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ScienceDirect Publication: Remote Sensing of Environment

  • The accuracy of snow melt-off day derived from optical and microwave radiometer data — A study for Europe
    Publication date: 15 June 2018
    Source:Remote Sensing of Environment, Volume 211

    Author(s): Sari Metsämäki, Kristin Böttcher, Jouni Pulliainen, Kari Luojus, Juval Cohen, Matias Takala, Olli-Pekka Mattila, Gabriele Schwaizer, Chris Derksen, Sampsa Koponen

    This paper describes the methodology for deriving yearly pixel-wise snow melt-off day maps from optical data-based FSC (Fractional Snow Cover) without conducting any interpolation for cloud-obscured pixels or otherwise missing data. The Copernicus CryoLand Pan-European FSC time series for 2001–2016 re-gridded to 0.1° serves as input for the production of 16 years of melt-off day maps for Europe. These maps are compared with passive microwave radiometer (MWR) melt retrievals, to compare the performance of these two independent datasets, particularly concerning the effect of physiographic and snow conditions on the accuracy of the melt-off day estimates. Both these datasets are evaluated against melt-off day derived from in situ snow depth (SD) time series observed at European weather stations. We also present the relationship of these snow melt-off day products to a passive microwave radiometer-derived landscape freeze/thaw product. Our results show that the melt-off day derived from optical springtime FSC time series provides the strongest correlation with the snow melt-off day with respect to the in situ data. Overall the deviation of CryoLand FSC data derived melt-off day to that indicated by in situ observations is quite small, with positive bias of 0.9 days, and RMSE of 13.1 days. For 85% of the analyzed cases the differences are between ±10 days. Across Europe the MWR-based detection of melt-off day is less accurate; the investigated method performs the best for areas with sustained seasonal snow cover. Based on the time series for MWR-based melt-off day (1980–2016) and FT-ESDR (1980–2014), separately for boreal forests and tundra, we also found a clear trend towards earlier snow clearance: a decrease of melt-off day by as much as ~5 days per decade in boreal forest region was observed.





  • LiDAR derived forest structure data improves predictions of canopy N and P concentrations from imaging spectroscopy
    Publication date: 15 June 2018
    Source:Remote Sensing of Environment, Volume 211

    Author(s): Michael Ewald, Raf Aerts, Jonathan Lenoir, Fabian Ewald Fassnacht, Manuel Nicolas, Sandra Skowronek, Jérôme Piat, Olivier Honnay, Carol Ximena Garzón-López, Hannes Feilhauer, Ruben Van De Kerchove, Ben Somers, Tarek Hattab, Duccio Rocchini, Sebastian Schmidtlein

    Imaging spectroscopy is a powerful tool for mapping chemical leaf traits at the canopy level. However, covariance with structural canopy properties is hampering the ability to predict leaf biochemical traits in structurally heterogeneous forests. Here, we used imaging spectroscopy data to map canopy level leaf nitrogen (Nmass) and phosphorus concentrations (Pmass) of a temperate mixed forest. By integrating predictor variables derived from airborne laser scanning (LiDAR), capturing the biophysical complexity of the canopy, we aimed at improving predictions of Nmass and Pmass. We used partial least squares regression (PLSR) models to link community weighted means of both leaf constituents with 245 hyperspectral bands (426–2425 nm) and 38 LiDAR-derived variables. LiDAR-derived variables improved the model's explained variances for Nmass (R2 cv 0.31 vs. 0.41, % RSMEcv 3.3 vs. 3.0) and Pmass (R2 cv 0.45 vs. 0.63, % RSMEcv 15.3 vs. 12.5). The predictive performances of Nmass models using hyperspectral bands only, decreased with increasing structural heterogeneity included in the calibration dataset. To test the independent contribution of canopy structure we additionally fit the models using only LiDAR-derived variables as predictors. Resulting R2 cv values ranged from 0.26 for Nmass to 0.54 for Pmass indicating considerable covariation between biochemical traits and forest structural properties. Nmass was negatively related to the spatial heterogeneity of canopy density, whereas Pmass was negatively related to stand height and to the total cover of tree canopies. In the specific setting of this study, the importance of structural variables can be attributed to the presence of two tree species, featuring structural and biochemical properties different from co-occurring species. Still, existing functional linkages between structure and biochemistry at the leaf and canopy level suggest that canopy structure, used as proxy, can in general support the mapping of leaf biochemistry over broad spatial extents.





  • Enhanced canopy growth precedes senescence in 2005 and 2010 Amazonian droughts
    Publication date: 15 June 2018
    Source:Remote Sensing of Environment, Volume 211

    Author(s): Yi Y. Liu, Albert I.J.M. van Dijk, Diego G. Miralles, Matthew F. McCabe, Jason P. Evans, Richard A.M. de Jeu, Pierre Gentine, Alfredo Huete, Robert M. Parinussa, Lixin Wang, Kaiyu Guan, Joe Berry, Natalia Restrepo-Coupe

    Unprecedented droughts hit southern Amazonia in 2005 and 2010, causing a sharp increase in tree mortality and carbon loss. To better predict the rainforest's response to future droughts, it is necessary to understand its behavior during past events. Satellite observations provide a practical source of continuous observations of Amazonian forest. Here we used a passive microwave-based vegetation water content record (i.e., vegetation optical depth, VOD), together with multiple hydrometeorological observations as well as conventional satellite vegetation measures, to investigate the rainforest canopy dynamics during the 2005 and 2010 droughts. During the onset of droughts in the wet-to-dry season (May–July) of both years, we found large-scale positive anomalies in VOD, leaf area index (LAI) and enhanced vegetation index (EVI) over the southern Amazonia. These observations are very likely caused by enhanced canopy growth. Concurrent below-average rainfall and above-average radiation during the wet-to-dry season can be interpreted as an early arrival of normal dry season conditions, leading to enhanced new leaf development and ecosystem photosynthesis, as supported by field observations. Our results suggest that further rainfall deficit into the subsequent dry season caused water and heat stress during the peak of 2005 and 2010 droughts (August–October) that exceeded the tolerance limits of the rainforest, leading to widespread negative VOD anomalies over the southern Amazonia. Significant VOD anomalies were observed mainly over the western part in 2005 and mainly over central and eastern parts in 2010. The total area with significant negative VOD anomalies was comparable between these two drought years, though the average magnitude of significant negative VOD anomalies was greater in 2005. This finding broadly agrees with the field observations indicating that the reduction in biomass carbon uptake was stronger in 2005 than 2010. The enhanced canopy growth preceding drought-induced senescence should be taken into account when interpreting the ecological impacts of Amazonian droughts.

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  • A novel method to retrieve the nocturnal boundary layer structure based on CCD laser aerosol detection system measurements
    Publication date: 15 June 2018
    Source:Remote Sensing of Environment, Volume 211

    Author(s): Yuxuan Bian, Chunsheng Zhao, Wanyun Xu, Ye Kuang, Jiangchuan Tao, Wei Wei, Nan Ma, Gang Zhao, Shaopeng Lian, Wangshu Tan, John E. Barnes

    Mixing layer height (MLH) is a key parameter for evaluating the transport and diffusion of atmospheric pollutants in both air quality forecasting and satellite data retrieval. However, there is a lack of methods for obtaining the nocturnal MLH. In this study, a novel instrument named the charge-coupled device-laser aerosol detection system (CCD-LADS) was developed to study the nocturnal MLH and boundary layer structure from the surface. The system mainly includes a continuous laser and a charge-coupled device camera with a fisheye lens. Structures of atmospheric layers characterized by the CCD-LADS were compared with those measured by a ceilometer. The heights of two atmospheric layers quantified by measurements with the CCD-LADS and the ceilometer show good agreement, with a relative difference of 5%. The results of this comparison demonstrated that the CCD-LADS is capable of distinctly identifying the nocturnal vertical structure of the atmosphere. The advantage of the CCD-LADS in retrieving the nocturnal MLH is that the CCD-LADS can provide the boundary layer structures under 100 m, while the ceilometer and other lidar measurements cannot retrieve the atmospheric structures below that altitude. CCD-LADS was deployed in a comprehensive field campaign measuring air pollution in the University of Chinese Academy of Sciences, located at the border between the North China Plain and Yanshan Mountain, during January 2016. The fine characteristics and patterns of the nocturnal boundary layer structures were derived with the CCD-LADS measurements.





  • Hierarchical Bayesian space-time estimation of monthly maximum and minimum surface air temperature
    Publication date: 15 June 2018
    Source:Remote Sensing of Environment, Volume 211

    Author(s): Ning Lu, Shunlin Liang, Guanghui Huang, Jun Qin, Ling Yao, Dongdong Wang, Kun Yang

    Surface air temperature (SAT) is a critical metric that is used to assess regional warming and cooling patterns, and maximum and minimum SATs are required to evaluate the model predictions of climate extremes. Since station SAT data are irregularly distributed, land surface temperature (LST) values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data are used to estimate regional SAT by using linear regression methods. The deviations between SAT and LST are largely dependent on space and time, which hampers the estimation of linear regression, especially for the maximum SAT. To obtain accurate regional SAT estimates, a three-stage hierarchical Bayesian (HB) model is proposed that incorporates the MODIS LSTs as model covariates and specifies the deviations with structured dependence of MODIS LST fields. Sampling of model parameters and estimation of SAT values are implemented under the Bayesian paradigm using a Markov Chain Monte Carlo algorithm. Sensitivity analyses involving various model configurations and running processes are discussed to help build a robust HB model. The model's performance is evaluated using station measurements that are not used in the modeling process, with RMSEs of 2.15 K (0.75%) and 1.97 K (0.73%) for monthly maximum and minimum SATs, respectively. The evaluation indicates that HB modeling is an effective method to estimate SAT from MODIS LST. The verified HB model with the covariate inputs of both MODIS daytime and nighttime LSTs is used to reproduce monthly maximum and minimum SATs that are spatially continuous over the Qinghai province in Northwestern China for 2003–2011. From the comparison between MODIS LST and HB-estimated SAT, it is found that the spatial structure and warming patterns of LST and SAT show significant distinctions, implying that they cannot be substituted for one another when assessing the regional warming trends. The spatial heterogeneity of HB model estimation is able to provide thorough insights into regional SAT status changes that could otherwise be biased by station deployment.





  • Capturing agricultural soil freeze/thaw state through remote sensing and ground observations: A soil freeze/thaw validation campaign
    Publication date: 15 June 2018
    Source:Remote Sensing of Environment, Volume 211

    Author(s): Tracy L. Rowlandson, Aaron A. Berg, Alexander Roy, Edward Kim, Renato Pardo Lara, Jarrett Powers, Kristin Lewis, Paul Houser, Kyle McDonald, Peter Toose, Albert Wu, Eugenia De Marco, Chris Derksen, Jared Entin, Andreas Colliander, Xiaolan Xu, Alex Mavrovic

    A field campaign was conducted October 30th to November 13th, 2015 with the intention of capturing diurnal soil freeze/thaw state at multiple scales using ground measurements and remote sensing measurements. On four of the five sampling days, we observed a significant difference between morning (frozen scenario) and afternoon (thawed scenario) ground-based measurements of the soil relative permittivity. These results were supported by an in situ soil moisture and temperature network (installed at the scale of a spaceborne passive microwave pixel) which indicated surface soil temperatures fell below 0 °C for the same four sampling dates. Ground-based radiometers appeared to be highly sensitive to F/T conditions of the very surface of the soil and indicated normalized polarization index (NPR) values that were below the defined freezing values during the morning sampling period on all sampling dates. The Scanning L-band Active Passive (SLAP) instrumentation, flown over the study region, showed very good agreement with the ground-based radiometers, with freezing states observed on all four days that the airborne observations covered the fields with ground-based radiometers. The Soil Moisture Active Passive (SMAP) satellite had morning overpasses on three of the sampling days, and indicated frozen conditions on two of those days. It was found that >60% of the in situ network had to indicate surface temperatures below 0 °C before SMAP indicated freezing conditions. This was also true of the SLAP radiometer measurements. The SMAP, SLAP and ground-based radiometer measurements all indicated freezing conditions when soil temperature sensors installed at 5 cm depth were not frozen.





  • Long-term record of top-of-atmosphere albedo over land generated from AVHRR data
    Publication date: 15 June 2018
    Source:Remote Sensing of Environment, Volume 211

    Author(s): Zhen Song, Shunlin Liang, Dongdong Wang, Yuan Zhou, Aolin Jia

    Top-of-atmosphere (TOA) albedo is a fundamental component of Earth's energy budget. To date, long-term global land TOA albedo products with spatial resolution higher than 20-km are not available. In this study, we propose a novel algorithm to retrieve TOA albedo from multispectral imager observations acquired by Advanced Very High Resolution Radiometer (AVHRR), which provides the longest continuous record of global satellite observations since 1981. Direct estimation models were established first to derive instantaneous TOA broadband albedo under various atmospheric and surface conditions, including cloudy-sky, clear-sky (snow-free) and snow-cover conditions. To perform long-term series analysis, the instantaneous TOA albedo were then converted to daily/monthly mean values based on the diurnal curves from multi-year Clouds and the Earth's Radiant Energy System (CERES) 3-hourly flux dataset. Calibration differences between sequential AVHRR sensors were further mitigated by invariant targets normalization. The retrieved TOA albedo at 0.05° × 0.05° was validated against two TOA albedo datasets, CM SAF (Climate Monitoring Satellite Application Facility) flux data and CERES flux data, at spatial resolutions of 0.05° × 0.05°, 20 km × 20 km and 1° × 1°. The instantaneous TOA albedo had an overall Root-Mean-Square-Error (RMSE) of 0.047 when compared with 20-km CERES fluxes, whereas the 1° by 1° monthly mean TOA albedo showed closer agreements with both CM SAF and CERES, with RMSE ranging from 0.029 to 0.040 and from 0.022 to 0.031, respectively. Moreover, our product was found to be highly consistent with both CERES and CM SAF at long-term trend detection. The extensive validation indicated the robustness of our algorithm and subsequently, comparable data quality with existing datasets. With global coverage and long time series (1981–2017), our product is expected to provide valuable information for climate change studies.





  • Swelling of transported smoke from savanna fires over the Southeast Atlantic Ocean
    Publication date: 15 June 2018
    Source:Remote Sensing of Environment, Volume 211

    Author(s): J. Kar, M. Vaughan, J. Tackett, Z. Liu, A. Omar, S. Rodier, C. Trepte, P. Lucker

    We use the recently released Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Version 4.1 (V4) lidar data to study the smoke plumes transported from Southern African biomass burning areas. Significant improvements in the CALIPSO V4 Level 1 calibration and V4 Level 2 algorithms lead to a better representation of their optical properties, with the aerosol subtype improvements being particularly relevant to smoke over this area. For the first time, we show evidence of smoke particles increasing in size, as demonstrated by their particulate color ratios, as they are transported over the South Atlantic Ocean from the source regions over Southern Africa. We hypothesize that this is due to hygroscopic swelling of the smoke particles and is reflected in the higher relative humidity in the middle troposphere for profiles with smoke. This finding may have implications for radiative forcing estimates over this area and is also relevant to the ORACLES field mission.





  • An improved approach to monitoring Brahmaputra River water levels using retracked altimetry data
    Publication date: 15 June 2018
    Source:Remote Sensing of Environment, Volume 211

    Author(s): Qi Huang, Di Long, Mingda Du, Chao Zeng, Xingdong Li, Aizhong Hou, Yang Hong

    Satellite altimetry is an important tool for monitoring water levels over oceans and inland water bodies, particularly over poorly gauged or ungauged areas. This study uses satellite altimetry (Jason-2/3 and Envisat) to derive water levels of the Great Brahmaputra River (GBR) originating from the Tibetan Plateau. Although the width of the river channels of the Lower Brahmaputra River (LBR) is ~1 km, the Upper Brahmaputra River (UBR) (which is part of the Yarlung Zangbo River of China) and the Middle Brahmaputra River (MBR) located in high-mountain regions have river widths that are generally less than 400 m. This poses considerable challenges for existing retracking algorithms to obtain accurately retrieved water levels. In this study, an improved approach for deriving water levels in high-mountain regions with complex terrain is proposed, comprising (1) an improved footprint selection and (2) an improved waveform retracking, called the 50% Threshold and Ice-1 Combined algorithm (TIC). It was applied to river channels of varying widths, ranging from 200 m in the UBR to more than 1 km in the LBR. Results show an increase in both the accuracy and sampling of water levels. Most of the derived water levels at 13 virtual stations (VSs) along the GBR agree reasonably well with gauged water levels (for VSs in the UBR) or published results (for VSs in the LBR). The standard deviation of the difference between the TIC-derived water levels and gauged data at the VSs ranges from 0.3 m to 0.8 m with the highest improvement percentage relative to the unretracked ranges reaching 80% in the UBR. In addition, the developed approach increases water level sampling by reasonably demarcating the buffer zone for footprint selection, thereby generating more water levels in the time series than the published results for VSs in the LBR. However, 3 out of the 13 virtual stations show poor performance for Envisat, primarily due to the extremely narrow river channels. Furthermore, TIC can potentially be applied to estimate water levels near ground tracks of altimetric missions, even where there is no crossover between the river and the track. It could also be applied to other altimetric missions, which would further contribute to monitoring water levels and potentially river discharge in high-mountain regions with narrow river channels.





  • A model to assess microphytobenthic primary production in tidal systems using satellite remote sensing
    Publication date: 15 June 2018
    Source:Remote Sensing of Environment, Volume 211

    Author(s): Tisja D. Daggers, Jacco C. Kromkamp, Peter M.J. Herman, Daphne van der Wal

    Quantifying spatial variability in intertidal benthic productivity is necessary to guide management of estuaries and to understand estuarine ecological processes, including the amount of benthic organic carbon available for grazing, burial and transport to the pelagic zone. We developed a model to assess microphytobenthic (MPB) primary production using (1) remotely sensed information on MPB biomass and remotely sensed information on sediment mud content, (2) surface irradiance and ambient temperature (both from local meteorological observations), (3) directly-measured photosynthetic parameters and (4) a tidal model. MPB biomass was estimated using the normalized-difference vegetation index (NDVI) and mud content was predicted using surface reflectance in the blue and near-infrared, both from Landsat 8 satellite imagery. The photosynthetic capacity (maximum photosynthesis rate normalized to MPB chl-a) was estimated from ambient temperature, while photosynthetic efficiency and the light saturation parameter were derived from in situ fluorometry-based production measurements (PAM). The influence of tides (submergence by turbid water) on MPB production was accounted for in the model. The method was validated on several locations in two temperate tidal basins in the Netherlands (Oosterschelde and Westerschelde). Model based production rates (mg C m−2 h−1) matched well with an independent set of in situ (PAM) measurement based production rates (Oosterschelde: RMSE = 9.7, mean error = 1.5, χ = 0.57; Westerschelde: RMSE = 46.7, mean error = −17.6, χ = 0.9). The relationship between photosynthetic capacity and temperature shows considerable variation and may be improved by using sediment surface temperature instead of ambient temperature. A sensitivity analysis revealed that emersion duration and mud content determine most of the variability in MPB production. Our results demonstrate that it is possible to derive a satellite remote sensing-based overview of average hourly and daily MPB primary production rates at the macro scale. As the proposed model is generic, the model can be applied to other tidal systems to assess spatial variability in MPB primary production at the macro scale after calibration at the site of interest. Model calibration, results and possible applications for regular monitoring of MPB production are discussed below.





  • Lidar supported estimators of wood volume and aboveground biomass from the Danish national forest inventory (2012–2016)
    Publication date: 15 June 2018
    Source:Remote Sensing of Environment, Volume 211

    Author(s): Steen Magnussen, Thomas Nord-Larsen, Torben Riis-Nielsen

    National forest inventories (NFI) provide estimates of forest resources at the national and regional level but are also increasingly used as basis for mapping forest resources based on remotely sensed data. Such maps procure local estimates of forest resources but may also improve precision of national and regional estimates. Supported by a countrywide airborne laser scanning (circa 2014) and a national land-use map (circa 2014), direct (DI), model-assisted (MA), and model calibrated (MC) estimates of wood volume (V) and aboveground biomass (AGB) densities in forest areas derived from the Danish NFI (2012–2016) are presented. Nonlinear models with three LiDAR metrics are used to predict V and AGB in forested areas. According to these models, the predicted values of V and AGB in sample plots missed in the field inventory was lower than in those visited in the field; we therefore opted for estimation with multiple (stochastic) imputations. MA estimates for the country suggested a 2% lower level of both V and AGB densities with errors 45% lower than estimated errors in DI results. National MC estimates were close to the DI estimates with an error approximately 40% lower than errors in DI estimates yet 5% greater than the MA estimates of error. Multiple imputations had the strongest impact on DI estimates, but only a weak impact on MA and MC results.





  • A data-driven framework to identify and compare forest structure classes using LiDAR
    Publication date: 15 June 2018
    Source:Remote Sensing of Environment, Volume 211

    Author(s): Christopher J. Moran, Eric M. Rowell, Carl A. Seielstad

    As LiDAR datasets increase in availability and spatial extent, demand is growing for analytical frameworks that allow for robust comparison and interpretation among ecosystems. We utilize data-driven classification in a hierarchical design to estimate forest structure classes with parsimony, flexibility, and consistency as priorities. We use an a priori selection of six input features derived from small-footprint (32 cm), high density (17 returns/m2) airborne LiDAR: four L-moments to describe the vertical distribution of canopy structure, canopy density as a measure of vegetation coverage, and standard deviation of canopy density to characterize within-cell horizontal variability. We identify 14 statistically-separated meta-classes characterizing six ecoregions over 168,117 ha in Montana, USA. Meta-classes follow four general vertical shapes: tall and continuous, short-single strata, tall-single strata, and broken strata over short strata. Structure classes that dominate locally but are rare overall are also identified. The approach outlined here allows for intuitive comparison and assessment of forest structure from any number of landscapes and forest types without need for field training data.





  • Editorial Board
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210









  • Remote sensing of surface [nitrite + nitrate] in river-influenced shelf-seas: The northern South China Sea Shelf-sea
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Xiaoju Pan, George T.F. Wong, Tung-Yuan Ho, Jen-Hua Tai, Hongbin Liu, Juanjuan Liu, Fuh-Kuo Shiah

    Sea surface concentrations of [nitrite + nitrate], [N + N], have been assessed successfully by remote sensing in the open oceans by utilizing its relationship to sea surface temperature (SST). A similar approach was not met with similar success in the river-influenced shelf waters in the northern South China Sea Shelf-sea (NoSoCS) where riverine inputs, in which the relationship between the concentration of [N + N] and SST was not straight forward, are significant. By considering river-influenced shelf waters as a mixture of open ocean water and riverine inputs and then combining SST with the absorption coefficient of colored dissolved organic matter (CDOM) at 412 nm, ag(412), which serves as an indicator of the riverine influence, an algorithm has been developed for remotely sensing the surface concentration of [N + N] in the NoSoCS. This satellite-derived concentration of [N + N] was validated by direct observations. In 16 match-up comparisons within a time window of ±24 h and concentrations of [N + N] ranging between 0.2 and 74 μM, the uncertainty was ±40%. The climatological distributions of the derived concentration of [N + N] in the NoSoCS and surrounding waters between 2002 and 2014 are consistent with the reported distributions based on ship-board observations. Thus, as a result of riverine inputs, the concentration of [N + N] generally increases towards the coasts, varying by more than two orders of magnitude from about 0.5 μM and near zero in the open northern South China Sea (NSCS) in January and July respectively to about 50 and 100 μM at the mouth of the Pearl River. In the open NSCS where nutrient availability is controlled primarily by the enhanced winter convective overturn, the intra-annual variations in the concentration of [N + N] follow a distinct seasonal cycle, reaching a minimum close to zero in the summer and a maximum, 0.7 μM, in the winter. In the NoSoCS, as a result of the high riverine input of nutrients in the summer, in addition to the maximum in the winter, there is a secondary summer maximum, which becomes increasingly prominent towards the coast. This work represents a first attempt to characterize the distributions of the surface concentration of [N + N] in river-influenced shelf-seas from space. With appropriate regional tuning, a similar approach may be applicable to other river-influenced shelf-seas such as the Texas-Louisiana Shelf.





  • Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): He Yin, Alexander V. Prishchepov, Tobias Kuemmerle, Benjamin Bleyhl, Johanna Buchner, Volker C. Radeloff

    Agricultural land abandonment is a common land-use change, making the accurate mapping of both location and timing when agricultural land abandonment occurred important to understand its environmental and social outcomes. However, it is challenging to distinguish agricultural abandonment from transitional classes such as fallow land at high spatial resolutions due to the complexity of change process. To date, no robust approach exists to detect when agricultural land abandonment occurred based on 30-m Landsat images. Our goal here was to develop a new approach to detect the extent and the exact timing of agricultural land abandonment using spatial and temporal segments derived from Landsat time series. We tested our approach for one Landsat footprint in the Caucasus, covering parts of Russia and Georgia, where agricultural land abandonment is widespread. First, we generated agricultural land image objects from multi-date Landsat imagery using a multi-resolution segmentation approach. Second, we estimated the probability for each object that agricultural land was used each year based on Landsat temporal-spectral metrics and a random forest model. Third, we applied temporal segmentation of the resulting agricultural land probability time series to identify change classes and detect when abandonment occurred. We found that our approach was able to accurately separate agricultural abandonment from active agricultural lands, fallow land, and re-cultivation. Our spatial and temporal segmentation approach captured the changes at the object level well (overall mapping accuracy = 97 ± 1%), and performed substantially better than pixel-level change detection (overall accuracy = 82 ± 3%). We found strong spatial and temporal variations in agricultural land abandonment rates in our study area, likely a consequence of regional wars after the collapse of the Soviet Union. In summary, the combination of spatial and temporal segmentation approaches of time-series is a robust method to track agricultural land abandonment and may be relevant for other land-use changes as well.





  • Can wetland plant functional groups be spectrally discriminated?
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Alanna J. Rebelo, Ben Somers, Karen J. Esler, Patrick Meire

    Plant functional traits (PFTs) underpin ecosystem processes and therefore ecosystem service provision. If PFTs are possible to detect and discriminate spectrally, then it may be possible to use remote sensing applications to map ecosystem processes or services within and across landscapes. As a first step towards this application, we explored whether functional groups of 22 dominant South African wetland species were spectrally separable based on their PFTs. We measured 23 biochemical and morphological PFTs in combination with spectra from 350 to 2349 nm using a handheld radiometer. First, we evaluated the possibility of accurately predicting morphological and biochemical PFTs from reflectance spectra using three approaches: spectrum averaging, redundancy analysis (RDA), and partial least squares regression (PLSR). Second, we established whether functional groups and species were spectrally distinguishable. We found seven PFTs to be important in at least two of the three approaches: four morphological and three biochemicals. Morphological traits that were important were leaf area (PLSR: r2 = 0.40, regression: r2 = 0.41), specific leaf area (r2 = 0.67), leaf mass (r2 = 0.43, r2 = 0.38), and leaf length/width ratio (r2 = 0.62). Biochemical traits that play a role in the structural composition of vegetation, like lignin content (r2 = 0.98, r2 = 0.54), concentration (r2 = 0.45) and cellulose content (r2 = 0.57, r2 = 0.49), were found to be important by at least two of the analyses. Three other traits were important in at least one of the analyses: total biomass (r2 = 0.56), leaf C/N ratio (r2 = 0.99), and cellulose concentration (r2 = 0.76). Redundancy analysis suggests that there is a large percentage (52%) of the spectrum not explained by the PFTs measured in this study. However, spectral discrimination of functional groups, and even species, appears promising, mostly in the ultraviolet A part of the spectrum. This has interesting applications for mapping PFTs using remote sensing techniques, and therefore for estimating related ecosystem processes and services.

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  • A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Yaping Cai, Kaiyu Guan, Jian Peng, Shaowen Wang, Christopher Seifert, Brian Wardlow, Zhan Li

    Accurate and timely spatial classification of crop types based on remote sensing data is important for both scientific and practical purposes. Spatially explicit crop-type information can be used to estimate crop areas for a variety of monitoring and decision-making applications such as crop insurance, land rental, supply-chain logistics, and financial market forecasting. However, there is no publically available spatially explicit in-season crop-type classification information for the U.S. Corn Belt (a landscape predominated by corn and soybean). Instead, researchers and decision-makers have to wait until four to six months after harvest to have such information from the previous year. The state-of-the-art research on crop-type classification has been shifted from relying on only spectral features of single static images to combining together spectral and time-series information. While Landsat data have a desirable spatial resolution for field-level crop-type classification, the ability to extract temporal phenology information based on Landsat data remains a challenge due to low temporal revisiting frequency and inevitable cloud contamination. To address this challenge and generate accurate, cost-effective, and in-season crop-type classification, this research uses the USDA's Common Land Units (CLUs) to aggregate spectral information for each field based on a time-series Landsat image data stack to largely overcome the cloud contamination issue while exploiting a machine learning model based on Deep Neural Network (DNN) and high-performance computing for intelligent and scalable computation of classification processes. Experiments were designed to evaluate what information is most useful for training the machine learning model for crop-type classification, and how various spatial and temporal factors affect the crop-type classification performance in order to derive timely crop type information. All experiments were conducted over Champaign County located in central Illinois, and a total of 1322 Landsat multi-temporal scenes including all the six optical spectral bands spanning from 2000 to 2015 were used. Computational experiments show the inclusion of temporal phenology information and evenly distributed spatial training samples in the study domain improves classification performance. The shortwave infrared bands show notably better performance than the widely used visible and near-infrared bands for classifying corn and soybean. In comparison with USDA's Crop Data Layer (CDL), this study found a relatively high Overall Accuracy (i.e. the number of the corrected classified fields divided by the number of the total fields) of 96% for classifying corn and soybean across all CLU fields in the Champaign County from 2000 to 2015. Furthermore, our approach achieved 95% Overall Accuracy by late July of the concurrent year for classifying corn and soybean. The findings suggest the methodology presented in this paper is promising for accurate, cost-effective, and in-season classification of field-level crop types, which may be scaled up to large geographic extents such as the U.S. Corn Belt.





  • Support vector regression snow-depth retrieval algorithm using passive microwave remote sensing data
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Xiongxin Xiao, Tingjun Zhang, Xinyue Zhong, Wanwan Shao, Xiaodong Li

    Snow cover is an informative indicator of climate change because it can affect local and regional surface energy and water balance, hydrological processes and climate, and ecosystem function. Passive microwave (PM) remote sensing data have long been used to retrieve snow depth and snow water equivalent with large uncertainties. The objective of this study is to develop snow-depth retrieval algorithm based on support vector regression (SVR) technique using PM remote sensing data and other auxiliary data. Ground-based daily snow depth data from 1223 stations across Eurasian continent were used to construct and validate the snow-depth retrieval algorithm. This SVR snow-depth retrieval algorithm partitioned three snow cover stages, and four land cover types then generated twelve snow-depth models for each phases. A non-linear regression method based on support vector regression (SVR) was used to retrieve snow depth with PM brightness temperatures, location (latitude and longitude), and terrain parameters (elevation) as input data and land cover as auxiliary data. In addition, we compared the performance of the SVR snow-depth retrieval algorithm with four alternative algorithms: the Chang algorithm, the Spectral Polarization Difference (SPD) algorithm, the Artificial/Neural Networks (ANN) and, an algorithm based on linear regression. Comparing results obtained from these five snow-depth retrieval algorithms against the ground-based daily snow depth data, the SVR snow-depth retrieval algorithm performs much superior with reduced uncertainties. We report the results aimed at evaluating the impact of the variation of snow cover stages and land cover types. The preliminary results suggest that the SVR snow-depth algorithm could detect deep snow with high accuracy and decrease the impact of saturation effects. These results suggest that the SVR snow-depth retrieval algorithm integrating PM remote sensing data and other auxiliary data (land cover types, location, terrain, snow cover stage with indirectly considering grain size variation) can be a more efficient and effective algorithm for retrieving snow depth and snow water equivalent over various scales.

    Graphical abstract

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  • How far are we from the use of satellite rainfall products in landslide forecasting?
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): M.T. Brunetti, M. Melillo, S. Peruccacci, L. Ciabatta, L. Brocca

    Satellite rainfall products have been available for many years (since '90) with an increasing spatial/temporal resolution and accuracy. Their global scale coverage and near real-time products perfectly fit the need of an early warning landslide system. Notwithstanding these characteristics, the number of studies employing satellite rainfall estimates for predicting landslide events is quite limited. In this study, we propose a procedure that allows us to evaluate the capability of different rainfall products to forecast the spatial-temporal occurrence of rainfall-induced landslides using rainfall thresholds. Specifically, the assessment is carried out in terms of skill scores, and receiver operating characteristic (ROC) analysis. The procedure is applied to ground observations and four different satellite rainfall estimates: 1) the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA, real time product (3B42-RT), 2) the SM2RASC product obtained from the application of SM2RAIN algorithm to the Advanced SCATterometer (ASCAT) derived satellite soil moisture (SM) data, 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), and 4) the Climate Prediction Center (CPC) Morphing Technique (CMORPH). As case study, we consider the Italian territory for which a catalogue listing 1414 rainfall-induced landslides in the period 2008–2014 is available. Results show that satellite products underestimate rainfall with respect to ground observations. However, by adjusting the rainfall thresholds, satellite products are able to identify landslide occurrence, even though with less accuracy than ground-based rainfall observations. Among the four satellite rainfall products, CMORPH and SM2RASC are performing the best, even though differences are small. This result is to be attributed to the high spatial/temporal resolution of CMORPH, and the good accuracy of SM2RSC. Overall, we believe that satellite rainfall estimates might be an important additional data source for developing continental or global landslide warning systems.





  • Retrievals of cloud droplet size from the research scanning polarimeter data: Validation using in situ measurements
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Mikhail D. Alexandrov, Brian Cairns, Kenneth Sinclair, Andrzej P. Wasilewski, Luke Ziemba, Ewan Crosbie, Richard Moore, John Hair, Amy Jo Scarino, Yongxiang Hu, Snorre Stamnes, Michael A. Shook, Gao Chen

    We present comparisons of cloud droplet size distributions (DSDs) retrieved from the research scanning polarimeter (RSP) data with correlative in situ measurements made during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). The airborne portion of this field experiment was based out of St. John's airport, Newfoundland, Canada with the focus of this paper being on the deployment in May–June 2016. RSP was onboard the NASA C-130 aircraft together with an array of in situ and other remote sensing instrumentation. The RSP is an along-track scanner measuring the polarized and total reflectance in 9 spectral channels. Its uniquely high angular resolution allows for characterization of liquid water droplet sizes using the rainbow structure observed in the polarized reflectance over the scattering angle range from 135° to 165°. The rainbow is dominated by single scattering of light by cloud droplets, so its structure is characteristic specifically of the droplet sizes at cloud top (within unit optical depth into the cloud, equivalent to approximately 50 m). A parametric fitting algorithm applied to the polarized reflectance provides retrievals of the droplet effective radius and variance assuming a prescribed size distribution shape (gamma distribution). In addition to this, we use a non-parametric method, the Rainbow Fourier Transform (RFT), which allows us to retrieve the droplet size distribution itself. The latter is important in the case of clouds with complex microphysical structure, or multiple layers of cloud, which result in multi-modal DSDs. During NAAMES the aircraft performed a number of flight patterns specifically designed for comparisons between remote sensing retrievals and in situ measurements. These patterns consisted of two flight segments above the same straight ground track. One of these segments was flown above clouds allowing for remote sensing measurements, while the other was near the cloud top where cloud droplets were sampled. We compare the DSDs retrieved from the RSP data with in situ measurements made by the Cloud Droplet Probe (CDP). The comparisons generally show good agreement (better than 1 μm for effective radius and in most cases better than 0.02 for effective variance) with deviations explainable by the position of the aircraft within the cloud, or by the presence of additional cloud layers between the cloud being sampled by the in situ instrumentation and the altitude of the remote sensing segment. In the latter case, the multi-modal DSDs retrieved from the RSP data were consistent with the multi-layer cloud structures observed in the correlative High Spectral Resolution Lidar (HSRL) profiles. The results of these comparisons provide a rare validation of polarimetric droplet size retrieval techniques, demonstrating their accuracy and robustness and the potential of satellite data of this kind on a global scale.





  • Glacier mass balance in the Qinghai–Tibet Plateau and its surroundings from the mid-1970s to 2000 based on Hexagon KH-9 and SRTM DEMs
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Yushan Zhou, Zhiwei Li, Jia Li, Rong Zhao, Xiaoli Ding

    In the context of global warming, glacier changes in the Qinghai–Tibet Plateau (QTP) and its surroundings have attracted a great amount of public attention. To date, there have been many studies of glacier mass balance across the QTP. However, given that most of the previous studies have focused on a short observation period (2000–2015), and that long-term mass change measurements are available only for some local regions, we utilized declassified KH-9 images and 1 arc-second Shuttle Radar Topography Mission (SRTM) digital elevation models (DEMs) to provide the region-wide mass balance (from the mid-1970s to 2000) for a larger scale (including 11 sample regions) across the QTP and its surroundings. The final results indicate that the glaciers in the northwest of the QTP have shown a less negative or near-zero mass balance, ranging from −0.11 ± 0.13 m w.e. a−1 to 0.02 ± 0.10 m w.e. a−1, compared to those in the southeast part, with a mass balance range of −0.30 ± 0.12 m w.e. a−1 to −0.11 ± 0.14 m w.e. a−1. The most serious mass loss has emerged in the central-eastern Himalaya. Integrating our results with the observations after 2000 suggests that, over the past four decades (mid-1970s to the mid-2010s), the glaciers in the Himalaya, Nyainqêntanglha, and Tanggula mountains, as a whole, have exhibited accelerated mass loss, and the most significant acceleration has occurred in the eastern Nyainqêntanglha. Moreover, the Hindu Raj glaciers have shown a stable rate of continuous mass loss, while a nearly stable or slight mass gain state in the western Kunlun region can be dated back to at least as far as the mid-1970s.





  • NASA's Black Marble nighttime lights product suite
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Miguel O. Román, Zhuosen Wang, Qingsong Sun, Virginia Kalb, Steven D. Miller, Andrew Molthan, Lori Schultz, Jordan Bell, Eleanor C. Stokes, Bhartendu Pandey, Karen C. Seto, Dorothy Hall, Tomohiro Oda, Robert E. Wolfe, Gary Lin, Navid Golpayegani, Sadashiva Devadiga, Carol Davidson, Sudipta Sarkar, Cid Praderas, Jeffrey Schmaltz, Ryan Boller, Joshua Stevens, Olga M. Ramos González, Elizabeth Padilla, José Alonso, Yasmín Detrés, Roy Armstrong, Ismael Miranda, Yasmín Conte, Nitza Marrero, Kytt MacManus, Thomas Esch, Edward J. Masuoka

    NASA's Black Marble nighttime lights product suite (VNP46) is available at 500 m resolution since January 2012 with data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP). The retrieval algorithm, developed and implemented for routine global processing at NASA's Land Science Investigator-led Processing System (SIPS), utilizes all high-quality, cloud-free, atmospheric-, terrain-, vegetation-, snow-, lunar-, and stray light-corrected radiances to estimate daily nighttime lights (NTL) and other intrinsic surface optical properties. Key algorithm enhancements include: (1) lunar irradiance modeling to resolve non-linear changes in phase and libration; (2) vector radiative transfer and lunar bidirectional surface anisotropic reflectance modeling to correct for atmospheric and BRDF effects; (3) geometric-optical and canopy radiative transfer modeling to account for seasonal variations in NTL; and (4) temporal gap-filling to reduce persistent data gaps. Extensive benchmark tests at representative spatial and temporal scales were conducted on the VNP46 time series record to characterize the uncertainties stemming from upstream data sources. Initial validation results are presented together with example case studies illustrating the scientific utility of the products. This includes an evaluation of temporal patterns of NTL dynamics associated with urbanization, socioeconomic variability, cultural characteristics, and displaced populations affected by conflict. Current and planned activities under the Group on Earth Observations (GEO) Human Planet Initiative are aimed at evaluating the products at different geographic locations and time periods representing the full range of retrieval conditions.





  • Inversion of deformation fields time-series from optical images, and application to the long term kinematics of slow-moving landslides in Peru
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Noélie Bontemps, Pascal Lacroix, Marie-Pierre Doin

    Slow-moving landslides are numerous in mountainous areas and pose a large threat to populations. Many observations show that their kinematics is driven by climatic forcings and earthquakes. In this study, we document the complex interaction between those two forcings on the slow-moving landslide kinematics, based on the retrieval of landslide displacements over 28-years using optical satellite images. To overcome the decorrelation effect over this large time-span, and possible misalignment between images, we develop a method that uses the redundancy of displacement fields from image pairs to derive a robust time-series of displacement. The method is tested on the 28-year long SPOT1/5-Pléiades archive, over an area in Peru affected by both earthquakes and rainfall. Errors are estimated on stable areas and by comparison with one 13-year long and eleven 3-year long GPS time-series on the Maca landslide. The methodology diminishes by up to 30% the uncertainty and reduces significantly the gaps due to decorrelation. The data set allows detecting 3 major landslides, moving at a rate of 35 to 50 m over 28 years, and smaller landslides with lower displacement rates. Time-series obtained over the three main landslides provide interesting results of their long-term kinematics, primarily driven by precipitation. We propose simple statistical hydro-kinematic models, relating yearly motion to seasonal rainfall, to explain the observed time-series. We found that annual precipitation is controlling the landslide displacements after a certain rainfall threshold is reached. Besides this control, we show the possible impact of a local Mw 5.4 earthquake in 1991 on the kinematics of the Maca landslide. Our results suggest that the earthquake accelerated the landslide and has an effect during several years on the precipitation threshold required for triggering a motion. These results suggest that the rainfall threshold can vary in time following strong earthquakes shaking.





  • Influences of multiple layers of air temperature differences on tidal forces and tectonic stress before, during and after the Jiujiang earthquake
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Ma Weiyu, Zhang Xuedong, Jun Liu, Qi Yao, Bo Zhou, Chong Yue, Chunli Kang, Xian Lu

    Using the air temperature data of the National Center for Environmental Prediction (NCEP), we compared multiple layers of air temperature differences before, during and after the Jiujiang earthquake, and explored its relationship with the additive tectonic stress caused by celestial tide-generating force (ATSCTF). The earthquake occurred at the 1 of 4 high phases of ATSCTF, while the temperature rise came from land surface to high sky. It indicated that the tide force could trigger an earthquake when the tectonic stress was in critical status, and the air temperature rise reflected the terra stress change modulated under the tidal force. During the shock period of ATSCTF, the distribution of air temperature changes both near land surfaces and upper multi-layers along the active fault zones showed a tectonic disturbance pattern of calm before earthquake, rise during earthquake, calm after earthquake as well as a heat distribution pattern of the surface air warmed by land, uplifted by heat flux, cooled and dissipated in the sky. The pattern of changes obeyed the rule of thermal rise of rocks broken under stress loading and the principle of atmospheric thermal dynamic diffusion in vertical. We argued that an earthquake may also be a reason for air temperature differences rather than a simple weather process. At the same time, the rise of air temperature was synchronized with the ATSCTF fluctuant, which showed that tidal force had a particular indicative significance for the identification of temperature anomaly on seismic faults. Because of the mechanical characteristics of the study of earthquake thermal anomalies, it could help to identify the earthquake thermal anomalies and the climatic thermal anomalies, and provided a clear time-indication for the choice of the background temperature in the seismic thermal anomaly recognition.





  • Earthquake damage mapping: An overall assessment of ground surveys and VHR image change detection after L'Aquila 2009 earthquake
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Roberta Anniballe, Fabrizio Noto, Tanya Scalia, Christian Bignami, Salvatore Stramondo, Marco Chini, Nazzareno Pierdicca

    Earth Observation (EO) data are used to map mostly affected urban areas after an earthquake generally exploiting change detection techniques applied at pixel scale. However, Civil Protection Services require damage assessment of each building according to a well-established scale to manage rescue operations and to estimate the economic losses. Considering the earthquake that hit L'Aquila city (Italy) on April 6, 2009, this work assess the feasibility of producing damage maps at the scale of single building from Very High Resolution (VHR) optical images collected before and after the seismic event. We considered the European Macroseismic Scale 1998 (EMS-98) and assessed the possibility to discriminate between collapsed or heavy damaged buildings (damage grade DG equal to 5 in the EMS-98 scale) and less damaged or undamaged buildings (DG < 5 in the EMS-98). The proposed approach relies on a pre-existing urban map to identify image objects corresponding to building footprints. The image analysis is carried out according to many different parameters with the objective of assessing their effectiveness in singling out changes associated to the building collapse. Features describing texture and colour changes, as well statistical similarity and correlation descriptors, such as the Kullbach Leibler Distance and the Mutual Information, were included in our analysis. Two supervised classification approaches, respectively, based on the use of the Bayesian Maximum A Posteriori (MAP) criterion and on Support Vector Machines (SVM), were compared. In our experiment, we considered the whole L'Aquila historical centre comparing classification results with the ground survey performed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV). The work represents one of the first attempt to detect damage at the scale of single building, validated against an extensive ground survey. It addresses methodological aspects, highlighting the potential of textural features computed at object scale and SVMs, and discuss potential and limitations of EO in this field compared to ground surveys.





  • Calibration of nationwide airborne laser scanning based stem volume models
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Eetu Kotivuori, Matti Maltamo, Lauri Korhonen, Petteri Packalen

    In-situ field measurements of sample plots are a major cost component in airborne laser scanning (ALS) based forest inventories. Field measurements on new inventory areas can be reduced by utilizing existing stand attribute models from former inventory areas. We constructed a nationwide model for stem volume, and examined seven different calibration scenarios using 22 inventory areas distributed evenly throughout Finland. These scenarios can be divided into three main categories: 1) calibration with additional predictor variables, 2) calibration with 200 geographically nearest sample plots, and 3) calibration with MS-NFI (Multi-source National Forest Inventory of Finland) volume at the target inventory area. Calibration with degree days, precipitation, and proportion of birch resulted in the greatest decrease in error rate of stem volume predictions. The mean of the root mean square errors (RMSE) among the 22 inventory areas decreased from 28.6% to 25.9%, and the standard deviation of RMSEs from 4.3% to 3.9% using three additional predictor variables. Correspondingly, the mean and standard deviation of absolute values of mean differences (|MD|) decreased from 8.3% to 5.6% and from 5.6% to 4.4%, respectively. All calibration scenarios decreased the error rate, especially the high |MDs| observed in the northern part of Finland. Calibration with sample plots from geographically nearest inventory areas was useful when there were sample plots that offered a good representation of the target area. MS-NFI based calibration was also a reasonable option if loggings and other inconsistencies between datasets could be detected and accounted for.





  • Improved mapping of forest type using spectral-temporal Landsat features
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Valerie J. Pasquarella, Christopher E. Holden, Curtis E. Woodcock

    Multi-spectral imagery from the Landsat family of satellites has been used to map forest properties for decades, but accurate forest type characterizations at a 30-m Landsat resolution have remained an ongoing challenge, especially over large areas. We combined existing Landsat time series algorithms to quantify both harmonic and phenological metrics in a new set of spectral-temporal features that can be produced seamlessly across many Landsat scenes. Harmonic metrics characterize mean annual reflectance and seasonal variability, while phenological metrics quantify the timing of seasonal events. We assessed the performance of spectral-temporal features derived from time series of all available observations (1985–2015) relative to more conventional single date and multi-date inputs. Performance was determined based on agreement with a reference dataset for eight New England forest types at both the pixel and polygon scale. We found that spectral-temporal features consistently and significantly (paired t-test, p ≪ 0.01) outperformed all feature sets derived from individual images and multi-date combinations in all measures of agreement considered. Harmonic features, such as annual amplitude and model fit error, aid in distinguishing deciduous hardwoods from conifer species, while phenology features, like the timing of autumn onset and growing season length, were useful in separating hardwood classes. This study represents an important step toward large-scale forest type mapping using spectral-temporal Landsat features by providing a quantitative assessment of the advantages of harmonic and phenology features derived from time series of Landsat data as compared with more conventional single-date and multi-date classification inputs.





  • Modeling the precision of structure-from-motion multi-view stereo digital elevation models from repeated close-range aerial surveys
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Jason Goetz, Alexander Brenning, Marco Marcer, Xavier Bodin

    The accuracy of digital elevation models (DEMs) derived from structure-from-motion (SFM) multi-view stereo (MVS) 3D reconstruction is commonly computed for a single realization of model elevations. This approach may be adequate to estimate an overall measure of systematic error; however, it cannot provide a good estimation of measurement precision. Knowing measurement precision is crucial for measuring elevation surface changes observed by DEM comparisons. In this paper, we illustrate an approach to characterize spatial variation in the precision for SFM-MVS derived DEMs. We use a snow-covered surface of an active rock glacier located in the southern French Alps as the case study. A spatially varying precision estimate is calculated from repeated close-range aerial surveys for a single acquisition period by calculating the standard deviation per grid cell between the DEMs created for each flight repetition. Regression analysis using a generalized additive model (GAM) is performed to model the estimated precision and provide insights regarding how sensor, survey design and field site conditions may spatially influence the measurement precision. Additionally, we define how DEM error can be described differently depending on the available validation data. In our study image height above ground level and distance to ground control points had the greatest explanatory power for spatial variation in DEM precision. Image overlap, mean reprojection error and saturation were also useful for explaining spatially varying measurement precision of the DEMs. Field site characteristics, such as slope angle and shading, had the least importance in our model of precision. From a practical point of view, regression-modeled relationships between precision and image and site characteristics can be utilized to design future surveys with similar sensing platforms and site conditions for improved DEM precision.





  • Mapping patterns of urban development in Ouagadougou, Burkina Faso, using machine learning regression modeling with bi-seasonal Landsat time series
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Franz Schug, Akpona Okujeni, Janine Hauer, Patrick Hostert, Jonas Ø. Nielsen, Sebastian van der Linden

    Rapid urban population growth in Sub-Saharan Western Africa has important environmental, infrastructural and social impacts. Due to the low availability of reliable urbanization data, remote sensing techniques become increasingly popular for monitoring land use change processes in that region. This study aims to quantify land cover for the Ouagadougou metropolitan area between 2002 and 2013 using a Landsat-TM/ETM+/OLI time series. We use a support vector regression approach and synthetically mixed training data. Working with bi-seasonal image stacks, we account for spectral variability between dry and rainy season and incorporate a new class - seasonal vegetation - that describes surfaces that are soil and vegetation during parts of the year. We produce fraction images of urban surfaces, soil, permanent vegetation and seasonal vegetation for each time step. Statistical evaluation shows that a temporally generalized, bi-seasonal model over all time steps performs equally or better than yearly or mono-seasonal models and provides reliable cover fractions. Urban fractions can be used to visualize pixel-based spatial-temporal patterns of urban densification and expansion. A simple rule set based on a seasonal vegetation to soil ratio is appropriate to delineate areas of unplanned and planned settlements and, thus, contributes to monitoring urban development on a neighborhood scale.





  • A method for combining SRTM DEM and ASTER GDEM2 to improve topography estimation in regions without reference data
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Hung T. Pham, Lucy Marshall, Fiona Johnson, Ashish Sharma

    Digital Elevation Models (DEMs) such as Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Models (ASTER GDEM), or Shuttle Radar Topography Mission DEM (SRTM) are widely used in remote areas and non-industrial countries because of their availability rather than their accuracy. Although a global DEM can be considerably enhanced using additional reference information such as higher resolution DEMs or ground truth points, improving accuracy in areas without reference data remains a challenge. This paper develops an approach to improve the accuracy of the estimated topography by combining two complementary DEMs (ASTER GDEM 1 arc-second and SRTM DEM 1 arc-second) in regions missing reference data. The combination approach is based on formulating relationships between slopes and weights in sites with reference data. Then the developed relationships are applied to sites with similar geomorphology to determine the combination weight for each DEM without using reference data. The results indicate that combined DEMs offer significant improvements of 47% and 20% in mean bias over a mountainous site, and 16% and 58% at a low-relief site when compared with the SRTM and ASTER GDEM products, respectively. DEM-derived drainages were also found to be more accurate for the combined DEMs than the near-global DEMs in areas where reference data is not available.





  • Deblurring DMSP nighttime lights: A new method using Gaussian filters and frequencies of illumination
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Alexei Abrahams, Christopher Oram, Nancy Lozano-Gracia

    A well known difficulty with the Defense Meteorological Satellite Program's nighttime lights series (DMSP-NTL 1992–2012) is that the images suffer from pervasive blurring, dubbed ‘overglow’ or ‘blooming’. In this paper we devise a new method that significantly mitigates blurring. We assemble a sample of isolated light sources around the globe and discover that blurring is governed by a symmetric Gaussian point-spread function (PSF), but that the brightness of sources widens the PSF. To make sense of this, we recreate step-by-step the satellite's data collection and storage process, and discover an important fact: any pixel containing a light source will tend to be lit at least as often as its neighbors. This regularity provides a second filter on the data that allows us to calibrate the dimensions of the PSF to each part of the globe, each satellite, and each year. We generate a user-friendly, open-access MATLAB script that deblurs all DMSP-NTL images for all years, and we showcase the enhanced images for a sample of locations around the globe.





  • Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Sory I. Toure, Douglas A. Stow, Hsiao-chien Shih, John Weeks, David Lopez-Carr

    Remote sensing data and techniques are reliable tools for monitoring and studying urban land cover and land use (LCLU) change. Fine spatial resolution (FRes) commercial satellite image in conjunction with geographic object-based image change analysis (GEOBICA) methods have been used to generate detailed and accurate urban LCLU maps. The integration of a backdating approach improves LCLU change classification results for the first date of a bi-temporal image sequences. Conversely, moderate spatial resolution satellite images such as those from Landsat sensors may not allow for detailed urban land use and land cover mapping. The objective of this study is to test a new bi-temporal change identification approach that integrates image classification of fine spatial resolution satellite imagery at time-2 and moderate spatial resolution satellite imagery (Landsat) at time-1, in a backdating and GEOBICA framework for mapping urban land use change. We compare the results from this approach to those of a GEOBICA approach based on fine spatial resolution imagery in both periods. The overall accuracy of the time-1 Landsat image classification is 0.82 and that of the fine spatial resolution image is 0.87. Moreover, the overall accuracy of the areal change data estimated from the pixel-wise spatial overlay of bi-temporal FRes LCLU maps is 0.80 while that from overlaying a time-2 FRes-generated map to that from a Landsat time-1 image is 0.81. The proposed method can be used in areas that lack FRes data due to limited coverage in the early 2000s.





  • Mapping population density in China between 1990 and 2010 using remote sensing
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Litao Wang, Shixin Wang, Yi Zhou, Wenliang Liu, Yanfang Hou, Jinfeng Zhu, Futao Wang

    Knowledge of the spatial distribution of populations at finer spatial scales is of significant value and fundamental to many applications such as environmental change, urbanization, regional planning, public health, and disaster management. However, detailed assessment of the population distribution data of countries that have large populations (such as China) and significant variation in distribution requires improved data processing methods and spatialization models. This paper described the construction of a novel population spatialization method by combining land use/cover data and night-light data. Based on the analysis of data characteristics, the method used partial correlation analysis and geographically weighted regression to improve the distribution accuracy and reduce regional errors. China's census data for the years 1990, 2000, and 2010 were assessed. The results showed that the method was better at population spatialization than methods that use only night-light data or land use/cover data and global linear regression. Evaluation of overall accuracies revealed that the coefficient of correlation R-square was >0.90 and increased by >0.13 in the years 1990, 2000, and 2010. Moreover, the local R-square of over 90% of the samples (counties) was higher than the adjusted R-square of the general linear regression model. Furthermore, the gridded population density datasets obtained by this method can be used to analyse spatial-temporal patterns of population density and provide population distribution information with increased accuracy and precision compared to conventional models.





  • Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Ran Meng, Jin Wu, Feng Zhao, Bruce D. Cook, Ryan P. Hanavan, Shawn P. Serbin

    Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not detectable by traditional optical remote sensing studies, largely attributing to the contaminated effect from understory recovery. Here, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1 m simultaneous airborne imaging spectroscopy and LiDAR and 2 m satellite multi-spectral imagery) to separate canopy recovery from understory recovery would enable to quantify post-fire forest recovery rate spanning a large gradient in burn severity over large-scales. Our study was conducted in a mixed pine-oak forest in Long Island, NY, three years after a top-killing fire. Our studies remotely detected an initial increase and then decline of forest recovery rate to burn severity across the burned area, with a maximum canopy area-based recovery rate of 10% per year at moderate forest burn severity class. More intriguingly, such remotely detected convex relationships also held at species level, with pine trees being more resilient to high burn severity and having a higher maximum recovery rate (12% per year) than oak trees (4% per year). These results are one of the first quantitative evidences showing the effects of fire adaptive strategies on post-fire forest recovery, derived from relatively large spatial-temporal scales. Our study thus provides the methodological advance to link multi-sensor remote sensing techniques to monitor forest dynamics in a spatially explicit manner over large-scales, with important implications for fire-related forest management and constraining/benchmarking fire effect schemes in ecological process models.





  • Quantifying vulnerability of Antarctic ice shelves to hydrofracture using microwave scattering properties
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): K.E. Alley, T.A. Scambos, J.Z. Miller, D.G. Long, M. MacFerrin

    Recent ice shelf disintegrations on the Antarctic Peninsula and subsequent increases in ice sheet mass loss have highlighted the importance of tracking ice shelf stability with respect to surface melt ponding and hydrofracture. In this study, we use active microwave scatterometry in time-series to estimate melt season duration, and winter backscatter levels as a proxy for relative concentration of refrozen ice lenses in Antarctic ice shelf firn. We demonstrate a physical relationship between melt days and firn/ice backscatter using scatterometry and field data from Greenland, and apply the observed relationship to derive and map a vulnerability index for Antarctica's ice shelves. The index reveals that some remaining Antarctic Peninsula ice shelves have already reached a firn state that is vulnerable to hydrofracture. We also show that the progression of an ice shelf towards vulnerability is affected by many factors, such as surface mass balance, internal stresses, and ice shelf geometry.





  • An algorithm for optically-deriving water depth from multispectral imagery in coral reef landscapes in the absence of ground-truth data
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Jeremy M. Kerr, Sam Purkis

    Although numerous approaches for deriving water depth from bands of remotely-sensed imagery in the visible spectrum exist, digital terrain models for remote tropical carbonate landscapes remain few in number. The paucity is due, in part, to the lack of in situ measurements of pertinent information needed to tune water depth derivation algorithms. In many cases, the collection of the needed ground-truth data is often prohibitively expensive or logistically infeasible. We present an approach for deriving water depths up to 15 m in Case 1 waters, whose inherent optical properties can be adequately described by phytoplankton, using multi-spectral satellite imagery without the need for direct measurement of water depth, bottom reflectance, or water column properties within the site of interest. The reliability of the approach for depths up to 15 m is demonstrated for ten satellite images over five study sites. For this depth range, overall RMSE values range from 0.89 m to 2.62 m when using a chlorophyll concentration equal to 0.2 mg m−3 and a generic seafloor spectrum generated from a spectral library of common benthic constituents. Accuracy of water depth predictions drastically decreases beyond these depths. Sensitivity analyses show that the model is robust to selection of bottom reflectance inputs and sensitive to parameterization of chlorophyll concentration.





  • A modified version of the kernel-driven model for correcting the diffuse light of ground multi-angular measurements
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Yadong Dong, Ziti Jiao, Anxin Ding, Hu Zhang, Xiaoning Zhang, Yang Li, Dandan He, Siyang Yin, Lei Cui

    When using the kernel-driven bidirectional reflectance distribution function (BRDF) model to process multi-angular measurements, the input multi-angular measurements must be corrected for atmospheric effects. However, in current databases, a significant number of ground-based multi-angular measurements contain either no corrections or only approximate corrections for atmospheric effects. Thus, the blended diffuse light in the total incident irradiance will result in considerable smoothing of the reflectance anisotropy retrieved by the kernel-driven model unless an atmospheric correction process is conducted. In this study, we propose a diffuse-light correction (DLC) form of the kernel-driven model that improves its ability to process multi-angular measurements blended with hemispherical diffuse light. The DLC form of the kernel-driven model can be used to retrieve the intrinsic reflectance anisotropy of the observed target from atmospheric-uncorrected multi-angular measurements. This study used multi-angular data simulated by the PROSAIL and Radiosity Applicable to Porous IndiviDual objects (RAPID) BRDF model, atmospheric-corrected Polarization and Directionality of the Earth's Reflectances (POLDER), Cloud Absorption Radiometer (CAR) multi-angular measurements and their simulated data based on the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) tools to validate the effectiveness of the DLC form of the kernel-driven model. The results indicated that the reflectance factors directly retrieved by the kernel-driven model are considerably smoothed by the blended diffuse light, especially in hotspot regions. Even under clear and cloudless sky conditions, the retrieved hotspot reflectance in the red band is still underestimated by an average of 9.25%, 7.72%, 11.0% and 13.8% for the PROSAIL, RAPID, POLDER and CAR data, respectively. In contrast, the hotspot reflectance retrieved by the DLC form of the kernel-driven model is very close to the intrinsic reflectance anisotropy of the targets; the average relative error of the DLC form of the kernel-driven model is only 1.99%, 1.50%, 4.57% and 3.42%, respectively. Although the reflectance reconstructed by the DLC form of the kernel-driven model in the hotspot region represents a considerable improvement compared with the reflectance retrieved by the original kernel-driven model, its improvement on the root mean square error (RMSE) and the bias of the entire datasets is not very apparent. Using the DLC form of the kernel-driven model can significantly improve the ability of the kernel-driven model to process multi-angular measurements blended with hemispherical diffuse irradiance.





  • Influence of reconstruction scale, spatial resolution and pixel spatial relationships on the sub-pixel mapping accuracy of a double-calculated spatial attraction model
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Shangrong Wu, Jianqiang Ren, Zhongxin Chen, Wujun Jin, Xingren Liu, He Li, Haizhu Pan, Wenqian Guo

    Mixed pixels universally exist in remote sensing images, and they are one of the main obstacles for further improving the accuracy of land cover recognition and classification. Since the concept of sub-pixel mapping (SPM) is proposed, SPM technology has rapidly become an important method to solve the problem of mixed pixels. To further improve SPM accuracy, this paper first proposes a double-calculated spatial attraction model (DSAM) combining the advantages of the spatial attraction model (SAM) and the pixel swap model (PSM). Then, based on the full validation of the proposed DSAM, how multiple factors affect the SPM accuracy is analyzed using the multispectral remote sensing (MRS) images. Finally, by analyzing the maximum variations in the ranges of the overall accuracy and the kappa coefficient under different multiple factors, the order of factors influencing SPM accuracy is determined as follows: reconstruction scale > image spatial resolution > pixel spatial relationships. The results can serve as a reference for other scholars in setting model parameters and selecting the appropriate remote sensing data, thereby helping them achieve more accurate SPM results.





  • Spatially-explicit monitoring of crop photosynthetic capacity through the use of space-based chlorophyll fluorescence data
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Yongguang Zhang, Luis Guanter, Joanna Joiner, Lian Song, Kaiyu Guan

    Plant functional traits such as photosynthetic capacity are critical parameters for terrestrial biosphere models. However, their spatial and temporal characteristics are still poorly represented. In this study, we used satellite observations of sun-induced fluorescence (SIF) to estimate top-of-canopy photosynthetic capacity (maximum carboxylation rate, V cmax at a reference temperature of 25 °C) for crops, which was in turn utilized to simulate regional gross primary production (GPP). We first estimate the key parameter, V cmax , in the widely-used FvCB photosynthesis model using field measurements of CO2 and water fluxes during 2007–2012 at seven crop eddy covariance flux sites over the US Corn Belt. The results showed that satellite far-red SIF retrievals have a stronger link to V cmax at the seasonal scale (R2 = 0.70 for C4 and R2 = 0.63 for C3 crop) as compared with widely-used vegetation indices. We calibrate an empirical model linking V cmax with SIF that was used to estimate spatially and temporally varying crop V cmax for the US Corn Belt region. The resulting V cmax maps are used together with meteorological data from MERRA reanalysis data and vegetation structural parameters derived from the satellite-based spectral reflectance data to constrain the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model in order to estimate regional crop GPP. Our results show a substantial improvement in the seasonal and spatial patterns of cropland GPP when compared with crop yield inventory data. The evaluation with tall tower atmospheric CO2 measurements further supports our estimation of spatiotemporal V cmax from space-borne SIF. Considering that SIF has a direct link to photosynthetic activity, our findings highlight the potential to infer regional V cmax using remotely sensed SIF data and to use this information for a better quantification of regional cropland carbon cycles.





  • Particle size effects on soil reflectance explained by an analytical radiative transfer model
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Morteza Sadeghi, Ebrahim Babaeian, Markus Tuller, Scott B. Jones

    Experimental evidence points to an intimate link between soil reflectance, R, and particle/aggregate diameter, D. Based on this strong correlation, various statistical methods for remote and proximal sensing of soil texture and hydraulic properties have been developed. In this paper, we derive a more fundamental and physically-based analytical radiative transfer model that yields a closed-form functional R(D) relationship for dry soils. Despite several simplifying assumptions, the proposed model shows good agreement with measured spectral reflectance (350–2500 nm) data of six soils covering a broad range of textures, colors, and mineralogies. The proposed S-shaped R(D) function resembles cumulative particle and pore size distributions as well as the soil water characteristic function. These analogies may potentially lead to new avenues for developing novel physical models for extracting important soil properties from remotely sensed reflectance data.





  • Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Yang Yang, Martha C. Anderson, Feng Gao, Brian Wardlow, Christopher R. Hain, Jason A. Otkin, Joseph Alfieri, Yun Yang, Liang Sun, Wayne Dulaney

    The Evaporative Stress Index (ESI) quantifies temporal anomalies in a normalized evapotranspiration (ET) metric describing the ratio of actual-to-reference ET (f RET ) as derived from satellite remote sensing. At regional scales (3–10 km pixel resolution), the ESI has demonstrated the capacity to capture developing crop stress and impacts on regional yield variability in water-limited agricultural regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to spatial and temporal limitations in the standard ESI products. In this study, we investigated potential improvements to ESI by generating maps of ET, f RET , and f RET anomalies at high spatiotemporal resolution (30-m pixels, daily time steps) using a multi-sensor data fusion method, enabling separation of landcover types with different phenologies and resilience to drought. The study was conducted for the period 2010–2014 covering a region around Mead, Nebraska that includes both rainfed and irrigated crops. Correlations between ESI and measurements of maize yield were investigated at both the field and county level to assess the potential of ESI as a yield forecasting tool. To examine the role of crop phenology in yield-ESI correlations, annual input f RET time series were aligned by both calendar day and by biophysically relevant dates (e.g. days since planting or emergence). At the resolution of the operational U.S. ESI product (4 km), adjusting f RET alignment to a regionally reported emergence date prior to anomaly computation improves r2 correlations with county-level yield estimates from 0.28 to 0.80. At 30-m resolution, where pure maize pixels can be isolated from other crops and landcover types, county-level yield correlations improved from 0.47 to 0.93 when aligning f RET by emergence date rather than calendar date. Peak correlations occurred 68 days after emergence, corresponding to the silking stage for maize when grain development is particularly sensitive to soil moisture deficiencies. The results of this study demonstrate the utility of remotely sensed ET in conveying spatially and temporally explicit water stress information to yield prediction and crop simulation models.





  • The impacts of spatial baseline on forest canopy height model and digital terrain model retrieval using P-band PolInSAR data
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Zhanmang Liao, Binbin He, Albert I.J.M. van Dijk, Xiaojing Bai, Xingwen Quan

    Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) has shown potential for the retrieval of a forest canopy height model (CHM) and the underlying solid earth digital terrain model (DTM). However, because of non-volume decorrelation and other unavoidable errors, the robustness of retrieval heights is sensitive to the spatial baseline of the selected InSAR pairs, which relates forest parameters to measured coherence. Within the context of the random volume over ground (RVoG) model and the three-stage inversion method, we aimed to quantify the influence of spatial baseline on the inversions at P-band, which are distinct from the inversions at higher frequency due to the non-negligible ground contributions. This information assists in optimal baseline selection and the development of robust inversion schemes. Assumptions about the extinction coefficient and additional DTM or DEM were used to reduce the influence of ground contribution on CHM and DTM inversion, respectively. Inversions from published airborne repeat-pass P-band PolInSAR data with four different spatial baselines were validated against LiDAR-derived DTM and CHM data. The results show that a longer spatial baseline performed better in DTM inversion. The longest baseline produced the best R2 of 0.995 and RMSE of 0.555 m, much better than the smallest baseline with an R2 of 0.794 and RMSE of 3.74 m. A threshold height could be identified that determines the overestimation and underestimation of CHM inversion due to the non-volume decorrelation. Different baselines produced different threshold heights, making CHM inversion only accurate for a limited range of forest height around the threshold. The optimal baseline produced a CHM with R2 of 0.605 and RMSE of 2.67 m. Additionally, we found that using multiple baselines has the potential to improve CHM inversion, improving the R2 to 0.827 and RMSE to 0.876 m in our study.





  • Drivers of spatial variability in greendown within an oak-hickory forest landscape
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): V.C. Reaves, A.J. Elmore, D.M. Nelson, B.E. McNeil

    Declining near-infrared (NIR) surface reflectance between early and late summer, here termed greendown, is a common, yet poorly understood phenomena in remote sensing time series of temperate deciduous forests. As revealed by phenology analysis of Landsat satellite data, there are strong spatial patterns in the rate of greendown across temperate deciduous forest landscapes, and analyzing these patterns could help advance our understanding of surface reflectance drivers. Within an oak-hickory (Quercus spp. – Carya spp.) forest landscape in western Maryland, USA, we tested how spatial patterns in greendown related to potential drivers at the landscape-, tree crown- and leaf-levels. We found that 50% of the spatial variability in greendown was explained by landscape variables, with greendown particularly higher in locations with higher maximum greenness, more southerly aspects, or locations with greater abundance of white oak (Quercus alba). The importance of species composition as a driver of greendown was supported at the tree crown level, where, relative to three other tree species, white oak exhibited the most consistent trend toward more vertical leaf angles later in the season. At the leaf level, NIR reflectance decreased in productive sites where %N increased, and δ13C decreased, through the season. However, among all sites, there were no consistent seasonal trends in foliar NIR reflectance, and we found no correlation among leaf-level NIR reflectance and satellite-observed greendown. Collectively, these results suggest that the spatial variability of greendown in this oak-hickory forest is most strongly controlled by an interaction of topographic and species compositional drivers operating at the landscape and tree crown levels. We found spatial analysis of greendown to be a useful approach to explore landscape-, tree crown-, and leaf-level controls on surface reflectance, and thereby help translate land surface phenology data into predictions of ecosystem structure and functioning.





  • Topographic controls on the surging behaviour of Sabche Glacier, Nepal (1967 to 2017)
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Arminel M. Lovell, J. Rachel Carr, Chris R. Stokes

    Using a combination of Landsat, Pléiades and CORONA satellite imagery from 1967 to 2017, we map changes in the terminus position, ice surface velocity and surface elevation of Sabche Glacier, and report the first observations of surging behaviour in central Nepal. Our observations show that Sabche Glacier surged four times over the last 50 years. The three most recent surges occurred at 10 to 11-year cycles, which is one of the shortest surge cycles ever recorded. Detailed analysis of the most recent surge (2012 onwards), indicates that the glacier advanced 2.2 km and experienced maximum velocities of 1.6 ± 0.10 m day−1. During this surge, there was a surface elevation gain at the terminus of up to 90 ± 6.19 m a−1, with a corresponding surface lowering of between 10 ± 6.19 and 60 ± 6.19 m a−1, 3 km up-glacier of the terminus. This transfer of mass amounted to a volume of ~2.7 × 107 ± 0.1 × 107 m3a−1. Sabche Glacier is the first surge-type glacier to be observed in the central Himalayas, but this is consistent with a previous global analysis which indicates that surge-type glaciers should exist in the region. We hypothesise that the surge is at least partially controlled by subglacial topography, whereby a major subglacial overdeepening and constriction 3 km up-glacier of the terminus provides resistance to glacier flow from the accumulation area to the ablation area. This overdeepening appears to store mass until a threshold is crossed, after which the glacier flows out of the subglacial depression and rapidly surges over a bedrock lip and down the valley. Thus, whilst the surges are likely to be facilitated by subglacial processes (e.g. changes in subglacial hydrology and/or basal thermal regime), the topographic setting of the glacier appears to be modulating both the timing and duration of each surge.





  • A continent-wide search for Antarctic petrel breeding sites with satellite remote sensing
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Mathew R. Schwaller, Heather J. Lynch, Arnaud Tarroux, Brandon Prehn

    The Antarctic petrel (Thalassoica antarctica) has been identified as a key species for monitoring the status and health of the Southern Ocean and Antarctic ecosystems. Breeding colonies of the Antarctic petrel are often found on isolated nunataks far from inhabited stations, some up to hundreds of kilometers from the shoreline. It is difficult therefore to monitor and census known colonies, and it is believed that undiscovered breeding locations remain to be found. We developed an algorithm that can detect Antarctic petrel colonies and used it to complete a continent-wide survey using Landsat-8 Operational Line Imager (OLI) imagery in Antarctica up to the southernmost extent of Landsat's orbital view at 82.68°S. Our survey successfully identified 8 known Antarctic petrel colonies containing 86% of the known population of Antarctic petrels. The survey also identified what appears to be a significant population of breeding birds in areas not known to host breeding Antarctic petrel colonies. Our survey suggests that the breeding population at Mt. Biscoe (66°13′S 51°21′E), currently reported to be in the 1000s, may actually be on the order of 400,000 breeding pairs, which would make it the largest known Antarctic petrel breeding colony in the world. The algorithm represents a first-ever attempt to apply satellite remote sensing to assess the distribution and abundance of the Antarctic petrel on a continent-wide basis. As such, we note several algorithm shortcomings and identify research topics for algorithm improvement. Even with these caveats, our algorithm for identifying Antarctic petrel colonies with Landsat imagery demonstrates the feasibility of monitoring their populations using satellite remote sensing and identifies breeding locations, including Mt. Biscoe, that should be considered high priorities for validation with directed field surveys.





  • Retrieving forest canopy clumping index using terrestrial laser scanning data
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Lixia Ma, Guang Zheng, Xiaofei Wang, Shiming Li, Yi Lin, Weimin Ju

    Quantitatively characterizing the non-random spatial distributions of foliage elements including coniferous needles is critical to map the radiation regime and retrieve the biophysical parameters of a given forest canopy from three-dimensional (3-D) perspective. Different experimental setups bring various challenges to the process of retrieving forest canopy clumping index (CI) using terrestrial laser scanning (TLS). In this paper, through developing a voxel-based gap size (VGS) algorithm, we compared the TLS-based forest canopy CIs with the ones obtained using the digital hemispherical photography (DHP)-based and tracing radiation and architecture of canopy (TRAC)-based approaches. Moreover, we investigated the effects of incident directions of solar beams, voxel size, and woody canopy components on the final retrieval accuracy of forest canopy CIs. Our results showed that: (1) TLS-based CIs accounted for 81% (N = 30, p < 0.001) of variations in the DHP-based method. (2) the anisotropic nature of forest canopy CIs suggested that a relatively comprehensive TLS data of a forest canopy was required to investigate the 3-D spatial variations of forest gap size distributions and CIs. (3) The user-defined laser sampling spacing was a reliable reference value to determine the voxel size when using the VGS algorithm. (4) It was recommended to separate woody canopy components when computing the forest canopy CI, especially for forest plots with higher proportions of woody material. (5) The effects of the penumbra on TLS-based forest canopy CIs were much more limited compared with the traditional optical instruments (i.e., DHP or TRAC). This work provides a solid foundation to dramatically improve the retrieval accuracy of leaf area index (LAI) using TLS.





  • A spatial ensemble approach for broad-area mapping of land surface properties
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Sam Hooper, Robert E. Kennedy

    Understanding rapid global change requires land cover maps with broad spatial extent, but also fine spatial and temporal resolution. Developing such maps presents a unique challenge, as variability in relationships between spectral characteristics (i.e., predictors) and a response variable is likely to increase with the size of the region across which a model is built and applied. Although most mapping approaches apply the same predictor-response relationships globally across the entire modeling region, learned relationships from one local area may be invalid for another when predicting across broad extents. Here, we adapted a spatial ensemble approach borrowed from species distribution modeling to land cover mapping, and evaluated whether the approach could faithfully represent spatial variation in relationships between land cover and spectral data. The spatiotemporal exploratory model (STEM) uses an ensemble of regression trees defined within spatially overlapping support sets, producing a broad-extent map that reflects variability at the spatial scale of each constituent support set. As test cases for reference maps, we used 30-m-resolution forest canopy and impervious surface cover layers from the 2001 U.S. National Land Cover Database (NLCD) for the states of Washington, Oregon, and California. When testing strategies for support set size and sampling intensity, we found that predictor-response relationships were strongest when individual components of the spatial ensemble were small and when sampling intensity was high. Compared to aspatial bagged decision tree and random forest models, we found that the STEM approach successfully captured variation in our source maps, both globally and at scales smaller than the modeling region. Leveraging the spatial structure of a STEM, we also mapped per-pixel spatial variation in prediction confidence and the importance of different predictor variables. After testing appropriate spatial ensemble and sampling strategies, we extended the predictor-response relationships gleaned from the 2001 source maps into a yearly time series based on temporally-smoothed spectral data from the LandTrendr algorithm. The end products were yearly forest canopy and impervious surface cover time series representing 1990–2012. Formal evaluation showed that our temporally extended maps also closely resembled NLCD maps from 2011. The aim of this research was to cultivate the implicit relationships between spectral data and a given map, not improve them, but as the need for time series maps produced at both broad extents and fine resolutions increases, our results demonstrate that an ensemble of locally defined estimators is potentially more appropriate than conventional ensemble models for land cover mapping across broad extents.





  • Mapping of forest alliances with simulated multi-seasonal hyperspectral satellite imagery
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Matthew L. Clark, Jennifer Buck-Diaz, Julie Evens

    A consistent and hierarchical classification of vegetation, such as the U.S. National Vegetation Classification (NVC) system, supports comprehensive conservation and management of natural ecosystems. At a detailed level, the NVC alliance is defined by diagnostic species and composition. Maps at this level of classification are often produced at local to regional scales (areas <25,000 km2) with costly manual to semi-automated interpretation of high resolution imagery. The main objective of this study was to assess the effectiveness of machine learning for automated, per-pixel (30 m) mapping of forest alliances with multi-seasonal hyperspectral imagery from a future satellite mission (HyspIRI), as simulated from Airborne Visible/Infrared Imaging Spectrometer Classic (AVIRIS-C) data. The study area was the San Francisco (S.F.) Bay Area, California, where we mapped forest alliances at regional and county scales. We implemented the Support Vector Machine (SVM) classifier in a two-stage approach, first mapping regional land cover followed by forest alliances in closed-canopy tree pixels. Predictor variables were reflectance bands and hyperspectral metrics based on indices, derivatives and absorption-fitting techniques applied to reflectance spectra, with data grouped into summer and three-season (spring, summer, fall) sets. For forest alliances, hyperspectral metrics improved overall accuracy of classifications by 2.9 to 6.4% relative to classifications based on the original reflectance bands. Multi-seasonal data improved overall accuracy by 1.3 to 6.2% relative to summer-only data. Using multi-seasonal metrics, the S.F. Bay Area regional classification with 21 alliances had an overall accuracy of 65.7% (Kappa 0.63), while the Sonoma County classification with 16 alliances had an accuracy of 75.9% (Kappa 0.72). Most forest alliances had internal variation in lifeform, species and structural properties that increased within-class spectral-temporal variation and complicated discrimination. Despite this challenge, classification accuracies were similar to regional NVC alliance reference data. We conclude that a hyperspectral satellite, with its repeat and global image acquisitions, has strong potential for accurate and economical mapping and monitoring the Earth's vegetation communities.





  • Tracking crop phenological development using multi-temporal polarimetric Radarsat-2 data
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Francis Canisius, Jiali Shang, Jiangui Liu, Xiaodong Huang, Baoluo Ma, Xianfeng Jiao, Xiaoyuan Geng, John M. Kovacs, Dan Walters

    Information on crop phenological development stages such as emergence, flowering, fruiting, maturing and senescence is essential for crop production surveillance and yield prediction. It has long been related to optical spectral signatures such as the Normalized Difference Vegetation Index (NDVI) or spectral shifts in the red-edge range. In recent years, more efforts have been made to explore the sensitivity of Synthetic Aperture Radar (SAR), particularly polarimetric SAR signatures, to crop biophysical parameters or phenological stages. In this study, phenological metrics of canola (Brassica napus) and spring wheat (Triticum spp.) are related with temporal evolution of polarimetric SAR parameters derived from the C-band RADARSAT-2 full polarimetric SAR data. Both crops are very common in north eastern Ontario, Canada, but have very anatomically different development processes. From multi-temporal RADARSAT-2 data acquired in three consecutive years (2012–2014), significant correlations were observed between a number of SAR polarimetric parameters and the growth parameters of both crops. Strong correlation was observed between plant height and the Alpha angle of the Cloude-Pottier decomposition, with the R2 of 0.91 and 0.66 for canola and wheat, respectively. The R2 increased when the polarimetric parameters were smoothed in the time domain (R2 of 0.98 for canola and 0.88 for wheat). Strong correlation was also observed for the two crops between the effective leaf area index (LAIe) and the Beta angle, and between days-after-seeding (DAS) and a combination of the Alpha and the Beta angles. These findings show that multi-temporal C-band polarimetric SAR parameters could be used for tracking crop phenological development stages.





  • Using picosatellites for 4-D imaging of volcanic clouds: Proof of concept using ISS photography of the 2009 Sarychev Peak eruption
    Publication date: 1 June 2018
    Source:Remote Sensing of Environment, Volume 210

    Author(s): Klemen Zakšek, Mike R. James, Matthias Hort, Tiago Nogueira, Klaus Schilling

    Volcanic ash clouds can present an aviation hazard over distances of thousands of kilometres and, to help to mitigate this hazard, advanced numerical models are used to forecast ash dispersion in the atmosphere. However, forecast accuracy is usually limited by uncertainties in initial conditions such as the eruption rate and the vertical distribution of ash injected above the volcano. Here, we demonstrate the potential of the Telematics Earth Observation Mission (TOM) picosatellite formation, due for launch in 2020, to provide valuable information for constraining ash cloud dispersion models through simultaneous image acquisition from three satellites. TOM will carry commercial frame cameras. Using photogrammetric simulations, we show that such data should enable ash cloud heights to be determined with a precision (~30–140 m depending on configuration) comparable to the vertical resolution of lidar observations (30–180 m depending on the cloud height). To support these estimates, we processed photographs taken from the International Space Station of the 2009 Sarychev Peak eruption, as a proxy for TOM imagery. Structure-from-motion photogrammetric software successfully reconstructed the 3-D form of the ascending ash cloud, as well as surrounding cloud layers. Direct estimates of the precision of the ash cloud height measurements, as well as comparisons between independently processed image sets, indicate that a vertical measurement precision of ~200 m was achieved. Image sets acquired at different times captured the plume dynamics and enabled a mean ascent velocity of 14 m s−1 to be estimated for regions above 7 km. In contrast, the uppermost regions of the column (at a measured cloud top height of ~11 km) were not ascending significantly, enabling us to constrain a 1-D plume ascent model, from which estimates for the vent size (50 m) and eruption mass flux (2.6 × 106 kg s−1) could be made. Thus, we demonstrate that nanosatellite imagery has the potential for substantially reducing uncertainties in ash dispersion models by providing valuable information on eruptive conditions.

    Graphical abstract

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  • Beihilfe-Affäre in Niederlanden: Jagd auf Schwache
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  • Kommentar zu Nawalny: Der Verlierer könnte im Kreml sitzen
    Kremlgegner Nawalny ist längst der zweitwichtigste Politiker in Russland, meint Ina Ruck. Die überstandene Nowitschok-Vergiftung könnte ihm am Ende sogar geholfen haben - und seine Verhaftung Präsident Putin schaden.
  • Marktbericht: Kursgewinne zum Börsenstart
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  • Nach CDU-Parteitag: Merz stellt sich hinter Laschet
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  • Corona-Krise: Darf es Privilegien für Geimpfte geben?
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  • Was Laschets Wahl zum CDU-Chef bedeutet
    Bergmannssohn Laschet hält die Rede seines Lebens und steht nun vor großen Baustellen. Friedrich Merz vergeigt zum zweiten Mal seine Rede und will Minister werden. Spahn schießt ein Eigentor. Die Erkenntnisse vom CDU-Parteitag. Von Wenke Börnsen.
  • Zeitgeschichte: Die tagesschau vor 20 Jahren
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  • Bundesregierung ordnet Suche nach Mutationen an
    Das Coronavirus mutiert - und wird ansteckender. Trotzdem wurden die Proben in deutschen Laboren bisher nur selten auf mögliche neue Virus-Varianten geprüft. Mit einer Such-Pflicht für die Labore soll sich das jetzt ändern.
  • Reportage: Sorge im Saarland über Corona-Lockerungen in Luxemburg
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  • Guide Michelin: Auszeichnung trotz geschlossener Restaurants
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  • Russland: Nawalny muss 30 Tage in Haft
    Ein russisches Gericht hat gegen den Oppositionellen Nawalny nach seiner Rückkehr aus Deutschland 30 Tage Haft verhängt. Es geht um Vorwürfe, der 44-Jährige habe gegen Meldeauflagen aus einem früheren Strafprozess verstoßen.
  • In Belgien und der Schweiz gilt Homeoffice-Pflicht
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  • Eishockey-Weltverband entzieht Belarus die WM
    Der Eishockey-Weltverband IIHF hat Belarus die Weltmeisterschaft in diesem Jahr entzogen. Nach monatelangen Protesten hatten Sponsoren zuletzt so massiv Druck gemacht, dass dem Weltverband keine Wahl blieb.

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