30-m Resolution Research Articles

Multi-factor weighted image fusion method for high spatiotemporal tracking of reservoir drawdown area and its vegetation dynamics

Reservoir drawdown areas (RDAs) with distinct dry-wet cycles and vegetation dynamics have emerged as significant hotspots for carbon-related activities. However, high-resolution spatiotemporal tracking of the variations and vegetation dynamics of RDAs remains challenging because they often change dramatically and are controlled by both human activities and natural factors. Herein, a modified image fusion method was proposed to capture rapid variations in RDAs by integrating impact factor information into the analysis. The capability of the proposed method was tested in the Danjiangkou (DJK) Reservoir as it is the largest artificial freshwater lake in Asia with a highly variable RDA, since it is surrounded by gently sloping plains or hills. The results showed that the modified workflow produced reliable predictions (r=0.83,RMSE=0.097) compared with the original Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) workflow (r=0.60,RMSE=0.195), demonstrating improved capability for mapping water surface changes and vegetation dynamics. Using the proposed method, the 15-d variations in the RDA were derived from 2013 to 2022 with a 30-m resolution. The interannual maximum RDA was estimated to be 278 km2 after the dam was elevated in 2013. The Normalized Difference Vegetation Index (NDVI) decreased as inundation frequency (IF) increased. Mean NDVI in the growing season (May–October) decreased by 0.109 (17.6 %) and 0.156 (26.0 %) under 30 %–40 % IF and 60 %–70 % IF, respectively, compared with the vegetation under 0 %–10 % IF, which was referred as “natural” vegetation considering its rare inundation. Moreover, the mean growing season length decreased to only 63 and 19 d for 30 %–40 % IF and 60 %–70 % IF, respectively. Furthermore, 77.3 % of the RDA exhibited a decrease in NDVI, whereas 22.7 % showed an unusual increase, possibly due to the selection of dominant species well-adapted to inundation during vegetation succession. Overall, this study not only proposed a new method for the high spatiotemporal monitoring of RDAs, but also highlighted the importance of variations and vegetation dynamics within RDAs for accurate estimation of their carbon budgets.

Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India.

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of - 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.

Detection of karst depression in Brazil comparing different semantic and instance segmentations and global digital elevation models

This research assessed the efficacy of deep segmentation in identifying and measuring natural karst depressions in the Bambuí Group's carbonate rocks in Western Bahia, Brazil. The investigation used five Global Digital Elevation Models (DEMs) with 30-m resolution: Advanced Spaceborne Thermal Emission and Reflection Radiometer - Global Digital Elevation Model version 3 (ASTER-GDEM v3), Advanced Land Observing Satellite World 3D – 30 m version 3.2 (AW3D30 v3.2), Copernicus 30 m global DEM (GLO-30), NASA Digital Elevation Model version 1 (NASADEM v1), and Shuttle Radar Topography Mission version 3 (SRTM v3). The karst feature detection analysis compared five semantic segmentation architectures: Feature Pyramid Networks (FPN), LinkNet, UNet, UNet++, DVL3+, using an EfficientNet-B7 backbone, one instance segmentation model (Mask-RCNN), and a semantic-to-instance conversion method. This comparison considers different datasets, including one, two, or eleven variables (DEM, DEM-based sink depth, and nine terrain attributes). Besides, we performed a combinatorial analysis of the DEMs to evaluate the improvement in karst feature detection. The methodology involved the generation of geomorphometric attributes, sample labeling from Sentinel-2 and Operational Land Imager-Landsat 8 datasets, training-validation-testing steps with 128 × 128 samples, reconstruction of large images for semantic and instance segmentation, and accuracy analysis. The findings revealed that GLO-30 and AW3D30 data were the most accurate, while ASTER GDEM performed poorly in both segmentation forms. Among the semantic models, FPN showed the highest accuracy. The 11-variable models preferentially outperformed those with fewer in both types of segmentation. The approach of semantic-to-instance conversion with Geographic Information System tools favored individualizing karst depressions and obtaining efficient quantification, consisting of an easy alternative to achieve instance segmentation. Reconstruction of large remote sensing images through sliding windows considering the specifics of instance and semantic segmentation demonstrated that smaller stride enhances object coverage and reduces the risk of losing crucial information, effectively improving the predictions. The combinatorial analysis for semantic and instance segmentation indicates that models incorporating more DEMs (4- and 5-DEM models) and variables achieve generally higher accuracy. The best result in semantic segmentation was combining the five DEM datasets using 11 variables, reaching an F-score of 85.06 and IoU of 74.00. In instance segmentation, the best result for the bounding box was the model that integrates AW3D30, GLO-30, NASADEM, and SRTM with eleven variables reaching Average Precision (AP) of 45.64, AP50 of 88.40 and AP75 of 39.38, while the best result for the segmentation mask was the model that integrates the five models with eleven variables with AP of 42.61, AP50 of 86.79 and AP75 of 35.43. These results demonstrate that integrating a wide range of DEM data, even the lowest performing ones, improves model generalization and accuracy in segmentation, leveraging strengths and mitigating individual weaknesses. Future work could explore high-resolution DEMs and the integration of various deep-learning methods.

Investigating the influence of sub-mesoscale current structures on Baltic Sea connectivity through a Lagrangian analysis

This study explores the impact of sub-mesoscale structures and vertical advection on the connectivity properties of the Baltic Sea using a Lagrangian approach. High-resolution flow fields from the General Estuarine Transport Model (GETM) were employed to compute Lagrangian trajectories, focusing on the influence of fine-scale structures on connectivity estimates. Six river mouths in the Baltic Sea served as initial positions for numerical particles, and trajectories were generated using flow fields with varying horizontal resolutions: 3D trajectories with 250m resolution as well as 2D trajectories with 250m and 1km resolutions. Several Lagrangian indices, such as mean transit time, arrival depths, and probability density functions of transit times, were analyzed to unravel the complex circulation of the Baltic Sea and highlight the substantial impact of sub-mesoscale structures on numerical trajectories. Results indicate that in 2D simulations, particles exhibit faster movement on the eastern side of the Gotland Basin in high-resolution compared to coarse-resolution simulations. This difference is attributed to the stronger coastal current in high-resolution compared to coarse-resolution simulations. Additionally, the study investigates the influence of vertical advection on numerical particle motion within the Baltic Sea, considering the difference between 3D and 2D trajectories. Findings reveal that denser water in the eastern and south-eastern areas significantly affects particle dispersion in 3D simulations, resulting in increased transit times. Conversely, regions in the North-western part of the basin accelerate particle movement in 3D compared to the 2D simulations. Finally, we calculated the average residence time of numerical particles exiting the Baltic Sea through the Danish strait. Results show an average surface layer residence time of approximately 790 days over an eight-year integration period, highlighting the relatively slow water circulation in the semi-enclosed Baltic Sea basin. This prolonged residence time emphasizes the potential for the accumulation of pollutants. Overall, the study underscores the pivotal role of fine-scale structures in shaping the connectivity of the Baltic Sea, with implications for understanding and managing environmental challenges in this unique marine ecosystem.

Remotely-sensed phenoclusters of Wisconsin’s forests, shrublands, and grasslands for biodiversity applications

Heterogeneous vegetation supports higher species richness than homogenous vegetation, which is why efficiently identifying heterogenous vegetation can be useful for biodiversity conservation. Satellite remote-sensing data provide an opportunity to generate vegetation heterogeneity metrics and to explore the phenology of vegetation patterns. Phenoclusters are vegetation types with similar phenological characteristics, and valuable for capturing vegetation habitat heterogeneity patterns. Our goal was to map phenoclusters for Wisconsin, USA, at 10-m spatial resolution based on land surface phenology metrics from EVI (Enhanced Vegetation Index) Sentinel-2 data. We characterized each phenocluster based on landcover composition and structure, phenology timing, and environmental factors, and compared them to bird species richness. We also calculated the diversity of phenoclusters at multiple spatial extents. We identified 14 phenoclusters in Wisconsin, each with distinct landcover composition and structure, and unique phenological characteristics. Our remotely-sensed phenoclusters effectively captured environmental gradients, with elevation and temperature emerging as the most important driving variables. Furthermore, the phenoclusters successfully captured bird biodiversity patterns, especially richness of forest and grassland specialist. Our results identified phenological patterns among Wisconsin’s forests, shrublands, and grasslands, capturing phenological timing both among and within the same tree species. Phenoclusters are a valuable tool for capturing vegetation habitat heterogeneity, phenology diversity and biodiversity patterns, as well as climate change effects.

Evaluating future flood risk mitigation capacities in major cities across China

Rapid urbanization and climate change have significantly altered urban hydrological processes. Previous research has highlighted their adverse effects on escalating risk of urban flood. Nevertheless, few assessments have quantified how future urban flood risk mitigation capacities evolve under various urban expansion and climate change scenarios. This study simulates urban expansion of major cities across China using future urban-regional environment simulation model at 30-m resolution under shared socioeconomic pathways scenarios (SSP). Concurrently, future precipitation intensity changes are assessed based on climate projection data. Finally, the urban flood risk mitigation capacity impacted by urban expansion and climate change is evaluated using the soil conservation service model. The results reveal: (1) The sizes of major cities are projected to expand 30% by 2050, and the north and east China are primary urban growth poles. (2) Major cities' precipitation intensity increase 0.4% in SSP126, but decline 5.4% and 2.3% in SSP245 and SSP585. (3) The flood risk mitigation capacities of major cities decrease 2.7% in SSP126 and 2.1% in SSP585, but increase 0.2% in SSP245. Most cities experiencing capacity decrease are located in the north and east China. These results provide valuable insights for the formulation of future urban flood adaptation and mitigation strategies.

Assessment of water shortage in underground aquifer in Abuja during dry season

Water shortage is a complex issue with severe implications for human well-being, agriculture, and ecosystems. Climate change, population growth, and inadequate water management practices contribute to this global problem. This study is to identify the causes of ground water scarcity and determine groundwater potential zones in the Abuja community. The research area's borehole coordinates, Landsat 8 satellite imagery, AsterDEM 30m resolution DEM data, and vertical electrical sounding (VES) data were all used to do this. Unsupervised classification of Landsat imagery was used to create a layer of land use and cover, image filtering techniques were used to improve the visualization of lineament extraction, and density analysis was used to quantify lineament density. AsterDEM data was subjected to surface analysis to acquire elevation data, while VES data were interpolated to produce spatial distributions of the resistivity and thickness parameters. To identify groundwater potential zones, multiple criterion analysis using the analytical hierarchical process (AHP) was utilized to reclassify variables such as land use, elevation, lineament density, resistivity, and thickness. To create the zones, these characteristics were weighted and layered. According to the findings, resistivity levels varied from 6.25 m to 676 m, and the geologic ground structure's thickness ranged from 0.27 m to 92.37 m. Elevation varied from 41 meters to 720 meters, while lineament density ranged from 0 to 0.04. 74% of the land was categorized as vegetated, 21% as built up, 2.7% as water bodies, and 2.3% as rock formations. Within Abuja, zones of groundwater potential were identified as being very low (5%), low (26.7%), marginally high (26.3%), moderately high (28.5%), and very high (13.4%)

High spatio-temporal resolution predictions of PM2.5 using low-cost sensor data

We generated PM2.5 predictions at a high spatio-temporal resolution in the Columbus, OH, Denver, CO, and Pittsburgh, PA metropolitan areas using low-cost PurpleAir sensor data. We used multiple modeling approaches, namely random forest (RF), random forest spatial interpolation (RFSI), space-time regression kriging (STRK), and random forest kriging (RFK). We trained separate models for each combination of hour, month, and city to predict PM2.5 concentrations at 8 a.m. and 6 p.m. on any specific day at a spatial resolution of 100m. In most cases, models that account for the spatio-temporal relationships (e.g., STRK, RFK, RFSI) show better performance than non-spatio-temporal machine learning models (e.g., RF). On average, considering all models of all cities, RFSI (mean MAE = 1.75, R2 = 0.67) and STRK (mean MAE = 1.74, R2 = 0.63) models perform better than RFK models (mean MAE = 2.11, R2 = 0.59), and STRK has clearest spatial patterns. We found that kriging models, especially STRK, are superior in capturing the spatio-temporal relationships and resemble the generic land use pattern of the city, while RFSI models are effective when dealing with very large datasets with missing cases. Our study demonstrates a multi-model approach that could inform low-cost sensor deployment to facilitate air quality modeling. Our high-resolution predictions could also facilitate studies on short-term, traffic-based exposure assessment.

Crowded and warmer: Unequal dengue risk at high spatial resolution across a megacity of India

The role of climate factors on transmission of mosquito-borne infections within urban landscapes must be considered in the context of the pronounced spatial heterogeneity of such environments. Socio-demographic and environmental variation challenge control efforts for emergent arboviruses transmitted via the urban mosquito Aedes aegypti. We address at high resolution, the spatial heterogeneity of dengue transmission risk in the megacity of Delhi, India, as a function of both temperature and the carrying-capacity of the human environment for the mosquito. Based on previous results predicting maximum mosquitoes per human for different socio-economic typologies, and on remote sensing temperature data, we produce a map of the reproductive number of dengue at a resolution of 250m by 250m. We focus on dengue risk hotspots during inter-epidemic periods, places where chains of transmission can persist for longer. We assess the resulting high-resolution risk map of dengue with reported cases for three consecutive boreal winters. We find that both temperature and vector carrying-capacity per human co-vary in space because of their respective dependence on population density. The synergistic action of these two factors results in larger variation of dengue’s reproductive number than when considered separately, with poor and dense locations experiencing the warmest conditions and becoming the most likely reservoirs off-season. The location of observed winter cases is accurately predicted for different risk threshold criteria. Results underscore the inequity of risk across a complex urban landscape, whereby individuals in dense poor neighborhoods face the compounded effect of higher temperatures and mosquito carrying capacity. Targeting chains of transmission in inter-epidemic periods at these locations should be a priority of control efforts. A better mapping is needed of the interplay between climate factors that are dominant determinants of the seasonality of vector-borne infections and the socio-economic conditions behind unequal exposure.

Mapping of secondary forest age in China using stacked generalization and Landsat time series

A national distribution of secondary forest age (SFA) is essential for understanding the forest ecosystem and carbon stock in China. While past studies have mainly used various change detection algorithms to detect forest disturbance, which cannot adequately characterize the entire forest landscape. This study developed a data-driven approach for improving performances of the Vegetation Change Tracker (VCT) and Continuous Change Detection and Classification (CCDC) algorithms for detecting the establishment of forest stands. An ensemble method for mapping national-scale SFA by determining the establishment time of secondary forest stands using change detection algorithms and dense Landsat time series was proposed. A dataset of national secondary forest age for China (SFAC) for 1 to 34 and with a 30-m spatial resolution was produced from the optimal ensemble model. This dataset provides national, continuous spatial SFA information and can improve understanding of secondary forests and the estimation of forest carbon storage in China.

Climate-induced tree-mortality pulses are obscured by broad-scale and long-term greening.

Vegetation greening has been suggested to be a dominant trend over recent decades, but severe pulses of tree mortality in forests after droughts and heatwaves have also been extensively reported. These observations raise the question of to what extent the observed severe pulses of tree mortality induced by climate could affect overall vegetation greenness across spatial grains and temporal extents. To address this issue, here we analyse three satellite-based datasets of detrended growing-season normalized difference vegetation index (NDVIGS) with spatial resolutions ranging from 30 m to 8 km for 1,303 field-documented sites experiencing severe drought- or heat-induced tree-mortality events around the globe. We find that severe tree-mortality events have distinctive but localized imprints on vegetation greenness over annual timescales, which are obscured by broad-scale and long-term greening. Specifically, although anomalies in NDVIGS (ΔNDVI) are negative during tree-mortality years, this reduction diminishes at coarser spatial resolutions (that is, 250 m and 8 km). Notably, tree-mortality-induced reductions in NDVIGS (|ΔNDVI|) at 30-m resolution are negatively related to native plant species richness and forest height, whereas topographic heterogeneity is the major factor affecting ΔNDVI differences across various spatial grain sizes. Over time periods of a decade or longer, greening consistently dominates all spatial resolutions. The findings underscore the fundamental importance of spatio-temporal scales for cohesively understanding the effects of climate change on forest productivity and tree mortality under both gradual and abrupt changes.

High-resolution land use/cover forecasts for Switzerland in the 21st century

We present forecasts of land-use/land-cover (LULC) change for Switzerland for three time-steps in the 21st century under the representative concentration pathways 4.5 and 8.5, and at 100-m spatial and 14-class thematic resolution. We modelled the spatial suitability for each LULC class with a neural network (NN) using > 200 predictors and accounting for climate and policy changes. We improved model performance by using a data augmentation algorithm that synthetically increased the number of cells of underrepresented classes, resulting in an overall quantity disagreement of 0.053 and allocation disagreement of 0.15, which indicate good prediction accuracy. These class-specific spatial suitability maps outputted by the NN were then merged in a single LULC map per time-step using the CLUE-S algorithm, accounting for LULC demand for the future and a set of LULC transition rules. As the first LULC forecast for Switzerland at a thematic resolution comparable to available LULC maps for the past, this product lends itself to applications in land-use planning, resource management, ecological and hydraulic modelling, habitat restoration and conservation.

Urban–Rural Boundary Delineation Based on Population Spatialization: A Case Study of Guizhou Province, China

Rational delineation of urban–rural boundaries is a foundational prerequisite for holistic urban and rural development planning and rational resource allocation. However, using a single data source for urban–rural boundaries yields non-comprehensive results. To address this problem, the present study proposes a method for extracting urban–rural boundaries using multiple sources such as population data, nighttime light data, land use, and points of interest (POI) data. Considering Guizhou Province for a case study, this study presents a two-step method for identifying urban–rural boundaries. First, the random forest model was combined with the dasymetric mapping method to obtain the province’s population spatialization data with a 30-m resolution. Second, based on the spatialized population, the urban–rural boundary for Guizhou Province in 2020 was extracted using the breaking point method. This method comprehensively integrated the benefits of various data and judiciously extracted the boundaries of the main urban areas and small and medium-sized towns of each city in the study province at the same spatial scale. The stratified random sampling method revealed an average overall accuracy of 88.05%. The proposed method has high universality and application value and can be useful for accurate and practical identification of urban–rural boundaries.

A new approach for Spatializing the CAnadian National Forest Inventory (SCANFI) using Landsat dense time series

Accurate and fine-scale forest data are essential to improve natural resource management, particularly in the face of climate change. Here, we present SCANFI, the Spatialized CAnadian National Forest Inventory, which provides coherent, 30m resolution 2020 wall-to-wall maps of forest attributes (land cover type, canopy height, crown closure, aboveground tree biomass, main species composition). These maps were developed using the National Forest Inventory (NFI) photo-plot dataset, a systematic regular sample grid of photo-interpreted high-resolution imagery covering all of Canada’s non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat imagery along with hundreds of tile-level regional models based on a multi-response k-nearest neighbours and random forest imputation method. This approach revealed the importance of radiometric variables in predicting vegetation attributes, namely winter radiometry, as the large-scale climate gradients were controlled at the tile-level. Cross-validation analyses were done, which revealed robust model performance for structural attributes (biomass R2=0.76; crown closure R2=0.82; height R2=0.78) and tree species cover. SCANFI attributes were also validated with several independent external products, ranging from ground plot-based tree species cover to satellite LiDAR height. The methodology can be used to map time series of these attributes and all other additional variables associated with the NFI photo-plots.

Temporal Analysis of Mangrove Forest Extent in Restoration Initiatives: A Remote Sensing Approach Using Sentinel-2 Imagery

The significance of mangroves and the associated risks they face have prompted government and the private sector to invest in projects aimed at conserving and restoring mangroves. Despite this interest, there is currently little information available on the effectiveness of these investments in mangrove conservation and restoration efforts. Therefore, this study aimed to use Sentinel-2 imagery with 10-m resolution through the Google Earth Engine to evaluate the effectiveness of these projects in mangrove areas in two regions: the Tahiry Honko project in Madagascar and the Abu Dhabi Blue Carbon Demonstration Project in the United Arab Emirates. The study compared the U-Net and SVM for mangrove classification. The U-Net model demonstrated superior performance, achieving an accuracy of 90%, with a Kappa coefficient value of 0.84. In contrast, the SVM had an overall accuracy of 86% and Kappa coefficient of 0.78. The analysis of changes in the mangrove area using U-Net model revealed a decline of 355 ha over four years in the Tahiry Honko project, while in the Abu Dhabi Project, the mangrove area increased by 5857 ha over 5 years. These findings can provide valuable information for policy-makers and management strategies.

Phasing segmented telescopes via deep learning methods: application to a deployable CubeSat.

Capturing high-resolution imagery of the Earth's surface often calls for a telescope of considerable size, even from low Earth orbits (LEOs). A large aperture often requires large and expensive platforms. For instance, achieving a resolution of 1m at visible wavelengths from LEO typically requires an aperture diameter of at least 30cm. Additionally, ensuring high revisit times often prompts the use of multiple satellites. In light of these challenges, a small, segmented, deployable CubeSat telescope was recently proposed creating the additional need of phasing the telescope's mirrors. Phasing methods on compact platforms are constrained by the limited volume and power available, excluding solutions that rely on dedicated hardware or demand substantial computational resources. Neural networks (NNs) are known for their computationally efficient inference and reduced onboard requirements. Therefore, we developed a NN-based method to measure co-phasing errors inherent to a deployable telescope. The proposed technique demonstrates its ability to detect phasing errors at the targeted performance level [typically a wavefront error (WFE) below 15nm RMS for a visible imager operating at the diffraction limit] using a point source. The robustness of the NN method is verified in presence of high-order aberrations or noise and the results are compared against existing state-of-the-art techniques. The developed NN model ensures its feasibility and provides a realistic pathway towards achieving diffraction-limited images.

USING MACHINE LEARNING ALGORITHMS FOR NATURAL HABITATS ASSESSMENT

The potential of AI to process and interpret large volumes of data can provide researchers with a powerful tool to understand and monitor biodiversity on a global scale. In this paper we aimed to identify dominant individual plant species in natural protected habitats. Mapping the dominant species from the targeted natural habitats was followed by testing machine learning algorithm for differentiating similar species using satellite images. In the end we validated the data generated by machine learning algorithms through extensive field observations. Using the Sentinel-2 mission 10m resolution data and comprehensive field mapping we managed to see different phenology variations between diverse types of plant communities. Using the NDVI and NDII vegetation indexes and Random Forest algorithm during the dominant species phenology stages for each consecutive 10-day periods between May 1st and September 10th, revealed distinct responses to climate fluctuations and environmental factors. The natural habitats different signatures are strongly influenced by their ecological and conservation status and are not yet suitable for identification, but could help improve AI’s automatic detection for multiannual analysis if a favorable conservation trend is reached. The main achievement of the proposed methodology is the ability to differentiate between different species of deciduous trees, with machine learning training accuracy generally exceeding 95% and classification accuracy surpassing 90%.

Using bi-temporal ALS and NFI-based time-series data to account for large-scale aboveground carbon dynamics: the showcase of mediterranean forests

ABSTRACT New remote-sensed biomass change products will transform our capacity to monitor and validate large-scale carbon dynamic in the next decade. In this study, we evaluated the use of multitemporal Airborne Laser Scanning (ALS) and the Climate Change Initiative (CCI) BIOMASS spaceborne mission to estimate AGB dynamics in different Mediterranean forest over an 8-year period (2010–2018). To do so, we evaluated different maps to estimate change in AGB, specifically indirect approach using forest-type specific ALS-based AGB maps using i) countrywide ALS coverage at 25 m resolution (2010–2018) and ii) the global, 100-m resolution CCI maps version 3 (2010–2018). The change in AGB (ΔAGB) was mapped across the study region to compute dynamics by forest type. Our results suggest that the indirect approach using ALS-model-based produced more accurate estimates in change of AGB than CCI when we compared with the design-based AGB estimation using Spanish National Forest Inventory (SNFI) at strata level. The spatial representation of the AGB change indicated that ΔAGB-ALS changes by forest type had an overall gain in biomass at regional level. The ΔAGB total and net annual changes by year and area (ΔAGB, Mg ha−1 year−1) were closed to the values obtained using SNFI at strata level. This study demonstrates the feasibility of enhancing carbon sequestration and stock capacity in Mediterranean forest using multitemporal ALS data and the limitations of global AGB maps at Regional Scale.

A 10-m annual grazing intensity dataset in 2015–2021 for the largest temperate meadow steppe in China

Mapping grazing intensity (GI) using satellites is crucial for developing adaptive utilization strategies according to grassland conditions. Here we developed a monitoring framework based on a paired sampling strategy and the classification probability of random forest algorithm to produce annual grazing probability (GP) and GI maps at 10-m spatial resolution from 2015 to 2021 for the largest temperate meadow in China (Hulun Buir grasslands), by harmonized Landsat 7/8 and Sentinel-2 images. The GP maps used values of 0–1 to present detailed grazing gradient information. To match widely used grazing gradients, annual GI maps with ungrazed, moderately grazed, and heavily grazed levels were generated from the GP dataset with a decision tree. The GI maps for 2015–2021 had an overall accuracy of more than 0.97 having significant correlations with the statistical data at city (r = 0.51) and county (r = 0.75) scales. They also effectively captured the GI gradients at site scale (r = 0.94). Our study proposed a monitoring approach and presented annual 10-m grazing information maps for sustainable grassland management.

Does spatial resolution matter in the estimation of average annual soil loss by using RUSLE?-a study of the Urmodi River Watershed (Maharashtra), India.

The study investigates the influence of multispectral satellite data's spatial resolution on land degradation in the Urmodi River Watershed in which Kaas Plateau, a UNESCO World Heritage site, is located. Specifically, the research focuses on soil erosion and its risk zonation. The study employs Landsat 8 (30-m resolution) and Sentinel-2 (10-m resolution) data to assess soil erosion risk. The Revised Universal Soil Loss Equation (RUSLE) is used to quantify the average annual soil erosion output denoted by (A), by using its factors such as rainfall (R), soil erodibility (K), slope-length (LS), cover management (C), and support practices (P). R-factor was computed from MERRA-2 rainfalldata, K-factor was derived from field soil sample-based analysis, LS factor was from Cartosat Digital Elevation Model-based data. The C factor was derived from NDVI of Landsat 8 and Sentinel-2, and the P factor was prepared from LULC derived from Landsat 8, and Sentinel-2 was incorporated in the final integration. The soil erosion hazard map ranged from slight to extremely severe. Remote sensing (RS)-based parameters like Land Use Land Cover (LULC) are derived from the Landsat 8 and Sentine-2 satellite data and used to compute the difference in the final outcome of the integration. The study found similarities in average annual soil loss (A) in plain areas, but differences in final soil erosion risk zone (A) were influenced by LULC map variations due to different cell sizes, P factor, and slope gradient. Notable differences were observed in soil erosion risk categories, particularly in high to very severe zones, with a cumulative difference of 73.85 km2. In addition to this, a scatterplot between the final outputs was computed and found the moderate (R2 = 42.08%) correlation between Landsat 8 and Sentinel-2 imagery-based final average annual soil erosion (A) of RUSLE. The study area encompasses various landforms ranging from the plateau to pediplain, and in such situation, the water-led soil erosion categories vary depending on terrain condition along with its biophysical factors and, hence, need to analyze the need of such factors on the average annual soil erosion quantification. Different spatial resolution has an effect on the final output, and hence, there is a need to track this change at various spatial resolutions. This analysis highlights the significant impact of spatial resolution on land degradation assessment, providing precise identification of surface features and enhancing soil erosion risk zoning accuracy.