The Massarosa wildfire, which occurred in July 2022 in Northwestern Tuscany (Italy), burned over 800 hectares, leading to significant environmental and geomorphological issues, including an increase in soil erosion rates. This study applied the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates with a multi-temporal approach, investigating three main scenarios: before, immediately after, and one-year post-fire. All the analyses were carried out using the Google Earth Engine (GEE) platform with free-access geospatial data and satellite images in order to exploit the cloud computing potentialities. The results indicate a differentiated impact of the fire across the study area, whereby the central parts suffered the highest damages, both in terms of fire-related RUSLE factors and soil loss rates. A sharp increase in erosion rates immediately after the fire was detected, with an increase in maximum soil loss rate from 0.11 ton x ha-1 x yr-1 to 1.29 ton x ha-1 x yr-1, exceeding the precautionary threshold for sustainable soil erosion. In contrast, in the mid-term analysis, the maximum soil loss rate decreased to 0.74 ton x ha-1 x yr-1, although the behavior of the fire-related factors caused an increase in soil erosion variability. The results suggest the need to plan mitigation strategies towards reducing soil erodibility, directly and indirectly, with a continuous monitoring of erosion rates and the application of machine learning algorithms to thoroughly understand the relationships between variables.

Despite its crucial role in flood defense for downstream regions, the catastrophic breach of the Kakhovka Dam on June 6, 2023, along the Dnipro River in Ukraine caused extensive flooding and damage both upstream and downstream. In addition, the subsequent significant drying up of the dam reservoir poses serious challenges, including hindered electricity generation, compromised flood control measures, and disrupted aquatic ecosystems. This study aims to address knowledge gaps related to the event by employing multi-temporal change detection of pre- and post-event Sentinel-1 synthetic aperture radar (SAR) imagery, analyzed using the Google Earth Engine (GEE) platform, to map flood extent and impacts. Furthermore, we assessed the impacts of dam breaches on soil organic carbon (SOC) sequestration potential in both the drying reservoir region upstream and the flooded areas downstream. The results estimated the total area of the flood extent to be approximately 379.41 km2, with an overall accuracy (OA) of 94% and a Kappa index (K) of 0.89. Quantitative analysis revealed that 81.15 km2 of urban areas, 82.59 km2 of agricultural lands, and 215.56 km2 of herbaceous wetlands were submerged by floodwaters. Both flooding and reservoir drawdown from dam collapses can significantly affect soil organic carbon (SOC) sequestration rates in affected soils. The quantification of post-disaster impacts underscores the pressing need for restoration practices and sustainable management efforts to lessen the environmental impacts and enhance the recovery of the affected regions.

Recent research on the Himalayan cryosphere has increasingly been focused on climate uncertainty and regional variations, considering features such as glacier recession, lake expansion, outburst floods, and regional hazards. The Bhilangana river basin, located in the central Himalayas, is predominantly characterized by increased elevation-dependent warming and declining seasonal precipitation. Our study shows that high-elevation temperature increased from 2000 to 2022 (0.05(degrees)C/year, p = 20 m/sec). Quantification of the regional hazard reveals potentially severe downstream challenges for low-to-medium-scale hydropower stations, local settlements, and road and railway bridges near Devling and Ghuttu villages.

Artificial human-induced soil sealing has numerous negative consequences. The extent of impervious surfaces is a key indicator of the location and intensity of human activity; however, it is also proof of damage to the natural environment as a result of the sealing and modification of ecosystems. Remote sensing techniques can help detect and monitor changes in land use and cover over an extended period. However, the limited availability of consistent satellite images with high spatiotemporal resolutions covering several decades poses major challenges for achieving high overall classification accuracy. An accurate methodology for the multitemporal detection of artificial land cover classes was developed and applied to a case study of the metropolitan area of Murcia (Spain) with its challenging landscape conditions due to the frequent presence of bare soil. For this purpose, a variety of high-resolution satellite images from SPOT 5, Rapid Eye, and PlanetScope covering a period of 20 years were used. To improve the automated detection of built-up areas, the reflectance values of the images, normalised difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), and a building surface digital model were used as inputs for the supervised classification model. We applied a random forest algorithm to non-public, high-resolution images in the Google Earth Engine (GEE) as a processing environment to identify eight target land cover classes. The results show that the proposed methodology leads to a substantial improvement, after including the indices and the digital building model, in the overall accuracy (from 93.16 to 95.97%) and in all classes. This improvement was significant for the artificial classes and was particularly noticeable for the built-up areas (from 91.1 to 95.64%) because their confusion with bare soil was considerably reduced. This work demonstrates the effectiveness of the building-surface digital model as a tool for training the classification model, as it reduces uncertainty in confusion with other spectrally similar classes and its applicability to multisource imagery.

Ecosystem Services Value (ESV) are the various beneficial functions and products that natural ecosystems provide to humans, and are important indicators for evaluating ecosystem conditions and human well-being. Opencast mining is one of the human activities that severely damage the surface environment, but its long-term impact on ecosystem services lacks systematic assessment. This study takes the Ordos opencast mining area as an example, and calculates the value of ESV from 1990 to 2020 based on the Google Earth Engine platform. Mann-Kendall Tau-b with Sen ' s Method (Sen + mk test) and Joinpoint regression model were used to analyzes its spatiotemporal variation characteristics. Further revealed the impacts of opencast mining on ESV as well as the trend of ESV changes. The results show that: (1) The dynamic ESV levels in the study area fluctuated considerably from 1990 to 2020 with an overall decreasing trend of 89.45%. (2) Among nine types ecosystem services, most of them were significantly different (p < 0.001) between mining areas and control areas, with biodiversity protection (BP), climate regulation (CR), gas regulation (GR), soil formation and retention (SFR), water supply (WS) and waste treatment (WT) showed a significant decrease between 1990 and 2020. (3) In the past 30 years, the ESV of the study area showed an overall improvement trend, where the improved area accounted for 48.45% of the total area of the study area. However, the degraded area also accounted for 21.28, and 17.19% of the area belonged to severe degradation. With 67% of the significantly degraded areas distributed within mining concessions. (4) The trend of ESV changes in the mining impact areas and the control area showed significant differences. The ESV of the control area increased continuously, with an average annual percentage change (AAPC) of 0.7(95%CI:0.50 similar to 0.9, P < 0.001) from 1990 to 2020; while the ESV of the mining impact areas first stabilized and then decreased significantly, with an AAPC of - 0.2(95%CI:- 0.3 similar to - 0.1,P < 0.001) from 1990 to 2020. This study provides scientific support for formulating ecosystem management, restoration plans, and payment for ecosystem service policies, which is conducive to achieving regional sustainable development and improving human well-being.

Soil erosion is caused by increased agricultural activities and a lack of necessary measures to prevent erosion. This leads to the destruction of soil, which takes thousands of years to regenerate. The study area in the Mediterranean Basin is one of the subbasins most affected by global climate change. Erosion in burned areas, especially after large forest fires, occurs as water can wash away the soil and increase the risk of erosion. Burned vegetation also reduces the soil's erosion resistance. The increase in erosion in burned areas can lead to a series of problems, such as water source pollution, damage to agricultural areas, and environmental pollution. The study aims to determine that the Google Earth Engine (GEE) platform is an effective tool for combating erosion after fire lands. Erosion is predicted using the RUSLE model on GEE in pre-fire (2020) and post-fire (2022). This study determined areas at risk of erosion, and preventative measures were taken to prevent environmental problems like soil loss, water pollution, habitat loss, and biodiversity loss. In the results of the study, it was determined that the average soil loss after forest fires in the Manavgat River Basin was 9.47 ton-1 ha-1 year-1. According to the study, changes in soil loss were found depending on land use during the pre-fire and post-fire periods, and there was a general increase in soil loss of 0.10 ton-1 ha-1 year-1 after the fire. It was found that soil loss was lower before the fires. The study area experienced soil loss higher than the Turkiye average. The RUSLE-GEE method used in the study and other methods for estimating soil loss emphasizes the need to use strategies such as changing agricultural methods, using sediment trapping systems, protecting soil cover, and implementing policies and laws together to reduce soil erosion.

Tropical Cyclone Yaas inflicted substanntial damage on Bhitarkanika National Park (BNP), an eminent wildlife sanctuary housing a vast diversity of flora and fauna, during its occurrence in 2021. The park has experienced a heightened frequency of cyclonic activity in recent years. This study undertakes a comprehensive analysis of the impacts of Tropical Cyclone Yaas on the mangrove forest within BNP, utilizing a broad array of physical, biological, and ecological indices. The assessment method employed in the study encompasses various indicators, such as ecological (Normalized Difference Vegetation Index - NDVI, Enhanced Vegetation Index - EVI, Leaf Area Index - LAI, Normalized Difference Water Index - NDWI, and Normalized Difference Salinity Index - NDSI), biological (Chlorophyll content and Gross Primary Productivity - GPP), and physical (flood monitoring and precipitation) measures. Our findings elucidate the destructive consequences of Cyclone Yaas on the mangrove forest, inflicting significant ecosystem loss attributable to the extreme precipitation and high wind speeds. The biophysical, ecological, and biological indicators reveal profound effects on the local ecosystem, manifested through a decline in vegetation vigor and alterations in soil conditions, notably marked by an increase in salinity.

The Granger Causality (GC) statistical test explores the causal relationships between different time series variables. By employing the GC method, the underlying causal links between environmental drivers and global vegetation properties can be untangled, which opens possibilities to forecast the increasing strain on ecosystems by droughts, global warming, and climate change. This study aimed to quantify the spatial distribution of four distinct satellite vegetation products' (VPs) sensitivities to four environmental land variables (ELVs) at the global scale given the GC method. The GC analysis assessed the spatially explicit response of the VPs: (i) the fraction of absorbed photosynthetically active radiation (FAPAR), (ii) the leaf area index (LAI), (iii) solar-induced fluorescence (SIF), and, finally, (iv) the normalized difference vegetation index (NDVI) to the ELVs. These ELVs can be categorized as water availability assessing root zone soil moisture (SM) and accumulated precipitation (P), as well as, energy availability considering the effect of air temperature (T) and solar shortwave (R) radiation. The results indicate SM and P are key drivers, particularly causing changes in the LAI. SM alone accounts for 43%, while P accounts for 41%, of the explicitly caused areas over arid biomes. SM further significantly influences the LAI at northern latitudes, covering 44% of cold and 50% of polar biome areas. These areas exhibit a predominant response to R, which is a possible trigger for snowmelt, showing more than 40% caused by both cold and polar biomes for all VPs. Finally, T's causality is evenly distributed amongst all biomes with fractional covers between similar to 10 and 20%. By using the GC method, the analysis presents a novel way to monitor the planet's ecosystem, based on solely two years as input data, with four VPs acquired by the synergy of Sentinel-3 (S3) and 5P (S5P) satellite data streams. The findings indicated unique, biome-specific responses of vegetation to distinct environmental drivers.

High-resolution permafrost mapping is an important direction in permafrost research. Arxan is a typical area with permafrost degradation and is situated on the southern boundary of the permafrost region in Northeast China. With the help of Google Earth Engine (GEE), the maximum entropy classifier (MaxEnt) is used for permafrost mapping using the land surface temperature (LST) of different seasons, deviation from mean elevation (DEV), solar radiation (SR), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) as the characteristic variables. The prior data of permafrost distribution were primarily based on 201 borehole data and field investigation data. A permafrost probability (PP) distribution map with a resolution of 30 m was obtained. The receiver operating characteristic (ROC) curve was used to test the distribution results, with an area under the curve (AUC) value of 0.986. The results characterize the distribution of permafrost at a high resolution. Permafrost is mainly distributed in the Greater Khingan Mountains (GKM) in the research area, which run from the northeast to the southwest, followed by low-altitude area in the northwest. According to topographic distribution, permafrost is primarily found on slope surfaces, with minor amounts present in peaks, ridges, and valleys. The employed PP distribution mapping method offers a suggestion for high-resolution permafrost mapping in permafrost degradation areas.

The growth of vegetation on the Qinghai Tibet Plateau (QTP) is experiencing significant changes due to climate change. There is still a lack of high -precision simulation methods for alpine grassland cover (AGC), and the climate feedback mechanisms of AGC remain unclear, which poses challenges for the production of highprecision AGC products and the formulation of ecological conservation policies. In this study, a transferable stacking deep learning (Stacking -DL) model is proposed based on a CNN, a DNN, and a GRU for AGC time series simulation. The applicability of deep learning models for AGC simulation is evaluated based on long time series of measured data, MODIS data, and environmental factors. Finally, the AGC spatiotemporal changes and controlling environmental factors in the alpine region were analyzed based on Sen 's slope and structural equation modeling (SEM). The results showed that feature selection and parameter optimization improved the applicability of the deep learning models in AGC simulations, and the DNN (R 2 = 0.899, RMSE = 0.078) model performed best among the base deep learning models. The Stacking -DL model combines the advantages of multiple models and achieves high transfer accuracy. In the YRSR, the AGC increase area (20.34 %) is greater than the AGC decrease area (3.34 %), the increase area is mainly located in the northeast, and the decrease area is mainly located in the southwest. AGC changes in the YRSR are mainly controlled by permafrost and climate. This study provides a high -precision and transferable vegetation monitoring model for alpine mountain regions based on advanced deep learning models and clarifies the response mechanism of AGC under climate change.