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The use of remote sensing for examining phenological variation in tropical forests is scarce. The major objectives of the study were to characterize the intra-annual variability of phenological cycle of the Biligiri Ranganathaswamy Temple Tiger Reserve (BRT) and the potentiality of these phenological metrices in defining species assemblages by classifying the forest. Sentinel-2 derived temporal Normalized Difference Vegetation Index (NDVI) data of 2019 was used to extract the vegetation trends and to derive phenological metrics using CropPhenology R package. Seasonal trends revealed that the highest greenness was associated with high NDVI values in September and October. We identified seven vegetation classes in the region and used Random Forest classifier to prepare a community level classification map with an overall classification accuracy of 68.9%. Our results revealed that incorporating the field sampling data and NDVI data can be effectively used for identifying, mapping and monitoring phenology of the BRT landscape.. [ABSTRACT FROM AUTHOR]

• An ensemble model is proposed for forest fire susceptibility modelling in the Western Ghats. • Ensemble model outperformed MAXENT, GLM, RF, MARS, GBM, and ANN. • Land use and land cover was the most important factor in explaining fire severity. • High number of fire occurrences are reported in deciduous forests, which occupy 40% of the landscape. • Machine learning models have been demonstrated as highly recommended tools for forest fire susceptibility modeling. In ecologically sensitive areas like the Western Ghats, fire is largely considered to be the most significant management concern. Therefore, identifying and predicting the fire susceptible areas is crucial for managing protected area networks. In this study, we investigated the factors affecting forest fires and modelled the fire susceptibility in a human-dominated landscape in the central Western Ghats, India, by employing remote sensing, geographic information systems and machine learning techniques. We used six machine learning models (MLMs), including artificial neural network, random forest, generalized linear model, maximum entropy, multivariate adaptive regression splines, gradient boosting machine and the ensemble model, to relate the occurrence data of fires to 14 predictor variables categorized as climate, topography, vegetation, and anthropogenic disturbances to generate fire susceptibility maps. The area under the receiver operating characteristic (AUC-ROC), true skill statistic (TSS) curves, accuracy and continuous Boyce index (CBI) were used to assess the model's accuracy. We found that all models achieved acceptable performance while validating the independent test data. However, ensemble model had the best overall performance with an AUC-ROC = 0.93; TSS = 0.72; ACCURACY = 0.89; and CBI = 0.95. The land-use and land-cover, and the distance to the agricultural fields and settlements were the key determinants of fire occurrences in the study area based on the ensemble model. The results showed that the landscape has a high to very high fire risk for about 26.5% of the total area. Forest fires have mostly occurred in the northeastern regions of the landscape, which are dominated by deciduous forests and plantations, whereas western regions are less susceptible to fires. Finally, we developed an ensemble-based fire susceptibility map with a resolution of 10 m that may be used as a tool for the prevention of forest fires. Further, it aids the relevant authorities in mitigating the disastrous effects and safeguarding the environment. The results demonstrated that ensemble-based MLMs can be employed effectively for fire prediction in other regions. [ABSTRACT FROM AUTHOR]

Tropical forest ecosystems are among the most essential habitats on Earth for conserving biological diversity and short-term climate regulation. For this reason, they are key areas of conservation policies in the world. In this paper, we aim to investigate the dynamics of forest cover and their changes in primary productivity by empowering information on historical forest management and fieldwork research with remote sensing vegetation monitoring methods. The study area falls within the central portion of the Indian Western Ghats, a global biodiversity hotspot. In particular, part of the analysis was performed on the Kadamakal Reserve Forest and Pushpagiri Wildlife Sanctuary, which harbours an endemic low elevation dipterocarp evergreen forest. This area was managed by selective logging and became fully protected in 1984. We performed multiple time series macroscale analyses between 1999 and 2020 on the Indian Central Western Ghats region, using satellite products at 1 km spatial resolution from the VITO Copernicus Global Land Service on Dry Matter Productivity, Fraction of Absorbed Photosynthetically Active Radiation, and Normalised Difference Vegetation Index. We also performed a very-high spatial resolution Normalised Difference Vegetation Index differential analysis between 2021 and 2016 with Sentinel 2-L2A products to investigate forest dynamics within the reserve. At the 1 km spatial resolution has been found an increase in all three vegetation indices, by employing the LOESS statistical method for the smoothed transition autoregressive model of raster data medians of our datasets. The boxplot raster distribution analysis also highlighted a significant imbalance in dry matter productivity in the last decade (2010−2020) comparing the previous one (1999–2009). The second part of the analysis, at 10 m spatial resolution within the reserve forest, revealed a growth in the vegetation cover on the top of the Pushpagiri Mountain ridge and in a previously landslide area. The study found new erosion channels down to the upper plateau on the South-West side of the reserve due to an increment of the run-off processes during the monsoon period. This satellite analysis highlighted generalised positive vegetation trends in the Central Western Ghats, India, over the last twenty-two years, enhancing an improvement in the ecosystem functioning and carbon storage ecosystem service. Notably, through this work, we also developed a standardised and open-access framework to monitor the vegetation remotely (SVIT) during periods of forest inaccessibility for fieldwork sampling. [Display omitted] • Tropical forest ecosystems are hotspots for conserving biological diversity • Natural conservation policies focus on the tropical region's legacy • Remote sensing tools as proxies for monitoring the vegetation functioning • Open-source ecology as a tool for standard methods and information consistency • Highlights of conservation effectiveness efforts in Central Western Ghats, India [ABSTRACT FROM AUTHOR]

Biomass and carbon stock assessments in data-deficient plantations and identifying the factors influencing tree growth, distribution, and carbon stocks are extremely important for implementing sound silvicultural management and monitoring practices to achieve REDD+ goals. We conducted carbon stock assessments in five major plantation types in a regional landscape in the central Western Ghats, India, by establishing fifty 0.1-ha plots across the landscape. We quantified the overall carbon stocks by summing the carbon pools across mature trees, deadwood, and soil (0 -15 cm) components. Allometric equations were compared to address the uncertainty in the tree biomass carbon. The tree biomass carbon and soil organic carbon varied significantly across the plantation types (F = 55.23, p < 0.00). The present study yielded the highest carbon stocks in Pinus plantation (201.91 ± 9.52 Mg ha -1 ) and the least in Eucalyptus (122.63 ± 9.73 Mg ha -1 ). The correlation analysis displayed a strong influence of mean annual precipitation and edaphic factors on soil organic carbon, while basal area and elevation were good predictors of tree biomass carbon. The principal component analysis revealed an association of predictor variables in the distribution of plantation types. We found a strong association between mean annual precipitation on Pinus plantation and mean annual temperature on Eucalyptus and Acacia plantations. On the other hand, teak pure plantation was associated with structural and topographic variables, while edaphic factors mainly influenced the distribution of teak mixed plantations. The findings of the present study conclude substantial carbon storage ability of the plantations in the studied landscape which can play a significant role in mitigating the effects of climate change and reaching carbon neutrality., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)