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Empirical analysis of the relative effectiveness of the Giant Panda National Park (GPNP) system can promote the optimization and improvement of its management level. Normalized Difference Vegetation Index (NDVI) is a key indicator to measure the health of ecosystems, which can effectively quantitatively reveal the spatial and temporal changes of ecological protection effects. This study evaluated the relative effectiveness of Normalized Difference Vegetation Index (NDVI) protection in the Sichuan area of the GPNP from 2000 to 2020 using the propensity score matching model (PSM). It also explored the influencing factors and interactions of each period by combining the Optimal Parameter-based Geographical Detector Model (OPGD). The results showed that: 1) The study area's Relative Effectiveness Index (REI) was positive, suggesting effective ecological protection. The REI fell from 0.044 in 2000 to 0.031 in 2015 and although it then increased to 0.034 in 2020 to a small extent, the REI showed an overall decreasing trend, and the conservation effect has weakened. 2)The REI change patterns varied in different functional zones of the area, with a general fluctuation and decline, in which the Minshan and Baishuijiang Core Protection Area (MBJ-CPA) as a whole first rise and then fall, and it is the area with the best relative effectiveness of protection. 3) Natural factors such as temperature and elevation are the main factors affecting NDVI, while the influence of policy and economic factors such as the level of protected areas and distance to towns are increasing. The Qionglaishan and Adjacent Areas General Control Area (QLA-GCA) is dominated by the interaction of landscape pattern index with its remaining factors, and the rest of the functional areas are dominated by the interaction of natural factors such as temperature, evapotranspiration with its remaining factors. Therefore, in future development, the Qionglaishan Areas need to pay more attention to the optimization of landscape patterns, while the other areas need to pay more attention to the impact of climate change on the ecosystem. This study can provide a reference for the improvement and management of ecological protection of the GPNP system in the future. [ABSTRACT FROM AUTHOR]

Extreme climate events can threaten food production and disrupt supply chains. For instance, the 2023 drought in Catalonia caused large areas of winter cereals to wilt and die early, yielding no grain. This study examined whether Sentinel-2 can detect total crop losses of winter cereals using ground truth data on crop failure. The methodology explored which Sentinel-2 phenological and greenness variables could best predict three drought impact classes: normal growth, moderate impact, and high impact, where the crop failed to produce grain. The results demonstrate that winter cereals affected by drought exhibit a premature decline in several vegetation indices. As a result, the best predictors for detecting total crop losses were metrics associated with the later stages of crop development. Specifically, the mean Normalized Difference Vegetation Index (NDVI) for the first half of May showed the highest correlation with drought impact classes (R2 = 0.66). This study is the first to detect total crop losses at the plantation level using field data combined with Sentinel-2 imagery. It also offers insights into rapid monitoring methods for crop failure, an event likely to become more frequent as the climate warms. [ABSTRACT FROM AUTHOR]

Landslide susceptibility evaluation is an indispensable part of disaster prevention and mitigation work. Selecting effective evaluation methods and models for landslide susceptibility assessment is of significant importance. This study focuses on selected areas in Yunyang County, Chongqing City. By interpreting high-resolution satellite remote sensing images from before and after heavy rainfall on 31 August 2014, the distribution of rainfall-induced accumulation landslides was obtained. To evaluate the susceptibility of accumulation landslides, we have equated evaluation factors to accumulation distribution prediction factors. Eight evaluation factors were extracted using multi-source data, including lithology, elevation, slope, remote sensing image texture features, and the normalized difference vegetation index (NDVI). Various machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and BP Neural Network models, were employed to assess the susceptibility of rainfall-induced accumulation landslides in the study area. Subsequently, the accuracy of the evaluation models was compared and verified using the Receiver Operating Characteristic (ROC) curve, and the evaluation results were analyzed. Finally, the developed Random Forest model was applied to Gongping Town in Fengjie County to verify its applicability in other regions. The findings indicate that the complex geological conditions and the unique tectonic erosion landform patterns in the northeastern region of Chongqing not only make this area a center of heavy rainfall but also lead to frequent and recurrent rainfall-induced landslides. The Random Forest model effectively reflects the development characteristics of accumulation landslides in the study area. High and very high susceptibility zones are concentrated in the northern and central regions of the study area, while low and moderate susceptibility zones predominantly occupy the mountainous and riverside areas. Landslide susceptibility mapping in the study area shows that the Random Forest model yields reasonably graded results. Elevation, remote sensing image texture features, and lithology are highly significant factors in the evaluation system, indicating that the development factors of slope geological disasters in the study area are mainly related to topography, geomorphology, and lithology. The landslide susceptibility evaluation results in Gongping Town, Fengjie County, validate the applicability of the Random Forest model developed in this study to other regions. [ABSTRACT FROM AUTHOR]

Spatiotemporal vegetation changes serve as a key indicator of regional ecological environmental quality and provide crucial guidance for developing strategies for regional ecological protection and sustainable development. Currently, vegetation change studies in the Yangtze River Basin primarily rely on the Normalized Difference Vegetation Index (NDVI). However, the NDVI is susceptible to atmospheric and soil conditions and exhibits saturation phenomena in areas with high vegetation coverage. In contrast, the kernel NDVI (kNDVI) demonstrates significant advantages in suppressing background noise and improving saturation thresholds through nonlinear kernel transformation, thereby enhancing sensitivity to vegetation changes. To elucidate the spatiotemporal characteristics and driving mechanisms of vegetation changes in the Yangtze River Basin, this study constructed a temporal kNDVI using MOD09GA data from 2000 to 2022. Considering sectional heterogeneity, rather than analyzing the entire region as a whole as in previous studies, this research examined spatiotemporal evolution characteristics by sections using four statistical metrics. Subsequently, Partial Least Squares Path Modeling (PLSPM) was innovatively introduced to quantitatively analyze the influence mechanisms of topographic, climatic, pedological, and socioeconomic factors. Compared to traditional correlation analysis and the geographical detector method, PLSPM, as a theoretically driven statistical method, can simultaneously process path relationships among multiple latent variables, effectively revealing the intensity and pathways of driving factors' influences, while providing more credible and interpretable explanations for kNDVI variation mechanisms. Results indicate that the overall kNDVI in the Yangtze River Basin exhibited an upward trend, with the midstream demonstrating the most significant improvement with minimal interannual fluctuations, the upstream displaying an east-increasing and west-stable spatial pattern, and the downstream demonstrating coexisting improvement and degradation characteristics, with these trends expected to persist. Driving mechanism analysis reveals that the upstream was predominantly influenced by the climatic factor, the midstream was dominated by terrain, and the downstream displayed terrain–soil coupling effects. Based on these findings, it is recommended that the upstream focus on enhancing vegetation adaptation management to climate change, the midstream need to coordinate the relationship between topography and human activities, and the downstream should concentrate on controlling the negative impacts of urban expansion on vegetation. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a critical process in the interaction between the terrestrial climate system and vegetation. In recent years, ET has undergone significant changes in the Jiziwan region of the Yellow River Basin, primarily due to the implementation of ecological restoration programs and the dual impacts of climate change. As a result, hydrological cycle processes have been profoundly affected, making it crucial to accurately capture trends in ET and its components, as well as to identify the key drivers of these changes. In this study, we first systematically analyzed the dynamic evolution of ET and its components in the Jiziwan of the Yellow River area between 1982 and 2018 from the perspective of land use change. To achieve accurate ET simulations, we introduced a multiple linear regression algorithm and quantitatively evaluated the specific contributions of five climate factors, including precipitation, temperature, wind speed, specific humidity, and radiation, as well as the normalized difference vegetation index (NDVI), a vegetation factor, to ET and its components. On this basis, we explored the combined influence mechanism of climate change and vegetation change on ET in detail. The results revealed that the structure of ET in the Jiziwan of the Yellow River area has changed significantly and that vegetation evapotranspiration has gradually replaced soil evaporation, occupies a dominant position, and has become the main component of ET in this area. Among the many factors affecting ET, the contribution of climate change is the most significant, with an average contribution rate of approximately 59%. Moreover, the influence of human activities on total ET and its components is also high. The factors that had the greatest impact on total ET, soil evaporation, and vegetation transpiration were precipitation, radiation, and the NDVI, respectively. In terms of spatial distribution, the eastern part of Jiziwan was more significantly affected by environmental changes, and the trends of the ET changes were more dramatic. This study not only enhances our scientific understanding of the changes in ET and their driving mechanisms in the Jiziwan area of the Yellow River but also provides a solid scientific foundation for the development of water resource management and ecological restoration strategies in the region. [ABSTRACT FROM AUTHOR]

Urban expansion encroaches on green spaces and weakens ecosystem services, potentially leading to a trade-off between ecological conditions and socio-economic growth. Effectively coordinating the two elements is essential for achieving sustainable development goals at the urban scale. However, few studies have measured urban–ecological linkage in terms of trade-off. In this study, we propose a framework by linking the degraded ecological conditions and urban land use efficiency from a return on investment perspective. Taking a rapidly expanding city as a case study, we comprehensively quantified urban–ecological conditions in four aspects: urban heat island, flood regulating service, habitat quality, and carbon sequestration. These conditions were assessed on 1 km2 grids, along with urban land use efficiency at the same spatial scale. We employed the slack-based measure model to evaluate trade-off efficiency and applied the geo-detector method to identify its driving factors. Our findings reveal that while urban–ecological conditions in Zhengzhou's periphery degraded over the past two decades, the inner city showed improvement in urban heat island and carbon sequestration. Trade-off efficiency exhibited an overall upward trend during 2000–2020, despite initial declines in some inner city areas. Interaction detection demonstrates significant synergistic effects between pairs of drivers, such as the Normalized Difference Vegetation Index and building height, and the number of patches of green spaces and the patch cohesion index of built-up land, with q-values of 0.298 and 0.137, respectively. In light of the spatiotemporal trend of trade-off efficiency and its drivers, we propose adaptive management strategies. The framework could serve as guidance to assist decision-makers and urban planners in monitoring urban–ecological conditions in the context of urban expansion. [ABSTRACT FROM AUTHOR]

Human exposure to mycotoxins is common and often severe in underregulated maize-based food systems. This study explored how monitoring of these systems could help to identify when and where outbreaks occur and inform potential mitigation efforts. Within a maize smallholder system in Kongwa District, Tanzania, we performed two food surveys of mycotoxin contamination at local grain mills, documenting high levels of aflatoxins and fumonisins in maize destined for human consumption. A farmer questionnaire documented diverse pre-harvest and post-harvest practices among smallholder farmers. We modeled maize aflatoxins and fumonisins as a function of diverse indicators of mycotoxin risk based on survey data, high-resolution geospatial environmental data (normalized difference vegetation index and soil quality), and proximal near-infrared spectroscopy. Interestingly, mixed linear models revealed that all data types explained some portion of variance in aflatoxin and fumonisin concentrations. Including all covariates, 2015 models explained 27.6% and 20.6% of variation in aflatoxin and fumonisin, and 2019 models explained 39.4% and 40.0% of variation in aflatoxin and fumonisin. This study demonstrates the value of using low-cost risk factors to model mycotoxins and provides a framework for designing and implementing mycotoxin monitoring within smallholder settings. [ABSTRACT FROM AUTHOR]

Accurate and near-real-time flood monitoring is crucial for effective post-disaster relief efforts. Although extensive research has been conducted on flood classification, efficiently and automatically processing multi-source imagery to generate reliable flood inundation maps remains challenging. In this study, a new automatic flood monitoring method, utilizing optical and Synthetic Aperture Radar (SAR) imagery, was developed based on the Google Earth Engine (GEE) cloud platform. The Normalized Difference Flood Vegetation Index (NDFVI) was innovatively combined with the Edge Otsu segmentation method, utilizing SAR imagery, to enhance the initial accuracy of flood area mapping. To more effectively distinguish flood areas from non-seasonal water bodies, such as lakes, rivers, and reservoirs, pre-flood Landsat-8 imagery was analyzed. Non-seasonal water bodies were classified using multi-index methods and water body probability distributions, thereby further enhancing the accuracy of flood mapping. The method was applied to the catastrophic floods in Poyang Lake, Jiangxi Province, in 2020, and East Dongting Lake, Hunan Province, China, in 2024. The results demonstrated classification accuracies of 92.6% and 97.2% for flood inundation mapping during the Poyang Lake and East Dongting Lake events, respectively. This method offers efficient and precise information support to decision-makers and emergency responders, thereby fully demonstrating its substantial potential for practical applications. [ABSTRACT FROM AUTHOR]

Grasslands are highly vulnerable to climate and changes in grazing management, yet little is known about the national rangeland response to long-term (>18 years) grazing management that may confound climate effects. This study assessed the correlation between Normalized Difference Vegetation Index (NDVI), i.e., productivity and Rangeland Condition Index (RCI) i.e., quality and predicted historical grazing management (26 years) using Ecological Index Method (EIM) analysis of 72 relevés in the Highland Sourveld (HSV). Relationships between 150 NDVI and 72 RCI samples showed a rate of 0.125 change in NDVI for every 12.5% change in RCI. In 1983, the HSV’s rangeland carrying capacity (RCC) ranged from 2.0 - 2.2 ha/AU/yr (land required to support one mature bovine for 1 year), with an NDVI of 0.43, like the benchmark. site. By 2009, the RCC decreased to 3.2 ha/AU/yr, with NDVI <0.30. Selective overgrazing, reduced RCC by increasing Increaser II species and reducing Decreaser species presence. Findings suggest combining NDVI and RCI is more effective than using either alone. Integrating remote sensing with traditional ecological data (Ecological Remote Sensing - eRS) improves our understanding of rangeland vulnarability, thus, ideal for permanent monitoring of public rangelands in South Africa. [ABSTRACT FROM AUTHOR]

Obtaining high temporal and spatial resolution spectral data is the key to revealing the influencing factors, effects, and mechanisms of land-atmosphere interactions in deserts. This study, we used MODIS and Sentinel-2 data as data sources to calculate daily reflectance and Normalized Difference Vegetation Index (NDVI) data with a spatial resolution of 10 m, based on the Spatiotemporal Fusion Incorporating Spectral Autocorrelation (FIRST) model, across different climatic zones in the Hobq Desert, northern China, in March. Then, we evaluated the adaptability of the FIRST model in the Hobq Desert based on spatial and textural characteristics, as well as spatial-temporal distribution characteristics, using qualitative analysis, quantitative analysis, and geographic detectors. The results show that the correlation coefficients of First fused data and Sentinel-2 data in red, green, blue, near-infrared bands, and NDVI were 0.574 (p < 0.01), 0.448 (p < 0.01), 0.485 (p < 0.01), 0.573 (p < 0.01), and 0.625 (p < 0.01), and the scatter points were evenly distributed on both sides of y = x. Meanwhile, FIRST NDVI and Sentinel-2 NDVI maintained consistency in spatial texture and hue changes, with similar value ranges. The daily scale coefficient of variation (CV) of FIRST NDVI in different desert types were less than that of MODIS NDVI. Among them, the variability of FIRST NDVI in fixed dunes was significantly smaller than that of MODIS NDVI, with the former's CV being 0.034 smaller than the latter's. Besides, it was found that there were significant differences in First NDVI among different desert types based on risk detection, while MODIS NDVI showed insignificant differences between fixed dunes and semi-fixed dunes. This suggests that First model integrated effectively various types of remote sensing data and had strong applicability in the eastern part of Hobq Desert, which could distinguish between fixed dunes and semi-fixed dunes, providing a more accurate monitoring tool for environmental zoning management in desert areas. [ABSTRACT FROM AUTHOR]

Flood events pose significant risks to infrastructure and populations worldwide, particularly in Punjab, Pakistan, where critical infrastructure must remain operational during adverse conditions. This study aims to predict flood-prone areas in Punjab and assess the vulnerability of critical infrastructures within these zones. We developed a robust Flood Susceptibility Model (FSM) utilizing the Maximum Likelihood Classification (MLC) model and Analytical Hierarchy Process (AHP) incorporating 11 flood-influencing factors, including "Topographic Wetness Index (TWI), elevation, slope, precipitation (rain, snow, hail, sleet), rainfall, distance to rivers and roads, soil type, drainage density, Land Use/Land Cover (LULC), and the Normalized Difference Vegetation Index (NDVI)". The model, trained on a dataset of 850 training points, 70% for training and 30% for validation, achieved a high accuracy (AUC = 90%), highlighting the effectiveness of the chosen approach. The Flood Susceptibility Map (FSM) classified high- and very high-risk zones collectively covering approximately 61.77% of the study area, underscoring significant flood vulnerability across Punjab. The Sentinel-1A data with Vertical-Horizontal (VH) polarization was employed to delineate flood extents in the heavily impacted cities of Dera Ghazi Khan and Rajanpur. This study underscores the value of integrating Multi-Criteria Decision Analysis (MCDA), remote sensing, and Geographic Information Systems (GIS) for generating detailed flood susceptibility maps that are potentially applicable to other global flood-prone regions. [ABSTRACT FROM AUTHOR]

The spectral vegetation indices derived from remote sensing data provide a detailed spectral analysis for assessing vegetation characteristics. This study investigated the relationship between cotton yield and canopy spectral indices to develop yield estimation models. Spectral reflectance data were collected at various growth stages using an ASD FieldSpec Pro VNIR 2500 spectrometer. Six prediction models were developed using spectral vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI), to estimate the Leaf Area Index (LAI) and above-ground biomass. For LAI estimation using the NDVI, the power function model (y = 10.083x11.298) demonstrated higher precision, with a multiple correlation coefficient of R2 = 0.8184 and the smallest root mean square error (RMSE = 0.3613). These results confirm the strong predictive capacity of NDVI for LAI, with the power function model offering the best estimation accuracy. In estimating above-ground biomass using RVI, the power function model of y = 6.5218x1.33917 achieved the higher correlation (R2 = 0.8851) for fresh biomass with an RMSE of 0.1033, making it the most accurate. For dry biomass, the exponential function model (y = 9.1565 × 10−5∙exp(1.1146x)) was the most precise, achieving an R2 value of 0.8456 and the lowest RMSE value of 0.0076. These findings highlight the potential of spectral remote sensing for accurately predicting cotton canopy structural parameters and biomass weights. By integrating spectral analysis techniques with remote sensing, this research offers valuable insights for precision cotton planting and field management, enabling optimized agricultural practices and enhanced vegetation health monitoring. [ABSTRACT FROM AUTHOR]

Due to the influences of climate change and human activities, the resources and environments of the "One Belt and One Road" initiative are facing severe challenges. Using drought indicators, this study aimed to analyze the spatiotemporal characteristics of the drought environment and the response of vegetation cover in the area to drought conditions. The Gravity Recovery and Climate Experiment (GRACE) drought severity index (GRACE-DSI), GRACE water storage deficit index (GRACE-WSDI) and standardized precipitation index (SPI) were calculated to measure hydrological drought. Additionally, based on GRACE and Global Land Data Assimilation System (GLDAS) data, groundwater data in Central Asia was retrieved to calculate the groundwater drought index using the GRACE Standardized Groundwater Level Index (GRACE-SGI). The findings indicate that, from 2000, Central Asia's annual precipitation decreased at a rate of 1.80 mm/year (p < 0.1), and its annual temperature increased slightly, at a rate of 0.008 °C/year (p = 0.62). Water storage decreased significantly at a rate of −3.53 mm/year (p < 0.001) and showed an increase-decrease-increase-decrease pattern. During the study period, the aridity in Central Asia deteriorated, especially on the eastern coast of the Caspian Sea and the Aral Sea basin. After 2020, most of Central Asia experienced droughts at both the hydrological and groundwater droughts levels and of varying lengths and severity. During the growing season, there was a substantial positive association between the Normalized Difference Vegetation Index (NDVI) and drought indicators such as GRACE-DSI and GRACE-WSDI. Nonetheless, the NDVI of cultivated land and grassland distribution areas in Central Asia displayed a strong negative correlation with GRACE-SGI. This study concludes that the arid environment in Central Asia affected the growth of vegetation. The ecological system in Central Asia may be put under additional stress if drought conditions continue to worsen. This paper explores the drought characteristics in Central Asia, especially those of groundwater drought, and analyzes the response of vegetation, which is very important for the ecological and environmental protection of the region. [ABSTRACT FROM AUTHOR]

In arid regions, climatic fluctuations significantly affect vegetation structure and function. Sun-induced chlorophyll fluorescence (SIF) can quantify certain physiological parameters of vegetation but has limitations in characterizing responses to climate change. This study analyzed the spatiotemporal differences in response to climate change across various ecological regions and vegetation types from 2000 to 2020 in Xinjiang. According to China's ecological zoning, R1 (Altai Mountains-Western Junggar Mountains forest-steppe) and R5 (Pamir-Kunlun Mountains-Altyn Tagh high-altitude desert grasslands) represent two ecological extremes, while R2–R4 span desert and forest-steppe ecosystems. We employed the standardized precipitation evapotranspiration index (SPEI) at different timescales to represent drought intensity and frequency in conjunction with global OCO-2 SIF products (GOSIF) and the normalized difference vegetation index (NDVI) to assess vegetation growth conditions. The results show that (1) between 2000 and 2020, the overall drought severity in Xinjiang exhibited a slight deterioration, particularly in northern regions (R1 and R2), with a gradual transition from short-term to long-term drought conditions. The R4 and R5 ecological regions in southern Xinjiang also displayed a slight deterioration trend; however, R5 remained relatively stable on the SPEI24 timescale. (2) The NDVI and SIF values across Xinjiang exhibited an upward trend. However, in densely vegetated areas (R1–R3), both NDVI and SIF declined, with a more pronounced decrease in SIF observed in natural forests. (3) Vegetation in northern Xinjiang showed a significantly stronger response to climate change than that in southern Xinjiang, with physiological parameters (SIF) being more sensitive than structural parameters (NDVI). The R1, R2, and R3 ecological regions were primarily influenced by long-term climate change, whereas the R4 and R5 regions were more affected by short-term climate change. Natural grasslands showed a significantly stronger response than forests, particularly in areas with lower vegetation cover that are more structurally impacted. This study provides an important scientific basis for ecological management and climate adaptation in Xinjiang, emphasizing the need for differentiated strategies across ecological regions to support sustainable development. [ABSTRACT FROM AUTHOR]

Vegetation indices, especially the normalized difference vegetation index (NDVI), are widely used in urban vegetation assessments. However, estimating the vegetation abundance in urban scenes using the NDVI has constraints due to the complex spectral signature related to the urban structure, materials and other factors compared to natural ground surfaces. This paper employs the 3D discrete anisotropic radiative transfer (DART) model to simulate the spectro-directional reflectance of synthetic urban scenes with various urban geometries and building materials using a flux-tracking method under shaded and sunlit conditions. The NDVI is calculated using the spectral radiance in the red (0.6545 μm) and near-infrared bands (0.865 μm). The effects of the urban material heterogeneity and 3D structure on the NDVI, and the performance of three NDVI-based fractional vegetation cover (FVC) inversion algorithms, are evaluated. The results show that the effects of the building material heterogeneity on the NDVI are negligible under sunlit conditions but not negligible under shaded conditions. The NDVI value of building components within synthetic scenes is approximately zero. The shaded road exhibits a higher NDVI value in comparison to the illuminated road because of scattering from adjacent pixels. In order to correct the effects of scattering caused by building geometry, the reflectance of the Landsat 8/OLI image is corrected using the sky view factor (SVF) and then used to calculate the FVC. Jilin-1 satellite images with high spatial resolution (0.5 m) are used to extract the vegetation cover and then aggregated to 30 m spatial resolution to calculate the FVC for validation. The results show that the RMSE is up to 0.050 after correction, while the RMSE is 0.169 before correction. This study makes a contribution to the understanding of the effects of the urban 3D structure and material reflectance on the NDVI and provides insights into the retrieval of the FVC in different urban scenes. [ABSTRACT FROM AUTHOR]

Climate-driven water challenges in the Pacific Northwest necessitate precise irrigation for sustainable vineyard management. In such scenarios, conservation of water using different approaches, including subsurface irrigation, becomes critical. Detecting crop water status becomes key to evaluating and managing such approaches. This study examines how multispectral, thermal, and hyperspectral proximal sensing data depict irrigation-induced variations in stomatal conductance in Cabernet Sauvignon vineyards during 2016 and 2017. The roles of individual and combined sensing modalities were analyzed, with key contributions including the identification of indices that characterize stomatal conductance. Data were collected at the following growth stages: 80 and 44 days before harvest (DBH) in 2016; and 64, 44, and 8 DBH in 2017. The vegetation indices analyzed included the green normalized difference vegetation index (GNDVI) and leaf area index (LAI) from multispectral data, crop water stress index (CWSI) from thermal data, and normalized difference spectral indices (NDSI) from hyperspectral data. Pearson's correlations at 80 and 44 DBH (2016) showed significant relationships between normalized stomatal conductance and multispectral indices (LAI: r = 0.59 to 0.66, GNDVI: r = 0.41 to 0.50, both p < 0.01). NDSI pairs (1380 nm with 1570 nm, 1570 nm with 1810 nm) at 80 DBH showed significant correlations (r = −0.27, 0.31, both p < 0.05). In 2017, the thermal data showed the strongest correlation with normalized stomatal conductance (r = −0.83) at 44 DBH. In the same year, NDSI pairs exhibited stronger correlations than multispectral indices as the DBH decreased (1380 nm with 1570 nm: r = −0.58 to −0.69, 1570 nm with 1810 nm: r = 0.64 to 0.48, both p < 0.05). Combining LAI with these NDSI pairs improved stomatal conductance predictions (2016: R2 = 0.37–0.50; 2017: R2 = 0.51–0.63, both p < 0.01). These results demonstrate the precision of a multimodal sensing approach, particularly integrating multispectral and hyperspectral data, to improve irrigation strategies and promote sustainable viticulture. [ABSTRACT FROM AUTHOR]

In estimating the global carbon cycle, the net ecosystem exchange (NEE) is crucial. The understanding of the mechanism of interaction between NEE and various environmental factors of ecosystems has been very limited, and the interactions between the factors are intricate and complex, which leads to difficulties in accurately estimating NEE. In this study, we propose the A-DMLP (attention-deep multilayer perceptron)-deep learning model for NEE simulation as well as an interpretability study using the SHapley Additive exPlanations (SHAP) model. The attention mechanism was introduced into the deep multilayer perceptual machine, and the important information in the original input data was extracted using the attention mechanism. Good results were obtained on nine eddy covariance sites in China. The model was also compared with the random forest, long short-term memory, deep neural network, and convolutional neural networks (1D) models to distinguish it from previous shallow machine learning models to estimate NEE, and the results show that deep learning models have great potential in NEE modeling. The SHAP method was used to investigate the relationship between the input features of the A-DMLP model and the simulated NEE, and to enhance the interpretability of the model. The results show that the normalized difference vegetation index, the enhanced vegetation index, and the leaf area index play a dominant role at most sites. This study provides new ideas and methods for analyzing the intricate relationship between NEE and environmental factors by introducing the SHAP interpretable model. These advancements are crucial in achieving carbon reduction targets. [ABSTRACT FROM AUTHOR]

Medium Resolution Spectral Imager-II (MERSI-II) on FengYun 3D (FY-3D) meteorological satellite is a visible and infrared spectral imaging instrument comparable with Moderate Resolution Imaging Spectroradiometer (MODIS), which provides ample opportunities for regional crop mapping. However, current research may have overlooked the potential of combining FY-3D MERSI-II data with deep neural network (DNN) algorithms for winter wheat identification. This research utilizes a DNN method to train a nonlinear model with the synthetic normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) at a 250 m resolution from FY-3D MERSI-II as the eigenvalues, enabling to accurately identify spatial distribution of winter wheat in China's main production regions and filling the gap in the application of FY-3D MERSI-II data. The results indicate that the winter wheat identification with the combination of NDVI and EVI achieved an accuracy of 95.32%, with a Kappa coefficient of 0.87 and a determination coefficient (R2) of 0.73, improving the overall accuracy by 1.27% and 1.07% compared to models trained solely using NDVI or EVI, respectively. In terms of spatial distribution, the overlap rate with the winter wheat dataset of China from MODIS at a 500 m resolution exceeded 95%, with an area bias of 2.67%. This study indicates that the medium resolution FY-3D MERSI-II remote sensing dataset combined with the DNN algorithm can accurately identify crop information over large areas. [ABSTRACT FROM AUTHOR]

Determining the factors that drive vegetation variation is complicated by the intricate interactions between climatic and anthropogenic influences. Neglecting the short-term time-lag and cumulative effects of climate on vegetation growth (i.e., temporal effects) exacerbates the uncertainty in attributing long-term vegetation dynamics. This study evaluated the climatic and anthropogenic influences on vegetation dynamics in China from 2000 to 2019 by analyzing normalized difference vegetation index (NDVI), temperature, precipitation, solar radiation, and ten anthropogenic indicators through linear regression, correlation, multiple linear regression (MLR), residual, and principal component analyses. Across most regions, growing season NDVI (G-NDVI) exhibited heightened sensitivity to climatic variables from earlier periods or from both earlier and current periods, signaling extensive temporal climatic effects. Constructing new time series for temperature, precipitation, and solar radiation from 2000 to 2019, based on the optimal vegetation response timing to each climatic variable, revealed significant correlations with G-NDVI across 27.9%, 26.7%, and 23.3% of the study area, respectively. Climate variability and anthropogenic activities contributed 45% and 55% to the G-NDVI increase in China, respectively. Afforestation significantly promoted vegetation greening, while agricultural development had a marginally positive influence. In contrast, urbanization negatively impacted vegetation, particularly in eastern China, where farmland conversion to constructed land has been prevalent over the past two decades. Neglecting temporal effects would significantly reduce the areas with robust MLR models linking G-NDVI to climatic variables, thereby increasing uncertainty in attributing vegetation changes. The findings highlight the necessity of integrating multiple anthropogenic factors and climatic temporal effects in evaluating vegetation dynamics and ecological restoration. [ABSTRACT FROM AUTHOR]

Environmental geohazard probabilities are considerably affected by multiple factors, and the reasonable selection of evaluation factors is crucial for evaluating environmental geohazard probability. This paper proposes a screening method for environmental geohazard probability factors based on a random forest (RF) model. The accuracy and reasonableness of the RF model are verified by comparison with those of the GeoDetector (GD) model with a confusion matrix, cross-validation and receiver operating characteristic (ROC) curves. In addition, the effectiveness of the RF model was analyzed in terms of the results of environmental geohazard probability zoning using information volume (IV), the frequency ratio (FR), and the mean absolute error (MAE). The results are shown for Panshi city, Jilin Province, China. The RF model screened nine factors, such as the normalized difference vegetation index (NDVI), elevation, population density, land use type, distance from river, aspect, topography, rainfall intensity and rock type, among which NDVI, elevation and population density were the key factors in the study area. The three factors of slope, profile curvature, and distance from fault eliminated by the RF model are more relevant to the key factors in the study area. Rainfall intensity is an important inducer of environmental geohazards in the study area, and it is unreasonable for the GD model to eliminate it; moreover, it is more reasonable for the RF model to screen the factor. Each evaluation indicator of the confusion matrix after RF model screening is improved and higher than that of the GD model, the model generalizability ability is stronger, and the RF model performance is better. The average accuracy of the model after RF model screening is improved by 13 %, the area under the curve (AUC) value is improved by 12 %, and the model accuracy is higher. After screening, the results of environmental geohazard probability zoning are more closely related to the distribution characteristics of key factors, and the density of disaster points in high-probability and very high-probability zones is increased, with the FR increasing by 0.68 % and 10.56 % respectively, which is conducive to targeted prevention and control of environmental geohazards. The information contribution rate (IN) after screening reached 93.57 %, the error of the environmental geohazard probability zoning results was reduced, the accuracy was improved, the results were more reasonable and effective, and the results could provide more targeted suggestions for the prevention and control of environmental geohazards. [ABSTRACT FROM AUTHOR]

In response to the impacts of climate change and the intensity of human activities in the alpine meadow region, there is an urgent need to determine the ecological quality and its drivers in alpine meadow areas. In this paper, Shangri-La was adopted as an example, the spatial and temporal evolution patterns of the ecological quality in Shangri-La were determined in both natural and social dimensions, and the contributions of various driving factors were analyzed. The conclusions are as follows: (1) the natural status index of Shangri-La from 2000 to 2020 generally showed a spatial distribution pattern that decreased from the central townships toward the north and south, and the social pressure index was irregularly distributed in high-value areas and continuously distributed in low-value areas. (2) From 2000 to 2020, the areas with high values of the ecological quality index were mainly distributed in central Shangri-La, with a maximum value of 0.91, while the low values were largely distributed in some townships in the north and south, with a minimum value of 0.26. (3) In the driving factors, the influences of the normalized difference vegetation index (NDVI) and net primary productivity (NPP) were greater than those of the other factors, among which the NDVI attained the largest mean value of 0.452, while the relative humidity (RHU) attained the lowest value of 0.036. (4) In terms of relative contributions, evapotranspiration (EVP) and precipitation (TEM) shifted from a positive drive to a negative drive from south to north. The contribution of the temperature to the ecological quality was the highest, at 64%. The spatial heterogeneity in the contributions of human disturbance activity factors to the ecological quality varied significantly, with the largest negative driving contribution of the NPP, at − 42.36%. The results could provide a basis for regional ecological quality protection and restoration. [ABSTRACT FROM AUTHOR]

Evaluating and predicting how carbon storage (CS) is impacted by land use change can enable optimizing of future spatial layouts and coordinate land use and ecosystem services. This paper explores the changes in and driving factors of Zunyi CS from 2000 to 2020, predicts the changes in CS under different development scenarios, and determines the optimal development scenario. Woodland and farmland are the main land use types in Zunyi. Land use change was reflected mainly in the mutual conversion among woodland, farmland, and grassland and by their conversion to construction land and water. In 2000, 2010, and 2020, the CS in Zunyi was 658.77 × 10^6 t, 661.44 × 10^6 t, and 658.35 × 10^6 t, respectively. Woodland, farmland and grassland conversions to construction land and water were primarily responsible for CS loss. The normalized difference vegetation index (NDVI) is the main factor influencing the pattern of CS (q > 10%). Furthermore, the impacts of the human footprint index and population density are increasing. In 2030, the CS of Zunyi is trending downward. Under the ecological-farmland conservation scenario (ECS), the CS is estimated to be 656.67 × 10^6 t, with the smallest decrease (− 0.26%) among timepoints. The effective control of woodland and farmland weakens the trend of CS reduction. [ABSTRACT FROM AUTHOR]

This study targeted the area surrounding Murakami City, Niigata Prefecture, Japan, which was the site of extensive sediment outflows due to heavy rainfall in August 2022. Specifically, the mudslide scar was estimated by calculating NDVI difference values (ΔNDVI) for four types of optical satellite data with different spatial resolutions. This study was carried out to support the work of engineers of government agencies in the early extraction of landslide sites after heavy rainfall by using optical satellite data. This study presented a set of ΔNDVI thresholds with high extraction accuracy at different spatial resolutions that were unexplored in similar studies. The data was extracted over a wide area and the effects of differences in spatial resolution on the applicability of the extraction method and the extraction rate were clarified. Selecting a spatial resolution that matches the area of the mudslide scar in the target area is considered to be effective. The two high-resolution satellite systems (Pleiades and SPOT) extracted approximately 50% of the total actual mudslide scar, and the two medium-resolution satellite systems (i.e., Planet and Sentinel-2) were able to extract about 70 % to 80 %. [ABSTRACT FROM AUTHOR]

The purpose of this work is to determine land-use and land-cover (LULC) patterns, land surface temperature (LST), and normalized difference vegetation index (NDVI) changes in Thatta district using Landsat data from 1991 to 2021 and evaluate the relationship between LST and NDVI. The research process employed the selection of the study area, data acquisition, preprocessing, and classification of remotely sensed images for the estimation of the land use land cover change (LULC), vegetation index (NDVI), and evaluation of LST using thermal bands in the Landsat dataset. The study revealed the area under built-up structures has increased from 1991 to 2021. Although the vegetation cover showed an increase, the bare soil showed a decreasing pattern, indicating a constant change in the LULC patterns in the region. The confusion matrix method for accuracy valuation of LULC data of 2021 revealed an overall accuracy of 88.24%, with a Kappa coefficient of 84.22%, while the Artificial Neural Network Multilayer Perceptron (ANN-MLP) model had a Kappa validation of 0.95 for 2021. The highest maximum temperature is observed for 2021, indicating a positive relationship between LST and built-up structures, while regression analysis found a negative correlation between LST and NDVI. This study provides a valuable monitoring framework to help resource managers develop strategies to manage land resources. [ABSTRACT FROM AUTHOR]

Wadi Al-Batin is an ancient valley that acts as the natural boundary between Iraq and Kuwait and is part of the larger Wadi Al-Rummah Basin in Saudi Arabia. Since the 1980s, human activities like military zones, oil fields, and camps have caused significant environmental changes in the area's geological, geomorphological, and hy drological conditions. This study investigates changes in the geomorphology, sedimentology, and topography of Wadi Al-Batin, land degradation and environmental changes caused by artificial landforms and natural factors and provides an update to the sedimentological map of Wadi Al-Batin. The study utilized field observations, geographical information systems (GIS), and remote sensing (RS) techniques to develop a geodatabase across various disciplines. A detailed map of the environmental geomorphological changes in Wadi Al-Batin was created using satellite imagery from 2000 to 2023 to provide up-to-date and precise information. The study also considered changes in the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) as indicators of environmental change. The results indicate that the surface deposits exhibited polymodal and trimodal modes (40% and 36%, respectively), were sandy and medium-coarse-grained, and showed vari ability in sorting, skewness, and kurtosis. They originated from the Upper Member of the Dibdibah Formation, were frequently fluvially reworked, and predominantly consisted of quartz with lower amounts of calcite. This study contributes to the preservation of environmental systems and assists decision-makers in protecting the rights of future generations to a sustainable environment. [ABSTRACT FROM AUTHOR]

As wild boar populations and their distribution ranges increase, human–wild boar conflicts have become increasingly prevalent in numerous regions across the globe. These conflicts have a profound impact on human livelihoods, resulting in significant economic losses. Understanding the habitat requirements and relative abundance of wild boars is crucial prior to implementing any conservation measures. However, studies on wild boar habitat and population in the central and eastern regions of the Tianshan Mountains in China are lacking. We assessed the activity patterns and relative abundance of wild boars in these areas and evaluated habitat suitability using a combination of camera trapping, line transects, species distribution modeling (maximum entropy model), and hierarchical abundance modeling (Bayesian N‐mixture model). We used 311 infrared cameras and 280 field‐based line transects to cover approximately 31,000 km² from September 2022 to May 2023 in the east‐central Tianshan Mountains. We used 240 wild boar distribution locations and 13 environmental predictors in the development of species distribution models. We also used species counts and associated environmental predictors in the N‐mixture model to estimate the relative abundance of wild boar. Wild boars were most active during crepuscular hours (1800), and relatively active in the diurnal period compared to the nocturnal period. The probability of wild boar occurrence increased with higher normalized difference vegetation index (NDVI), the minimum temperature of the coldest month, and annual temperatures below 39°C. Boars were most likely to be found in closed deciduous‐coniferous forests. The relative abundance of wild boars was positively affected by NDVI and negatively affected by the minimum temperature of the coldest month and temperature annual range. Based on our results, we suggest areas of management priority. In particular, extensive and intact habitat with substantial wild boar populations, such as the Banfanggou, the South Mountain of Urumqi, and the Hutubi, should be prioritized for long‐term wild boar population monitoring and management so the adverse impacts of increasing wild boar populations in the study region can be minimized. [ABSTRACT FROM AUTHOR]

Aim: This study aims to describe the latitudinal pattern in plant functional groups' (PFGs') biomass and cover in Nunavik to test whether PFG attributes are better Normalized Difference Vegetation Index (NDVI) predictors than climate. Location: The study spans a 700‐km latitudinal gradient from the lichen woodland to prostrate shrub tundra vegetation zones across Nunavik, Canada. Taxon: Our analysis focuses on the following PFGs: erect and prostrate shrubs, herbaceous plants, bryophytes, and lichens. Methods: Biomass and cover data of the different PFGs were sampled in 40 sites distributed across the latitudinal gradient. NDVI data were obtained through remote sensing, while climatic, permafrost depth, and surficial deposits were derived from various databases. The PFG models were built to explore relationships between average NDVI (2016–2020) at the sampling site and ecological attributes such as PFG biomass or cover but also other variables such as surficial deposits and permafrost depth. A second series of models, the climatic models, were built using only climatic variables such as seasonal temperature and precipitation. Results: The most parsimonious PFG model was built with the biomass data of erect shrubs, herbaceous plants, bryophytes, and lichens and included surficial deposits and permafrost depth (R2 = 0.74). This biomass model performed better than the most parsimonious cover model (cover of erect shrubs and herbaceous, surficial deposits, permafrost depth; R2 = 0.63). However, the most parsimonious climatic model (fall temperature, annual, and winter precipitations) exhibited superior predictive power compared to the ecological ones (R2 = 0.87). Conclusions: PFG models built with PFGs aboveground biomass or cover are good predictors of NDVI of the plant formations sampled along the latitudinal gradient in Nunavik. Despite the intrinsic association between NDVI and vegetation attributes, our study emphasizes the importance of the regional climate in the control of primary productivity in Arctic and subarctic ecosystems. This study provides new insights into the interpretation of NDVI data and enhances our understanding of Arctic vegetation responses under rapid climate change. Furthermore, it underscores the balance between climatic drivers and ecological dynamics in shaping fragile Arctic ecosystems. [ABSTRACT FROM AUTHOR]

The pressing need to address climate change and advance global sustainable development has heightened the emphasis on ecosystem services, especially carbon sequestration. This research assesses the supply and demand dynamics of carbon sequestration services on Hainan Island, China, highlighting its significant contributions to global biodiversity conservation and carbon balance. The analysis considers the spatial distribution and interrelation of these services in light of recent land use and ecological policy changes. The methodology incorporates land use and land cover data, the Normalized Difference Vegetation Index (NDVI), meteorological data, and soil data. A gravity model is employed to elucidate the supply–demand relationship for carbon sequestration services, examining the flow across different regions and identifying spatial connections and their intensities. The results indicate a notable increase in carbon sequestration supply in Hainan from 2000 to 2020, particularly in the central mountainous areas. Conversely, the demand for these services has risen, especially in the northern plains' urban areas and southern coastal towns. The gravity model reveals a strong spatial interdependence between the central mountainous supply zones and the high-demand urban locales. This study underscores the disparities in carbon sequestration supply and demand on Hainan, emphasizing the need for the strategic management of these elements. It provides critical data for ecological compensation policies and offers insights into the roles of regional ecosystems in climate change mitigation. The research highlights the necessity of incorporating ecosystem services into land-use planning and decision-making to foster sustainable development and strengthen climate resilience. [ABSTRACT FROM AUTHOR]

OIL salinization is a major environmental issue in Egypt's semi-arid region, characterized by low precipitations rate, high temperatures, and high evaporation rates, ultimately lowers agricultural sustainability. Consequently, effective soil salinity observation, evaluation and mapping are vital to mitigate the negative impacts of soil degradation and ensure sustainable land management. Therefore, this study focused on estimating and mapping soil salinity in semi-arid lands (Sohag Governorate, Egypt), using PRISMA (PRecursore IperSpettrale della Missione Applicativa) visible-Near-Infra-Red (vis-NIR) hyperspectral images as an advanced tool that offers significant promise for extracting detailed information about the composition and condition of the observed areas. For this study, seventeen representative soil profiles were collected in more than 150 cm depth from the field and subsequently analyzed in the lab to determine their EC (dS/m) values. Moreover, PRISMA hyperspectral image of the investigated area was acquired, processed, and some soil salinity hyperspectral indices were applied such as; Salinity Index (SI), Brightness Index (BI), Normalized Differential Salinity Index (NDSI), Combined Spectral Response Index (COSRI) and Coloration Index (CI). Among the PRISMA hyperspectral image, six bands were significant (12, 23, 25, 31, 32, and 49) and used for calculating the hyperspectral indices. Soil salinity spatial variability maps of the study area were generated using the relation between the developed hyperspectral indices and the observed soil salinity as EC values (dS/m). The study area demonstrated a relatively consistent level of salinity, with soil EC values ranging from 0.9 to 2.10 dS/m and averaging 1.40 dS/m, this moderate salinity, evident in the generated maps, suggests a relatively stable soil condition. The COSRI and CI hyperspectral salinity indices achieved the highest accuracy in estimating soil salinity (EC) with coefficient off determination (R² ) values of 0.68 and 0.64, respectively; and the root mean square error (RMSE) values were 0.26 and 0.29 dS/m, respectively. Other indices such as; normalized difference vegetation index (NDVI), NDSI, BI, and SI had lower capability for estimating soil salinity over the study area. The PRISMA hyperspectral data is a very potential tool for estimating soil salinity in the studied area, therefore, monitoring and analyzing soil salinity are essential for developing effective land management strategies. [ABSTRACT FROM AUTHOR]

This study presents an advanced phenology-based approach to map rice fields and their growth stages using Sentinel-1 and Sentinel-2 time-series data combined with k-means clustering and fuzzy deep neural networks (FDNN). The method relies on two main observations: the detection of flooding during transplanting through Sentinel-1 VH backscatter analysis and the identification of growth phase fluctuations using normalized difference vegetation index (NDVI) time series. This approach was applied to Gilan, Iran (14,042 km²), and Niigata, Japan (12,584 km²). Data from January 2019 to December 2020 were processed on the Google Earth engine (GEE) platform, resulting in high-resolution maps at a 10-meter scale. The methodology integrated unsupervised classification and FDNN to effectively identify areas with similar phenological characteristics and detect plant diseases. The resulting maps detailed the extent, intensity, and schedules of rice cultivation, showcasing the seasonal rhythms of planting and harvesting. Validation against high-resolution Google Earth street viewpoints confirmed the accuracy and reliability of the generated maps, demonstrating the robustness and efficiency of the proposed approach. The findings underscore the cost-effectiveness of this method, which accurately delineates rice fields, identifies growth stages, and detects plant diseases over large areas. This capability is crucial for monitoring progress towards self-sufficiency in rice production and for calculating methane emissions from rice paddies. By providing detailed insights into the spatial and temporal dynamics of rice farming, the study offers a valuable tool for agricultural planning and environmental management. The approach's success in diverse geographic regions highlights its potential for broader application in other rice-producing areas worldwide. [ABSTRACT FROM AUTHOR]

The intensity and frequency of tropical cyclones (TC) are on the rise due to climate change, resulting in severe damage to coastal regions. Hence, the mitigation of socioeconomic and environmental consequences of cyclones has attained paramount importance in the recent years. In this study, the rapid impact of a very severe cyclonic storm "Titli" on land cover (LC) changes were evaluated using Moderate Resolution Imaging Spectroradiometer (MODIS) and high-resolution Sentinel-2 data. The cyclonic event caused substantial modifications in land use and land cover with nearly 46% decrease in dense vegetation, 129% increase in fallow land, and 111% increase in water body, over the study region. Widespread damage (dense to less dense vegetation) was evident on the left side of the cyclone track as compared to the right. The analysis revealed a 98.3% decrease in dense vegetation, marked by a decrease in the normalized difference vegetation index (NDVI) from 0.73 to 0.44 over the landfall region. This NDVI decrease continued for nearly 3 months before the onset of vegetation regrowth. Change in vegetation into other LCs over the landfall region resulted in an increase of the mean daytime land surface temperature by ~ 6 °C. The analysis highlights the magnitude of spatiotemporal scale damages to LULC and consequent loss in seasonality that can be ushered in by a single short-duration extreme weather event like TC and thus emphasizes the need for well-formulated mitigation strategies. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) serves as a crucial indicator for evaluating the effects of different environmental factors on the ecosystem, including alterations in land use, climate variations, and emissions of greenhouse gases. This comprehensive study used remote sensing data to analyze changes and effects of land use/land cover (LULC) on LST in Tay Ninh province, Vietnam, from 1988–2023. Landsat satellite images in 1988, 2004, and 2023 were preprocessed and supervised classification on ArcGIS 10.8 software. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and LST in the study area were determined using the Landsat image data. The classification results showed a decrease in the area of agricultural land, barren land, and forest classes by 8.30%, 8.82%, and 15.93%, respectively, from 1988 to 2023. Conversely, the area of built-up and waterbodies classes indicated an increase of 33.00% and 0.06%, respectively, during the same period. In terms of LST, the study area exhibited temperature ranges of approximately 19.75 °C—35.28 °C, 26.26 °C—46.33 °C, and 21.05 °C—40.60 °C in 1988, 2004, and 2023, respectively. Contribution Index (CI) and multiple regression analysis evaluated the relationship between land cover, LST, NDVI, and NDBI. The regression analysis preliminary showed a negative correlation between NDVI and LST, while a positive correlation was observed between NDBI and LST. The CI of built-up areas has increased from 0.01 in 1988 to 0.77 in 2023, which shows that this coating has contributed to rising temperatures in the study area. Meanwhile, the forest and water body classes have consistently negative CI throughout the period 1988–2023, which has contributed to the decrease in temperature. This comprehensive study provides policymakers with valuable information regarding LULC and LST, instilling confidence in developing effective policies for land resource management. [ABSTRACT FROM AUTHOR]

In recent years, mudflows have emerged as a significant threat to infrastructure and lives, exhibiting increased frequency and severity. This study collectively addresses the rising threat of natural hazards, specifically mudflows in mountain-foothill zones in Tajikistan, utilizing UAV and remote sensing technologies to monitor their impact. From 2020 to 2023, we employed a QC-2 Micro UAV and a DJI Phantom 4 quadcopter to capture high-resolution aerial imagery across multiple affected districts, supplemented by pre- and post-event Sentinel-2 images analyzed using the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). Our analysis revealed that 49 districts and cities experienced significant mudflow events, resulting in estimated damages and economic losses of 30.2 million USD, with particularly severe impacts noted in the Khuroson, Tojikobod, Vakhsh, Panjakent, Lakhsh, and Varzob. The composition of mudflows in the six studied areas predominantly consisted of clay, with some regions featuring mudstone and a few exhibiting a mixture of both materials. Our findings indicate the predominance of rain-induced mudflows in foothill areas, contrasting with rain-snow, glacial, and lake-outburst-induced mudflows in mountainous regions. This underscores the urgent need for effective disaster risk management strategies in vulnerable locations. Integrating UAV and remote sensing technologies provides crucial information for analyzing damages, identifying vulnerable areas, and advising emergency response efforts, thereby enhancing disaster risk management in Tajikistan. [ABSTRACT FROM AUTHOR]

Climate change and human activities have significantly influenced soil loss and the soil conservation service, posed threats to regional ecological sustainability. However, the relationships and underlying driving forces between potential soil loss, actual soil loss, and soil conservation service have not been well understood. Utilizing the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, we evaluated the soil conservation service on the Tibetan plateau from 1990 to 2020. We analyzed the spatial and temporal trends and examined the driving factors using linear regression, Pearson correlation, and random forest regression. The soil conservation service exhibited a complex pattern of increase followed by a decrease, with a turning point around 2010. Soil conservation service and soil loss demonstrated non-trade-off changes. The potential soil loss dominated the spatiotemporal patterns of soil conservation service on the Tibetan Plateau. Climatic factors significantly influenced the spatiotemporal patterns of soil conservation service, with annual precipitation emerging as the dominant driving factor, contributing approximately 20%. However, the impacts of human activities became more pronounced since 2010, and the contribution of vegetation to changes in soil conservation service was increased. The impact of the Normalized Difference Vegetation Index (NDVI) on soil conservation service for the grades I, II, and III increased by 13.19%, 3.08%, and 3.41%, respectively. Conversely, in northern Tibet before 2010 and eastern Three-River-Source after 2010, soil conservation service exhibited an increasing trend driven by both climate factors and human activities. Which indicates that the implementation of ecological restoration measures facilitated vegetation improvement and subsequently reduced actual soil loss. This study provides a scientific basis for resource management, land development strategies, and the formulation of ecological restoration measures on the Tibetan Plateau. Highlights: Climatic factors dominated the spatiotemporal patterns of soil conservation service in the majority of the Tibetan Plateau. The contribution of vegetation changes to soil conservation service changes has increased since 2010. Initial success has been observed in the implementation of ecological protection measures in northern Tibet and the eastern Three-River-Source region. [ABSTRACT FROM AUTHOR]

Solar radiation (Rs) is a major renewable energy source and also a crucial factor in designing solar panels, determining water requirement, and irrigation scheduling. In this study, meteorological parameters (air temperature, average air temperature, and relative humidity; Scenario 1), satellite image–based indices (normalized difference vegetation index: NDVI and land surface temperature: LST; Scenario 2), and their combination (Scenario 3) were used as predictors of Rs simulator models in Mashhad watershed (2005–2015). To this end, three different transfer function algorithms of the multi-layer perceptron (MLP), namely Levenberg–Marquardt backpropagation (MLP-LVM), gradient descend backpropagation (MLP-GDB), and batch training with weight and bias learning rules (MLP-BTWB), as well as two other machine learning models, M5 Tree and XGB (eXtreme Gradient Boosting), were employed. In addition, hybrid models obtained through coupling the best MLP transfer function with genetic algorithm (MLP-LVM-GA) and coupling Variational Mode Decomposition with M5 Tree and XGB (VMD-M5 Tree and VMD-XGB, respectively) were also investigated. In the case of MLP-based models, the third scenario proved to be more efficient, with RMSEs equal to 404.2, 424.5, and 423.8 J.cm−2.day−1 for the aforementioned transfer functions, respectively. The results showed that GA coupled with the best transfer function algorithm of MLP improved Rs estimates, decreasing estimation error by 14.7, 30.65, and 32.67% in the three defined scenarios, respectively. In training set, VMD-M5 Tree and VMD-XGB hybrid models outperformed corresponding base models under all three scenarios; but in testing set, estimation error was decreased only in the third scenario (by 37 and 42%, respectively). Overall, all models (base and hybrid) had the least Rs estimation errors in the third scenario (application of both meteorological parameters and satellite image–based data). [ABSTRACT FROM AUTHOR]

This study provides an in-depth analysis of specific land use areas within a semi-arid rangeland region by utilizing the normalized difference vegetation index (NDVI). The stream areas, local roads, main roads, and rock mining areas were subjected to NDVI analysis, revealing distinct vegetation health patterns. The stream areas, encompassing a 10-meter buffer, exhibited NDVI values ranging from 0.0098 to 0.447, covering 0.3% of the total study area. NDVI values for local roads (5 m buffer) ranged from 0.07 to 0.438, while main roads (10 m buffer) showed values between 0.017 and 0.172. In the rock mining areas, NDVI values varied at 10-meter and 20-meter buffer distances, with a polygon region indicating values from 0.012 to 0.276. The findings underscore the impact of specific land use practices on rangeland health and advocate for integrating NDVI techniques in monitoring and decision-making processes. The study also emphasizes the importance of selective management strategies to preserve healthy rangeland areas and mitigate the negative effects of degradation drivers, such as population density, grazing intensity, deforestation, unmanaged mining, and unplanned road networks. These insights contribute to of developing sustainable land use practices and ecological resilience in semi-arid rangeland ecosystems. [ABSTRACT FROM AUTHOR]

This study is devoted to the use of varied analytical methods to elucidate the complex relationship between normalized difference vegetation index (NDVI) and wheat production in Taounate, Morocco based on MODIS Satellite data. Linear regression (LR), with a coefficient of determination (R²) of 0.93, provided a solid basis, while the decision tree (DT) showed significant performance with an R² of 0.81. Support vector regression (SVR) performed well with an R² of 0.96, highlighting its ability to capture the nonlinear nuances of the data. Given the complexity inherent in the observed relationships, characterized by non-linear variations, we opted for a combined approach. K-means, closely linked to SVR, was integrated for its ability to identify homogeneous subgroups in the data (R2 up to 0.98). This combination made it possible to circumvent the limits of strictly linear methods, thus reinforcing the robustness of our analysis. These results underline the capacity of the chosen methodology to decode the interactions between NDVI and wheat production in the complex context of Taounate. By providing clear and nuanced perspectives, this study helps inform agricultural decisions and build resilience to climate challenges in the region. [ABSTRACT FROM AUTHOR]

Development of long-term continues, consistent and high-quality satellite remote sensing aerosol and surface products is crucial to constrain climate models and improve our understanding on climate change. Particulate Observing Scanning Polarization (POSP) is the first space-borne multi-spectral (UV-VIS-NIR-SWIR) cross-track scanning polarimeter dedicated to complement Directional Polarimetric Camera (DPC) multi-angular polarimetric measurements and perform synergetic observations namely the Polarization Cross-Fire suite (PCF) onboard Chinese Gaofen series GF-5(02) satellite. The POSP unique single-viewing spectral (UV-VIS-NIR-SWIR) high-precision polarimetric measurements provide rich information for atmospheric aerosol and surface characterization. Here we developed aerosol and surface products from POSP/GF-5(02) based on the Generalized Retrieval of Atmosphere and Surface Properties (GRASP)/Models approach. The detailed retrieval approach and processing scheme are provided. The baseline level 2 product was generated for the first 18 months POSP measurements from December 2021 to May 2023 and publicly available and registered at doi.org/10.57760/sciencedb.14748. The obtained POSP/GF-5(02) aerosol and surface products are validated and intercompared with ground-based AERONET reference aerosol dataset and other independent satellite products, such as NOAA-20 VIIRS/DB aerosol product and MODIS MCD43 surface product. The results show generally good consistency of POSP products with AERONET, VIIRS/NOAA-20 aerosol dataset and MODIS surface product. Moreover, it is possible to conclude the developed POSP product includes reliable estimates of not only total Aerosol Optical Depth (AOD), but also detailed properties of aerosol such as aerosol size, absorption, layer height, type, etc., as well as full surface Bidirectional Reflectance Distribution Function (BRDF), Bidirectional Polarization Distribution Function (BPDF), and black-sky, white-sky albedos and Normalized Difference Vegetation Index (NDVI). These parameters are of high importance to constrain the Earth-atmosphere radiation budget. The retrieval of these properties seems to be possible due to the polarimetric capabilities and wide UV-VIS-NIR-SWIR spectral range of from POSP observations and advances of used GRASP/Models retrieval approach. Finally, some potential improvements for POSP level 1 to level 2 processing chain are identified, and limitations and lessons learned are discussed. [ABSTRACT FROM AUTHOR]

The study focuses on identifying fireburning and burnt areas in a large-scale forest fire that occurred in Xintian County, China, in October 2022. To investigate the adaptability of machine learning methods in various scenarios for mapping forest fire areas, this study presents a comparative study on the recognition and mapping accuracy of three machine learning algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and Neural Network (NN), based on Sentinel-1B and 2A imagery. Initially, three sets of pre-fire, during-fire, and post-fire remote sensing data were preprocessed. Various feature parameters from Sentinel-1B and 2A imagery were combined to identify firerelated land cover types. The experimental results revealed that: (i) During the pre-fire period, the SVM method demonstrated superior accuracy compared to the other two methods. The combination of spectral and Normalized Difference Vegetation Index (NDVI) features achieved an optimal accuracy for identifying forest areas with an overall accuracy (OA) of 93.52%. (ii) In the during-fire period, RF method exhibited higher accuracy compared to the other two methods with peak fire identification accuracy reached by combining spectral and Normalized Burn Ratio (NBR) index features at an OA of 95.43%. (iii) In the post-fire period, SVM demonstrated superior accuracy compared to other methods. The highest accuracy of 94.97% was achieved when combining spectral and radar features from Sentinel-1B imagery, highlighting the effectiveness of using spectral and radar backward scattering coefficients as feature parameters to enhance forest fire recognition accuracy for burnt areas. These findings suggest that appropriate machine learning algorithms should be employed under different conditions to obtain more precise identification of forest fire areas. This study provides technical support and empirical evidence for extracting and mapping forest fire areas while assessing damage caused by fires. [ABSTRACT FROM AUTHOR]