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.

Abstract

No Results Found

Abstract

No Results Found

Abstract

No Results Found

Abstract

Researchers quantify vegetation characteristics using phytosociology studies. The data collected from vegetation surveys, such as tree girth and height measurements, are used to calculate various phytosociological attributes like frequency, density, basal cover, important value index, etc. In general, they are calculated using an Excel spreadsheet and a lengthy mathematical process. There are a few open-source or free software available to compute some of these parameters. However, they are not user friendly for most researchers. Also, these software primarily doesn’t compute basic phytosociological attributes. In view of these constraints and to ease researchers work, we have developed a simple tool, “PAT” Phytosociology Analysis Tool,” that computes the maximum attributes from the field collected vegetation data. The PAT GUI is simple. PAT computes results using standard formulas after users upload a csv file in the required format. Users can download the table's results and figures in PNG format.

Abstract

No Results Found

Abstract

The global impact of coal mining and associated activities on land use/land cover (LULC) changes is significant. This study used Landsat satellite images from 1990 to 2020 to assess LULC changes and their impact on land surface temperature (LST) in four districts of Chhattisgarh state, India. Over three decades, Korba and Raigarh districts saw expansion in coal mines, built-up areas, and water bodies, while forest areas diminished by 711.3 km2 and 212.87 km2, respectively. Koriya district saw coal mine expansion of 5.68 km2 (1990–2010), later declining to 2.85 km2, alongside growth in built-up regions, and forest cover reduction by 251.31 km2 in 2020. Surguja district experienced coal mine and built-up area expansion (1990–2020), with initial forest decline of 160.21 km2 in 2010 followed by recovery in 2020. LST was determined using the Mono-window algorithm. LST increased during winter and summer, with the most significant rise in summer. Vegetation-rich regions had lower LST, while coal mines had the highest temperatures. There was a positive relationship between mining land patch size and patch temperatures. This study underscores the need for vegetation restoration in mining areas, particularly abandoned sites, and sustainable mining practices to mitigate coal mining's warming effects.