Understanding how land cover and seasonal variations influence soil microbial communities and nutrient cycling is crucial for sustainable land management in tropical forests. However, such investigations are limited in Madagascar's tropical ecosystems. This study investigated the impacts of land cover types and seasonal variations on soil properties and microbial communities in the tropical forest region of Andasibe, Madagascar. Soil samples were collected from four land cover types-tree fallow (TSA), shrub fallow (SSA), eucalyptus forest (EUC), and degraded land (TM)-across three seasonal periods: the dry season, the start of the rainy season, and the end of the rainy season. Both land cover and sampling season affected soil pH and available P, whereas total nitrogen, soil organic carbon, and the C/N ratio were affected only by land cover. The soil organic carbon and total nitrogen concentrations were greater in TM. NextSeq sequencing of the 16S rRNA gene and ITS regions of the nuclear rRNA operon revealed distinct microbial community compositions across land covers, with greater diversity in the TSA and SSA. Bacteria are more sensitive to seasonal changes than are fungi, with phosphate-solubilizing (gcd) and phosphate-mineralizing (phoD) genes being more abundant in the rainy season, emphasizing the role of microbes in nutrient availability under different climatic conditions. Principal component analysis highlighted SSA as a hotspot for microbial activity, reinforcing the potential of shrub ecosystems in soil restoration. These findings reveal strong land cover and seasonal effects on soil microbial functions, with implications for nutrient cycling, ecosystem resilience, and sustainable land management in tropical forest landscapes.

The Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) system is a combination of polarimetric SAR and interferometric SAR, which can simultaneously obtain the power information, polarimetric information, and interferometric information of land cover. Traditional land cover classification methods fail to fully utilize these information types, resulting in limited classification types and low accuracy. This paper proposes a PolInSAR land cover classification method that fuses power information, polarimetric information, and interferometric information, aiming to enrich the classification types and improve the classification accuracy. Firstly, the land cover is divided into strong scattering areas and weak scattering areas by using the power information to avoid the influence of weak scattering areas on the classification results. Then, the weak scattering areas are distinguished into shadows and water bodies by combining the interferometric information and image corners. For the strong scattering areas, the polarimetric information is utilized to distinguish vegetation, buildings, and bare soil. For the vegetation area, the concept of vegetation ground elevation is put forward. By combining with the anisotropy parameter, the vegetation is further subdivided into tall coniferous vegetation, short coniferous vegetation, tall broad-leaved vegetation, and short broad-leaved vegetation. The effectiveness of the method has been verified by the PolInSAR data obtained from the N-SAR system developed by Nanjing Research Institute of Electronics Technology. The overall classification accuracy reaches 90.2%, and the Kappa coefficient is 0.876.

Flood is among the most disastrous natural disasters since they are responsible for massive damage to infrastructure, severe fatalities and injuries, innumerable economic losses, and social disruptions worldwide. These damages caused by floods have been worsening in recent years worldwide because of environmental degradation, climatic change, and high-speed urbanization. A rising precipitation rate increases the chances of floods in flood-vulnerable areas. A flash flood is a rapid flooding of geomorphic low-lying regions caused by remarkably high rainfall in a short duration. On September 23rd, 2023 a flooding event in the Nagpur, Maharashtra, it is directly impact on the human death and economic loss entire city. In the present study, the change in the dynamics of Nagpur city was analysed by employing remote sensing and GIS techniques to assess the change in the land use and land cover patterns. Landsat imagery of year 2000, 2010, 2020, and 2023 was used for land use and land cover classification. This analysis reveals that there is an increase in built-up area from 72.85 sq. km in year 2000 to 185.4 sq. km in year 2023. The built up land is increased this changes where directly affects the infiltration rate of rainwater into the soil. The total area covered by water bodies is reduced to 2.29 sq. km in 2023 which were 12.2 sq. km in year 2000. It is indicates the encroachment of built-up land on the water bodies. On the day of flash flood occurrence, it was observed that Nagpur city received 145 mm rainfall which is highest in the month of September, 2023. The Shannon entropy model was used to estimate the population dynamics and growth patterns of Nagpur city. Higher entropy values were obtained during the analysis which indicates the rapid transformation of city in all directions. Population dynamics of Nagpur city also indicate the inflation in population from 4,067,637 in 2000 to 4,653,570 in 2010. The SAR water index was calculated using Google Earth Engine to detect the water surges in residential areas during the flood. Precautionary measures should be taken by governing authorities to avoid such disasters. Proper city planning and improvements in drainage systems are recommended within the city. It is needed for an hour to develop a river monitoring system and early warning system, as well as preventive measures that should be implemented, like the construction of retaining walls to control the flood water.

The 2018 Sulawesi Earthquake and Tsunami serves as a backdrop for this work, which employs simple and straightforward remote sensing techniques to determine the extent of the destruction and indirectly evaluate the region's vulnerability to such catastrophic events. Documenting damage from tsunamis is only meaningful shortly after the disaster has occurred because governmental agencies clean up debris and start the recovery process within a few hours after the destruction has occurred, deeming impact estimates unreliable. Sentinel-2 and Maxar WorldView-3 satellite images were used to calculate well-known environmental indices to delineate the tsunami-affected areas in Palu, Indonesia. The use of NDVI, NDSI, and NDWI indices has allowed for a quantifiable measure of the changes in vegetation, soil moisture, and water bodies, providing a clear demarcation of the tsunami's impact on land cover. The final tsunami inundation map indicates that the areas most affected by the tsunami are found in the urban center, low-lying regions, and along the coast. This work charts the aftermath of one of Indonesia's recent tsunamis but may also lay the groundwork for an easy, handy, and low-cost approach to quickly identify tsunami-affected zones. While previous studies have used high-resolution remote sensing methods such as LiDAR or SAR, our study emphasizes accessibility and simplicity, making it more feasible for resource-constrained regions or rapid disaster response. The scientific novelty lies in the integration of widely used environmental indices (dNDVI, dNDWI, and dNDSI) with threshold-based Decision Tree classification to delineate tsunami-affected areas. Unlike many studies that rely on advanced or proprietary tools, we demonstrate that comparable results can be achieved with cost-effective open-source data and straightforward methodologies. Additionally, we address the challenge of differentiating tsunami impacts from other phenomena (et, liquefaction) through index-based thresholds and propose a framework that is adaptable to other vulnerable coastal regions.

Siberia occupies vast areas underlain by permafrost, and understanding its land cover changes is important for ecological environmental protection in a warming climate. Based on the land cover and climate datasets, we analyzed the land cover changes and their drivers in Siberia from 1992 to 2020. The results show that (1) From 1992 to 2020, the areas of evergreen needleleaf trees and deciduous needleleaf trees in Siberia decreased by 9% and 2.5%, and the areas of grassland, shrub, cropland, and construction land increased by 1.5%, 14.2%, 2.8%, and 39.2%, respectively. Cropland expansion had the fastest rate of 1.85% in the continuous permafrost zone, and construction land expansion had the fastest rate of 3.07% in the non-permafrost zone. (2) The center of gravity of agricultural land continues to migrate to the northeast, and the center of gravity of construction land continues to migrate to the southwest. (3) The primary drivers for the land cover changes were temperature and precipitation, and active layer thickness also affected grassland, cropland, and deciduous needleleaf trees. The correlation coefficient between active layer thickness and cropland area is 0.74 in the continuous permafrost zone. The interaction between factors is mostly manifested as a two-factor enhancement, with the highest q-value of the interaction of temperature and precipitation for explanatory power. Our results suggest that climate change and permafrost degradation significantly changed land cover in Siberia. This finding deepens our understanding of the mechanisms of land cover change under the influence of permafrost degradation and provides a new perspective on the land cover changes in permafrost regions.

There is 78 % permafrost and seasonal frozen soil in the Yangtze River's Source Region (SRYR), which is situated in the middle of the Qinghai-Xizang Plateau. Three distinct scenarios were developed in the Soil and Water Assessment Tool (SWAT) to model the effects of land cover change (LCC) on various water balance components. Discharge and percolation of groundwater have decreased by mid-December. This demonstrates the seasonal contributions of subsurface water, which diminish when soil freezes. During winter, when surface water inputs are low, groundwater storage becomes even more critical to ensure water supply due to this periodic trend. An impermeable layer underneath the active layer thickness decreases GWQ and PERC in LCC + permafrost scenario. The water transport and storage phase reached a critical point in August when precipitation, permafrost thawing, and snowmelt caused LATQ to surge. To prevent waterlogging and save water for dry periods, it is necessary to control this peak flow phase. Hydrological processes, permafrost dynamics, and land cover changes in the SRYR are difficult, according to the data. These interactions enhance water circulation throughout the year, recharge of groundwater supplies, surface runoff, and lateral flow. For the region's water resource management to be effective in sustaining ecohydrology, ensuring appropriate water storage, and alleviating freshwater scarcity, these dynamics must be considered.

Soil erosion is a severe issue posing a number of adverse effects on the environment. It is a prominent hazard damaging the fertile agricultural land. Therefore, in this study, a spatio-temporal assessment of soil erosion was carried out in the Swat River Basin, Pakistan by employing the Revised Universal Soil Loss Equation (RUSLE). The parameters of the RUSLE model are rainfall erosivity, soil erodibility, slope length and steepness, land management and support practice. These factors were developed from monthly mean rainfall data obtained from the Regional Metrology Department Peshawar, FAO soil database, land use data prepared from Landsat-5 and 8 satellite imageries, topographic data obtained from the ALOS PALSAR Digital Elevation Model (DEM). The analysis discovered that 13% of the study area experienced severe erosion. Results of the spatial distribution and vulnerability to erosion within the Swat River Basin have been categorized into different zones such as very low (59.7%), low (19.5%), moderate (5.37%), high (6.86%), and very high (5.96%). These results accentuate the necessity for mitigation measures in the study area to mitigate the loss of valuable topsoil. This research possesses the potential to offer valuable insights into decision-making and planning to reduce the risk of erosion.

In March 2020, an extreme rainfall in Baixada Santista, Brazil, led to a series of landslides affecting more than 2800 people and resulting losses exceeding USD 43 million. This attribution study compared extreme rainfall in two large ensembles of the UK Met Office Hadley Centre HadGEM3-GA6 model that represented the event with and without the effects of anthropogenic climate change. Antecedent rainfall conditions on two different timescales are considered, namely extreme 60-day rainfall (Rx60day) which relates to the soil moisture conditions and extreme 3-day rainfall (Rx3day) which represents landslide triggering heavy rainfall. In the scenario including both natural and human-induced factors the antecedent 60 day rainfall became 74% more likely, while the short-term trigger was 46% more likely. The anthropogenic contribution to changes in rainfall accounted for 20-42% of the total losses and damages. The greatest economic losses occurred in Guaruj & aacute; (42%), followed by S & atilde;o Vicente (30%) and Santos (28%). Landslides were responsible for 47% of the homes damaged, 85% of the homes destroyed, all reported injuries, and 51% of the deaths associated with heavy rainfall. Changes in land cover and urbanization showed a pronounced increase in urbanized area in Guaruj & aacute; (107%), S & atilde;o Vicente (61.7%) and Santos (36.9%) and a reduction in farming area. In recent years, the region has experienced an increase in population growth and a rise in the proportion of irregular and/or precarious housing in high-risk areas. Guaruj & aacute; has the highest number of such dwellings, accounting for 34.8%. Our estimates suggest that extreme precipitation events are having shorter return periods due to climate change and increased urbanization and population growth is exposing more people to these events. These findings are especially important for decision-makers in the context of disaster risk reduction and mitigation and adaptation to climate change.

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.

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.