The Indian Council of Forestry Research and Education (ICFRE) is an autonomous organisation or governmental agency under the Ministry of Environment and Forests, Government of India. Headquartered in Dehradun, its functions are to conduct forestry research; transfer the technologies developed to the states of India and other user agencies; and to impart forestry education. The council has 9 research institutes and 4 advanced centres to cater to the research needs of different bio-geographical regions. These are located at Dehradun, Shimla, Ranchi, Jorhat, Jabalpur, Jodhpur, Bengaluru, Coimbatore, Prayagraj, Chhindwara, Aizawl, Hyderabad and Agartala.
Abandoned sandstone mines pose significant threats to the environment and safety. Despite the threats, the mined-out pits in arid areas of Rajasthan remain unreclaimed. The major challenges associated with the reclamation of these degraded lands include lack of awareness, harsh climatic conditions, low water availability, and the unavailability of abundant backfilling materials, coupled with topsoil deficit and nutrient-poor conditions. Reclamation of small-scale sandstone mines remains underexplored in scientific literature, limiting the development of evidence-based restoration strategies. Therefore, this review synthesizes the possible approaches for active reclamation of sandstone mined-out pits in the arid regions of Rajasthan. The key steps of active reclamation of the exhausted mine pits are topographic reconstruction through backfilling, topsoil application, soil amendments, and phytorestoration. This manuscript extensively explored the utilization of non-hazardous industrial waste as sustainable backfilling materials, different soil amendments in the context of the backfilled mine land, and revegetation strategies. In our view, the selection of native species is the most critical step of the revegetation strategy, closely followed by post plantation care. In addition, microbial interventions, especially the inoculation of beneficial microbes like plant growth-promoting bacteria, mycorrhiza, and others, have been discussed for promoting soil health and enhancing plant growth.
Accurate measurement and mapping of above-ground biomass (AGB) is critical for SDG 15, facilitating comprehensive ecosystem assessments and carbon sequestration evaluations within biodiversity hotspots like the Indo-Myanmar region. This study employs Random Forest (RF), Classification and Regression Trees (CART), and Gradient Tree Boosting (GTB) models in Google Earth Engine (GEE) to predict AGB in Manipur, India. Using remote sensing data (NDVI, EVI, LAI, FPAR, land cover, and DEM), models were trained on 2010 reference data for 2024 predictions. The Random Forest model achieved an R² of 0.797, outperforming CART and GTB in predictive accuracy (RMSE: 13.678 Mg/ha). NDVI emerged as the key predictor. Prediction maps reveal 24–75
Accurate estimation of grassland aboveground biomass (AGB) is crucial for terrestrial carbon cycling, global climate change research, degradation assessment, and sustainable land management. This study employs XGBoost model, combined with feature selection via Random Forest & Pearson correlation, alongside SHapley Additive exPlanations (SHAP), to enhance AGB predictions across diverse grassland ecosystems in China. Results indicate that incorporating vegetation height significantly improves model performance, increasing test R2 values by 0.01-0.07 (final range: 0.59 to 0.68), and reducing the errors nRMSE to <= 0.04. This underscores the critical role of vegetation height in improving biomass estimation accuracy. SHAP analysis further reveals the relative importance of key predictors, offering insights into their individual contributions to model performances. Spatiotemporal analysis (2001-2021) reveals rising AGB trends in highly productive regions, whereas arid and degraded grasslands exhibit stability or continue to decline, highlighting their vulnerability to climatic changes and anthropogenic pressures. Although the model demonstrates strong predictive capability, regional heterogeneity and complex feature interactions warrant further investigation. This research highlights the effectiveness of machine learning combined with remote sensing in monitoring grassland degradation, providing valuable insights for ecosystem restoration, carbon sequestration strategies, and policy-driven conservation efforts.
Elevational gradients integrate coordinated variation in temperature, water availability, and soil conditions, providing a natural experiment for examining large-scale trait-environment relationships. Using a nationwide dataset of 20,774 vascular plant species across China, we quantified elevational patterns in leaf area, plant height, seed size, and cone size, evaluated environmental drivers, and explored potential responses under future climate scenarios. All four traits exhibited significant nonlinear relationships with elevation, with statistically supported breakpoints at approximately 1838 m (leaf area), 2925 m (plant height), 2585 m (seed size), and 2275 m (cone size). Beyond these breakpoints, vegetative traits showed steeper negative scaling with elevation, whereas reproductive traits varied more gradually, suggesting differential sensitivity to high-elevation constraints. Structural equation modeling indicated that trait-environment coupling differed between elevation zones: indirect environmental mediation was more pronounced at low elevations, while direct elevation effects became relatively stronger at high elevations. Scenario-based projections derived from core climatic drivers suggested that vegetative traits may exhibit greater sensitivity to future hydrothermal changes than reproductive traits. Together, these findings highlight elevation-dependent shifts in trait-environment relationships and emphasize trait-specific sensitivities across heterogeneous landscapes.
Forest Fire in the North-Western Himalaya becomes a pressing issue. Forest fire poses a severe threat to biodiversity, soil fertility, and local livelihoods, especially in the ecologically sensitive Himalayan region and Uttarakhand is one of the major susceptible states. Therefore, this study focuses on comprehensive analysis of forest fire susceptibility in Uttarakhand, India using the Random Forest (RF) machine learning model integrated with geospatial data. By incorporating various topographic, climatic, and anthropogenic factors - ments, and wind speed, this study generates a high-resolution susceptibility map. The analysis reveals that approximately 33.60% of Uttarakhand's regions fall under high forest fire susceptibility, primarily concentrated in the southern and central zones, while only 21.33% regions are deemed insusceptible. Seasonal forest fire trend maps from 2000 to 2020 highlight that the pre-monsoon months (March- May) are the most fire-prone, with pronounced activity also observed in January-February. Validation of the RF model using the receiver operating characteristic (ROC) curve yields an AUC of 85.4%, indicating excellent predictive performance. Additionally, the study explores the role of Chir Pine (Pinus roxburghii) in exacerbating fire risks due to its resinous and highly flammable foliage. These findings underscore the critical need for proactive forest fire management strategies, including early warning systems, community engagement, and sustainable landscape practices. Policymakers, forest managers, and disaster mitigation organizations can use the integrated approach presented here as a useful tool to pinpoint hotspots and distribute resources efficiently to lessen the effects of forest fires in the Himalayas. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar
