How natural are the forests in Rajiv Gandhi (Nagarhole) Tiger Reserve? A multi-source data approach.

This study aimed to monitor long-term land use dynamics for understanding the natural forest integrity and intactness of the Rajiv Gandhi (Nagarhole) Tiger Reserve (RTR) pre- and post-declarations as TR. We employed multi-source data from Survey of India Toposheets (1:50 k), Landsat-7, and Sentinel-2A along with Global Ecosystem Dynamics Investigation (GEDI) vegetation canopy height (10 m) data, a high-spatial resolution CORONA (1970) images and temporal Google Earth Pro images for mapping and validation. To map vegetation type, land use and land cover (LULC) transitions, and fragmentation (1980-2022) we employed a hybrid classification approach. This study also analyzed decadal forest dynamics within TRs using India's State of Forest Reports (ISFR). Findings reveal significant forest fragmentation and habitat loss due to anthropogenic activities in the TR. Mono-plantations (teak and eucalyptus) were found inside TR, while the buffer (1 km) was occupied mainly with coffee and orange plantations which indicates the prevalence of human footprint. The overall accuracy of the current period (2022) is 92.0% with a kappa coefficient value of 0.90. These plantations were established during the British colonial period (early 1900s) for commercial purposes by clearing natural forests. The present study highlights that mono-plantations have not transitioned into natural forests even after a century. This lack of transformation could potentially compromise the integrity of the native forest. Despite its ecological significance, RTR has experienced disturbance due to human footprint. Hence, there is a need for an action plan to protect this vital landscape by replacing mono-plantations with suitable species to preserve the integrity of the forest. These issues extend beyond the protected areas, impacting surrounding regions and require regular monitoring. The proposed methods can be applied to other protected areas facing similar problems in the country and world.

Rubber expansion and age-class mapping in the state of Tripura (India) 1990-2021 using multi-year and multi-sensor data.

The present study focuses on the spread of rubber monoculture in the state of Tripura during past three decades (1990-2021) in the northeast region of India which is known for its rich biodiversity, shifting cultivation, and extensive forest dynamics. Earth observation (EO) data of seven time periods from Landsat missions (1990, 1995, 2000, 2004, and 2009) and Sentinel-2 (2016 and 2021) were the main source for mapping and were supplemented with MODIS-EVI temporal spectral profiles, GEDI-derived vegetation heights (2019), and Google Earth high-resolution historical images for additional cues to support discrimination, mapping, and accuracy assessment. The methodology for rubber used its unique phenology from spectral-temporal profile and multi-year comparison of patches and their dynamics for age-class mapping. The results indicate that in the state of Tripura (geographic area 1.08 Mha), the area under rubber increased from 0.3% in 1990 to 8.9% of the geographic area in 2021. The overall classification accuracy for the maps created for the years 1990, 1995, 2000, 2004, 2009, 2016, and 2021 was 84.2%, 83.9%, 84.8%, 88.0%, 86.0%, 86.7%, and 89.5%, respectively. New areas under rubber originated from various land cover classes including open forests, shifting cultivation lands, and scrub. Recent expansion has resulted in 84.3% of rubber plantations under the 10-year age class. Implications of this transformation of the natural landscape, biodiversity and biomass, and carbon pool assessment are discussed.

Seasonal and annual variations of CO2 and CH4 at Shadnagar, a semi-urban site.

Carbon dioxide (CO2) and methane (CH4) are the most important greenhouse gases (GHGs) due to their significant role in anthropogenic global climate change. The spatio-temporal variations of their concentration are characterized by the terrestrial biosphere, seasonal weather patterns and anthropogenic emissions. Hence, to understand the variability in regional surface GHG fluxes, high precision GHGs measurements were initiated by the National Remote Sensing Center (NRSC) of India. We report continuous CO2 and CH4measurements during 2014 to 2017 for the first time from Shadnagar, a suburban site in India. Annual mean CO2 and CH4 concentrations are 399.56 ± 5.46 ppm and 1.929 ± 0.09 ppm, respectively, for 2017. After the strong El Niño of 2015-2016, an abnormal rise in CO2 growth rate of 5.5 ppm year-1 was observed in 2017 at the study site, compared to 3.03 ppm year-1 at Mauna Loa. Thus, the repercussion of the El Niño effect diminishes the net uptake by the terrestrial biosphere accompanied by increased soil respiration. Seasonal tracer to tracer correlation between CO2 and CH4 was also analyzed to characterize the possible source-sink relationship between the species. We compared CO2 and CH4 concentrations to simulations from an atmospheric chemistry transport model (ACTM). The seasonal phases of CH4 were well captured by the ACTM, whereas the seasonal cycle amplitude of CO2 was underestimated by about 30%.

Dynamics of CO2 fluxes and controlling environmental factors in sugarcane (C4)-wheat (C3) ecosystem of dry sub-humid region in India.

In this study, CO2 exchange over sugarcane and wheat growing season was quantified by continuous measurement of CO2 fluxes using eddy covariance (EC) system from January 2014 to June 2015. We also elaborated on the response of CO2 fluxes to environmental variables. The results show that the ecosystem has seasonal and diurnal dynamics of CO2 with a distinctive U-shaped curve in both growing seasons with maximal CO2 absorption reaching up to -8.94 g C m-2 day-1 and -6.08 g C m-2 day-1 over sugarcane and wheat crop, respectively. The ecosystem as a whole acted as a carbon sink during the active growing season while it exhibits a carbon source prior to sowing and post-harvesting of crops. The cumulative net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (Reco) were -923.04, 3316.65, and 2433.18 g C m-2 over the sugarcane growing season while the values were -192.30, 621.47, and 488.34 g C m-2 over the wheat growing season. The sesbania (green manure) appeared to be a carbon source once it is incorporated into soil. The response of day-time NEE to photosynthetically active radiation (PAR) under two vapor pressure deficit (VPD) sections (0-20 h Pa and 20-40 h Pa) seems more effective over sugarcane (R2 = 0.41-0.61) as compared to the wheat crop (R2 = 0.25-0.40). A decrease in net CO2 uptake was observed under higher VPD conditions. Similarly, night-time NEE was exponentially related to temperature at different soil moisture conditions and showed higher response to optimum soil moisture conditions for sugarcane (R2 = 0.87, 0.33 ≤ SWC < 0.42 m3 m-3) and wheat (R2 = 0.75, 0.31 ≤ SWC < 0.37 m3 m-3) crop seasons. The response of daily averaged NEE to environmental variables through path analysis indicates that PAR was the dominant predictor with the direct path coefficient of -0.65 and -0.74 over sugarcane and wheat growing season, respectively. Satellite-based GPP products from Moderate Resolution Imaging Spectroradiometer (GPPMOD) and Vegetation Photosynthetic model (GPPVPM) were also compared with the GPP obtained from EC (GPPEC) technique. The seasonal dynamics of GPPEC and GPPVPM agreed well with each other. This study covers the broad aspects ranging from micro-meteorology to remote sensing over C4-C3 cropping system.

Development of deforestation and land cover database for Bhutan (1930-2014).

Bhutan is a mountainous country located in the Himalayan biodiversity hotspot. This study has quantified the total area under land cover types, estimated the rate of forest cover change, analyzed the changes across forest types, and modeled forest cover change hotpots in Bhutan. The topographical maps and satellite remote sensing images were analyzed to get the spatial patterns of forest and associated land cover changes over the past eight decades (1930-1977-1987-1995-2005-2014). Forest is the largest land cover in Bhutan and constitutes 68.3% of the total geographical area in 2014. Subtropical broad leaved hill forest is predominant type occupies 34.1% of forest area in Bhutan, followed by montane dry temperate (20.9%), montane wet temperate (18.9%), Himalayan moist temperate (10%), and tropical moist sal (8.1%) in 2014. The major forest cover loss is observed in subtropical broad leaved hill forest (64.5 km2) and moist sal forest (9.9 km2) from 1977 to 2014. The deforested areas have mainly been converted into agriculture and contributed for 60.9% of forest loss from 1930 to 2014. In spite of major decline of forest cover in time interval of 1930-1977, there is no net rate of deforestation is recorded in Bhutan since 1995. Forest cover change analysis has been carried out to evaluate the conservation effectiveness in "Protected Areas" of Bhutan. Hotspots that have undergone high transformation in forest cover for afforestation and deforestation were highlighted in the study for conservation prioritisation. Forest conservation policies in Bhutan are highly effective in controlling deforestation as compared to neighboring Asian countries and such service would help in mitigating climate change.

Nationwide classification of forest types of India using remote sensing and GIS.

India, a mega-diverse country, possesses a wide range of climate and vegetation types along with a varied topography. The present study has classified forest types of India based on multi-season IRS Resourcesat-2 Advanced Wide Field Sensor (AWiFS) data. The study has characterized 29 land use/land cover classes including 14 forest types and seven scrub types. Hybrid classification approach has been used for the classification of forest types. The classification of vegetation has been carried out based on the ecological rule bases followed by Champion and Seth's (1968) scheme of forest types in India. The present classification scheme has been compared with the available global and national level land cover products. The natural vegetation cover was estimated to be 29.36% of total geographical area of India. The predominant forest types of India are tropical dry deciduous and tropical moist deciduous. Of the total forest cover, tropical dry deciduous forests occupy an area of 2,17,713 km(2) (34.80%) followed by 2,07,649 km(2) (33.19%) under tropical moist deciduous forests, 48,295 km(2) (7.72%) under tropical semi-evergreen forests and 47,192 km(2) (7.54%) under tropical wet evergreen forests. The study has brought out a comprehensive vegetation cover and forest type maps based on inputs critical in defining the various categories of vegetation and forest types. This spatially explicit database will be highly useful for the studies related to changes in various forest types, carbon stocks, climate-vegetation modeling and biogeochemical cycles.

Geospatial characterization of deforestation, fragmentation and forest fires in Telangana state, India: conservation perspective.

Conservation of biodiversity has been put to the highest priority throughout the world. The process of identifying threatened ecosystems will search for different drivers related to biodiversity loss. The present study aimed to generate spatial information on deforestation and ecological degradation indicators of fragmentation and forest fires using systematic conceptual approach in Telangana state, India. Identification of ecosystems facing increasing vulnerability can help to safeguard the extinctions of species and useful for conservation planning. The technological advancement of satellite remote sensing and Geographical Information System has increased greatly in assessment and monitoring of ecosystem-level changes. The areas of threat were identified by creating grid cells (5 × 5 km) in Geographical Information System (GIS). Deforestation was assessed using multi-source data of 1930, 1960, 1975, 1985, 1995, 2005 and 2013. The forest cover of 40,746 km(2), 29,299 km(2), 18,652 km(2), 18,368 km(2), 18,006 km(2), 17,556 km(2) and 17,520 km(2) was estimated during 1930, 1960, 1975, 1985, 1995, 2005 and 2013, respectively. Historical evaluation of deforestation revealed that major changes had occurred in forests of Telangana and identified 1095 extinct, 397 critically endangered, 523 endangered and 311 vulnerable ecosystem grid cells. The fragmentation analysis has identified 307 ecosystem grid cells under critically endangered status. Forest burnt area information was extracted using AWiFS data of 2005 to 2014. Spatial analysis indicates total fire-affected forest in Telangana as 58.9% in a decadal period. Conservation status has been recorded depending upon values of threat for each grid, which forms the basis for conservation priority hotspots. Of existing forest, 2.1% grids had severe ecosystem collapse and had been included under the category of conservation priority hotspot-I, followed by 27.2% in conservation priority hotspot-II and 51.5% in conservation priority hotspot-III. This analysis complements assessment of ecosystems undergoing multiple threats. An integrated approach involving the deforestation and degradation indicators is useful in formulating the strategies to take appropriate conservation measures.

Estimating inter-annual diversity of seasonal agricultural area using multi-temporal resourcesat data.

The present study aims at analysis of spatial and temporal variability in agricultural land cover during 2005-6 and 2011-12 from an ongoing program of annual land use mapping using multidate Advanced Wide Field Sensor (AWiFS) data aboard Resourcesat-1 and 2. About 640-690 multi-temporal AWiFS quadrant data products per year (depending on cloud cover) were co-registered and radiometrically normalized to prepare state (administrative unit) mosaics. An 18-fold classification was adopted in this project. Rule-based techniques along with maximum-likelihood algorithm were employed to deriving land cover information as well as changes within agricultural land cover classes. The agricultural land cover classes include - kharif (June-October), rabi (November-April), zaid (April-June), area sown more than once, fallow lands and plantation crops. Mean kappa accuracy of these estimates varied from 0.87 to 0.96 for various classes. Standard error of estimate has been computed for each class annually and the area estimates were corrected using standard error of estimate. The corrected estimates range between 99 and 116 Mha for kharif and 77-91 Mha for rabi. The kharif, rabi and net sown area were aggregated at 10 km × 10 km grid on annual basis for entire India and CV was computed at each grid cell using temporal spatially-aggregated area as input. This spatial variability of agricultural land cover classes was analyzed across meteorological zones, irrigated command areas and administrative boundaries. The results indicate that out of various states/meteorological zones, Punjab was consistently cropped during kharif as well as rabi seasons. Out of all irrigated commands, Tawa irrigated command was consistently cropped during rabi season.

Geo-spatial analysis of the temporal trends of kharif crop phenology metrics over India and its relationships with rainfall parameters.

The Global Inventory Modeling and Mapping Studies bimonthly Normalized Difference Vegetation Index (NDVI) data of 8 × 8 km spatial resolution for the period of 1982-2006 were analyzed to detect the trends of crop phenology metrics (start of the growing season (SGS), seasonal NDVI amplitude (AMP), seasonally integrated NDVI (SiNDVI)) during kharif season (June to October) and their relationships with the amount of rainfall and the number of rainy days over Indian subcontinent. Direction and magnitude of trends were analyzed at pixel level using the Mann-Kendall test and further assessed at meteorological subdivision level using field significance test (α = 0.1). Significant pre-occurrence of the SGS was observed over northern (Punjab, Haryana) and central (Marathwada, Vidarbha and Madhya Maharashtra) parts, whereas delay was found over southern (Rayalaseema, Coastal Andhra Pradesh) and eastern (Bihar, Gangetic West Bengal and Sub-Himalayan West Bengal) parts of India. North, west, and central India showed significant increasing trends of SiNDVI, corroborating the kharif food grain production performance during the time frame. Significant temporal correlation (α = 0.1) between the rainfall/number of rainy days and crop phenology metrics was observed over the rainfed region of India. About 35-40 % of the study area showed significant correlation between the SGS and the rainfall/number of rainy days during June to August. June month rainfall/number of rainy days was found to be the most sensitive to the SGS. The amount of rainfall and the number of rainy days during monsoon were found to have significant influence over the SiNDVI in 24-30 % of the study area. The crop phenology metrics had significant correlation with the number of rainy days over the larger areas than that of the rainfall amount.

Assessment and monitoring of long-term forest cover changes in Odisha, India using remote sensing and GIS.

Deforestation and fragmentation are important concerns in managing and conserving tropical forests and have global significance. In the Indian context, in the last one century, the forests have undergone significant changes due to several policies undertaken by government as well as increased population pressure. The present study has brought out spatiotemporal changes in forest cover and variation in forest type in the state of Odisha (Orissa), India, during the last 75 years period. The mapping for the period of 1924-1935, 1975, 1985, 1995 and 2010 indicates that the forest cover accounts for 81,785.6 km(2) (52.5 %), 56,661.1 km(2) (36.4 %), 51,642.3 km(2) (33.2 %), 49,773 km(2) (32 %) and 48,669.4 km(2) (31.3 %) of the study area, respectively. The study found the net forest cover decline as 40.5 % of the total forest and mean annual rate of deforestation as 0.69 % year(-1) during 1935 to 2010. There is a decline in annual rate of deforestation during 1995 to 2010 which was estimated as 0.15 %. Forest type-wise quantitative loss of forest cover reveals large scale deforestation of dry deciduous forests. The landscape analysis shows that the number of forest patches (per 1,000) are 2.463 in 1935, 10.390 in 1975, 11.899 in 1985, 12.193 in 1995 and 15.102 in 2010, which indicates high anthropogenic pressure on the forests. The mean patch size (km(2)) of forest decreased from 33.2 in 1935 to 5.5 in 1975 and reached to 3.2 by 2010. The study demonstrated that monitoring of long term forest changes, quantitative loss of forest types and landscape metrics provides critical inputs for management of forest resources.

Dual character of Sundarban estuary as a source and sink of CO2 during summer: an investigation of spatial dynamics.

A comprehensive attempt has been made to evaluate the diurnal and spatial pattern of CO2 exchange between the atmosphere and water along the estuarine track of Indian Sundarbans during the two summer months, April and May, 2011. Rigorous field observations were carried out which included the hourly measurements of total alkalinity, pH, fugacity of CO2 in ambient air and water surface, dissolved oxygen, and chlorophyll a. The estuarine water was found rich in total alkalinity and was oversaturated with CO2 throughout the diurnal cycle in the two stations situated at the inner and middle estuary, respectively, whereas an entirely reverse situation was observed in the outer fringes. The fugacity of CO2 in water ranged from 152 to 657 µatm during the study period. The percentage of over-saturation in inner and middle estuary varied from 103 to 168 and 103 to 176 %, respectively, whereas the degree of under-saturation in the outer estuary lied between 40 and 99 %. Chlorophyll a concentrations were found higher in the outer estuary (12.3 ± 2.2 mg m(-3)) compared to the middle (6.4 ± 0.6 mg m(-3)) and inner parts (1.6 ± 0.2 mg m(-3)), followed by a similar decreasing pattern in nutrient availability from the outer to inner estuary. The sampling stations situated at the inner and middle estuary acted as a net source of 29.69 and 23.62 mg CO2 m(-2) day(-1), respectively, whereas the outer station behaved as a net sink of -33.37 mg CO2 m(-2) day(-1). The study of primary production and community respiration further supports the heterotrophic nature of the estuary in the inner region while the outer periphery was marked by dominant autotrophic character. These contrasting results are in parity with the source characters of many inner estuaries and sinking characters of the outer estuaries situated at the distal continental shelf areas.

Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data.

The most commonly used normalized difference vegetation index (NDVI) from remote sensing often fall short in real-time drought monitoring due to a lagged vegetation response to drought. Therefore, research recently emphasized on the use of combination of surface temperature and NDVI which provides vegetation and moisture conditions simultaneously. Since drought stress effects on agriculture are closely linked to actual evapotranspiration, we used a vegetation temperature condition index (VTCI) which is more closely related to crop water status and holds a key place in real-time drought monitoring and assessment. In this study, NDVI and land surface temperature (T (s)) from MODIS 8-day composite data during cloud-free period (September-October) were adopted to construct an NDVI-T (s) space, from which the VTCI was computed. The crop moisture index (based on estimates of potential evapotranspiration and soil moisture depletion) was calculated to represent soil moisture stress on weekly basis for 20 weather monitoring stations. Correlation and regression analysis were attempted to relate VTCI with crop moisture status and crop performance. VTCI was found to accurately access the degree and spatial extent of drought stress in all years (2000, 2002, and 2004). The temporal variation of VTCI also provides drought pattern changes over space and time. Results showed significant and positive relations between CMI (crop moisture index) and VTCI observed particularly during prominent drought periods which proved VTCI as an ideal index to monitor terminal drought at regional scale. VTCI had significant positive relationship with yield but weakly related to crop anomalies. Duration of terminal drought stress derived from VTCI has a significant negative relationship with yields of major grain and oilseeds crops, particularly, groundnut.

Estimation and analysis of terrestrial net primary productivity over India by remote-sensing-driven terrestrial biosphere model.

In the present study, the Carnegie-Ames-Stanford Approach (CASA), a terrestrial biosphere model, has been used to investigate spatiotemporal pattern of net primary productivity (NPP) during 2003 over the Indian subcontinent. The model drivers at 2-min spatial resolution were derived from National Oceanic and Atmospheric Administration advanced very high resolution radiometer normalized difference vegetation index, weather inputs, and soil and land cover maps. The annual NPP was estimated to be 1.57 Pg C (at the rate of 544 g C m(-2)), of which 56% contributed by croplands (with 53% of geographic area of the country (GAC)), 18.5% by broadleaf deciduous forest (15% of GAC), 10% by broadleaf evergreen forest (5% of GAC), and 8% by mixed shrub and grassland (19% of GAC). There is very good agreement between the modeled NPP and ground-based cropland NPP estimates over the western India (R2=0.54; p=0.05). The comparison of CASA-based annual NPP estimates with the similar products from other operational algorithms such as C-fix and Moderate Resolution Imaging Spectroradiometer (MODIS) indicate that high agreement exists between the CASA and MODIS products over all land covers of the country, while agreement between CASA and C-Fix products is relatively low over the region dominated by agriculture and grassland, and the agreement is very low over the forest land. Sensitivity analysis suggest that the difference could be due to inclusion of variable light use efficiency (LUE) across different land cover types and environment stress scalars as downregulator of NPP in the present CASA model study. Sensitivity analysis further shows that the CASA model can overestimate the NPP by 50% of the national budget in absence of downregulators and underestimate the NPP by 27% of the national budget by the use of constant LUE (0.39 gC MJ(-1)) across different vegetation cover types.