Ecosystem Health Assessment Using a Fuzzy Spatial Decision Support System in Taleghan Watershed Before and After Dam Construction

Jafary, P.; Sarab, A Alimohammadi; Tehrani, Nadia Abbaszadeh

doi:10.1007/s40710-018-0341-4

Cited by 38 publications

(12 citation statements)

References 88 publications

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“…With the help of literatures investigation [1], [38]- [40], [45] and the acceptability of the data, we selected 17 indicators to construct PSR model (Table 2). We adopted AHP method, assigning weights to each indicator, to integrate these 17 indicators as one ecological index [51], [63], which can be expressed as:…”

Section: Construction Of Psrmentioning

confidence: 99%

Eco-Environmental Quality Assessment in China’s 35 Major Cities Based On Remote Sensing Ecological Index

et al. 2019

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The ecological conditions in urban area are greatly changed during the process of industrialization and urbanization of China. The pressure-state-response (PSR) framework is the most popular method to evaluate the ecological quality by integrating a set of remote sensing and statistical indicators into one index through a weighting method. However, a completely remote-sensed ecological index (RSEI), integrating normalized difference vegetation index (NDVI), Wet, land surface temperature (LST), and the normalized differential build-up and bare soil index (NDBSI) through principal components analysis (PCA) method, has been proposed to assess the regional ecological quality. The publications about urban ecological evaluation by RSEI often focus on only one city or a certain area and there are few types of research on the ecological quality assessment by RSEI of 35 major cities in China. In this paper, we employed RSEI to monitor the changes in the ecological quality in China' 35 major cities. The results of RSEI were compared to that of PSR and stepwise regression method was applied to establish the quantitative relationship among RSEI, NDVI, Wet, NDBSI, and LST. The results show that there are 18 cities with ecological quality deteriorated, mainly located in the east and southwest of China

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Section: Construction Of Psrmentioning

confidence: 99%

Eco-Environmental Quality Assessment in China’s 35 Major Cities Based On Remote Sensing Ecological Index

et al. 2019

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“…Landscape metrics that indicate ecosystem resilience, structural stability, and primary productivity are usually used to assess ecosystem conditions [7,8]. For example, Jafary et al [9] used five metrics (normalized difference vegetation index (NDVI), grassland pattern, land use change, landscape diversity, and vegetation coverage) to assess the condition of the Taleghan watershed, Iran. Peng et al [8] assessed the ecosystem health of Lijiang City using land use and landscape derived indicators, including the average normalized difference vegetation index (NDVI), landscape heterogeneity index, patch cohesion of forest land, and ecosystem elasticity coefficient.…”

Section: Introductionmentioning

confidence: 99%

Development of a Landscape-Based Multi-Metric Index to Assess Wetland Health of the Poyang Lake

Liu

You

et al. 2022

Remote Sensing

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Human-induced changes in landscapes are one of the major drivers of wetland loss and degradation. The Poyang Lake wetland in China has been experiencing severe degradation due to human disturbance and landscape modification. Indicators to assess the condition of this wetland are thus needed urgently. Here, a landscape-based multi-metric index (LMI) is developed to evaluate the condition of the Poyang Lake wetland. Twenty-three candidate metrics that have been applied to wetland health assessment in published studies were tested. Metrics that show strong discriminative power to identify reference and impaired sites, having significant correlations with either benthic macroinvertebrate- or vegetation-based indices of biotic integrity (B-IBI or V-IBI), were chosen to form the LMI index. Five of these metrics (largest patch index, modified normalized differential built-up index, Shannon’s diversity index, connectance index, and cultivated land stress index) were selected as our LMI metrics. A 2 km buffer zone around sample sites had the strongest explanatory power of any spatial scale on IBIs, suggesting that protecting landscapes at local scales is essential for wetland conservation. The LMI scores ranged between 1.05 and 5.00, with a mean of 3.25, suggesting that the condition of the Poyang Lake wetland is currently in the “fair” category. The areas along lakeshores were mainly in poor or very poor conditions, while the less accessible inner areas were in better conditions. This study demonstrates significant links between landscape characteristics and wetland biotic integrity, which validates the utility of satellite imagery-derived data in assessing wetland health. The LMI method developed in this study can be used by land managers to quickly assess broad regions of the Poyang Lake wetland.

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“…Slopes > 0 indicates that variables are increasing in n years, while slopes < 0 means a downward trend. The more the slope values are near to 0, the much less changes are occurred in the trends (Jafary et al 2018). To detect statistical significance of the variables trends in karst regions from 2001 to 2020, a Pearson correlation analysis was used and significance level was set to 0.05 (P < 0.05) in this study.…”

Section: Time Series Analysesmentioning

confidence: 99%

Climate change and ecological engineering jointly induced vegetation greening in global karst regions from 2001 to 2020

Jing

Huang

et al. 2021

Plant Soil

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Backgrounds Vegetation dynamic plays a dominant role in the global carbon cycle and climate, especially in vulnerable karst ecosystem. Many studies have examined past several decades changes in vegetation greenness and the associated with climate drivers. Yet, few studies have analyzed the vegetation change in global karst regions particularly in the last decades when climate change and anthropogenic disturbance widely occurred.Methods In this study, we investigated the spatio-temporal variations of vegetation dynamic using the Seasonally Integrated Normalized Difference Vegetation Index (SINDVI) and examined their relationship to climate changes by correlation analysis, the ordinary least squares method investigate the variation trends and the Mann-Kendal test to detect the turning point from 2001 to 2020. ResultsAs expected, there have greening trend in global karst SINDVI from 2001 to 2020, with significant increasing trend in China (range = 0.836, P < 0.05), Europe (range = 0.456, P < 0.05) and many other regions.According to correlation analyses, SINDVI is water-limited in arid and semi-arid regions, such as Middle East and central Asia, and temperature-limited in northern high-latitude.Conclusions consistent with previous studies, our results suggest that anthropogenic activities are mainly responsible for increasing vegetation greenness in tailoring management measures (e.g., Ecological Engineering, the Grain to Green Project) of China and Europe, intensive farmed in Middle East. Coupling warming temperature and increasing precipitation, southeastern Asia and Russia showed an increasing trend in SINDVI. In general, climate factors were the dominant drivers of the variation in vegetation greenness in globally karst regions during research period.

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Ecosystem Health Assessment Using a Fuzzy Spatial Decision Support System in Taleghan Watershed Before and After Dam Construction

Cited by 38 publications

References 88 publications

Eco-Environmental Quality Assessment in China’s 35 Major Cities Based On Remote Sensing Ecological Index

Eco-Environmental Quality Assessment in China’s 35 Major Cities Based On Remote Sensing Ecological Index

Development of a Landscape-Based Multi-Metric Index to Assess Wetland Health of the Poyang Lake

Climate change and ecological engineering jointly induced vegetation greening in global karst regions from 2001 to 2020

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