Forest ecosystems serve as principal repositories of terrestrial biodiversity and major carbon sinks globally. However, widespread degradation from deforestation, mining, overgrazing, agricultural expansion and climate change, has critically impaired forest cover and ecosystem services in India. This review synthesizes restoration ecology, across tropical, temperate, alpine, mine degraded and grassland systems. It optimizes biodiversity recovery and carbon sequestration through evidence based species selection (native vs. exotic), active/ passive restoration techniques, Species Distribution Modeling (SDM), litter mediated soil recovery, invasive species control and enhanced carbon storage potential. These approaches provide a unified framework for science driven climate mitigation.
Adoption of drought management strategies becomes essential to reduce the adverse effects of drought. This study has developed a new extent (intensity) of adoption, using crop and dairy drought management strategies in different vulnerable districts of Tamil Nadu, India. Most of the studies have measured the intensity of adoption in either crop or dairy separately, but not combined. In addition, there is no appropriate measurement for extent of adoption. Hence, this study fulfils this research gap using gross intensity of adoption. There were 6, 7 and 5 paddy strategies identified in high (Ramanathapuram), moderate (Nagapattinam) and less vulnerable districts (Erode), respectively, whereas six dairy strategies were found in each district. The high-level and low-level adopters were significantly different in all the districts. The study found that paddy intensity of adoption was highest in Ramanathapuram (0.58), followed by Erode (0.47) due to more adoption of drought management strategies in crops. In contrast, the dairy intensity of adoption was highest in Erode (0.50), followed by Nagapattinam (0.46) because of more adoption of drought management strategies in dairy. The gross intensity of adoption was highest in Erode (0.52), followed by Ramanathapuram (0.51), whereas least was observed in Nagapattinam (0.44). This shows that households in Erode have more intensity to adopt the management strategies in comparison to the other two districts. This could be attributed to the prevailing drought condition in Ramanathapuram and combination of frequent occurrences of cyclones and prevailing drought situation in Nagapattinam. Adoption could be improved by reducing the adoption gap and increasing awareness through various programmes.
A study was carried out to predict the future distribution range of Chironji (Buchanania lanzan) and characterization of ecological niche in the degraded ecosystem of Vindhya region of Uttar Pradesh. Two different ecological niche models (BioClim and MaxEnt) were applied to determine future species distribution ranges and identify limiting bioclimatic variables for real occurrence data of 57 locations. The GeoCAT and Digital Elevation Model (DEM) were used to find out the population size and topography. Results of the study showed soil pH varied from 6.46 to 7.92, Nitrogen from low (213 kg/ha) to medium (389 kg/ha), Phosphorus was medium (13.50-22.50 kg/ha), and Potassium low (84 kg/ha) to high (662 kg/ha), with 210m to 520m elevation and population size 4.515km2 (Extent of Occurrence) and 23 km2 (Area of Occupancy) with Mahua (Madhuka longifolia var. latifolia), Tendu (Diospuros melanoxylon), and Sal (Shorea robusta) as associates. The distribution ranges were found with 80% overlap between the baseline (2030) and predicted (2100) habitat suitability for the focal species, which were primarily determined by the mean temperature of the warmest quarter (Bio_10) and precipitation of the driest quarter (Bio_17) that emerged to be the most sensitive with the contribution of 29.5% and 22.3% respectively. The present (2030) and future (2100) projections suggest 13.73% low suitable, 86.27% medium suitable and 1.52% low suitable, 98.48% medium suitable area respectively for the distribution and cultivation of the chironji. The findings of the study provide insight into the suitable habitats of B. lanzan for its promotion and conservation in Banda, Hamirpur, Kaushambi, Bhadohi, Prayagraj, and Chitrakoot forest divisions of Uttar Pradesh.
Land degradation and declining ecosystem services have emerged as major environmental concerns in North East India. Agroforestry, through the deliberate integration of trees, crops, livestock, shrubs, and herbs within the same landscape, offers a sustainable land-use approach for restoring degraded landscapes, improving soil health, and strengthening rural livelihoods. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach, this review provides a comprehensive overview of 68 peer-reviewed articles published since 1992, accessed through the search engines “Google Scholar”, “Scopus” and “Web of Science” using specific keywords. This study systematically evaluates the role of agroforestry in land restoration through improved soil properties, erosion control, forest regeneration, carbon management, biodiversity conservation, and the securing of local livelihoods, while contributing to climate change adaptation and mitigation in North East India. This review also evaluates the role of agroforestry in promoting gender inclusion and community participation. Evidence from various agro-climatic regions of North East India demonstrates that agroforestry systems improve soil health while contributing to food security, diversified income, and sustainable rural development. Overall, agroforestry systems play a significant role in enhancing both the physical and chemical properties of soil while simultaneously reducing soil bulk density. The study concludes that agroforestry represents a multifunctional, cost-effective, and socially inclusive nature-based solution for combating land degradation while aligning with national priorities and the United Nations Sustainable Development Goals. Strengthening institutional support, extension services and promoting region-specific agroforestry models is essential for scaling up agroforestry adoption.
Land degradation is a major global challenge, with up to 40% of the world's land estimated to be degraded, affecting more than 3 billion people and weakening food security, biodiversity, climate resilience and ecosystem services. In this context, the objective of this review is to examine how artificial intelligence (AI), remote sensing and geospatial analytics can support land restoration by integrating three connected domains: land degradation mapping, climate-risk prediction and ecosystem service valuation. The review focuses on AI-based approaches for identifying degradation hotspots, assessing vegetation and soil stress, predicting drought, heat, evapotranspiration and fire-related risks, and estimating ecosystem services such as carbon sequestration, soil retention, water regulation, forage productivity and biodiversity support. Methodologically, the paper adopts a thematic synthesis of peer-reviewed literature, with emphasis on machine learning, deep learning, hybrid geospatial models and multi-source data integration. The synthesis indicates that AI can improve restoration planning by strengthening spatial diagnosis, capturing nonlinear land-climate interactions, anticipating future risk and estimating likely ecosystem service gains from restoration interventions. However, the review also finds that operational adoption remains constrained by data gaps, limited field validation, scale mismatch, uncertainty and weak interpretability of complex models. The paper therefore argues for a shift from isolated AI applications towards integrated, explainable and decision-oriented frameworks that combine Earth Observation, climate data, field evidence and ecosystem service indicators. Future research should prioritise groundvalidated datasets, multi-scale modelling, uncertainty reporting, local ecological knowledge and operational decision-support systems. Such integration can help identify where restoration is most urgently needed, where it is most likely to succeed, and what ecological and livelihood benefits it can generate. This review provides state-of-the-art insights on using AI-enabled land degradation mapping, climate-risk prediction and ecosystem service valuation as decision-support tools for sustainable land management, ecosystem resilience and evidence-based restoration of degraded landscapes.
Forests cover approximately 4.14 billion hectares globally and play an important role in carbon sequestration, biodiversity conservation, and rural livelihoods. However, accelerating climate change is reshaping forest structure, species composition, and ecosystem functioning at an unprecedented pace, creating an urgent need for robust predictive tools. This review examines the major categories of forest simulation models, empirical, process-based, gap, landscape, and hybrid, evaluating their theoretical foundations, practical strengths, and inherent limitations in the context of climate change research. A historical timeline traces model development from 18th-century yield tables to contemporary Earth System Models. Four case studies drawn from India, Spain, and the United States demonstrate how different modelling frameworks perform across contrasting ecological and management contexts, confirming that no single model type is universally appropriate. The review identifies the near complete absence of process-based forest modelling applications in Indian ecosystems as the most significant knowledge gap in the field. Future research priorities include physiological parameterization of Indian tree species, long-term monitoring network development, and the application of multi-model ensemble frameworks to tropical and subtropical forest systems.
Degradation of forests, marked by a slow but continuous deterioration of forest structure, diversity, and ecosystem functioning, is now regarded as an important environmental concern in light of the ongoing climate change. Forest degradation differs from deforestation in that it occurs gradually but still makes major contributions to global carbon emissions and ecological disturbance. Innovations in monitoring methods, ecological restoration techniques, and policies are among the ways to tackle this problem. This paper presents current trends in the areas of remote sensing, LiDAR, artificial intelligence, genomic assessments, and adaptive management, like climate-smart forestry and Ecosystem-Based Adaptation, in the field of forest conservation. In addition, policies such as the REDD+ mechanism, national forest policy, and carbon financing are explored along with some illustrative examples from around the world and India. It is seen that although there have been significant advances in technology, which help with detection and predictive analytics in the field of forest management, the actual process of mitigation needs proper policy-making and active involvement of local communities as well as region-specific approaches.
Urban regions account for around 70% of worldwide Carbon dioxide (CO ) 2 emissions despite comprising less than 2% of the land; yet urban green infrastructure (UGI) remains inadequately employed as a climate mitigation approach. This paper formulates a cohesive framework that identifies carbon sequestration, land restoration, and biodiversity conservation as interconnected elements of urban climate initiatives. Using peer-reviewed literature, it demonstrates that UGI types vary in carbon storage capacity; however, results are significantly affected by species composition, soil health, management approaches, and urban environmental limitations. Land restoration strategies, such as brown field rehabilitation, phytoremediation, and soil supplements, coupled with biodiversity-focused design that prioritizes native species and ecological linkages, are recognized as essential for enduring carbon sequestration. Case studies from Singapore, Medellín, New York City, and several Indian cities illustrate that UGI can yield quantifiable climatic and ancillary benefits, such as urban cooling, storm water management, and biodiversity restoration, when underpinned by sustained governance and oversight. Nonetheless, fragmented planning, insufficient funding, the absence of uniform carbon accounting, and equity issues, including green gentrification, hinder broader implementation. The article advocates for enforceable planning objectives, standardized monitoring, reporting, and verification mechanisms, incorporation into national climate programs, and equity safeguards to facilitate scalable and equitable implementation.
Global forest ecosystems, which occupy about 4 billion hectares (or 30%) of the world's land mass, are the most visible indicators of the health of the Planet and act as critical carbon sinks, storing 66% of all terrestrial carbon. Yet, the world has lost 32% of forest cover due to industrialization and urban development, compounded by climate change effects such as severe weather events, changing drought patterns, and increasing wildfires. This paper presents the use of Artificial Intelligence (AI) and Machine Learning (ML) in rehabilitating these ecosystems. Through integration of the "scorpan" variables - soil, climate, organisms, relief, parent material, age, and space - AI models offer unprecedented predictive accuracy in predictions for species survival, growth and carbon storage. This paper explores key algorithms such as Random Forests (RF), Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, as well as use of Unmanned Aerial Vehicles (UAVs) for targeted reforestation and decision-making. The results suggest that AI-based restoration efforts not only improve ecosystem resilience but are crucial to reaching global carbon neutrality, provided regional data gaps and domain shift issues are overcome.
Land degradation and climate change threaten ecosystem sustainability, particularly in tropical forests. Spatial assessment of forest carbon stock is essential for supporting Land Degradation Neutrality (LDN). This study integrates optical and Synthetic Aperture Radar (SAR) remote sensing data to estimate and map forest carbon stock in reserve and protected forests of Khasi Hills, Meghalaya. Field data from 221 plots were used to derive aboveground biomass (AGB) and estimate total carbon. A multi-linear regression model using vegetation indices and SAR backscatter achieved R2 = 0.66 (RMSE = 50.34 Mg ha-1), while total carbon estimation showed a validation accuracy of R2 = 0.67 (RMSE = 26.27 Mg ha-1). Vegetation carbon was largely concentrated between 100–150 Mg ha-1 and soil organic carbon between 60–90 Mg ha-1, leading to total carbon values predominantly in the range of 150–250 Mg ha-1 with some areas exceeding 250 Mg ha-1. The study demonstrates the effectiveness of multi-sensor approaches for carbon assessment, supporting ecosystem restoration and LDN objectives.
Agroforestry systems are increasingly recognized as sustainable land-use strategies that simultaneously enhance agricultural productivity, maintain ecological stability and improve climate resilience. This study evaluated biomass, productivity, carbon sequestration, crop yield and soil nutrient dynamics across three agroforestry systems in the Tarai region: agrisilviculture (AS), agri-horticulture (AH) and homegarden system (HG). Seasonal variation in productivity and weed diversity reflected differences in system structure, species composition and management intensity. Among the systems, agri-silviculture (Populus deltoides) recorded a total biomass of 89.94 ± 4.44 t ha-1, grain yield of 5350 ± 131.92 kg ha-1 and the highest carbon sequestration rate of 2.53 ± 0.12 t C ha-1 yr-1. Agri-horticulture (Litchi chinensis) exhibited the highest biomass (114.31 ± 4.38 t ha-1) and carbon sequestration of 2.27 ± 0.15 t C ha-1 yr -1. Home garden system, characterized by diverse perennial species such as Mangifera indica and Syzygium cumini, showed comparatively lower biomass (69.94 ± 3.24 t ha-1) and NPP (0.17 ± 0.12 –1.97 ± 0.50 t ha-1 yr-1) but demonstrated superior soil fertility, with higher surface soil increment of phosphorus (3.43 ± 0.21 kg ha-1 ) and potassium (13.1 ± 0.43 kg ha-1). Annual nutrient increment was higher in the topsoil (0–15 cm), indicating stronger surface-driven nutrient cycling. Overall, agroforestry systems in the Tarai region demonstrate substantial potential for carbon sequestration while simultaneously supporting agrobiodiversity and long-term soil health, thereby reinforcing their role as effective climate-resilient and sustainable land-use systems.
This study assesses the forest carbon sequestration in a Chir pine (Pinus roxburghii) dominated landscape of the Almora Forest Division, Uttarakhand, India, and examines its implications for climate change mitigation. Given the spatial heterogeneity of forest ecosystems and the limitations of conventional field-based methods, an integrated approach combining field observations, multi-source satellite data, and machine learning was adopted to generate wall-to-wall estimates of aboveground carbon (AGC). A total of 40 sample plots (0.1 ha each) were established across the study area, where tree-level measurements were used to estimate aboveground biomass (AGB) through volumetric and allometric equations, subsequently converted to carbon stock using a standard factor (0.47). Satellite-derived variables from Sentinel-1 (SAR), Sentinel-2 (optical) and topographic variables derived from SRTM data were utilized as predictors in Random Forest algorithm for modeling AGB/AGC estimation. The results revealed substantial spatial variability in AGC, ranging from 47 to 121 Mg ha-1, with a mean value of 75.21 Mg ha-1. The integration of optical and radar data provided complementary insights into vegetation structure and condition, enhancing model robustness. The findings underscore the significance of Chir pine forests as regional carbon reservoirs despite their relatively lower biomass compared to broadleaf systems. The study demonstrates the effectiveness of machine learning and remote sensing integration for largescale carbon mapping and highlights its potential for supporting climate change mitigation strategies, carbon accounting frameworks, and sustainable forest management in Himalayan ecosystems.
Silica mining in the Shankargarh region of Prayagraj, India, has caused extensive land degradation and biodiversity loss over four decades. This study evaluates the ecological recovery of these mine spoils thirty years after the initiation of strategic ecorestoration. Using a successional restoration framework, fifteen resilient native species were introduced alongside moisture conservation techniques. Comparative analysis of vegetation dynamics across three strata (herbs, shrubs, and trees) revealed significant ecological uplift. Post-restoration, species richness surged across all layers, with the herbaceous understory showing the most dramatic increase (from 22 to 107 species). True Diversity (D) effectively doubled in the tree layer (from 8.50 to 17.29) and increased nearly five-fold in the herbs (from 16.59 to 75.19). Concomitant improvements in soil health, including a reduction in pH (6.5 to 6.2) and increased Electrical Conductivity (0.13 to 0.18 dS/m), signify the re-establishment of a functional, nutrient-cycling ecosystem. These results provide a robust scientific framework for the sustainable restoration of silica-mined landscapes in tropical dry deciduous regions.
Red mud, the highly alkaline bauxite residue generated during alumina refining, poses formidable challenges to vegetation establishment owing to extreme pH (>=11), elevated electrical conductivity, high exchangeable sodium, and near-zero organic matter (Nayak et al., 2024). The present study documents the large-scale ecological rehabilitation of red mud disposal areas at Hindalco Industries Limited, Muri, Jharkhand, where The Energy and Resources Institute (TERI) has undertaken restoration of 99 acres since 2021, of which 50 acres (20.24 ha) have been planted. An integrated amendment strategy combining gypsum, farmyard manure (FYM), fly ash, and mycorrhiza was applied to ameliorate baseline conditions (pH 11.1; EC 5.7 dS m-1; OC 0.4%). A multi-tier, multi-species plantation of 44 tree species, 10 shrub species, and 6 grass species was established. Of 20,116 plants established across RMP 3 and RMP 4 areas, 16,588 survived (overall survival rate 82.46%). Biodiversity assessment of 54 species (44 tree, 10 shrub) and 5,183 individuals recorded a Shannon-Wiener diversity index of 3.23 and Simpson diversity index of 0.944, indicating high vegetation diversity and low species dominance. A vegetation inventory of 3,595 tree individuals recorded a mean height of 1.89 m (SD = 0.95 m) and mean GBH of 8.63 cm (SD = 5.62 cm). Two-way ANOVA confirmed highly significant differences in growth performance among species (F = 74.51 for height, F = 69.14 for GBH; both p < 0.001) after accounting for plantation area. Babool (Vachellia nilotica) and Subabul (Leucaena leucocephala) emerged as the topperforming species by composite growth index and are recommended for priority deployment in red mud rehabilitation. Natural regeneration of ten plant species and return of diverse fauna confirm progressive ecological recovery. The study provides a replicable, evidence-based model for rehabilitation of industrial red mud wastelands.
This study assessed the decadal changes in Urban Green Spaces (UGS) of Dehradun city, Uttarakhand, India, using Landsat satellite images for the years 2000, 2010, 2020. The study aimed to classify major land cover classes, map UGS, and assess temporal changes caused by rapid urbanisation. Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI datasets were processed using a hybrid classification approach integrating Maximum Likelihood Classification and Iterative Self Organising (ISO) data clustering techniques. The imagery was classified into agriculture, built-up, forest, scrub, and water classes. Accuracy assessment using 350 field verification points produced an overall classification accuracy of 90.9% with a Kappa coefficient of 0.886. The results showed a rapid increase in built-up area from 14.85% in 2000 to 47.33% in 2020, while agricultural land and scrub areas declined significantly. The study also revealed that institutional campuses and roadside corridors showed noticeable greening trends. The study demonstrates the usefulness of remote sensing and GIS techniques for monitoring urban expansion and sustainable management of UGS. The findings can support future urban planning and ecological conservation strategies.
In recent times, escalations in aggression, conflict over natural resources, and even the fatalities amongst the mammals, especially in monkeys and human beings, occur now and then throughout localities, state, country, and carry significant losses in terms of life and property. This study examines feeding behavior in monkeys for a proper understanding of their diet, food preferences, and habitat. The types of food and edibles items and time spent on feeding by macaques, concerning their impacts on general behavioral modifications, were extensively studied with standard protocols in the Sirmaur district of Himachal Pradesh, India. The results showed that these factors account for various serious behavioral alterations. This study concluded that Rhesus macaque prefer human habitation compared to forest area.
Himalayan Gentian (Gentiana kurroo Royle) a member of family Gentianaceae, is a critically endangered perennial herb of high medicinal and ornamental value. About 80 percent of its geographical range lies in India, particularly in the North Western Himalayan states of Jammu & Kashmir, Himachal Pradesh and Uttarakhand. The species is under threat due to loss of habitat, destructive harvesting for roots and rhizomes and climate change. Naturally, the species propagates through seeds. Seed germination studies revealed that freshly harvested seeds fail to germinate promptly. Seeds loses viability and hardly germinate after one year. After two to three months of seed collection seed germination was 79.01 per cent in lab conditions. Cold stratification at -5±2°C for three months and treatment with 60 ppm GA3 solution were comparable and improved germination to 91 percent and 95 percent respectively. Soil from natural habitat recorded to be preferred growing media followed by sandy loam soil and mix of soil + sand + FYM (1:1:1). Seedlings at two leaf stage be transplanted in ploy bags for higher field survival. Seedlings exhibited field survival of 75 percent. Plant divisions gave 54.80 percent field survival. Shoot cuttings treated with 500ppm IBA gave 84.96 per cent field survival. The multiplication through seeds and shoot cuttings may help in conservation of species through enrichment of existing populations in natural range and bringing the species under cultivation to meet its market demand.
The people of Northeast India use edible insects as both a nutritional source and cultural heritage, mainly within tribal communities. The research was conducted across five districts of Assam from April 2024 to April 2025 to study traditional insect knowledge, species diversity, and pattern of entomophagy. A total of 500 respondents were surveyed, of whom 405 (81%) reported consuming edible insects, while 95 (19%) reported not consuming them. This study identified 26 insect species. The preparation methods for insects included frying, roasting, smoking and consuming them raw, depending on both species' characteristics and local traditions. Insect collection and trading activities primarily carried out by women and marginalized groups who often obtain high market prices for certain species. Statistical analyses indicated significant associations between community category and insect consumption (² = 114.83, p < 0.001), as well as between age group and consumption (² = 20.28, p < 0.001). Traditional practices face threats because of cultural erosion and changes modern dietary habits. The combination of insect farming with traditional knowledge and scientific methods provides sustainable solutions to enhance nutrition while protecting cultural heritage and improving rural economic conditions in areas facing environmental and socio-economic challenges.
