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The complex channel planforms dynamics of river systems have attracted a lot of attention worldwide because of the tremendous effects that morphological changes have on nearby ecosystems and human populations. The present research aims at understanding intricate changes in the Rel river's channel as well as the erosion and deposition taking place over the past 48 years (1975–2023) through the application of Geographic Information System (GIS) and remote sensing. Spatial data within GIS were scrutinized to identify alterations in sinuosity, centreline migration, and large-scale dynamics of the river. A synergetic approach employing earth observation data, topographic mapping, and GIS processing, the research underscores the pivotal role of geospatial analysis in providing actionable spatiotemporal variations insights in the length of the river varying from 49.61 to 71 km, sinuosity index ranging from 1.25 to 1.79 and the maximum erosion and deposition were observed in year 1990 and 2015, respectively. This study's relevance extends to the broader context of river management and sustainable development, emphasizing the need for a holistic understanding of river systems to address contemporary challenges. In essence, the research contributes valuable insights for both scientific understanding and practical applications in the field of river dynamics, flood, drought, and environmental sustainability.

Forest fires are a recurring issue in many parts of the world, including India. These fires can have various causes, including human activities (such as agricultural burning, campfires, or discarded cigarettes) and natural factors (such as lightning). The present study presents a comprehensive and advanced methodology for assessing wildfire susceptibility by integrating diverse environmental variables and leveraging cutting-edge machine learning techniques across Gujarat State, India. The primary goal of the study is to utilize Google Earth Engine to compare locations in Gujarat, India, before and after forest fires. High-resolution satellite data were used to assess the amount and types of changes caused by forest fires. The present study meticulously analyzes various environmental variables, i.e., slope orientation, elevation, normalized difference vegetation index (NDVI), drainage density, precipitation, and temperature to understand landscape characteristics and assess wildfire susceptibility. In addition, a sophisticated random forest regression model is used to predict land surface temperature based on a set of environmental parameters. The maps that result depict the geographical distribution of normalized burn ratio and difference normalized burn ratio and land surface temperature forecasts, providing valuable insights into spatial patterns and trends. The findings of this work show that an automated temporal analysis utilizing Google Earth Engine may be used successfully over a wide range of land cover types, providing critical data for future monitoring of such threats. The impact of forest fires can be severe, leading to the loss of biodiversity, damage to ecosystems, and threats to human settlements.