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With the threats of climate change, the forest cover in India necessitates the study of its survival probability and the precipitation thresholds value trigger life form regime shift. With a mega-biodiversity ecosystem, the assessment of forest cover resilience will enhance the effectiveness of climate adaptive conservation strategies. In the current study, we have used an open source tree canopy cover percentage (TCC %) data to map the spatial distribution of forest, scrub, grassland and treeless, and to relate with long term annual precipitation. The natural occurrences forest, scrub, grassland and treeless were identified in the precipitation ranges as 340–8650 mm, 196–1018 mm, 167–995 mm, and 34–965 mm precipitation, respectively; whereas their mean values were observed as 1952 mm, 779 mm, 760 mm, and 322 mm respectively. We applied binary logistic regression with the binary presence and absence of life forms, and used the probability value to define the resilience state and precipitation thresholds. Only 0.02% of the total forest covers in India are estimated least resilient observed in the dry regions in the trans-Himalaya. Whereas, the forest covers in the wet climate regimes as the Western Ghats, Western Himalaya, Eastern Ghats and North-East (NE) India are predicted highly resilient. The forest cover resilience curve saturates about 1400 mm precipitation, indicating majority forest covers in India are extremely resilient that can withstand large precipitation alterations in addition to the shorter drought periods. However, the TCC % loss and gain during 2000–2017 were observed dominantly in highly resilient forest covers areas may be indicating its anthropogenic origin. The precipitation thresholds of each life forms and forest cover resilience are critically important in ecological research. Moreover, the spatially explicit forest cover resilience map offers to integrate with other spatial and non-spatial data to frame uniform and improved conservation and management policies in India under the threats to climate alteration.

Gross primary productivity (GPP) is a vital ecosystem variable that is used as a proxy to study the functional behaviour of a terrestrial ecosystem and its ability to regulate atmospheric CO 2 by working as a carbon pool. India, having the potential terrestrial ecosystem dynamics to absorb the atmospheric carbon dioxide to some extent, is one of the least-explored regions in terms of carbon monitoring studies. The current study evaluates the applicability of a newly developed, quantum yield–based, remote sensing data–driven diagnostic model called the Southampton Carbon Flux (SCARF). This model was used to estimate the annual and seasonal variability of the terrestrial GPP over the Indian region with a spatial resolution of 1 km during 2008. This modified version of the conventional production efficiency model successfully predicted GPP using meteorological variables (PAR, air temperature and dew point temperature), the fraction of photosynthetically active radiation and quantum yield of C3 and C4 plants as the key input parameters. The annual GPP values were in the range from 0 to 4147.55 g C m −2 year −1 , with a mean value of 1507.32 g C m −2 year −1 . The maximum and minimum GPP were during the summer monsoon and pre-monsoon, respectively. The seasonal and annual distributions of GPP over the study area obtained using the SCARF model, and the MODIS GPP product (MOD17A2H) were similar. However, MODIS was found to underestimate the GPP in all regions and an overestimation in eastern Himalaya region. The study reveals that environmental scalars, specifically water stress, are the pivotal controlling variables responsible for the variation of GPP in India. The estimates of the GPP in different regions of the study area were made using SCARF, and an eddy covariance technique was similar. The SCARF model can be used to estimate GPP on a global scale. SCARF appears to be a better model in terms of the simplicity of the algorithm, performance and resolution. Thus, it may give higher accuracy in carbon monitoring studies.

The papers presented in this special issue cover a wide range of topics including the impact of climate change on Indian biodiversity through modelling approaches, the resilience of ecosystems to climate-induced shifts, and the role of remote sensing in monitoring forest dynamics. This body of research focuses on various ecological systems, highlighting the consequences of climate change and providing actionable insights for conservation policy and practice. By addressing data gaps, enhancing model precision, and integrating technological advancements like GIS and Earth Observation systems, this research supports the ongoing efforts to conserve biodiversity in fragile ecosystems, such as the Himalaya. Furthermore, studies synthesising and collecting data on India’s biodiversity help in understanding patterns of biodiversity and their biotic and abiotic drivers in the country’s varied landscapes, which offer valuable perspectives on conservation and sustainable development strategies. By highlighting these diverse research efforts, this special issue seeks to advance our understanding of climate change impacts on biodiversity and foster the development of resilient ecosystems in alignment with the UN Sustainable Development Goals, particularly Goal 13 (Climate Action) and Goal 15 (Life on Land).

The ‘spectral variation hypothesis (SVH)’ assumes spectral variability as a result of variation in species richness. In the present study, we explore the potential of satellite datasets in identifying the patterns in species richness in part of three global biodiversity hotspots falling in India viz., Himalaya, Indo-Burma, and Western Ghats. We used generalized linear models to correlate remote sensing based vegetation indices (VIs) and physiographic indices (PIs) with plant richness calculated using 1264, 1114, and 1004 field plots across 21 different forest vegetation classes in Himalaya, Indo-Burma, and Western Ghats respectively. Three different vegetation indices ranked highest in explaining the variance in plant richness in the three hotspots. The variance in species richness explained by models based on only VIs was highest (69%, P < 0.01) for Bamboo vegetation in Indo Burma hotspot with Normalized Difference Vegetation Index, followed by that for dry deciduous forest in Western Ghats (57%, P < 0.001) with Normalized Difference Water Index, and for grasslands (54%, P < 0.05) in Himalaya by Modified Soil Adjusted Vegetation Index. The explained variance increased with combined models that are based on PIs and VIs to up to 85% (P < 0.05). Overall, we observed very high correlation between VIs and plant richness in open canopy vegetation classes with low species richness such as grasslands, scrubs, and dry deciduous forests, followed by vegetation classes with moderately dense canopy. Our study provides crucial insights on utility of satellite datasets as a proxy for estimating plant richness for better conservation of diverse ecosystems.

Ecotourism is the greener variant of tourism which advocates conservation of biodiversity and acts as a development strategy to build a self-sustainable system to help protect and further enhance the ecosystem through the income generated by ecotourism activities. There is a strong linkage between biodiversity conservation and ecotourism which has also been recognized by the UN and finds its place under the technical note on “Biodiversity and the 2030 Agenda for Sustainable Development”. But, are the stakeholders of the ecotourism destinations religiously following practices that will ensure biodiversity conservation at all times is something that needs continuous evaluation and validation. The authors have worked in the past on developing multi-stage methodology (ESA Framework, ESM Model and their validation) using Qualitative and Quantitative techniques and successfully developed Ecotourism Sustainability Assessment Method (ESAM) for such an appraisal. The present paper aims at devising a process which will show the applicability of ESM model in identifying the biodiversity related and other environmental factors, adversely impacting the ecotourism destination at present, or may impact it in times to come. In addition, it also offers the prescription to solve these issues and achieve the goal of ecotourism sustainability at the operational level. This proposed process initially will be defined with the help of a site-level case study of Mt. Kangchendzonga Base Camp Trek, Sikkim. Later, this would be extended to other ecologies and geographies so that in the future, a robust and useful model, applicable for most of the ecotourism destinations, can be developed.

Plant phenology regulates ecosystem functions at diverse scales but is impacted by micro and macro climatic variations, and climate change. In India, precise estimations of pheno-phase transition dates remain scarce at different spatial and temporal scales, necessitating comprehensive research efforts. This study aims to gather continuous intra-day ground data about vegetation and climate conditions using PhenoCam (optical RGB and IR images) along with meteorological sensors, at Dalma Wildlife Sanctuary (DWS), Jharkhand. To derive phenological metrics, different indices were computed from images captured by PhenoCam sensors and Satellite derived Normalized Difference Vegetation Index (NDVI). Since the PhenoCam covers diverse vegetation species in the frame, the analysis was performed over three specific subset Region of Interests (ROI): Bombax ceiba (Semal) tree, background cluster of vegetation and a sample tree. MODIS NDVI data revealed that most of the area is highly deciduous with major greening in the 1st half of April and senescence during 2nd half of March. The study found that Green Chromatic Coordinate Index (GCC) and Blue Chromatic Coordinate Index (BCC) results could reveal greening and senescence phases correctly. The timing of start of leaf flush (SOLF), end of leaf flush (EOLF) and end of leaf maturity (EOLM) estimated based on inflection point method from Pheno-Cam images are: for Semal tree: 5th April, 2nd May, 10th June, 2022; for background vegetation: 15th March, 28th March and 2nd May, 2022; and for sample tree: 15th March, 28th March and 25th April, 2022, respectively. The dates of SOLF differed in 2023 and it occurred twice for Semal and background vegetation: for Semal tree: 20th February and 3rd April 2023, and for background vegetation: 20th January and 8th March, 2023, respectively. The rate of leaf flush and rate of leaf maturity was not similar in different years as the rates were much higher in 2023 than in 2022. The temperature and rainfall during winter and spring played an important role in greening, senescence, and its sustenance. These findings revealed the micro-climatic effect on plant phenology in the Dalma Wildlife Sanctuary, as well as the importance of integrating PhenoCam and satellite data in accurate monitoring of phenological phases.

Population growth rate indicates the proportional rate of settlement expansion and landscape modification in any river basin. The Mahanadi River basin (MRB), which is a densely populated, cropland and forest-dominated landscape, is selected as a case study area for studying the nature of built-up expansion and the corresponding land cover modifications. Satellite data-derived land use/land cover (LU/LC) maps for the years 1995, 2005, and 2015 were used for identification of landscape changes during the past three decades. One of the major LU/LC changes are observed in terms of increase in the water, which may be attributed to construction of new dams at the cost of the croplands and forest areas. Conversion of forest to cropland and expansion and densification of built-up areas in and around the existing built-up areas are also identified as a major LU/LC change. The geostatistical analysis was performed to identify the relationship between LU/LC classes with drivers, which showed that built-up areas were more in topographically flat terrain with higher soil depth, and expanded more around the existing built-up areas; cropland areas were more at lower elevation and less sloppy terrain, and forest areas were more at higher elevation. The LU/LC scenario of 2025 was projected using a spatially explicit dynamic conversion of land use and its effects (Dyna-CLUE) modeling platform with the LU/LC change trends of past 10 years (2005–2015) and 20 years (1995–2015). The major LU/LC changes observed during 2005–2015 were built-up expansion by 36.53% and deciduous forest and cropland reduction by 0.35% and 0.45%, respectively. Thus, the corresponding predicted change during 2015–2025 estimated built-up expansion by 25.70% and deciduous forest and croplands loss by 0.43% and 0.35%, respectively. On the other hand, during 1995 to 2015, the total built-up expansion and deciduous forest and cropland reduction were observed 50.79%, 0.45%, and 0.73%, respectively. Thus, the predicted changes during 2015–2025 were estimated as 18.48% built-up expansion and 0.22% and 0.21% deciduous forest and cropland loss. However, with the conditions of restricted deforestation and less landscape modification, the LU/LC projections show less built-up area expansion, reducing the cropland, fallow land, plantation, and waste land. The reduced numbers of land cover conversions types during 2005–2015 compared with 1995–2005 indicate more stabilized landscape. The input LU/LC maps and statistical analysis demonstrated the landscape modifications and causes observed in the basin. The model projected LU/LC maps are giving insights to possible changes under multiple pathways, which will help the agriculture, forest, urban, and water resource planners and managers in improved policy-making processes.

Harvesting surface runoff during monsoon season for further utilization in crop production during the post-monsoon season is now becoming an effective solution to mitigate water scarcity problems. In this study, multi-criteria analysis–analytic hierarchy process (MCA–AHP)–based approach was envisaged for rainwater harvesting (RWH) zoning for a case study area, i.e., two districts of Odisha state situated in Eastern India. In spite of having a large irrigation network in the study area, major portion of these two densely populated and agriculture dominated districts remains fallow during dry seasons. Suitable locations for RWH structures such as farm pond, check dam, and percolation tanks were identified through Boolean conditions. RWH potential map was generated using different thematic layers namely land use/land cover (LU/LC), geomorphology, slope, stream density, soil type, and surface runoff. AHP-based MCA technique was used to integrate these thematic layers by assigning weights to the thematic layers and ranks to the individual theme features on 1–9 AHP Saaty’s scale, considering their relative importance on RWH potential of the study area. The Natural Resources Conservation Service-Curve Number method was used to derive surface runoff using Climate Hazards Group Infra-Red Precipitation with Station rainfall data, satellite-derived LU/LC and FAO soil maps. In comparison to single cropped areas in 48% of the total study area, only 4% area was under double and triple cropped areas during 2016–2017. Moderate runoff was observed in > 50% of the study area dominated by agricultural landscape. Nearly 40%, 25.11%, and 32.45% of the study area indicated very high, high, and moderate RWH potentials, respectively. Particularly, very high RWH potential is observed in the eastern and central portion of the study area. The use of appropriate RWH structures in less irrigated areas will facilitate multiple cropping and will substitute the use of sub-surface water harvesting practices. In these two districts, 73 check dams and 153 percolation tanks are prescribed along the 2nd- and 3rd-order streams. In coarser textured soil, nearly 306 km2 and 608 km2 areas are identified as moderate and highly suitable zones for percolation tank construction on ground, while in fine soil, around 786 km2 area is identified as suitable for farm pond construction. Majority of the suitable zones for percolation tanks is found in Jajpur district, while suitability for adoption of farm pond and check dam is more in Bhadrak district. It is expected that implementation of the prescribed RWH structures can mitigate the threats of flood, drought, soil erosion, and enhance the soil moisture and cropping intensity significantly. The use of GIS platform with the spatial layers and the methodology adopted can be updated and replicated in larger regions in a shorter time. The spatially explicit maps are offering insights to different themes, providing useful information to the water resource managers, and may improve the decision-making process.

With the availability of satellite data from free data domain, remote sensing has increasingly become a fast-hand tool for monitoring of land and water resources development activities with minimal cost and time. Here, we verified construction of check dams and implementation of plantation activities in two districts of Tripura state using Landsat and Sentinel-2 images for the years 2008 and 2016–2017, respectively. We applied spectral reflectance curves and index-based proxies to quantify these activities for two time periods. A subset of the total check dams and plantation sites was chosen on the basis of site condition, nature of check dams, and planted species for identification on satellite images, and another subset was randomly chosen to validate identification procedure. The normalized difference water index (NDWI) derived from Landsat and Senitnel-2 were used to quantify water area evolved, qualify the water quality, and influence of associated tree shadows. Three types of check dams were observed, i.e., full, partial, and fully soil exposed on the basis of the presence of grass or scrub on the check dams. Based on the nature of check dam and site characteristics, we classified the water bodies under 11-categories using six interpretation keys (size, shape, water depth, quality, shadow of associated trees, catchment area). The check dams constructed on existing narrow gullies totally covered by branches or associated plants were not identified without field verification. Further, use of EVI enabled us to approve the plantation activities and adjudge the corresponding increase in vegetation vigor. The plantation activities were established based on the presence and absence of existing vegetation. Clearing on the plantation sites for plantation shows differential increase in EVI values during the initial years. The 403 plantation sites were categorized into 12 major groups on the basis of presence of dominant species and site conditions. The dominant species were Areca catechu , Musa paradisiaca , Ananas comosus , Bambusa sp., and mix plantation of A. catechu and M. paradisiaca . However, the highest maximum increase in average EVI was observed for the pine apple plantation sites (0.11), followed by Bambussa sp. (0.10). These sites were fully covered with plantation without any exposed soil. The present study successfully demonstrates a satellite-based survey supplemented with ground information evaluating the changes in vegetation profile due to plantation activities, locations of check dams, extent of water bodies, downstream irrigation, and catchment area of water bodies.

The land, oceans, and atmosphere are tightly linked and form the most dynamic component of the climate system. Studies on terrestrial and ocean science enhance the understanding on the impacts of climate change. Across India and the world over, human-driven land use and climate changes are altering the structure, function, and extent of natural terrestrial ecosystems and in turn regional biogeochemical feedbacks. In this special issue, we present 29 manuscripts; those discuss wide-ranging aspects of terrestrial and oceanic characterization and dynamics. These contributions are based on selected presentations made at the 2nd International Workshop on Biodiversity and Climate Change (BDCC-2018) held on 24–27 February 2018 at the Indian Institute of Technology Kharagpur, India. The manuscripts are arranged in five sections such as Ecological Assessment, Plant Invasion, Carbon Dynamics, Ecosystem Characterization, and Ocean Dynamics. We realized that the utility of satellite remote sensing data has been emerging as a dominant trend in environmental monitoring and assessment studies in India.