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The present study evaluates the potential of airborne hyperspectral and space-borne multispectral satellite datasets to determine alpha (α) and beta (β) diversity combining in-situ floristic composition in tropical deciduous forests (TDFs) of Mudumalai Tiger Reserve (MTR), India. 43 forest plots (each of 0.04 ha) were sampled to record the species diversity in MTR. The very high-resolution AVIRIS-NG hyperspectral images and temporal Sentinel-2 multispectral images were used to extract spectral variables by associating each field plot with corresponding pixels of the satellite images. A high positive correlation of field-based α-diversity (Shannon-Wiener diversity index (H′)) with AVIRIS-NG based α-diversity (R2: 0.83 (2016); R2: 0.82 (2018)) was observed compared to Sentinel-2 based α-diversity (R2: 0.50 to 0.63) with high correlation during post-monsoon (R2: 0.63). Principal coordinate analysis of AVIRIS-NG based β-diversity signifies a low diversity (H′: <1.00) in the eastern parts (0.13% of MTR) in contrast to moderate (H′: 1.00 to 2.00; 13.39% of MTR) to high diversity (H′ >2.00; 86.49% of MTR) in the remaining parts of MTR. The study highlights the application of high spectral-spatial AVIRIS-NG images for precise tree diversity mapping and the high temporal-spatial Sentinel-2 satellite images for assessing the phenological variations of the forest species. The integration of high spatial-hyperspectral and temporal multispectral satellite datasets with field-based observations for fine-scale tree species diversity mapping at geographical and temporal scales is the novelty of the study that may effectively contribute in devising effective forest management plans for biodiversity conservation. [ABSTRACT FROM AUTHOR]

The understating of natural resources and hazards is very fundamental from spatial perspective to reduce loss of resource and human lives. For this, the main aim of the current research is to synthesize the three natural resources (groundwater, wetland, and forest) into one multi-resource (MR) potentiality map and three natural hazards (flood, landslide, and shoreline erosion) into one multi-hazard (MH) susceptibility map. To achieve this goal, several inventories, geo-environmental factors, satellite bands, and indices have been taken as input data sets. Random forest (RF) model has been considered for individual mapping (except shoreline erosion mapping) due to its high precision. For shoreline erosion mapping, digital shoreline analysis system (DSAS) model has been employed. The accuracies of the model have been determined from the area under receiver operating characteristic (AUROC) curve, producer accuracy (PA), and user accuracy (UA). RF model has more than 90% prediction accuracy in different resource and hazard mapping, whereas for the forest mapping, PA and UA are 90% and 80%, respectively. The results of this study for multi-resource mapping show forest (24.05%) and groundwater (22.28%) as the major resources, whereas flood (17.27%) is the most destructive in comparison with other hazards. The multi-resource and multi-hazard maps of the research area provide an important tool to land managers and policymakers for sustainable development and management. [ABSTRACT FROM AUTHOR]

In developing countries, both deforestation and forest degradation are of serious environmental concern due to various driving factors. This can be more difficult to manage due to the lack of quality data and the unavailability of appropriate mapping techniques. Based on remote sensing data, this study examined an integrated approach to improve the mapping capability of forest change monitoring for data deficient areas. The study is carried out integrating ground-based information with Landsat 5 TM, Landsat 8 OLI and TIRS in the Man River Basin, India. The findings of the current study suggest that the integrated approach enhances the ability of modeling to estimate deforestation and forest degradation associated with responsible drivers, especially in countries such as India where grassroots data are infrequent. [ABSTRACT FROM AUTHOR]

The archaeology of historical India has usually been perceived through the lens of cities and states, leaving forest tracts to a large extent unexamined. This article considers the historical signature in a segment of the Bandhavgarh National Park and Tiger Reserve, Madhya Pradesh, India, in order to understand how histories of occupation in jungles and wilderness where no settlements presently exist can be studied. Our survey involved a combination of ground-level investigations using GPS devices along with an analysis of satellite images to explore whether such images can be used for locating structures and sites. The earliest archaeological markers in Bandhavgarh are cave shelters of the 2nd century CE, which form the subject of this article. [ABSTRACT FROM AUTHOR]

This study's objective was to develop a method by which smallholder forest plantations can be mapped accurately in Andhra Pradesh, India, using multitemporal visible and near‐infrared (VNIR) bands from the sentinel‐2 multispectral instruments (MSIs). Conversion to cropland, coupled with secondary dependencies on and scarcity of wood products, has driven the deforestation and degradation of natural forests in Southeast Asia. Concomitantly, forest plantations have been established both within and outside of forests, with the latter (as contiguous blocks) and are the focus of this study. Accurately mapping smallholder forest plantations in South and Southeast Asia is difficult using remotely sensed data due to the plantations' small size (average of 2 hectares), short rotation ages (4–7 years for timber species), and spectral similarities to croplands and natural forests. Cloud‐free Harmonized landsat sentinel‐2 (HLS) S10 data were acquired over six dates, from different seasons, over four years (2015–2018). Available in situ data on forest plantations was supplemented with additional training data resulting in 2230 high‐quality samples aggregated into three land cover classes: nonforest, natural forest, and forest plantations. Image classification used random forests on a thirty‐band stack consisting of the VNIR bands and NDVI images for all six dates. The median classification accuracy from the 5‐fold cross‐validation was 94.3%. Our results, predicated on high‐quality training data, demonstrate that (mostly smallholder) forest plantations can be separated from natural forests even using only the sentinel‐2 VNIR bands when multitemporal data (across both years and seasons) are used. [ABSTRACT FROM AUTHOR]

Forest fire mapping is essential for monitoring significant ecosystem losses. Geospatial techniques play a considerable role in the monitoring of these events across the globe. The present study observes and compares various indices like NDVI, NBR and RBR used to estimate burn severity, which can be helpful to measure vegetation loss in the study area during 2018. The study shows a strong correlation between NDVI and dNBR, and better differentiation between the burned and unburned regions using RBR to create a better forest action plan for the local state departments to combat forest fires. [ABSTRACT FROM AUTHOR]

Indian natural forest has a high ecological significance as it holds much biodiversity and is primarily affected due to deforestation. The present study exhibits the forest cover change on Global Forest Non-Forest (FNF) data for India and greenness trend using MOD15A2H LAI product, which is the best product available till date. JAXA uses of SAR datasets for forest classification based on FAO definitions. Later, Forest Survey of India (FSI) used different definitions for forest classification from FAO and was to compare with JAXA based forest cover. The global FNF study exhibited that total forest cover was reduced from 568249 Km2 to 534958 Km2 during 2007–17 in India. The significant loss of forest cover (33291.59 Km2; by −5.85% change) was primarily evident in Eastern Himalayas followed by Western Himalayas. Whereas forest cover increase was observed in Eastern and the Western Ghats from 2007 to 2017. The state of forest report by FSI states an increase in the forest cover from 690889 Km2 to 708273 Km2 during 2007–17 by 2.51%. The difference in forest cover as estimated by JAXA global FNF datasets and FSI report is attributed to differences in forest cover mapping definitions by both the agencies and use of varied datasets (SAR datasets by JAXA and optical datasets by FSI). It is to note that SAR is highly sensitive to forest cover and vegetation's as compare to optical datasets. Recent satellite-based (2000–2018) LAI product reveals the increase in leaf area of vegetation during 2000–18. It may be attributed to proper human land use management and implications of green revolutions in the region. The greening in India is most evident from the croplands with insignificant contribution from forest cover. [ABSTRACT FROM AUTHOR]

India is home of the largest remaining population of the Asian elephant (Elephas maximus L.) in the South and Southeast Asia. The forest loss and fragmentation is the main threat to the long-term survival of Asian elephants. In the present study, we assessed forest loss and fragmentation in the major elephant ranging provinces in India, viz., north-eastern, north-western, central, and southern since the 1930s. We quantified forest cover changes by generating and analyzing forest cover maps of 1930, 1975, and 2013, whereas fragmentation of contiguous forest areas was quantified by applying landscape metrics on the temporal forest cover maps. A total of 21.49% of the original forest cover was lost from 1930 to 1975, while another 3.19% forest cover was lost from 1975 to 2013 in the elephant ranges in India. The maximum forest loss occurred in the southern range (13,084 km2) followed by north-eastern (10,188 km2), central (5614 km2), and north-western (4030 km2) elephant ranges in the past eight decades. The forests in the central range were the most fragmented followed by southern, north-eastern, and north-western elephant ranges. The forest fragmentation in the southern range occurred at the fastest rate than central, north-eastern, and north-western ranges. The core forest areas shrunk by 39.6% from 1930 to 2013. The causative factors of forest change and situation of elephant-human conflict have been discussed. Study outcomes would be helpful in planning effective conservation strategies for Asian elephants in India. [ABSTRACT FROM AUTHOR]

Forests play an essential role towards net primary productivity, biological cycles and provide habitat to flora & fauna. To monitor key physiological activities in forest canopies such as photosynthesis, respiration, transpiration, spatially-explicit and precise information of the biochemical (biological) variables such as Leaf Chlorophyll Content (LCC) is required. While lookup-table (LUT)-based Radiative Transfer Model (RTM) inversion against optical remote sensing imagery is regarded as a physically sound and robust approach for retrieving biochemical and biophysical variables, regularization procedures are required to offset the problem of ill-posedness. To optimize the RTM inversion of LCC over a sub-tropical pine forest plantation, in the Western Himalaya, we investigated the role of: (1) cost functions (CFs), (2) added noise, and (3) multiple finest solutions in LUT inversion. Principal CFs were evaluated belonging to three categories: information measures, M-estimates, and minimal contrast approaches. The inversion approaches were applied to a LUT produced by the coupled leaf-canopy model known as PROSAIL RTM and tested in contrast field spectral data obtained from reflectance data derived from UAV (Unmanned Aerial Vehicle) images taken over the canopies of covered pine forests. The Bhattacharyya divergence, an information measure, outperformed all other CFs in LCC inversion, with R2 of 0.94, RMSE of 6.20 μg/cm2 and NRMSE of 12.27% during the validation. The optimized inversion strategy was subsequently applied to a UAV-acquired multispectral image at an 8.2 cm pixel resolution for detailed landscape forest LCC mapping. The associated residuals as provided by the LUT-based inversion provided insights in the spatial consistency of the LCC map. • First time radiative transfer model of LCC has been established in the Pine Forest of Western Himalaya of India. • First time comprehensive evaluation of drone datasets was provided for high resolution LCC estimation. • The inversion approaches were applied through LUT produced by the coupled leaf-canopy model for Pine Forest. • Final outcome provided the fine scale inventory of LCC for Pine Forest validated through ground measurements. [ABSTRACT FROM AUTHOR]

Bamboo is used by different communities in India to develop indigenous products, maintain livelihood and sustain life. Indian National Bamboo Mission focuses on evaluation, monitoring and development of bamboo as an important plant resource. Knowledge of spatial distribution of bamboo therefore becomes necessary in this context. The present study attempts to map bamboo patches using very high resolution (VHR) WorldView 2 (WV 2) imagery in parts of South 24 Parganas, West Bengal, India using both pixel and object-based approaches. A combined layer of pan-sharpened multi-spectral (MS) bands, first 3 principal components (PC) of these bands and seven second order texture measures based Gray Level Co-occurrence Matrices (GLCM) of first three PC were used as input variables. For pixel-based image analysis (PBIA), recursive feature elimination (RFE) based feature selection was carried out to identify the most important input variables. Results of the feature selection indicate that the 10 most important variables include PC 1, PC 2 and their GLCM mean along with 6 MS bands. Three different sets of predictor variables (5 and 10 most important variables and all 32 variables) were classified with Support Vector Machine (SVM) and Random Forest (RF) algorithms. Producer accuracy of bamboo was found to be highest when 10 most important variables selected from RFE were classified with SVM (82%). However object-based image analysis (OBIA) achieved higher classification accuracy than PBIA using the same 32 variables, but with less number of training samples. Using object-based SVM classifier, the producer accuracy of bamboo reached 94%. The significance of this study is that the present framework is capable of accurately identifying bamboo patches as well as detecting other tree species in a tropical region with heterogeneous land use land cover (LULC), which could further aid the mandate of National Bamboo Mission and related programs. [ABSTRACT FROM AUTHOR]

Abstract: Forest fragmentation is considered as one of the major conservation issue. The purpose of this study is to contribute to the understanding of the national assessment of spatial patterns of forest fragmentation and influence of deforestation on fragmentation in the Indian forests during three decade interval (1975-2005). Mapping of forest cover was carried out based on Landsat MSS and IRS P6 AWiFS data using hybrid classification techniques on 1:250,000 scale. The present study considered biogeographic zones as a major stratum for national level assessment. The forest fragmentation was analysed based on landscape indices which allow quantification and categorisation of complex forest landscapes and depict landscape composition, structure and scenario of fragmentation. The analysis revealed that in all biogeographic zones, more than 90% of total number of forest fragments consists of patches having area less than 1km2. At national level the mean forest patch size stands at 187ha. The total core area was highest for Islands (87.4%) followed by Eastern Himalayas (82.5%), Deccan (78.9%), Deserts (76.4%) and Eastern Ghats (76%). The temporal analysis shows that the loss of Indian forest area as 5.8% during 1975–2005. The annual rate of deforestation has been computed as 0.20% at national level. The decreased mean patch size, increased edge density and increased number of patches from 1975 to 2005 indicates ongoing fragmentation in biogeographic zones. The very high fragmentation in Trans Himalayas is contributed mostly by the natural factors while in other biogeographic zones, increased fragmentation is due to deforestation. [Copyright &y& Elsevier]

The present study describes the night-time active forest fire detection capabilities of Defense Meteorological Satellite Program - Operational Linescan System (DMSP-OLS) satellite data over the forest region of Kerala State, India in 2004. Kerala State had a high incidence of forest fires during 2004 because of the extended dry season, with the unusual absence of intermittent rainfall from January to April. DMSP-OLS data were processed to detect active night-time forest fires over Kerala State and validated with ground data and fine-resolution Indian Remote Sensing (IRS)-P6 Advanced Wide Field Sensor (AWiFS) satellite imagery. DMSP-OLS-derived night-time fire products were compared with synchronous Moderate Resolution Imaging Spectroradiometer (MODIS)-derived daytime fire products to check for the spatial agreement and continuity of fires. To estimate the burnt areas, different atmospheric correction algorithms were applied to the IRS-P6 AWiFS dataset; these included the cosine approximation model (COST), ATCOR2 in ERDAS Imagine and the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) code. Atmospheric corrections to the satellite data indicated significant improvement in burnt area estimates. The results of the study suggest a good correlation between AWiFS data-derived burnt areas, DMSP-OLS-derived fire counts and MODIS-derived fire products. The fire occurrences derived from DMSP-OLS and MODIS data were validated with field records on fire occurrences over the study area. [ABSTRACT FROM AUTHOR]

Phenological studies involve capturing information on dates of recurrent and seasonal biological events in plants and animals. In plants, phenological events of leaf flushing, full bloom, autumn discolouration, leaf fall etc. can be used to distinguish vegetation into separate classes. We refer here such phenological distinction as Phenological Functional Types (PhFT). The PhFT can be considered as the precursor of Plant Functional Types (PFTs) where PFT uses additional traits (e.g. leaf area, tree height, leaf structure, rate of evapotranspiration and photosynthesis, etc.) to classify vegetation. The PFT classification is essentially needed for developing and running dynamic global vegetation models (DGVMs) used in studying impacts of climate change on vegetation. We used archived long time series satellite remote sensing data, Landsat 5,7 and 8 (1985–2019), for classifying a landscape into appropriate PhFT. Normalized Difference Vegetation Index (NDVI) was calculated for the given period in the Google Earth Engine (GEE). The GEE is a cloud-based platform developed for the retrieval and processing of remotely sensed images and other data. The monthly median values of NDVI for the mentioned period was used to label each pixel into appropriate PhFT classes using Random Forest (RF) algorithm in GEE to obtain four distinct classes of evergreen forest, deciduous forest, agriculture and non-forest. The comparison of PhFT map was done with reference maps of global MCD12Q1 and the forest type map of India; with an overall moderate agreement of 68.55% and 66.22%, respectively. MCD12Q1 has an accuracy of 73% for the land cover map and the forest type map of India has 75% accuracy, whereas, we achieved an overall accuracy of 78%. The PhFT classification accuracy can further be improved using additional indices and topographic variables. The methodology demonstrated in this study can be adopted for classifying a landscape into distinct PhFT/PFT classes. • 4 Phenological Functional Types (PhFTs) retrieved using Google Earth Engine (GEE). • Landsat images (1985–2019) processed to obtain median NDVI values using GEE. • Monthly NDVI median values could segregate individual PhFT in the study area. • Random Forest implemented successfully in GEE to classify image pixels into PhFTs. • PhFT classification can further be transcribed into PFT using additional traits. [ABSTRACT FROM AUTHOR]

The recurrent forest fires have been a serious management concern in southern Western Ghats, India. This study investigates the applicability of various geospatial data, machine learning techniques (MLTs) and spatial statistical tools to demarcate the forest fire susceptible regions of the forested landscape of the Wayanad district in the southern Western Ghats (Kerala, India). The inventory map of 279 forest fire locations (period = 2001–2018) was developed via Sentinel 2A satellite images, NASA fire archives, and field visits. The forest fire susceptibility modelling involves twelve influencing factors, such as ambient air temperature, wind speed, rainfall, relative humidity, atmospheric water vapor pressure (WVP), elevation, slope angle, topographical wetness index (TWI), slope aspect, land use/land cover (LU/LC), distance from the road and distance from the villages. Considering the varying level of performances (i.e., receiver operating characteristics-area under curve (ROC-AUC) values ranging from 0.869 to 0.924 in the testing phase) of the MLTs, viz., artificial neural network (ANN), generalized linear model (GLM), multivariate adaptive regression splines (MARS), Naïve Bayesian classifier (NBC), K-nearest neighbour (KNN), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), adaptive boosting (AdaBoost) and maximum entropy (MaxEnt), we propose a weighted approach to characterize the forest fire susceptibility of the region using the outputs of the different MLTs. The proposed method demonstrates improvement in accuracy (AUC = 0.890) for mapping the forest fire susceptibility of the region compared to the individual MLTs (AUC = 0.715 to 0.869) while validating with the recent forest fire data (i.e., 2019–2021). This study suggests that roughly one-third of the study area is highly susceptible to the occurrence of forest fires, implying the severity of the disturbance regime. The analysis also indicates the role of anthropogenic factors in the occurrence of forest fires in the region. It is expected that the demarcation and prioritization of the forest fire susceptibility zones in the region, which is a part of one of the global biodiversity hotspots, have significant implications on biodiversity conservation at a regional scale. • We compare the efficiency of ten machine learning techniques for forest fire susceptibility modelling in a tropical region • Moran's I Spatial autocorrelation, and Getis-Ord Gi* hotspot analysis help identify forest fire pattern of the region • We propose a weighted approach to integrate the forest fire susceptibility outputs of different machine learning techniques • One-third of the study area is highly susceptible to forest fires implying the severity of the fire disturbances • The proposed approach has better accuracy for mapping forest fire susceptibility zones compared to the individual techniques [ABSTRACT FROM AUTHOR]

Although studies on species-level classification and mapping using multisource data and machine learning approaches are plenty, the use of data with ideal placement of central wavelength and bandwidth at appropriate spatial resolution, for the classification of mangrove species is underreported. The species composition of a mangrove forest has been estimated utilising the red-edge spectral bands and chlorophyll absorption information from AVIRIS-NG and Sentinel-2 data. In this study, three dominant species, Heritiera fomes, Excoecaria agallocha and Avicennia officinalis, have been classified using the random forest (RF) model for a mangrove forest in Bhitarkanika Wildlife Sanctuary, India. Various combinations of reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1 were used for species-level discrimination and mapping. The RF model showed maximum accuracy using Sentinel-2, followed by the AVIRIS-NG, in discriminating three dominant species and two mixed compositions. This study indicates the potential of Sentinel-2 data for discriminating various mangrove species owing to the appropriate placement of central wavelength and bandwidth in Sentinel-2 at ≥10 m spatial resolution. The variable importance plots proved that species-level classification could be attempted using red edge and chlorophyll absorption information. This study has wider applicability in other mangrove forests around the world. [ABSTRACT FROM AUTHOR]

• Forest plantations are major driver of tropical natural forest conversion. • Natural forest loss threatens indigenous communities' livelihoods. • Conserving forests is key to protect socio-economic and cultural traits of tribes. • Operational areas of hunter-gatherers must be mapped and protected. • Indian official forest system must disaggregate plantations from natural forests. Conversions of natural forests to forest plantations threaten ecosystems and forest-dependent indigenous populations in many parts of the world. It is crucial to understand the scale and nature of such forest conversions and how they affect forest-dependent communities as a basis for prudent forest management and protection of indigenous livelihoods. This study analyses natural forest conversions and their impacts on tribal communities in the southern Western Ghats, a globally significant biodiversity hotspot. It focuses on two tribal settlements in protected forests within the catchment of the Achankovil River. The study involves time series analysis of forest cover change, based on a historical map and satellite images, field mapping of changes in the tribal communities' areas of operation, household surveys, and focus group discussions. The analysis shows that the area of protected forest decreased by 53 % from 1978 to 2015. As a result, the size of the tribal communities' gathering areas considerably shrunk, resulting in a decline in their collection of non-timber forest products and increased dependency on forest plantations labour for their livelihood. This is especially critical as the tribal communities still prefer to depend on natural forests for their sustenance. Preservation of natural forests is hence crucial not only for biodiversity conservation, but also to protect socio-economic and cultural traits of tribal populations. [ABSTRACT FROM AUTHOR]