Ecosystem service value assessment of a natural reserve region for strengthening protection and conservation

Ecosystem Services (ESs) refer to the direct and indirect contributions of ecosystems to human well-being and subsistence. Ecosystem valuation is an approach to assign monetary values to an ecosystem and its key ecosystem goods and services, generally referred to as Ecosystem Service Value (ESV). We have measured spatiotemporal ESV of 17 key ESs of Sundarbans Biosphere Reserve (SBR) in India using temporal remote sensing (RS) data (for years 1973, 1988, 2003, 2013, and 2018). These mangrove ecosystems are crucial for providing valuable supporting, regulatory, provisioning, and cultural ecosystem services. We have adopted supervised machine learning algorithms for classifying the region into different ecosystem units. Among the used machine learning models, Support Vector Machine (SVM) and Random Forest (RF) algorithms performed the most accurate and produced the best classification estimates with maximum kappa and an overall accuracy value. The maximum ESV (derived from both adjusted and non-adjusted units, million US$ year −1 ) is produced by mangrove forest, followed by the coastal estuary, cropland, inland wetland, mixed vegetation, and finally urban land. Out of all the ESs, the waste treatment (WT) service is the dominant ecosystem service of SBR. Additionally, the mangrove ecosystem was found to be the most sensitive to land use and land cover changes. The synergy and trade-offs between the ESs are closely associated with the spatial extent. Therefore, accurate estimates of ES valuation and mapping can be a robust tool for assessing the effects of poor decision making and overexploitation of natural resources on ESs. </p

Examining effects of climate change and land use dynamic on biophysical and economic values of ecosystem services of a natural reserve region

Ecosystem Service Valuation (ESV) is a process of evaluating and quantifying the monetary values of ESs and their functions. Using both biophysical and spatially explicit integrated models, biophysical and monetary values of key Ecosystem Services (ESs) were estimated in the Sundarbans Biosphere Region (SBR), India. Quantification was made both in time series (1982–2017) and individual years (1973, 1988, 2003, 2013, 2018, 2025, 2035, 2045) to understand the impact of climate change and land-use dynamics on the long-term ecological status of the region. The monetary and biophysical values of the ESs were then obtained from Net Primary Productivity (NPP) models, Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), and Cellular Automata Markov Chain Model (CA-Markov). NPP increased significantly during the first half period (1982–1999), but significantly declined during the second period (2000–2017). The highest estimated ESVs (US$ ha−1) was found for habitat service (30780), nutrient cycling (12626), and gas regulation (7224.81), whereas, lower ESVs were approximated for water regulation (347.81), raw material production (777.82) and waste treatment (13.57) services. Among the nine ESs evaluated, climate regulation, gas regulation, and disturbance regulation were the most important regulating services of the SBR. The combined effects of climate change and land-use dynamics on ESs are much stringent in a vulnerable region like the SBR. Most of the regulating services were closely associated with the fluctuation of land use land cover input. Thus, land management policies and land reform strategies that will encourage the conversion of productive land, especially the highly productive mangrove forest, for the development or any other financial benefits, would disturb the ideal human-nature balance of this ecosystem. The outcomes of this study also provide an important reference to the land administrators, researchers, and decision-makers to comprehend the expected social-ecological juxtaposition in a protected natural reserve region like the Sundarbans.</p

Examining the effects of forest fire on terrestrial carbon emission and ecosystem production in India using remote sensing approaches

Remote sensing techniques are effectively used for measuring the overall loss of terrestrial ecosystem productivity and biodiversity due to forest fires. The current research focuses on assessing the impacts of forest fires on terrestrial ecosystem productivity in India during 2003–2017. Spatiotemporal changes of satellite remote sensing derived burn indices were estimated for both fire and normal years to analyze the association between forest fires and ecosystem productivity. Two Light Use Efficiency (LUE) models were used to quantify the terrestrial Net Primary Productivity (NPP) of the forest ecosystem using the open-source and freely available remotely sensed data. A novel approach (delta NPP/delta burn indices) is developed to quantify the effects of forest fires on terrestrial carbon emission and ecosystem production. During 2003–2017, the forest fire intensity was found to be very high (>2000) across the eastern Himalayan hilly region, which is mostly covered by dense forest and thereby highly susceptible to wildfires. Scattered patches of intense forest fires were also detected in the lower Himalayan and central Indian states. The spatial correlation between the burn indices and NPP were mainly negative (−0.01 to −0.89) for the fire-prone states as compared to the other neighbouring regions. Additionally, the linear approximation between the burn indices and NPP showed a positive relation (0.01 to 0.63), suggesting a moderate to high impact of the forest fires on the ecosystem production and terrestrial carbon emission. The present approach has the potential to quantify the loss of ecosystem productivity due to forest fires.</p

Examining the effects of green revolution led agricultural expansion on net ecosystem service values in India using multiple valuation approaches

Ecosystem Services (ESs) are bundles of natural processes and functions that are essential for human well-being, subsistence, and livelihoods. The ‘Green Revolution’ (GR) has substantial impact on the agricultural landscape and ESs in India. However, the effects of GR on ESs have not been adequately documented and analyzed. This leads to the main hypothesis of this work – ‘the incremental trend of ESs in India is mainly prompted by GR led agricultural innovations that took place during 1960 - 1970’. The analysis was carried out through five successive steps. First, the spatiotemporal Ecosystem Service Values (ESVs) in Billion US$ for 1985, 1995, and 2005 were estimated using several value transfer approaches. Second, the sensitivity and elasticity of different ESs to land conversion were carried out using coefficient of sensitivity and coefficient of elasticity. Third, the Geographically Weighted Regression model was performed using five explanatory factors, i.e., total crop area, crop production, crop yield, net irrigated area, and cropping intensity, to explore the cumulative and individual effects of these driving factors on ESVs. Fourth, Multi-Layer Perceptron based Artificial Neural Network was employed to estimate the normalized importance of these explanatory factors. Fifth, simple and multiple linear regression modeling was done to assess the linear associations between the driving factors and the ESs. During the observation periods, cropland, forestland and water bodies contributed to 80%–90% of ESVs, followed by grassland, mangrove, wetland and urban built-up. In all three evaluation years, the highest estimated ESVs among the nine ES categories was provided by water regulation, followed by soil formation and soil-water retention, biodiversity maintenance, waste treatment, climate regulation, and greenhouse gas regulation. Among the five explanatory factors, total crop area, crop production, and net irrigated area showed strong positive associations with ESVs, while cropping intensity exhibited a negative association. Therefore, the study reveals a strong association between GR led agricultural expansion and ESVs in India. This study suggests that there should be an urgent need for formulation of rigorous ecosystem management strategies and policies to preserve ecological integrity and flow of uninterrupted ESs and to sustain human well-being

Mechanism of iron integration into LiMn1.5Ni0.5O₄ for the electrocatalytic oxygen evolution reaction

Abstract Spinel-type LiMn1.5Ni0.5O₄ has been paid temendrous consideration as an electrode material because of its low cost, high voltage, and stabilized electrochemical performance. Here, we demonstrate the mechanism of iron (Fe) integration into LiMn1.5Ni0.5O₄ via solution methods followed by calcination at a high temparature, as an efficient electrocatalyst for water splitting. Various microscopic and structural characterizations of the crystal structure affirmed the integration of Fe into the LiMn1.5Ni0.5O₄ lattice and the constitution of the cubic LiMn1.38Fe0.12Ni0.5O₄ crystal. Local structure analysis around Fe by extended X-ray absorption fine structure (EXAFS) showed Fe3+ ions in a six-coordinated octahedral environment, demonstrating incorporation of Fe as a substitute at the Mn site in the LiMn1.5Ni0.5O₄ host. EXAFS also confirmed that the perfectly ordered LiMn1.5Ni0.5O₄ spinel structure becomes disturbed by the fractional cationic substitution and also stabilizes the LiMn1.5Ni0.5O₄ structure with structural disorder of the Ni²⁺ and Mn⁴⁺ ions in the 16d octahedral sites by Fe²⁺ and Fe³⁺ ions. However, we have found that Mn³⁺ ion production from the redox reaction between Mn⁴⁺ and Fe²⁺ influences the electronic conductivity significantly, resulting in improved electrochemical oxygen evolution reaction (OER) activity for the LiMn1.38Fe0.12Ni0.5O4 structure. Surface-enhanced Fe in LiMn1.38Fe0.12Ni0.5O₄ serves as the electrocatalytic active site for OER, which was verified by the density functional theory study