Invasive alien plant species (IAPS) can pose severe threats to biodiversity and stability of native ecosystems, therefore, predicting the distribution of the IAPS plays a crucial role in effective planning and management of ecosystems. In the present study, we use Maximum Entropy (MaxEnt) modelling approach to predict the potential of distribution of eleven IAPS under future climatic conditions under RCP 2.6 and RCP 8.5 in part of Kailash sacred landscape region in Western Himalaya. Based on the model predictions, distribution of most of these invasive plants is expected to expand under future climatic scenarios, which might pose a serious threat to the native ecosystems through competition for resources in the study area. Native scrublands and subtropical needle-leaved forests will be the most affected ecosystems by the expansion of these IAPS. The present study is first of its kind in the Kailash Sacred Landscape in the field of invasive plants and the predictions of potential distribution under future climatic conditions from our study could help decision makers in planning and managing these forest ecosystems effectively.

Flood inundation maps provide valuable information towards flood risk preparedness, management, communication, response, and mitigation at the time of disaster, and can be developed by harnessing the power of satellite imagery. In the present study, Sentinel-1 Synthetic Aperture RADAR (SAR) data and Otsu method were utilized to map flood inundation areas. Google Earth Engine (GEE) was used for implementing Otsu algorithm and processing Sentinel—1 SAR data. The results were assessed by (i) calculating a confusion matrix; (ii) comparing the submerge water areas of flooded (Aug 2018), non-flooded (Jan 2018) and previous year’s flooded season (Aug 2016, Aug 2017), and (iii) analyzing historical rainfall patterns to understand the flood event. The overall accuracy for the Sentinel-1 SAR flood inundation maps of 9th and 21st August 2018 was observed as 94.3% and 94.1% respectively. The submerged area (region under water) classified significant flooding as compared to the non-flooded (January 2018) and previous year’s same season (August 2015–2017) classified outputs. Summing up, observations from Sentinel-1 SAR data using Otsu algorithm in GEE can act as a powerful tool for mapping flood inundation areas at the time of disaster, and enhance existing efforts towards saving lives and livelihoods of communities, and safeguarding infrastructure and businesses.

Forest fire has been one of the compelling issues in the Hindu Kush Himalayan (HKH) region. To promote regeneration, clearing fields for agriculture, hunting, and security reasons, local people deliberately set forests on fire. In this paper, active fire incidents, temperature, precipitation, and the changes of Aerosol Optical Depth (AOD) and Carbon monoxide (CO) value associated with forest fire were evaluated. The active forest fire incidents obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite are supplemented by the ERA5-land dataset to see the relation between precipitation and temperature with forest fires. MODIS and Tropospheric Monitoring Instrument (TROPOMI) sensor datasets were used to see the changes in AOD and CO in the region. MODIS sensor detected more than 30,462 active fires incidents in March and April 2021 in the study areas. Shan State of Myanmar recorded the maximum number of active fire incidents which is due to the practice of shifting cultivation and minimum in Bhutan due to the awareness campaigns and technology improvement. The temperature recorded in the study sites shows an increasing trend as compared to the reference period (2010-2020). Apart from Shan and Bago of Myanmar, precipitation in the study sites is also less during the study period. AOD and CO values show prominent peaks in a fire season which coincide with days of the maximum number of fire counts inferring the influence of forest fire on air quality. Developing countries like Nepal, India, Myanmar, and Bhutan are willing to take part in climate finance and are bound to accept expensive insurance premium due to forest fire incidents. Unless forest fires are effectively managed and mitigated, achieving Nationally Determined Contributions (NDCs) and global agendas, including United Nations Decade of Ecosystem Restoration is onerous.

Forest fire is one of the major global environmental issues, causing havoc in places as disparate as cold Siberia, tropical Amazon, and the temperate HKH region (Fig. 8.1).

Forest fires in the Hindu Kush Himalaya (HKH) region are increasing in frequency and severity, driven by climate variability, prolonged dry periods, and human activity. Nepal, a critical part of the HKH, recorded over 22,700 forest fire events in the past decade, with fire incidence nearly doubling in 2023. Despite this growing threat, operational early warning systems remain limited. This study presents Nepal’s first high-resolution early fire risk outlook system, developed by adopting the Canadian Fire Weather Index (FWI) using meteorological forecasts from the High-Impact Weather Assessment Toolkit (HIWAT). The system generates daily and two-day forecasts using a fully automated Python-based workflow and publishes results as Web Map Services (WMS). Model validation against MODIS, VIIRS, and ground fire records for 2023 showed that over 80% of fires occurred in zones classified as Moderate to Very High risk. Spatiotemporal analysis confirmed fire seasonality, with peaks in mid-April and over 65% of fires occurring in forested areas. The system’s integration of satellite data and high-resolution forecasts improves the spatial and temporal accuracy of fire danger predictions. This research presents a novel, scalable, and operational framework tailored for data-scarce and topographically complex regions. Its transferability holds substantial potential for strengthening anticipatory fire management and climate adaptation strategies across the HKH and beyond.

Fire regimes are intensifying globally under climate change, yet comprehensive multi-seasonal assessments integrating fire activity with environmental drivers remain scarce for tropical and subtropical landscapes. This study quantifies 24-year fire susceptibility trends across Queensland, Australia, using a novel Weighted Combined Index synthesizing seven environmental variables: fire frequency, burned area, climate anomalies (temperature, vapor pressure deficit, soil moisture, precipitation), and vegetation productivity. Analysis of 1.5 million MODIS and VIIRS active fire detections (2001-2024) revealed pronounced spatiotemporal heterogeneity, with spring accounting for 57.4% of total fire activity and annual burned area varying sixfold (61,000-252,000 km²). Northern tropical savannas exhibited persistent high susceptibility (WCI > 0.50) across all seasons, while southern coastal regions remained consistently low-risk (WCI < 0.25). Three distinct temporal phases emerged: extensive burning in the early 2000s, suppressed activity from 2013-2020, and recent resurgence in 2023-2024. Winter displayed maximum mean susceptibility (0.41), reflecting cumulative fuel moisture deficits preceding peak spring fire activity. Semi-arid interior bioregions exhibited greatest seasonal amplitude (0.12), contrasting with temporally stable tropical systems (0.03). Model validation demonstrated good autumn performance (AUC = 0.744), moderate winter-spring performance (AUC = 0.61-0.62), and poor summer discrimination (AUC = 0.564), reflecting distinct mechanistic controls across seasons. These findings demonstrate the utility of multidimensional environmental indices for characterizing landscape-level fire risk and provide critical guidance for adaptive, regionally tailored fire management under accelerating climate change.