Purpose: The purpose of the study is to analyse the occurrence and distribution of different tree species in Gilgit-Baltistan, Pakistan, as a baseline for further inventories, and estimate the biomass per species and plot. Furthermore, it aims to measure forest biodiversity using established formulae for tree species diversity index, richness, evenness and accumulative curve.Design/methodology/approach: Field data were collected, including stratification of forest sample plots. Statistical analysis of the data was carried out, and locally appropriate allometric equations were applied for biomass estimation.Findings: Representative circular 556 forest sample plots of 1,000 m²contained 13,135 trees belonging to nine tree species with a total aboveground biomass of 12,887 tonnes. Sixty-eight per cent of the trees were found between 2,600 and 3,400 masl; approximately 63 per cent had a diameter at breast height equal to 30 cm, and 45 per cent were less than 12 m in height. The Shannon diversity index was 1.82, and Simpson’s index of diversity was 0.813.Research limitations/implications: Rough terrain, long distances, harsh weather conditions and location of forest in steep narrow valleys presented challenges for the field crews, and meant that fieldwork took longer than planned.Practical implications: Estimating biomass in Gilgit-Baltistan’s forests using locally developed allometric equations will provide transparency in estimates of forest reference levels, National Forest Monitoring System in Pakistan and devising Reducing Emissions from Deforestation and Forest Degradation national strategies and for effective implementation.Originality/value: This paper presents the first detailed forest inventory carried out for the dry temperate and semi-arid cold region of Gilgit-Baltistan, Pakistan.

Highland ecologies are the most susceptible to climate change, often experiencing intensified impacts. Due to climate change and human activities, there were dramatic changes in the alpine domain of the China–Pakistan Economic Corridor (CPEC), which is a vital project of the Belt and Road Initiative (BRI). The CPEC is subjected to rapid infrastructure expansion, which may lead to potential land surface susceptibility. Hence, focusing on sustainable development goals, mainly SDG 9 (industry, innovation, and infrastructure) and SDG 13 (climate action), to evaluate the conservation and management practices for the sustainable and regenerative development of the mountainous region, this study aims to assess change detection and find climatic conditions using multispectral indices along the mountainous area of Gilgit and Hunza-Nagar, Pakistan. It has yielded practical and highly relevant implications. For sustainable and regenerative ecologies, this study utilized 30 × 30 m Landsat 5 (TM), Landsat 7 (ETM+), and Landsat-8/9 (OLI and TIRS), and meteorological data were employed to calculate the aridity index (AI). The results of the AI showed a non-significant decreasing trend (−0.0021/year, p > 0.05) in Gilgit and a significant decreasing trend (−0.0262/year, p < 0.05) in Hunza-Nagar. NDVI distribution shows a decreasing trend (−0.00469/year, p > 0.05), while NDWI has depicted a dynamic trend in water bodies. Similarly, NDBI demonstrated an increasing trend, with rates of 79.89%, 87.69%, and 83.85% from 2008 to 2023. The decreasing values of AI mean a drying trend and increasing drought risk, as the study area already has an arid and semi-arid climate. The combination of multispectral indices and the AI provides a comprehensive insight into how various factors affect the mountainous landscape and climatic conditions in the study area. This study has practical and highly relevant implications for policymakers and researchers interested in research related to land use and land cover change, environmental and infrastructure development in alpine regions.