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Remote Sensing of Environment
Elsevier
Remote Sensing of Environment

Elsevier

0034-4257

Remote Sensing of Environment/Journal Remote Sensing of EnvironmentSCIISTPEI
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    Assessing biodiversity from space: Impact of spatial and spectral resolution on trait-based functional diversity

    Helfenstein, Isabelle S.Schneider, Fabian D.Schaepman, Michael E.Morsdorf, Felix...
    16页
    查看更多>>摘要：Observing functional diversity continuously in time and space using satellite imagery forms the basis for studying impact, interactions, and feedback of environmental change mechanisms on ecosystems and biodiversity globally. Functional diversity of plant traits links ecosystem functioning and biodiversity. This work presents an approach to map and quantify functional diversity of physiological forest traits derived from 20 m Sentinel-2 data in a temperate forest ecosystem. We used two complementary data sources, namely high-resolution, as well as spatially resampled airborne imaging spectroscopy data and Sentinel-2 data, to ensure our methods support consistently mapping functional diversity from space. We retrieved three physiological traits related to forest health, stress, and potential productivity, namely chlorophyll, carotenoid, and water content, from airborne imaging spectroscopy and Sentinel-2 data using corresponding spectral indices as proxies. We analyzed changes in two functional diversity metrics, namely functional richness and divergence, at different spatial resolutions. Both functional diversity metrics depend on the size and number of pixels to derive functional diversity as a function of distance, leading to different interpretations. When mapping functional diversity using Sentinel-2 data, small-scale patterns <1.1 ha were no longer visible, implying a minimum calculation area with 60 m radius recommended for retrieval of functional diversity metrics. The spectrally convolved and spatially resampled airborne spectroscopy data and the native Sentinel-2 data were correlated with r = 0.747 for functional richness and r = 0.709 for divergence in a 3.1 ha neighborhood. Functional richness was more affected by the differences in trait maps between the acquisitions resulting from effects in illumination and topography compared with functional divergence. Further differences could be explained by varying illumination/observation effects and phenological status of the vegetation at acquisition. Our approach demonstrates the importance of spatial and spectral resolution when scaling diversity assessments from regional to continental scales.
      原文链接:
    • NSTL
    • Elsevier

    Deforestation detection using scattering power decomposition and optimal averaging of volume scattering power in tropical rainforest regions

    Sugimoto, RyuKato, SoushiNakamura, RyosukeTsutsumi, Chiaki...
    13页
    查看更多>>摘要：Synthetic aperture radar (SAR) has been a powerful tool for deforestation detection in tropical rainforests. Polarimetric SAR (POLSAR) data are acquired in a quad-polarization mode, and L-band POLSAR data in particular are one of the few SAR data types that preserve the dielectric properties and structures of the scatterer. POLSAR data consist of 2 x 2 scattering matrices and consequently offer superior target recognition compared with dual-polarization data. In this study, we applied scattering power decomposition suitable for detecting deforestation in near real-time to POLSAR data obtained from the Earth observation sensor Phased Array type L band Synthetic Aperture Radar-2 (PALSAR-2). The reflection symmetry condition is known to apply to natural distributed objects (i.e., the cross-correlation between co-and cross-polarization data is zero). Inspired by this, we theoretically and experimentally examined the volume scattering power component to distinguish natural forests from the surrounding area. Two important results were verified for natural forests: as the window size of the ensemble average increases, (i) the coherency matrix approaches a simple theoretical form and (ii) the volume scattering power becomes dominant among the scattering power components. Based on these results, we constructed an algorithm that applies scattering power decomposition for detecting deforestation. We produced reference data using high-resolution optical images and evaluated the performance of the derived deforestation map in the Amazon natural forest when employing various window sizes for the ensemble average. At an optimal window size of 15 x 15 pixels, the deforestation detection performance reached a user's accuracy of 94.9% +/- 1.5%, a producer's accuracy of 72.3% +/- 1.2%, and a kappa coefficient of 0.816 +/- 0.0039. Sparse trees left after logging increased the volume scattering power and reduced the producer's accuracy. The proposed algorithm can contribute to deforestation detection with slightly lower accuracy than that of the annual map provided by Global Forest Change. Further, the proposed algorithm is robust to the seasonal variations in tropical rainforests and temporal variations in the deforestation process. Consequently, the proposed algorithm employing the six component scattering power decomposition method can be utilized in near real-time without considering applicable areas in tropical rainforests. Subsequently, we applied our algorithm to dual-polarization data, which are acquired by PALSAR-2 much more frequently than POLSAR data. The false detection rate did not increase when using the dual-polarization data; however, the omission error increased considerably compared with that when using POLSAR data owing to the low total power obtained from the dual-polarization data.
      原文链接:
    • NSTL
    • Elsevier

    Evolution of geodetic mass balance over the largest lake-terminating glacier in the Tibetan Plateau with a revised radar penetration depth based on multi-source high-resolution satellite data

    Zhou, YushanLi, XinZheng, DonghaiLi, Zhiwei...
    17页
    查看更多>>摘要：The southeast Tibetan Plateau has become the region with the fastest rate of mass loss within High Mountain Asia (HMA), and the mass loss from lake-terminating glaciers in the region is the most significant. However, the evolution of the mass change of lake-terminating glaciers is still unclear, and accurate penetration depth estimates of C-band and X-band radar in glacierized areas are unknown, which greatly hinders the monitoring and understanding of the glacier evolution. Hence, in this study, taking the largest lake-terminating glacier in HMA (the Yanong Glacier) as the study object, we first evaluated the X-band penetration depth using Ple ' iades and TanDEM-X data, and further combined the penetration depth difference between the C-band and X-band radar (1.14 +/- 0.14 m) to estimate the C-band penetration depth based on a newly proposed area-weighted strategy, according to surface categories. The results indicate a region-wide average penetration depth of 1.84 +/- 0.59 m and 2.98 +/- 0.61 m for the X-band and C-band radar, respectively, demonstrating either an underestimation of 60% or an overestimation of 202% for the previous C-band penetration corrections. On this basis, we determined the multi-temporal glacier mass balance for the Yanong Glacier, using KH-9, SRTM DEM, SPOT-7, and multi-orbit TanDEM-X data. The results show that the Yanong Glacier has been in a state of serious mass loss (at a rate of -0.73 +/- 0.13 m w.e./a) during 1974-2015, and the rate of mass loss has doubled since 2000 (i.e., -0.48 +/- 0.20 (1974-2000), -0.95 +/- 0.20 (2000-2012), and - 1.02 +/- 0.53 m w.e./a (2012-2015)). Moreover, the interannual mass change has shown a highly volatile and accelerating trend during 2012-2015 (i.e., -0.47 +/- 0.85 (2011-2012), -0.87 +/- 0.66 (2012-2013), -1.26 +/- 0.72 (2013-2014), and - 1.58 +/- 0.44 m w.e./a (2014-2015)). By further analyzing the ERA5 reanalysis data and the changes in the proglacial lake and glacier dynamics, we can conclude that, qualitatively: 1) the long-term acceleration of mass loss at the Yanong Glacier has mainly been controlled by climate change, but has also been affected by the glacier dynamics to a limited extent; and 2) the inter-annual acceleration of mass loss has, to some extent, been affected by subglacial/englacial melting. The findings of this study will not only provide an accurate penetration depth correction for future relevant studies, but will also significantly improve the understanding of the evolution of lake-terminating glaciers in HMA and other high mountain areas of the world.
      原文链接:
    • NSTL
    • Elsevier

    Mapping snow cover in forests using optical remote sensing, machine learning and time-lapse photography

    Zhao, LiqiangMenzel, LucasLin, KairongLuo, Jianfeng...
    14页
    查看更多>>摘要：The accurate spatial information of snow cover is useful for understanding the impact of global warming, and it is of high significance for hydrological disaster prediction, water resources management, and climate change research. The Normalized Difference Snow Index (NDSI) based approach has been used extensively around the world for mapping snow, and they displayed high accuracy in open areas. However, capturing snow cover in forests remains problematic due to the obstruction effects of the forest canopy, which causes the snow cover area to be seriously underestimated. In this paper, we present a new algorithm based on machine learning (ML) technology to improve the accuracy of binary snow cover (BSC) mapping in forests, using the remotely sensed surface reflectance and ground truth data. A time-lapse photography network with a two-hour resolution was established in the eastern Qilian Mountains in northwestern China to obtain the ground truth data both in forests and open areas. We trained Random Forests (RF) with the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data from bands 1-7 to generate BSC results (RF-BSC). Then we evaluated RF-BSC and the NDSI-derived BSC maps with three different NDSI thresholds (i.e., 0.10, 0.29, and 0.40) against ground truth data. The results indicate that the proposed algorithm has a high performance in forest BSC mapping in this area, compared to the NDSI-threshold approach. The RF-BSC can retrieve 67% of all real forest snow pixels, while the NDSI-based BSC can only detect 8-14%. We also find that the performance of the algorithm seems to be sensitive to changes in solar illumination conditions and forest coverage. This study suggests that machine learning with the fusion of optical remote sensing and ground-based observations is an effective approach for improving the accuracy of forest snow cover mapping at regional scales.
      原文链接:
    • NSTL
    • Elsevier

    Annual forest disturbance intensity mapped using Landsat time series and field inventory data for the conterminous United States (1986-2015)

    Lu, JiamingHuang, ChengquanTao, XinGong, Weishu...
    21页
    查看更多>>摘要：Forest disturbances can have broad impact on the climate, local environment, and the regeneration of the forest ecosystem. The nature and magnitude of such impact is largely driven by disturbance intensity. In this study, by integrating field plot measurements collected by the Forest Inventory and Analysis program with time series Landsat observations, we produced the first set of annual forest disturbance intensity map products quantifying the percentage of basal area removal (PBAR) at the 30-m resolution for the conterminous United States (CONUS) from 1986 to 2015. The derived map products revealed that during the 30-year study period, the annual average PBAR values of all disturbed pixels across CONUS ranged from 66% to 70%, and the proportion of those pixels having stand-clearing disturbances ranged from 40% to 58%. High disturbance intensities were concentrated in the Southeastern states from Texas to Virginia and along the Pacific coast and the Cascades in the West. At the national scale, the annual mean PBAR and proportion of stand clearing area (PSCA) values both appeared to follow second order trajectories, with increasing trends at the beginning, decreasing trends towards the end, and turning points around 2003. Overall, there is a net increase of 2% in PBAR and 3% in PSCA from 1986 to 2015. The temporal trends of PBAR and PSCA were also investigated at state and ecoregion levels, with substantial differences found among many states and ecoregions. While states and ecoregions generally follow second order trajectories, the majority had increasing trends throughout much of the study period, reflecting higher disturbance intensities during the later years compared to earlier years. Large increase (>10%) in PBAR was seen in several states (e.g., Virginia, Arkansas, and Minnesota) and ecoregions (e.g., Northern Minnesota Wetlands); however, large decreases (>10%) in PBAR were not observed in any states, and were seen in only one ecoregion, the Blue Mountains in the southeast. The disturbance intensity maps are available from a web portal of the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL-DAAC) at https://doi.org/10.3334/ORNL DAAC/2059.
      原文链接:
    • NSTL
    • Elsevier

    Inversion of the refractive index of marine spilled oil using multi-angle sun glitter images acquired by the ASTER sensor

    Wang, ChenZhang, HuaguoXu, QingCao, Wenting...
    14页
    查看更多>>摘要：Oil spills in the ocean pollute and harm the marine environment. Remote sensing has been widely used to examine and analyze marine oil spills. Refractive index is an important parameter for monitoring, evaluating, and mitigating marine oil spills, but its quantitative detection through remote sensing is still in the exploratory stages. In this study, we developed a quantitative inversion model at a pixel-scale to determine the equivalent refractive index (ERI) of the sea surface. The proposed model involved two important steps. First, we performed simulation experiments and found that the multi-angle Fresnel reflection coefficient ratio is not sensitive to the refractive index, which was further verified via real image geometry. Second, we proposed a sun glitter (SG) correction method by reanalyzing wind data and seawater refractive index in the background seawater area as constraints. High-resolution multi-angle sun glitter (MSG) images of the oil spill in the Gulf of Mexico acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer sensor were used to estimate the ERI. The ERI images with the resolution same as that of the original images (15 m) exhibited significant spatial variability. The estimated ERI exhibited a reasonable range as compared to the existing oil spill refractive index data in relevant literature, which were derived from laboratory measurements and through remote sensing inversion. Finally, the sensitivities of the SG correction model and multi-angle Fresnel ratio model were dis-cussed. The results show that the MSG images can estimate high-resolution ERI of sea surface that plays a sig-nificant role in the observations of marine oil spills.
      原文链接:
    • NSTL
    • Elsevier

    Mapping actual evapotranspiration using Landsat for the conterminous United States: Google Earth Engine implementation and assessment of the SSEBop model

    Senay, Gabriel B.Friedrichs, MacKenzieMorton, CharlesParrish, Gabriel E. L....
    16页
    查看更多>>摘要：The estimation and mapping of actual evapotranspiration (ETa) is an active area of applied research in the fields of agriculture and water resources. Thermal remote sensing-based methods, using coarse resolution satellites, have been successful at estimating ETa over the conterminous United States (CONUS) and other regions of the world. In this study, we present CONUS-wide ETa from Landsat thermal imagery-using the Operational Simplified Surface Energy Balance (SSEBop) model in the Google Earth Engine (GEE) cloud computing platform. Over 150,000 Landsat satellite images were used to produce 10 years of annual ETa (2010-2019) at unprecedented scale. The accuracy assessment of the SSEBop results included point-based evaluation using monthly Eddy Covariance (EC) data from 25 AmeriFlux stations as well as basin-scale comparison with annual Water Balance ETa (WBET) for more than 1000 sub-basins. Evaluations using EC data showed generally mixed performance with weaker (R-2 < 0.6) correlation on sparsely vegetated surfaces such as grasslands or woody savanna and stronger correlation (R-2 > 0.7) over well-vegetated surfaces such as croplands and forests, but location-specific conditions rather than cover type were attributed to the variability in accuracy. Croplands performed best with R-2 of 0.82, root mean square error of 29 mm/month, and average bias of 12%. The WBET evaluation indicated that the SSEBop model is strong in explaining the spatial variability (up to R-2 > 0.90) of ETa across large basins, but it also identified broad hydro-climatic regions where the SSEBop ETa showed directional biases, requiring region-specific model parameter improvement and/or bias correction with an overall 7% bias nationwide. Annual ETa anomalies over the 10-year period captured widely reported drought-affected regions, for the most part, in different parts of the CONUS, indicating their potential applications for mapping regional-and field-scale drought and fire effects. Due to the coverage of the Landsat Path/Row system, the availability of cloud-free image pixels ranged from less than 12 (mountainous cloud-prone regions and U.S. Northeast) to more than 60 (U.S. Southwest) per year. However, this study reinforces a promising application of Landsat satellite data with cloud computing for quick and efficient mapping of ETa for agricultural and water resources assessments at the field scale.
      原文链接:
    • NSTL
    • Elsevier

    Cloud identification and property retrieval from Himawari-8 infrared measurements via a deep neural network

    Wang, XinyueIwabuchi, HironobuYamashita, Takaya
    13页
    查看更多>>摘要：Clouds constitute a key component of weather and climate systems, whereas the uniform retrieval of cloud properties, such as cloud top height (CTH) and cloud optical thickness (COT), requires accuracy and computational efficiency improvements. In this study, an image-based deep neural network (DNN) model for cloud identification and simultaneous retrieval of CTH and ice-COT is developed for Himawari-8 satellite infrared measurements. The DNN model is trained with brightness temperature data from four months in 2016 as the input, and cloud properties of an active remote sensing product from CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) as the target truth. Supplementary variables, including the vertical temperature profile, the surface elevation, and the geometrical parameters, are added as the input data. DNN model performance is first tested with an independent dataset, and then cases over a CloudSat track and a Himawari-8 granule (85?E-205?E, 60?S-60?N) are selected for further validation of the model by comparing its results with those from two physics-based models. For both the water-and ice-CTH estimates, the DNN model shows high consistency with the target values, with an overall CTH correlation coefficient of 0.90 for high ice clouds with COT >= 0.3. Notably, as an infrared method in nature, the DNN extends the predictable ice-COT to similar to 200, with relative biases of similar to 20% for high ice clouds with COT > 1. The strong accuracy of the DNN model is primarily derived from its ability to learn from the spatial features imprinted on the input brightness temperature image, and its integration of information from neighboring pixels in a three-dimensional space. A single full disk estimation with the DNN model takes about 20 min using one processor; therefore, near-real-time cloud property retrieval that is uniformly available over 24 h can be obtained for severe weather monitoring and mesoscale cloud-system studies.
      原文链接:
    • NSTL
    • Elsevier

    Characterizing seasonal variation in foliar biochemistry with airborne imaging spectroscopy

    Chlus, AdamTownsend, Philip A.
    14页
    查看更多>>摘要：Foliar biochemical traits are important indicators of ecosystem functioning and health that are impractical to characterize at large spatial and temporal scales using traditional measurements. However, comprehensive inventories of foliar traits are important for understanding ecosystem responses to anthropogenic and natural disturbances, as inputs into ecosystem process models, and for quantifying spatial variation in functional diversity. Imaging spectroscopy has been demonstrated as a valuable tool for developing maps of ecologically important foliar traits at large scales, but its application to mapping foliar traits over the course of the growing season has been limited. We collected high-resolution imaging spectroscopy data over Blackhawk Island, Wisconsin, USA at eight time points during the 2018 growing season (May - October). Using partial least squares regression (PLSR) we developed predictive models applicable to all dates to produce canopy-level maps of eight traits related to ecophysiological function: chlorophyll content, leaf mass per area and concentrations of calcium, nitrogen, phosphorus, potassium, phenolics and lignin. The accuracy of our models varied across traits (R2: 0.25-0.86); traits with well-defined absorption features were retrieved with high accuracy including chlorophyll (R2: 0.86; %RMSE: 11.0) and total phenolics (R2: 0.86; %RMSE: 11.0). We also assessed how well our models estimated biochemistry on novel species and new dates using a cross-validation analysis. Chlorophyll and total phenolics were well estimated across withheld dates and species, whereas calcium was estimated poorly on both withheld species (R2: 0.08) and dates (R2: 0.07). Our canopy-level maps of macronutrients (N, P and K) showed general trends of decreasing concentration over the course of the year, reflecting dilution by carbon-rich compounds during the growing season and resorption during senescence. Conversely, recalcitrant compounds including lignin and calcium increased until late summer, after which they stabilized. These results demonstrate the potential of current and proposed spaceborne imaging spectroscopy missions for mapping seasonal patterns in foliar biochemistry at a global scale.
      原文链接:
    • NSTL
    • Elsevier

    Refined InSAR tropospheric delay correction for wide-area landslide identification and monitoring

    Wang, YianDong, JieZhang, LuZhang, Li...
    18页
    查看更多>>摘要：SAR Interferometry (InSAR) proves to be effective for investigating landslides. However, its measurement accuracy is largely limited by the complex atmospheric delay distortion in alpine valley regions, resulting in poor performance of landslides detection and monitoring. Particularly, the spatial atmospheric heterogeneity over wide areas cannot be accurately reflected by conventional empirical phase-elevation models or external databased methods. In this study, we proposed a multi-temporal moving-window linear model (MMLM) to correct the tropospheric delay for wide-area landslides investigation. This is a linear regression model based on the elevation-phase relationship for modeling multi-temporal phases within a sliding local window. It mitigates the influence of local turbulent phase, local landslide deformation, and phase unwrapping error on parameter estimation, providing precise heterogeneous atmospheric corrections for wide-area InSAR landslide identification and monitoring. A simulation experiment was conducted to analyze the sensitivity of model parameters settings and evaluate the effectiveness of the MMLM model. Furthermore, we demonstrated the performance of the MMLM model through a comparison with the ERA5, GACOS, spatial-temporal filtering, and traditional linear model using descending and ascending Sentinel-1 data over the reservoir area of the Lianghekou hydropower station. Among the above-mentioned methods, the standard deviation of original unwrapped phases achieved the largest decrease of more than 35% and 50% after correction by the MMLM model for the descending and ascending Sentinel-1 tracks, respectively. In addition, the accurate deformation corrected by the MMLM model improved the landslides investigation, not only can help for delineating landslide boundaries in space but also retrieving movement evolution in time.
      原文链接:
    • NSTL
    • Elsevier