Abstract

The forest cover in Mizoram State is essential for the region’s socio-economic development and environmental sustainability. This state features abundant natural resources and diverse biodiversity, predominantly harbored within its forests. However, forest fires pose a significant risk, especially during dry seasons. Factors such as human activities, weather conditions, and traditional bamboo and Jhum cultivation practices contribute to the occurrence of forest fires in Mizoram. It’s crucial to map forest fire susceptibility to manage this risk effectively. This proactive approach enables better fire management, reduces risks, and facilitates informed decision-making. By identifying areas vulnerable to wildfires, it’s possible to promote sustainable land use practices, preserve ecosystems, and safeguard people and property. Statistical techniques such as frequency ratio (FR) and analytic hierarchy process (AHP) are used to generate the fire susceptibility maps for Mizoram based on satellite datasets. The MODIS Terra and Aqua active fire points (MCD14) are the basis, divided into training and testing datasets. AHP and FR techniques establish relationships between the training dataset and fourteen key factors, including slope, aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), Normalized Multiband Drought Index (NMDI), rainfall, temperature, wind speed, and proximity to settlements and roads. Various datasets such as MODIS Terra (surface reflectance, land surface temperature, vegetation indices), SRTM Digital Elevation Model, ERA-5, and CHRS are utilized in this study. The accuracy of the susceptibility maps generated by FR and AHP is validated against active fire test datasets, ensuring reliability in predicting fire-prone areas.

Abstract

Landscapes are shaped by both natural and cultural factors, influencing their management. In the Andaman and Nicobar Islands, rapid land-use changes from tsunamis and human activities threaten both ecological integrity and local communities. While the Andaman Islands have been extensively studied, research on the Nicobar Islands remains limited, prompting this investigation of historical land-use and land-cover changes using geospatial data. Multi-temporal satellite imagery revealed significant changes across selected Nicobar Islands. Trinket Island, heavily impacted by the 2004 tsunami, experienced considerable forest loss, mudflat formation, and fluctuations in grassland and water body areas. Katchal Island saw major forest declines from the tsunami and human activity, with partial recovery hindered by ongoing seawater intrusion and erosion. Camorta Island experienced reduced forest and grassland areas post-tsunami, followed by gradual forest recovery and water body expansion. Nancowry Island, though minimally affected initially, later faced significant forest loss due to seawater intrusion, followed by recovery and settlement growth. Car Nicobar Island suffered severe forest loss from the tsunami but has seen partial vegetation recovery alongside settlement expansion. Additionally, the Nicobar Islands' tropical grasslands, crucial for biodiversity and local livelihoods, are under threat from environmental changes and proposed oil palm plantations, which could lead to further ecological disruption and cultural impacts.

Abstract

Sugarcane is a vital cash crop with substantial significance in both global sugar production and the biofuel industry. However, its sustainable cultivation faces persistent challenges from environmental stressors, particularly salinity and water scarcity. In recent years, the integration of artificial intelligence (AI) and remote sensing (RS) technologies has proven to be a transformative approach for detecting and evaluating these stress conditions, offering critical insights for advancing precision agriculture (PA). This review explores the utilization of satellite imagery and sensor-based data including RGB, multispectral, hyperspectral imaging, and unmanned aerial vehicles (UAVs) to monitor stress-related parameters in sugarcane farming. It emphasizes key indices used to assess water stress, generate salinity stress maps, and estimate nitrogen levels, demonstrating their role in equipping farmers with actionable information to optimize irrigation and nutrient management strategies. These innovations significantly enhance crop productivity while promoting environmental sustainability. The review sets out three core objectives: (i) to evaluate the contribution of AI and RS in assessing water stress in sugarcane cultivation, (ii) to examine methods for mapping salinity stress using RS and AI tools, and (iii) to highlight the relevance of spectral indices in tracking nitrogen status in sugarcane crops. Drawing upon reputable bibliographic sources such as Google Scholar, Scopus, ResearchGate, and Web of Science, along with current literature on AI and RS applications in sugarcane stress assessment, the review consolidates detailed information on advanced sensors and UAV technologies. It also introduces novel deep learning models and sensor platforms that have received limited attention in prior studies. In conclusion, the review affirms that AI-driven remote sensing is a highly effective approach for monitoring and managing critical stress factors in sugarcane production. It not only contributes to enhanced yield and crop quality but also delivers significant socio-economic and environmental benefits, marking a major step forward in achieving sustainable and efficient sugarcane cultivation.

Abstract

Different regions of the tropics vary in overall tree species diversity, with the tropical Americas exhibiting strikingly higher regional tree species richness than Africa and Southeast Asia. We investigated whether these differences also occur at the local scale, and whether the environmental conditions associated with tree species richness are consistent across tropical regions despite highly dissimilar species pools. A spatial random forest (RF) model was trained using a network of 429 one-hectare plots across the tropics, together with 24 environmental variables, to predict plot-level tree α diversity. A combination of climatic, soil and topographical variables explained around 86% of variation in richness. Despite differences in regional species pools and potentially disruptive effects of different geological, climatic and evolutionary histories, the relationship between environmental variables and local scale tree species richness is closely similar across different continents. Our findings imply a pervasive role of niche-based mechanisms in structuring local tree species richness, regardless of regional species assemblages. This pantropical convergence in the richness-environment relationship poses a challenge for ecology to explain.

Abstract

This research utilises the capabilities of Google Earth Engine, a cloud-based computing platform, to conduct a comprehensive spatiotemporal assessment of the Coringa mangrove. Situated within the Coringa Wildlife Sanctuary in Andhra Pradesh, India, this mangrove – the country's second largest – is evaluated across various timeframes: 1999, 2002, 2007, 2013, 2017, and 2022. ML algorithms – Random Forest and Support Vector Machine with an RBF kernel – are employed. These algorithms analyze band composites derived from Landsat-7 ETM+ (1999, 2002, 2007), Landsat-8 (2013, 2017), and Sentinel-2 MSI (2022) satellite images. This analysis incorporates key spectral indices and utilises two spectral index thresholds for mangrove classification: one derived from established literature that identifies common thresholds at which mangroves typically occur (standard threshold), and the other is a customised threshold obtained from individual spectral index maps, tailored specifically to delineate mangrove areas in the study area (customized threshold). The results show that RF, particularly with the CT, outperforms all other methods, including RF with the ST and SVM with both thresholds, in terms of training and testing accuracy. These findings affirm the effectiveness of RF with the CT approach in accurately differentiating mangrove areas, emphasizing the critical role of threshold selection in enhancing the accuracy and competence of classification methods for mapping mangrove ecosystems.

Abstract

A number of anthropogenic and natural disasters that have impacted the fragile ecosystems of the Baratang group of islands in the Andaman and Nicobar archipelago, resulting in the degradation and depletion of their natural environment and biodiversity, have been identified. It is found that, in the early 20th century, the resources of the archipelago, especially Baratang Island, were exploited for infrastructure and plantations, resulting in a decrease in forest cover and an increase in anthropogenically transformed landscapes. However, the most recent available information on land use (LU) and land cover is only from 2013. This study aims to understand the dynamics of LU and land cover change in the Baratang Island group using multitemporal satellite data for 1976, 1993, 2004, 2014, and 2023. Using visual interpretation techniques, spatial and temporal change maps were created to represent the different LU and land cover features in the study area. An analysis of these maps revealed an interesting trend: there is an increase in forest area with a simultaneous decrease in settlements and agricultural land, which contradicts the findings of previous studies. This work has identified several factors contributing to this trend, namely natural regeneration, a reduction in logging and human activities, government policies and laws, and the implementation of conservation measures by the forest department. However, the evidence on mangrove decline attributed to both human activities and natural disasters is consistent with the results of other studies. This study emphasizes the importance of regularly monitoring the island’s landscape dynamics to understand changes in LU and land cover and to take appropriate conservation measures.

Abstract

Aim We test the hypothesis that wind dispersal is more common among emergent tree species given that being tall increases the likelihood of effective seed dispersal. Location Americas, Africa and the Asia-Pacific. Time period 1970–2020. Major taxa studied Gymnosperms and Angiosperms. Methods We used a dataset consisting of tree inventories from 2821 plots across three biogeographic regions (Americas, Africa and Asia-Pacific), including dry and wet forests, to determine the maximum height and dispersal strategy of 5314 tree species. A web search was used to determine whether species were wind-dispersed. We compared differences in tree species maximum height between biogeographic regions and examined the relationship between species maximum height and wind dispersal using logistic regression. We also tested whether emergent tree species, that is species with at least one individual taller than the 95% height percentile in one or more plots, were disproportionally wind dispersed in dry and wet forests within each biogeographic region. Results Our dataset provides maximum height values for 5314 tree species, of which more than half (2914) had no record of this trait in existing global databases. We found that, on average, tree species in the Americas have lower maximum heights compared to those in Africa and the Asia Pacific. The probability of wind dispersal increased significantly with tree species maximum height and was significantly higher among emergent than non-emergent tree species in both dry and wet forests in all three biogeographic regions. Main conclusion Wind dispersal is more prevalent in tall, emergent tree species than in non-emergent species and may thus be an important factor in the evolution of tree species maximum height. By providing the most comprehensive dataset so far of tree species maximum height and wind dispersal strategies, this study paves the way for advancing our understanding of the eco-evolutionary drivers of tree size.

Abstract

Wetlands, which serve as transitional zones between terrestrial and aquatic environments, play a crucial role in providing diverse ecosystem services for the sustenance of wildlife and mankind. Unfortunately, these delicate systems have experienced illicit human interventions in recent times, resulting in degradation and deterioration. Despite being designated as protected areas, wetlands are being exploited beyond their ecological capacity, posing a threat to their sustainability and potential existence in the near future. The present study aims to analyse the historical spatial changes in three wetlands located in Kerala, India, both before and after their declaration as Ramsar sites. Ashtamudi Lake, Sasthamkotta Lake, and Vembanad Lake were the focus areas, and the study employed multi-temporal satellite data sets from 1988 to 2023 to generate land use and land cover maps. The objective was to assess the ecological transitions, particularly the expansion of urbanization and subsequent reclamation of wetlands. By analysing the changes in aerial extent of each land cover class within the study area, the study identified spatio-temporal transitions and modifications occurring in and around the Ramsar sites. A comparison of the thematic maps from 1988 to 2023 revealed a significant increase in built-up areas and a noticeable reduction in vegetation, agriculture, and water bodies, both in terms of extent and quality. The study identified various socio-economic drivers that are responsible for these changes and emphasized the urgent need for regular monitoring and management of these wetland ecosystems. It stresses the importance of treating wetland conservation as a priority.

Abstract

Sugarcane holds a critical position in global agriculture, serving as a basis for the sugar and bioenergy sectors. The integration of remote sensing technologies and sophisticated machine learning approaches and related models has revolutionized sugarcane research. These tools ofer efcient, noninvasive, and large-scale assessment methods. This review highlights the utilization of satellite imagery and sensor data, encompassing RGB, multispectral, hyperspectral, and unmanned aerial vehicles (UAVs) in sugarcane agriculture. It addresses crop identifcation, pest and disease management, yield and acreage estimation, modeling, phenotypic measurement, and their impact on empowering farmers with insights for optimal irrigation, fertilizer application, and overall crop management. These advancements signifcantly increase productivity and foster environmental sustainability. The review had dual aims: (1) consolidate RS data applications in India’s sugarcane research and development, and (2) examine the pros and cons of RS and AI methods in sugarcane farming. The review employed prominent bibliographic databases—google scholar, scopus, researchgate, and web of science—along with pertinent research articles on RS and AI applications in sugarcane, and comprehensive data on sensors and UAVs retrieved from these databases. The study concludes that AI-driven crop RS stands as an efective method for monitoring and managing sugarcane, contributing signifcantly to improving yield and quality, while simultaneously ofering substantial benefts in social, economic, and environmental realms. However, challenges in the sugar industry, such as adapting technology, high initial costs, climate impact, communication, policy, and regulation, must be addressed.

Abstract

Coastal waters are complex, dynamic, and sensitive, and any change in the system impacts the marine environment and life. Coastal water quality has been decreasing due to the incursion of anthropogenic derived waste and toxins into the ocean. This study investigates water quality along the Kollam coast of Kerala State, India, using Sentinel-2 Multispectral Imager (MSI) data for the period of 2019–2022. Four key water quality parameters, chlorophyll (Chl-a), total suspended matter (TSM), turbidity, and coloured dissolved organic matter (CDOM), were analysed for seasonal variations and driving factors. The study highlights the potential of web-based platforms like Google Earth Engine for facilitating large-scale water quality assessments. The results reveal a distinct seasonal pattern in all parameters, primarily influenced by monsoonal riverine discharge and anthropogenic activities as contributing factors to water quality degradation. Overall, the study emphasises the need for comprehensive monitoring and management strate- gies to ensure the long-term sustainability of the coastal ecosystem.