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Zhou J, Wang X, Wang X, Yao W, Tu Y, Sun Z, Feng X. Evaluation of ecosystem quality and stability based on key indicators and ideal reference frame: A case study of the Qinghai-Tibet Plateau. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 370:122460. [PMID: 39288498 DOI: 10.1016/j.jenvman.2024.122460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 09/06/2024] [Accepted: 09/06/2024] [Indexed: 09/19/2024]
Abstract
China has explicitly prioritized the enhancement of ecosystem quality and stability(EQS) as a governmental objective. However, our understanding of systematic and comprehensive assessment methods for EQS remains limited. The development and investigation of corresponding evaluation frameworks and their underlying mechanisms remain insufficiently explored. This study employs the concept of an "ideal reference system and key indicators," integrating diverse ecosystem and human activity characteristics from perspectives such as ecosystem structure, function, and landscape vulnerability, to determine indicator weights using the Analytic Hierarchy Process(AHP) and entropy weight method, thereby constructing an evaluation framework for assessing the quality and stability of the Qinghai-Tibet Plateau(QTP) ecosystem. The spatiotemporal variations in EQS from 2000 to 2018 were examined, and the key driving factors were identified using the optimal parameter-based geographical detector (OPGD). The results indicate that the EQS of the QTP exhibit a spatial distribution pattern characterized by higher values in the southeast and lower values in the northwest. From 2000 to 2018, there has been a consistent improvement in the overall ecosystem quality and stability across the QTP. The EQS exhibit a significant synergistic effect, with high-high(26.59 ± 1.26%) and low-low(32.61 ± 1.45%) matching combinations becoming the predominant regional patterns. However, in climatic transition zones and glacial areas, the relationship between these factors is particularly distinctive, indicating ecosystem response mechanisms specific to certain natural environmental conditions. Vegetation cover(>0.697), evapotranspiration(>0.620), and precipitation(>0.688) are the primary natural factors influencing EQS, while the impact of human activities has become increasingly significant. Furthermore, the research findings underscore the positive effects of the variable climatic conditions of the QTP on ecosystems within the context of global climate warming, while the stringent implementation of ecological protection measures has collectively contributed to the enhancement of EQS. The proposed evaluation framework not only facilitates a comprehensive and precise assessment of regional EQS, but also provides a scientific basis for understanding and managing the adaptive responses of plateau ecosystems under the complex interplay of natural and anthropogenic factors.
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Affiliation(s)
- Jitao Zhou
- School of Land Engineering, Chang'an University, Xi'an, 710054, China
| | - Xiaofeng Wang
- School of Land Engineering, Chang'an University, Xi'an, 710054, China; Key Laboratory of Xi'an Territorial and Spatial Information, Xi'an, 710054, China.
| | - Xiaoxue Wang
- School of Land Engineering, Chang'an University, Xi'an, 710054, China
| | - Wenjie Yao
- School of Land Engineering, Chang'an University, Xi'an, 710054, China
| | - You Tu
- School of Land Engineering, Chang'an University, Xi'an, 710054, China
| | - Zechong Sun
- School of Land Engineering, Chang'an University, Xi'an, 710054, China
| | - Xiaoming Feng
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Bejing, 100085, China
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Ning Q, Ouyang X. Spatio-temporal characteristics and mechanism of ecological degradation in a hilly southern area-a case study of Dongting Lake Basin. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:45274-45284. [PMID: 36705836 DOI: 10.1007/s11356-023-25514-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 01/19/2023] [Indexed: 06/18/2023]
Abstract
Understanding the spatio-temporal characteristics of ecological degradation and its mechanism is the key to implementing national land space ecological restoration. Currently, there is a lack of knowledge about identifying ecologically degraded areas from a structure-function angle. This paper used the Dongting Lake Basin (DLB) as the research area, with the landscape pattern index and InVest model utilized to analyze the landscape distribution characteristics and ecosystem service functions in 2000 and 2018. Based on this, a fuzzy inference approach and geographic detectors were used to explore the characteristics and driving mechanism of ecological degradation in the DLB from 2000 to 2018. The results found are the following: (1) The overall landscape of the DLB was fragmented, the landscape shape tended to be complex, the degree of aggregation declined, and the landscape types were more discrete than before. In terms of the landscape-level index, the overall indicators of the landscape pattern in the DLB showed little change from 2000 to 2018, and the overall landscape pattern change was reasonably stable. (2) The three ecological services exhibited prominent spatial distribution features during the study period. In particular, food supply services showed a steady upward trend, while habitat quality and carbon storage services generally declined. (3) The ecological degradation in the DLB demonstrated striking spatial and temporal differences during the study period, and the ecological situation improved. The ecological degradation areas were mainly distributed in urban areas with denser populations and a higher level of urbanization, while the ecological restoration areas were mainly in the mountainous and hilly areas far away from the urban centers. (4) Among the influential factors, the production potential of urban land and farmland is the main factor that affects the ecological environment degradation and spatial distribution difference in the DLB. The interactive detection results indicate that the driving mechanism exhibits a two-factor enhancement or nonlinear increase.
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Affiliation(s)
- Qimeng Ning
- College of Architecture and Urban Planning, Hunan City University, Yiyang, 413000, China
- Key Laboratory of Urban Planning Information Technology of Hunan Provincial Universities, Yiyang, 413000, China
- Key Laboratory of Digital Urban and Rural Spatial Planning of Hunan Province, Yiyang, 413000, China
| | - Xiao Ouyang
- Hunan Institute of Economic Geography, Hunan University of Finance and Economics, Changsha, 410205, China.
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Zhang M, Zhang L, He H, Ren X, Lv Y, Niu Z, Chang Q, Xu Q, Liu W. Improvement of ecosystem quality in National Key Ecological Function Zones in China during 2000-2015. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 324:116406. [PMID: 36352714 DOI: 10.1016/j.jenvman.2022.116406] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/31/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
Improving ecosystem quality is the ultimate goal of ecological restoration projects and sustainable ecosystem management. However, previous results of ecosystem quality lack comparability among different regions when assessing the effectiveness of ecological restoration projects on the regional or national scales, due to the influence of geographical and climatic background conditions. Here we proposed a new index, ecosystem quality ratio (EQR), by integrating the status of landscape structure, ecosystem services, ecosystem stability, and human disturbance relative to their reference conditions, and assessed the EQR changes in China's counties and National Key Ecological Function Zones (NKEFZs) from 1990 to 2015. The results showed that the average ecosystem quality of China's counties deviated from the reference condition by 28%. EQR decreased by 1.2% during 1990-2000 but increased by 3.7% during 2000-2015. Those counties with increasing EQR in 2000-2015 occupy 64.7%, with obviously increasing counties mainly located in the water conservation, biodiversity maintenance, and water and soil conservation types of NKEFZs. The EQR increase in counties within NKEFZs was 3.65 times that outside of NKEFZs. Remarkable improvement of ecosystem quality occurred in the forest region in Changbai Mountain, biodiversity and soil conservation region in Wuling Mountains, and hilly and gully region of Loess Plateau, where EQR increases mainly resulted from the conversion of farmland to forest or grassland and consequent increases in ecosystem services and stability. The magnitude of EQR enhancement showed a positive relationship with the increase in forest and grassland coverage in NKEFZs. Our results highlight the important role of ecological restoration projects in improving ecosystem quality in China, and demonstrate the feasibility of the new index (EQR) for the assessment of ecosystem quality in terms of ecosystem management and restoration.
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Affiliation(s)
- Mengyu Zhang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; National Ecosystem Science Data Center, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Li Zhang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; National Ecosystem Science Data Center, Beijing, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China.
| | - Honglin He
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; National Ecosystem Science Data Center, Beijing, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China.
| | - Xiaoli Ren
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; National Ecosystem Science Data Center, Beijing, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Yan Lv
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; National Ecosystem Science Data Center, Beijing, 100101, China
| | - Zhong'en Niu
- School of Resources and Environmental Engineering, Ludong University, Shandong, 264025, China
| | - Qingqing Chang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; National Ecosystem Science Data Center, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qian Xu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; National Ecosystem Science Data Center, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Weihua Liu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; National Ecosystem Science Data Center, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China
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Developing an Enhanced Ecological Evaluation Index (EEEI) Based on Remotely Sensed Data and Assessing Spatiotemporal Ecological Quality in Guangdong–Hong Kong–Macau Greater Bay Area, China. REMOTE SENSING 2022. [DOI: 10.3390/rs14122852] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Ecological changes affected by increasing human activities have highlighted the importance of ecological quality assessments. An appropriate and efficient selection of ecological parameters is fundamental for ecological quality assessments. On the basis of remote sensing data and methods, this study developed an enhanced ecological evaluation index (EEEI) with five integrated ecological parameters by containing pixel and sub-pixel information: normalized difference vegetation index, impervious surface coverage, soil coverage, land surface temperature, and wetness component of tasseled cap transformation. Significantly, the EEEI simultaneously considered the five aspects of land surface ecological conditions (i.e., greenness, human activities, dryness, heat, and moisture), which provided an effective guide for the systematic selection of ecological parameters. The EEEI has a clear theoretical framework, and all the parameters can be obtained quickly on the basis of the remote sensing datasets and methods, which is suitable for the promotion and application of ecological quality assessments to various areas and scales. Furthermore, the EEEI was applied to assess and detect the ecological quality of the Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China. Assessment results indicated that the ecological quality of the GBA is currently facing great challenges with a degradation trend from 2000 to 2020, which emphasizes the significance and urgency for eco-environmental protection of the GBA. This provided evidence that the EEEI can be used as an effective index for scientific, objective, quantitative, and comprehensive ecological quality assessment, which can also aid regional environmental management and ecological protection.
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Multi-Scenario Simulation of Production-Living-Ecological Space in the Poyang Lake Area Based on Remote Sensing and RF-Markov-FLUS Model. REMOTE SENSING 2022. [DOI: 10.3390/rs14122830] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
With industrialization and urbanization, the competition among land production, living, and ecological (PLE) spaces has intensified. Particularly in ecological reserves, competition among various types of land use restricts the coordinated development of PLE space. To explore spatial sustainable development, this study starts from a PLE spatial perspective, based on Landsat long time series images. Object-based image analysis (OBIA) and landscape index analysis were selected to monitor the spatial and temporal land use and landscape pattern changes in the Poyang Lake region (PYL region) from 1989 to 2020. The RF-Markov-FLUS coupled model was used to simulate spatial changes in 2030 under four scenarios: production space priority (PSP), living space priority (LSP), ecological space priority (ESP), and an integrated development (ID). Finally, the goal-problem-principle was used to enhance PLE space. The results showed that: (1) production space and ecological spaces decreased in general from 1989 to 2020 by 3% and 7%, respectively; living space increased by 11%. (2) From 1989 to 2020, the overall landscape spread in the Poyang Lake (PYL) area decreased, connectivity decreased, fragmentation increased, landscape heterogeneity increased, and landscape geometry became more irregular. (3) Compared with the other three scenarios, the ID scenario maintained steady production space growth in 2030, the expansion rate of living space slowed, and the area of ecological space decreased the least. (4) Spatial pattern optimization should start with three aspects: the transformation of the agricultural industry, improving the efficiency of urban land use, and establishing communities of “mountains, water, forests, fields, lakes and grasses”. The results provide scientific planning and suggestions for the future ecological protection of Poyang Lake area with multiple scenarios and perspectives.
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Yang R, Ren F, Xu W, Ma X, Zhang H, He W. China's ecosystem service value in 1992-2018: Pattern and anthropogenic driving factors detection using Bayesian spatiotemporal hierarchy model. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 302:114089. [PMID: 34775337 DOI: 10.1016/j.jenvman.2021.114089] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 09/30/2021] [Accepted: 11/08/2021] [Indexed: 06/13/2023]
Abstract
Maintaining ecosystem services (ESs) and reducing ecosystem degradation are important goals for achieving sustainable development. However, under the influence of various anthropogenic factors, the total ecosystem service value (ESV) of China continues to decline, and the detailed processes involved in this decline are unclear. In this paper, a new long-term annual land cover dataset (the Climate Change Initiative Land Cover or CCI-LC dataset) with a spatial resolution of 300 m was employed to estimate the ESV of China, and Bayesian spatiotemporal hierarchy models were built to examine the detailed patterns and anthropogenic driving factors. From 1992 to 2018, the total ESV of China fluctuated and decreased from 3265.3 to 3253.29 billion US$ at an average rate of 0.55 billion US$ per year. Furthermore, the model revealed the spatiotemporal variations in the ESV pattern, and simultaneously detected the influences of 9 variables related to economic factors, population, infrastructure, energy, agriculture and ecological restoration, providing a convenient and effective method for ESV spatiotemporal analysis. The results enrich our understanding of the detailed spatiotemporal variation and anthropogenic driving factors underlying the declining ESV in China. These findings have substantial guiding implications for adjusting ecological regulation policies.
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Affiliation(s)
- Renfei Yang
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, China.
| | - Fu Ren
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, China; Key Laboratory of GIS, Ministry of Education, Wuhan University, Wuhan, 430079, China.
| | - Wenxuan Xu
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, 210023, China.
| | - Xiangyuan Ma
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, China.
| | - Hongwei Zhang
- Electronic Information School, Wuhan University, Wuhan, 430079, China.
| | - Wenwen He
- School of Resource and Environmental Science, Wuhan University, Wuhan, 430079, China.
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Delimitating the Ecological Spaces for Water Conservation Services in Jilin Province of China. LAND 2021. [DOI: 10.3390/land10101029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Mastering the spatial distribution of water retention capacity and scientifically delineating the ecological space for water conservation are of great significance to the management of regional land and water resources. In this paper, a water conservation ecological spatial delimitation framework suitable for water-deficient areas was put forward. The water retention capacity of the study area in 1983, 1990, 2000, 2010, and 2016 was evaluated by using the InVEST Water Yield model and water balance method, respectively. On this basis, a flexible inflection point model based on the contribution degree of functional units was established. Then the ecological space for water conservation was delimited. The framework was applied to the delimitation of the key water conservation areas in Jilin province, China. The results showed that: (1) the spatial distribution pattern of water conservation in Jilin province gradually decreased from east to west. The spatial difference was significant. The maximum value of water conservation in Jilin province was 730 mm. From 1983 to 2016, water conservation, which accounted for 75.71% of the area, showed an upward trend. The overall water retention capacity showed the characteristics of the overall increase and the local decline. (2) From the absolute amount of the effect of unit area change on water conservation, the intensity from the high to the low was forestland, cultivated land, grassland, unused land, buildings, and water. (3) The area of water conservation less than 474 mm accounted for more than 80% of the total study area. The overall water retention capacity was low. High importance ecological space area of water conservation was comprehensively defined as 36.97%, which was mainly distributed in the natural forest area of Changbai Mountain in the east and the south of Song Liao Plain. Therefore, this study provided a basic layout of relatively concentrated ecological spatial distribution for water conservation types at different levels in Jilin province. The study results and conclusions of this paper will provide a reference for water conservation assessment and the regional land’s natural resources management.
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Integrating Ecological Assessments to Target Priority Restoration Areas: A Case Study in the Pearl River Delta Urban Agglomeration, China. REMOTE SENSING 2021. [DOI: 10.3390/rs13122424] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The identification and management of ecological restoration areas play important roles in promoting sustainable urban development. However, current research lacks a scientific basis for the scope and scale of ecological restoration. Further, the absence of a framework to assess policy goals and public preferences that leads to identification of ecological restoration areas across the science-policy interface is difficult, and the existing frameworks’ performance has little applicability. We proposed a transdisciplinary framework to combine ecological quality, ecological health, and ecosystem services as an assessment endpoint to identify priority restoration areas. Further, we classified the ecological restoration areas on a township scale by K-means. Based upon policy goals and public preferences of the Pearl River Delta urban agglomeration, we chose air quality, biodiversity, soil fragility, recreation quality, ecosystem vigor, landscape metrics, and the water supply ecosystem service as elements of the evaluation system. This study showed that priority restoration areas accounted for 10.8% of the urban agglomeration area and classified township, largely in the difference between natural and semi-natural ecosystems and the human environment. Policymakers can use this framework comprehensively and flexibly to identify and classify ecological restoration areas to achieve policy goals and fulfil public preferences.
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Quantifying the Compound Factors of Forest Land Changes in the Pearl River Delta, China. REMOTE SENSING 2021. [DOI: 10.3390/rs13101911] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Forestland has been a focus of urbanization research, yet the effect of urbanization on forest land change on an urban agglomeration scale still remains unclear. Screening and quantifying the main factors affecting forest land changes have practical significance for land planning and management. Considering the characteristics of the region and referring to related studies, 26 natural, social, and economic factors were screened in the Pearl River Delta (PRD), where land-use changes are intense. Geographically weighted regression and the relative importance were used to quantify the spatial heterogeneity of these main factors. There was still a large area of deforestation evident in the PRD with its afforestation area of 604.3 km2 (mainly converted from cropland) and a deforestation area of 1544.6 km2 (mainly converted from built-up land). The effects of socio-economic factors were the main factors for these forest land changes, especially the rural population and migration. Deforestation mainly occurs in urban growth boundaries, which will be the focus area for further land management. These main factors have the potential to provide a methodological contribution to land-use changes, and the results of this study can provide a solid theoretical basis for forest land management and urban planning (e.g., balancing expansion of built-up land and ecological protection that advances forest land protection and restoration).
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Karbalaei Saleh S, Amoushahi S, Gholipour M. Spatiotemporal ecological quality assessment of metropolitan cities: a case study of central Iran. ENVIRONMENTAL MONITORING AND ASSESSMENT 2021; 193:305. [PMID: 33900465 DOI: 10.1007/s10661-021-09082-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 04/15/2021] [Indexed: 06/12/2023]
Abstract
The present study used the recently developed Remote Sensing-Based Ecological Index (RSEI) to assess the temporal-spatial variation of ecological quality in the metropolitan city of Isfahan (Iran) as a member of the UNESCO Creative Cities Network. This study was conducted from the Landsat TM/OLI satellite images of 2004, 2009, 2014 and 2019. The RSEI was synthesized by principal component analysis for four indices of Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Land Surface Moisture (LSM) and Normalized Differential Build-up, and Bare Soil Index (NDBSI) based on the framework of the Pressure-State-Response (PSR) in the aforementioned years. The ecological quality of the city was assessed by RSEI over a 15-year period. The index has a value range of 0 (completely poor ecological quality) to 1 (completely desirable). In addition, the spatial heterogeneity of RSEIs at different intervals was assessed by the Moran index. The results showed that the RSEI value was always less than 0.4, which indicated the unfavourable ecological quality of the city. This index was 0.34, 0.37, 0.26 and 0.30 in 2004, 2009, 2014 and 2019, respectively. Therefore, the ecological quality of the city did not have a constant trend during the studied period and had several fluctuations, which could be attributed to the natural and anthropogenic changes in the studied period. Additionally, the results of the Moran index showed a steady decline, which indicated a declining homogeneity during this period. Matching the calculated RSEIs with the realities of the region at each time interval suggested that the index could be a useful tool for assessing urban ecological quality.
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Affiliation(s)
- Sajjad Karbalaei Saleh
- Department of Environmental sciences, Faculty of Fisheries and Environmental sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Golestan, Iran
| | - Solmaz Amoushahi
- Department of Environmental sciences, Faculty of Fisheries and Environmental sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Golestan, Iran.
| | - Mostafa Gholipour
- Department of Environmental sciences, Faculty of Fisheries and Environmental sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Golestan, Iran
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Investigation of Dynamic Coupling Coordination between Urbanization and the Eco-Environment—A Case Study in the Pearl River Delta Area. LAND 2021. [DOI: 10.3390/land10020190] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The interaction between urbanization and the eco-environment is usually viewed as an effect–feedback framework. Its coupling system is composed of urbanization and eco-environment subsystems. In this paper, the coupling degree (CD) and the coupling coordinated degree (CCD) are used to reflect the coupling interaction and coupling coordination between the urbanization subsystem and the eco-environment subsystem. Based on the dynamic relative quantities of urbanization and eco-environment data in the Pearl River Delta, CD and CCD values were calculated, and the spatiotemporal evolution trend of coordination was analyzed. The results show that (1) from 2000 to 2015, the nine cities in the Pearl River Delta had high CD values and CCD values. Though they had different performances in different periods, they were all in a coordinated class, including good coordination (GC), moderate coordination (MC), and bare coordination (BC). (2) In terms of temporal evolution, the coupling coordination between urbanization and the eco-environment in the entire Pearl River Delta greatly improved. (3) From the perspective of spatial distribution, the coupling coordination of the central region was higher than that of the peripheral regions, and that of the west bank of the Pearl River was higher than that of the east bank of the Pearl River. These results can help local policy makers enact appropriate measures for sustainable development.
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A Remotely Sensed Assessment of Surface Ecological Change over the Gomishan Wetland, Iran. REMOTE SENSING 2020. [DOI: 10.3390/rs12182989] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Due to the excessive use of natural resources in the contemporary world, the importance of ecological and environmental condition modeling has increased. Wetlands and cities represent the natural and artificial strategic areas that affect ecosystem conditions. Changes in the ecological conditions of these areas have a great impact on the conditions of the global ecosystem. Therefore, modeling spatiotemporal variations of the ecological conditions in these areas is critical. This study was aimed at comparing degrees of variation among surface ecological conditions due to natural and unnatural factors. Consequently, the surface ecological conditions of Gomishan city and Gomishan wetland in Iran were modeled for a period of 30 years, and the spatiotemporal variations were evaluated and compared with each other. To this end, 20 Landsat 5, 7, and 8, and 432 Moderate Resolution Imaging Spectroradiometer (MODIS), monthly land surface temperature (LST) (MOD11C3) and normalized difference vegetation index (NDVI) (MOD13C3) products were utilized. The surface ecological conditions were modeled according to the Remote Sensing-based Ecological Index (RSEI), and the spatiotemporal variation of the RSEI values in the study area (Gomishan city, Gomishan wetland) were evaluated and compared with each other. According to MODIS products, the mean of the LST and NDVI variance values for the study area (Gomishan city, Gomishan wetland) were obtained to be 6.5 °C (2.1, 12.1) and 0.009 (0.005, 0.013), respectively. The highest LST and NDVI temporal variations were found for Gomishan wetland near the Caspian Sea. According to Landsat images, Gomishan wetland and Gomishan city have the highest and lowest temporal variations in surface biophysical characteristics, respectively. The mean RSEI for the study area (Gomishan city, Gomishan wetland) was 0.43 (0.65, 0.29), respectively. Additionally, the mean Coefficient of Variation (CV) of RSEI for the study area (Gomishan city, Gomishan wetland) was 0.10 (0.88, 0.51), respectively. The surface ecological conditions of Gomishan city were worse than those of the Gomishan wetland at all dates. Temporal variations in the surface ecological conditions of Gomishan wetland were greater than those of the study area and Gomishan city. These results can provide useful and effective information for environmental planning and decision-making to improve ecological conditions, protect the environment, and support sustainable ecosystem development.
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