Science of Remote Sensing最新文献

Snow albedo is a key geophysical parameter that controls the energy exchanges between the atmosphere and Earth's surfaces and has been widely utilized in climatic and environmental change studies. However, recent studies have demonstrated that current albedo satellite products still have large uncertainties in snow-covered areas. In this study, we estimated the blue-sky shortwave albedo of snow surfaces using the eXtreme Gradient Boosting (XGBoost) algorithm with Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance values, ERA-5 land reanalysis snow parameters (e.g., snow cover, snow density and snow depth water equivalent) and in situ measurements. In the XGBoost model, the MODIS MCD43 albedo values were input as prior knowledge, and the random sample validation results showed that the R² and root mean square error (RMSE) values of this model were approximately 0.953 and 0.044, respectively. The typical sites for independent validation were subjected to in situ measurements at the UPE_L, AWS5, and CA_ARB sites. Finally, the retrieved XGBoost albedo values were compared with the official NASA MODIS (MCD43, collection 6), the Global Land Surface Satellite (GLASS), and the National Oceanic and Atmospheric Administration (NOAA) Visible Infrared Imaging Radiometer Suite (VIIRS) SURFALB albedo products. The validation results indicated that the proposed approach achieved much greater accuracy (RMSE = 0.052, bias = 0.002) than did the corresponding official MODIS (RMSE = 0.087, bias = −0.033), GLASS (RMSE = 0.089, bias = −0.031) and VIIRS SURFALB albedo (RMSE = 0.100, bias = −0.032) products. The improved shortwave albedo captured the rapid temporal changes in surface snow conditions.

Lakes provide societies and natural ecosystems with valuable services such as freshwater supply and flood control. Water level changes in lakes reflect their natural responses to climatic and anthropogenic stressors; however, their monitoring is costly due to installation and maintenance requirements. With its advanced hardware and computational capabilities, altimetry has become a popular alternative to conventional in-situ gauging, although subject to the temporal availability of altimetric observations. To further improve the temporal resolution of altimetric measurements, we here combine radar altimetry data with Differential Interferometric Synthetic Aperture Radar (D-InSAR), using ten lakes in Sweden as a testing platform. First, we use Sentinel-1A and Sentinel-1B SAR images to generate consecutive six-day baseline interferograms across 2019. Then, we accumulate the phase change of coherent pixels to construct the time series of InSAR-derived water level anomalies. Finally, we retrieve altimetric observations from Sentinel-3, estimate their mean and standard deviation, and apply them to the D-InSAR standardized anomalies. In this way, we build a water-level time series with more temporal observations. In general, we find a strong agreement between water level estimates from the combination of D-InSAR and Satellite Altimetry (DInSAlt) and in-situ observations in eight lakes (Concordance Correlation Coefficient - CCC >0.8) and moderate agreement in two lakes (CCC >0.57). The applicability of DInSAlt is limited to lakes with suitable conditions for double-bounce scattering, such as the presence of trees or marshes. The accuracy of the water level estimates depends on the quality of the altimetry observations and the lake's width. These findings are important considering the recently launched Surface Water and Ocean Topography (SWOT) satellite, whose capabilities could expand our methodology's geographical applicability and reduce its reliance on ground measurements.

Savannas cover more than 20% of the Earth and account for the third largest stock of global aboveground biomass yet estimates of their above ground biomass density (AGBD) are very inaccurate. The Global Ecosystem Dynamic Investigation (GEDI) sensor provides near-global full-waveform LiDAR data with 25 m footprints, from which various structural metrics are derived that are used to predict footprint level AGBD. The current GEDI L4A AGBD product uses a comprehensive Forest Structure and Biomass Database (FSBD) to develop models for specific plant functional types and geographic regions, but southern African savannas have been underrepresented in the reference data. The objectives of this study were to (i) validate GEDI L4A AGBD in South African savannas using field measurements and ALS datasets and (ii) develop and evaluate local GEDI footprint-level AGBD estimates from multiple L2A and L2B metrics. The local GEDI AGBD models outperformed GEDI L4A AGBD (R² = 0.42, RMSE = 12 Mg/ha, %RMSE = 79.5%) with higher R² and smaller error measures. The local GEDI AGBD using a random forest model (RF) had the highest R² of 0.71 and lowest %RMSE of 53.3%, while the generalized linear model (GLM) results provided the lowest Relative Mean Systematic Deviation (RMSD) of 9.2%, which was half that of RF model. L4A significantly underestimated AGBD with an RMSD up to −37%. This highlights the importance and benefits of local calibration of biomass models to unlock the full potential of GEDI metrics for estimating AGBD. The field and ALS data have subsequently been contributed to the GEDI FSBD and should be used in calibration of future versions of GEDI L4A AGBD product. This research paves the way for the integration of the local GEDI AGBD estimates with other sensors, notable the eminent NISAR mission, to derive regional to global gridded AGBD products that will enable the monitoring of savanna carbon stocks.

While Leaf Area Index (LAI) is critical for understanding forest canopy, photosynthesis and forest growth, traditional field-based LAI measurements are laborious and costly. Remote sensing offers a practical alternative for extensive assessments. Satellite imagery provides broad-scale, long-term monitoring; however, may lack detail needed to guide specific forest management actions. Conversely, Airborne Laser Scanning (ALS) provides accurate LAI estimates at fine spatial detail but is limited by cost and temporal monitoring constraints. Combining ALS data with satellite observations could enhance plantation management decisions by balancing extensive coverage with detailed observations. This study explores the integration of ALS and satellite remote sensing as a comprehensive alternative for assessing LAI and stand volume growth rate (m³/ha/year) in operational Pinus radiata plantations in central-south Chile. Our approach comprised four major steps. First, we applied the Beer-Lambert law using ALS vertical profiles to estimate LAI across a forest plantation (LAI_ALS). We found that ALS accurately estimated LAI across 121 plots (R² = 0.82 and RMSE = 0.51). Second, we built a simple linear regression to link LAI_ALS with the Normalized Difference Moisture Index (NDMI) derived from surface reflectance information from the Landsat/Sentinel-2 satellites, resulting in an R² of 0.53 and an RMSE of 1.17. This step showed a higher correlation with satellite data compared to using only ground-based LAI estimates (R² = 0.38; RMSE = 1.18). Third, we transformed biweekly NDMI time series to LAI, then derived peak annual LAI as an indicator of mean annual increment (MAI) (R² = 0.51; RMSE = 5.27 m³/ha/year). This allowed us to characterize stand growth and LAI on a yearly wall-to-wall basis. Throughout the modelling steps, we incorporated error propagation, allowing final estimates to be error bounded. This integrated approach serves as a tool for identifying and visualizing growth irregularities, guiding adaptive management strategies to maintain or enhance stand productivity over time.

Canopy cover (CC) quantifies the proportion of canopy materials projected vertically onto the ground surface. CC is a crucial canopy structural variable and is commonly used in many ecological and climatic models. The vertical CC profile product is currently available from the Global Ecosystem Dynamics Investigation (GEDI). However, detailed information about the accuracy and uncertainty of the GEDI vertical CC profile product remains limited. The objective of this study is to validate the GEDI CC product over selected forest sites using reference values derived from digital hemispherical photography (DHP), airborne laser scanning (ALS) point clouds, and simulated waveforms. The accuracy of CC was quantified and analyzed regarding GEDI observation conditions, waveform processing, and estimation methods. The results show that the GEDI total CC correlates well with those estimated from DHP, ALS, and simulated waveform data (r² = 0.65, 0.71, and 0.71, respectively) but is systematically underestimated (bias = −0.05, −0.11, and −0.07, respectively) based on reference data. Compared with the ALS-estimated CC, needleleaf forest shows the highest correlation for vertical CC (r² ≥ 0.65) and shrubland shows the lowest bias for total CC (bias = −0.13). The mean absolute error (MAE) of the GEDI CC decreases from 0.15 to 0.09 as the estimation height increases from ground to 35 m. The GEDI total CCs derived from the waveform interpretation algorithms A2 and A6 display the highest r² (≥ 0.6) and smallest RMSE (≤ 0.23) compared to those of the other algorithms. The CC accuracy increases with beam sensitivity and decreases with increasing canopy cover. The GEDI CC was improved at moderate CC values using a canopy-to-ground backscattering coefficient ratio (${\rho }_v/{\rho }_g$) determined with the regression method. The partial difference between GEDI CC and ALS CC is attributed to definitional discrepancies. Further improvement of the CC algorithm can be made by using vegetation-specific waveform processing algorithms and realistic ${\rho }_v/{\rho }_g$ values.

Characterizing the extent, severity, and persistence of natural disturbances in forests is crucial in areas as large and heterogeneous as the Canadian boreal forest. Non-stand replacing (NSR) disturbances, in particular, can produce subtle and lagged impacts to forest canopy and structure with mechanisms that remain elusive, and they are challenging to discern using typical remote sensing approaches including aerial photointerpretation and spectral analysis of satellite imagery. Consequently, there is a need for timely and accurate information on the structural modifications due to NSR disturbances to inform proactive forest management practices. To address these needs, we leveraged a unique bitemporal airborne laser scanning (ALS) dataset to characterize changes in the forest structure caused by eastern spruce budworm (ESB, Choristoneura fumiferana (Clem.)), responsible for one of the greatest tree mortality in Canada. A range of infestation severity with varying impacts to forest structure are examined in a mixedwood boreal forest in Lac-Saint Jean, Quebec, Canada. We derived 14 ALS structural change metrics at 10 m spatial resolution, including height, cover, and gappiness 7 years apart (2014–2020). Six distinct structural responses to cumulative ESB infestations severity were identified using cluster analysis from the combination of the 14 change metrics, with canopy cover, the 75th and 25th height percentiles (p75-25) driving cluster separability. Canopy cover and p25 consistently decreased as cumulative infestation severity increased, whereas p75 showed greater variability across the landscape. Photointerpretation of aerial imagery over the same period confirmed the validity of the structural characterization. Further, we studied the role of initial forest structures in modulating the severity of the infestation and found that sparser canopies with cover <65% and shorter trees (p75 < 7.5 m, p25 < 2.5 m) were associated with less severe ESB infestations after 7 years, and controlling for underlying environmental factors. These findings showed the potential of bitemporal ALS data in characterizing structural changes due to ESB infestations at fine scale based on canopy cover and height, relevant for forest management strategies to better target current and future infestations.

Himalayan glacial lakes have been rapidly developing and expanding in recent decades under climate change and glacier mass loss. These growing glacial lakes can produce glacial lake outburst floods (GLOFs) events with far-reaching and devastating consequences. However, the latest spatial distribution and temporal evolution of the Himalayan glacial lakes is not timely updated due to the inaccessibility of high mountain areas and the lack of an effective automated mapping method that can leverage the availability of wide-ranging remote sensing data. To frequently update glacial lake inventory in GLOF-vulnerable regions, we developed the state-of-the-art glacial lake mapping approaches based on deep learning technique and multi-source remote sensing imagery. DeepLabv3+, an advanced semantic segmentation algorithm, was trained to delineate glacial lakes with areas larger than 0.005 km² from multi-source imagery and their derivatives, including PlanetScope red-green-blue (RGB), PlanetScope-derived Normalized Difference Water Index (NDWI), Sentinel-2 RGB, Sentinel-2-derived NDWI, Sentinel-1 Synthetic Aperture Radar (SAR), and Landsat-8 RGB images. The well-trained deep learning models achieved high mapping accuracy in the northern Bhutan test region, with the F1 score varying from 0.74 (Sentinel-1) to 0.91 (Planet-RGB) among the six types of images. We applied the well-trained models to automatically map the glacial lakes from multi-source satellite imagery. After manually cataloging the mapping results, we compiled a glacial lake inventory for the Bhutan Himalaya in 2021 that includes 2563 glacial lakes with a total area of 153.85 ± 9.33 km². Our results demonstrated the mapping capability of deep learning on multiple satellite imagery, the key roles of PlanetScope optical images for accurate glacial lake mapping, and the essential supplementary usage of SAR images and NDWI images to complement the glacial lake inventory over Bhutan Himalaya. This study provides an advanced and transferable workflow for inventorying glacial lakes from multi-source satellite imagery, as well as provides a high-quality and comprehensive glacial lake inventory for outburst flood studies.