Sundarban mangroves: diversity, ecosystem services and climate change impacts

The Bengal delta coast harboring the famous Sundarban mangroves is extremely vulnerable to climate change. Already, salinity intrusion, increasing cyclones and anomalies in rainfall, and temperature, are causing many social and livelihood problems. However, our knowledge on the diversified climate change impacts on Sundarban ecosystems services, providing immense benefits, including foods, shelters, livelihood, and health amenities, is very limited. Therefore, this article has systematically reviewed the major functional aspects, and highlights on biodiversity, ecosystem dynamics, and services of the Sunderban mangroves, with respect to variations in climatic factors. The mangrove ecosystems are highly productive in terms of forest biomass, and nutrient contribution, especially through detritus-based food webs, to support rich biodiversity in the wetlands and adjacent estuaries. Sundarban mangroves also play vital role in atmospheric CO2 sequestration, sediment trapping and nutrient recycling. Sea level rise will engulf a huge portion of the mangroves, while the associated salinity increase is posing immense threats to biodiversity and economic losses. Climate-mediated changes in riverine discharge, tides, temperature, rainfall and evaporation will determine the wetland nutrient variations, influencing the physiological and ecological processes, thus biodiversity and productivity of Sundarban mangroves. Hydrological changes in wetland ecosystems through increased salinity and cyclones will lower the food security, and also induce human vulnerabilities to waterborne diseases. Scientific investigations producing high resolution data to identify Sundarban‟s multidimensional vulnerabilities to various climatic regimes are essential. Sustainable plans and actions are required integrating conservation and climate change adaptation strategies, including promotion of alternative livelihoods. Thus, interdisciplinary approaches are required to address the future climatic disasters, and better protection of invaluable ecosystem services of the Sunderban mangroves.Fil: Neogi, Sucharit Basu. Coastal Development Partnership; Bangladesh. Osaka Prefecture University; Japón. Leibniz Center for Tropical Marine Ecology GmbH; AlemaniaFil: Dey, Mouri. University of Chittagong; BangladeshFil: Lutful Kabir, S. M.. Bangladesh Agricultural University; BangladeshFil: Masum, Syed Jahangir H.. Coastal Development Partnership; BangladeshFil: Kopprio, Germán Adolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Leibniz Center for Tropical Marine Ecology GmbH; AlemaniaFil: Yamasaki, Shinji. Osaka Prefecture University; JapónFil: Lara, Ruben Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentin

Identifying and Forecasting Potential Biophysical Risk Areas within a Tropical Mangrove Ecosystem Using Multi-Sensor Data

Mangroves are one of the most productive ecosystems known for provisioning of various ecosystem goods and services. They help in sequestering large amounts of carbon, protecting coastline against erosion, and reducing impacts of natural disasters such as hurricanes. Bhitarkanika Wildlife Sanctuary in Odisha harbors the second largest mangrove ecosystem in India. This study used Terra, Landsat and Sentinel-1 satellite data for spatio-temporal monitoring of mangrove forest within Bhitarkanika Wildlife Sanctuary between 2000 and 2016. Three biophysical parameters were used to assess mangrove ecosystem health: leaf chlorophyll (CHL), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). A long-term analysis of meteorological data such as precipitation and temperature was performed to determine an association between these parameters and mangrove biophysical characteristics. The correlation between meteorological parameters and mangrove biophysical characteristics enabled forecasting of mangrove health and productivity for year 2050 by incorporating IPCC projected climate data. A historical analysis of land cover maps was also performed using Landsat 5 and 8 data to determine changes in mangrove area estimates in years 1995, 2004 and 2017. There was a decrease in dense mangrove extent with an increase in open mangroves and agricultural area. Despite conservation efforts, the current extent of dense mangrove is projected to decrease up to 10% by the year 2050. All three biophysical characteristics including GPP, LAI and CHL, are projected to experience a net decrease of 7.7%, 20.83% and 25.96% respectively by 2050 compared to the mean annual value in 2016. This study will help the Forest Department, Government of Odisha in managing and taking appropriate decisions for conserving and sustaining the remaining mangrove forest under the changing climate and developmental activities

Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu

Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. There was a significant difference in vegetation intensity and status of vegetation over time, with NDVI having a larger value than EVI, indicating that biomass intensity varies over time in Tamil Nadu. Among the land cover classes, the deciduous forest showed the highest mean values for NDVI (0.83) and EVI (0.38), followed by cropland mean values of NDVI (0.71) and EVI (0.31) and the lowest NDVI (0.68) and EVI (0.29) was recorded in the scrubland. The study demonstrated that vegetation indices extracted from MODIS offered valuable information on vegetation status and condition at a short temporal time period

Elucidating the impact of temperature variability and extremes on cereal croplands through remote sensing

Remote sensing-derived wheat crop yield-climate models were developed to highlight the impact of temperature variation during thermo-sensitive periods (anthesis and grain-filling; TSP) of wheat crop development. Specific questions addressed are: can the impact of temperature variation occurring during the TSP on wheat crop yield be detected using remote sensing data and what is the impact? Do crop critical temperature thresholds during TSP exist in real world cropping landscapes? These questions are tested in one of the world's major wheat breadbaskets of Punjab and Haryana, north-west India. Warming average minimum temperatures during the TSP had a greater negative impact on wheat crop yield than warming maximum temperatures. Warming minimum and maximum temperatures during the TSP explain a greater amount of variation in wheat crop yield than average growing season temperature. In complex real world cereal croplands there was a variable yield response to critical temperature threshold exceedance, specifically a more pronounced negative impact on wheat yield with increased warming events above 35 °C. The negative impact of warming increases with a later start-of-season suggesting earlier sowing can reduce wheat crop exposure harmful temperatures. However, even earlier sown wheat experienced temperature-induced yield losses, which, when viewed in the context of projected warming up to 2100 indicates adaptive responses should focus on increasing wheat tolerance to heat. This study shows it is possible to capture the impacts of temperature variation during the TSP on wheat crop yield in real world cropping landscapes using remote sensing data; this has important implications for monitoring the impact of climate change, variation and heat extremes on wheat croplands

ASSESSING IMPACTS OF CLIMATE CHANGE ON TEAK AND SAL LANDSCAPE USING MODIS TIME SERIES DATA

Climate change poses a severe threat to the forest ecosystems by impacting its productivity, species composition and forest biodiversity at global and regional level. The scientific community all over the world is using remote sensing techniques to monitor and assess the impact of climate change on forest ecosystems. The consistent time series data provided by MODIS is immensely used for developing a different type of Vegetation indices like NDVI (Normalized difference vegetation indices) products at different spatial and temporal resolution. These vegetation indices have significant potential to detect forest growth and health, vegetation seasonality and different phenological events like budding and flowering. The current study aims to understand the impact of climate change on Teak and Sal forest of STR (Satpura tiger reserve) in central India by using Landsat and MODIS time series data. The rationale for taking STR as study site was to attribute the changes exclusively to climate change as there is no anthropogenic disturbance in STR. A change detection analysis was carried out to detect changes between the period 2017 and 1990 using Landsat data of October month. To understand the inter-annual and seasonal variation of Teak and Sal forests, freely available MOD13Q1 product (250 m, 16 days’ interval) was used to extract NDVI values for each month and four seasons (DJF, JJAS, ON, MAM) for the period 2000 to 2015. The climatic data (rainfall and temperature) was sourced from IMD (India Meteorological Department) at different resolutions (1, 0.5 and 0.25 degree) for the given period of the study. A correlation analysis was done to establish a causal relationship between climate variable (temperature and rainfall) and vegetation health (NDVI) on a different temporal scale of annual, seasonal and month. The study found an increasing trend in annual mean temperature and no consistent trend in total annual rainfall over the period 2000 to 2015. The maximum percentage change was observed in minimum temperature over the period 2000 to 2015. The average annual NDVI of Teak and Sal forests showed an increasing trend however, no trend was observed in seasonal and monthly NDVI over the same period. The maximum and minimum NDVI was found in the post-monsoon months (ON) and summer months (MAM) respectively. As STR is a Teak and Sal dominated landscape, the findings of the current study can also be applied in developing silvicultural and adaptation strategies for other Teak and Sal dominated landscapes of central India

Assessment and attribution of mangrove forest changes in the indian sundarbans from 2000 to 2020

The Indian Sundarbans, together with Bangladesh, comprise the largest mangrove forest in the world. Reclamation of the mangroves in this region ceased in the 1930s. However, they are still subject to adverse environmental influences, such as sediment starvation due to migration of the main river channels in the Ganges–Brahmaputra delta over the last few centuries, cyclone landfall, wave action from the Bay of Bengal—changing hydrology due to upstream water diversion—and the pervasive effects of relative sea-level rise. This study builds on earlier work to assess changes from 2000 to 2020 in mangrove extent, genus composition, and mangrove ‘health’ indicators, using various vegetation indices derived from Landsat and MODIS satellite imagery by performing maximum likelihood supervised classification. We show that about 110 km2 of mangroves disappeared within the reserve forest due to erosion, and 81 km2 were gained within the inhabited part of Sundarbans Biosphere Reserve (SBR) through plantation and regeneration. The gains are all outside the contiguous mangroves. However, they partially compensate for the losses of the contiguous mangroves in terms of carbon. Genus composition, analyzed by amalgamating data from published literature and ground-truthing surveys, shows change towards more salt-tolerant genus accompanied by a reduction in the prevalence of freshwater-loving Heiritiera, Nypa, and Sonneratia assemblages. Health indicators, such as the enhanced vegetation index (EVI) and normalized differential vegetation index (NDVI), show a monotonic trend of deterioration over the last two decades, which is more pronounced in the sea-facing parts of the mangrove forests. An increase in salinity, a temperature rise, and rainfall reduction in the pre-monsoon and the post-monsoon periods appear to have led to such degradation. Collectively, these results show a decline in mangrove area and health, which poses an existential threat to the Indian Sundarbans in the long term, especially under scenarios of climate change and sea-level rise. Given its unique values, the policy process should acknowledge and address these threats

A long-term spatiotemporal analysis of vegetation greenness over the himalayan region using google earth engine

The Himalayas constitute one of the richest and most diverse ecosystems in the Indian sub-continent. Vegetation greenness driven by climate in the Himalayan region is often overlooked as field-based studies are challenging due to high altitude and complex topography. Although the basic information about vegetation cover and its interactions with different hydroclimatic factors is vital, limited attention has been given to understanding the response of vegetation to different climatic factors. The main aim of the present study is to analyse the relationship between the spatio-temporal variability of vegetation greenness and associated climatic and hydrological drivers within the Upper Khoh River (UKR) Basin of the Himalayas at annual and seasonal scales. We analysed two vegetation indices, namely, normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) time-series data, for the last 20 years (2001–2020) using Google Earth Engine. We found that both the NDVI and EVI showed increasing trends in the vegetation greening during the period under consideration, with the NDVI being consistently higher than the EVI. The mean NDVI and EVI increased from 0.54 and 0.31 (2001), respectively, to 0.65 and 0.36 (2020). Further, the EVI tends to correlate better with the different hydroclimatic factors in comparison to the NDVI. The EVI is strongly correlated with ET with R2 = 0.73 whereas the NDVI showed satisfactory performance with R2 = 0.45. On the other hand, the relationship between the EVI and precipitation yielded R2 = 0.34, whereas there was no relationship was observed between the NDVI and precipi-tation. These findings show that there exists a strong correlation between the EVI and hydroclimatic factors, which shows that changes in vegetation phenology can be better captured using the EVI than the NDVI

Spatiotemporal distribution patterns of climbers along an abiotic gradient in Jhelum District, Punjab, Pakistan

Climber–abiotic parameter interactions can have important ramifications for ecosystem’s functions and community dynamics, but the extent to which these abiotic factors influence the spatial distributions of climber communities in the western Himalayas is unknown. The purpose of this study was to examine the taxonomic diversity, richness, and distribution patterns of climbers in relation to abiotic variables in the Jhelum District. The data were collected from 120 random transects between 2019 and 2021, from 360 sites within triplet quadrats (1080 quadrats), and classification and ordination analyses were used to categorize the sample transects. A total of 38 climber species belonging to 25 genera and 11 families were recorded from the study area. The Convolvulaceae were the dominant family (26.32%), followed by the Apocynaceae (21.05%), and Leguminosae (15.79%). The majority of the climbers were herbaceous in nature (71.05%), followed by woody (23.68%). Based on the relative density, the most dominant species was Vicia sativa (12.74). The majority of the species flowered during the months of March–April (28.04%), followed by August–September (26.31%). Abiotic factors have a significant influence on the distribution pattern and structure of climbers in the study area. The results show that the climbers react to the biotic environment in different ways. The findings will serve as the foundation for future botanical inventories and will be crucial for understanding the biological, ecological, and economic value of climbers in forest ecosystems. This will help forest management, conservation, and ecological restoration in the Himalayas

Effects of training parameter concept and sample size in possibilistic c-means classifier for pigeon pea specific crop mapping

4openInternationalInternational coauthor/editorThis research work aims to study the effect of training parameter concept and sample size in the process of classification by using a fuzzy Possibilistic c-Means (PCM) approach for Pigeon Pea specific crop mapping. For specific class extraction, the “mean” of the training data is considered as a training parameter of the classification algorithm. In this study, we proposed an “Individual Sample as Mean” (ISM) approach where the individual training sample is accounted as a mean parameter for the fuzzy PCM classifier. In order to avoid the spectral overlap of target Pigeon pea crop with other crops in the study area, a temporal indices database was generated from Sentinel 2A/2B satellite images acquired during the 2019–2020 Pigeon Pea crop cycle. The spectral dimensionality of temporal data was reduced to extract the required bands to achieve maximum enhancement of the target crop class in the temporal data. Further, the training sample size was increased to study the heterogeneity within the class in the classified output. The proposed ISM approach delivered a higher mean membership difference (MMD) between the Pigeon Pea crop and the co-cultivated Cotton crop as compared to the conventional mean method. This indicated that a better separation was achieved between the target crop and the spectrally similar crop grown, that were cultivated in the same study area. When the sample size was gradually increased from 5 to 60, the MMD values within the Pigeon Pea test fields remained in the range 0.013–0.02, thereby implying that the proposed algorithm works better even with a small number of training samples. The heterogeneity was better handled using the proposed ISM approach since the variance obtained within Pigeon Pea field was only 0.008, as compared to that of 0.02 achieved using the conventional mean approachopenSivaraj, Priyadarsini; Kumar, Anil; Koti, Shiva Reddy; Naik, ParthSivaraj, P.; Kumar, A.; Koti, S.R.; Naik, P