Comprehensive evaluation of ecological vulnerability based on the AHP-CV method and SOM model: A case study of Badong County, China

As a result of rapid economic development, ecological environmental problems have become increasingly serious. Ecological vulnerability (EV) assessment can scientifically analyse the causal mechanism and change laws of ecosystem vulnerability, providing a basis for decision-making for ecological env...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wu, Xueling [verfasserIn] Tang, Shiyi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Ecological vulnerability PSR model AHP-CV method SOM model GeoDetector

Übergeordnetes Werk:	Enthalten in: Ecological indicators - Amsterdam [u.a.] : Elsevier Science, 2001, 137
Übergeordnetes Werk:	volume:137

DOI / URN:	10.1016/j.ecolind.2022.108758

Katalog-ID:	ELV007629257

Internformat


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520			\|a As a result of rapid economic development, ecological environmental problems have become increasingly serious. Ecological vulnerability (EV) assessment can scientifically analyse the causal mechanism and change laws of ecosystem vulnerability, providing a basis for decision-making for ecological environmental protection. Based on the pressure-state-response (PSR) model, this paper selects 13 indicators, including the geological hazard index, soil erosion sensitivity, population density, proportion of developed land, remote sensing ecological index, and landscape adaptability index, to construct an EV evaluation index system for Badong County, China. Furthermore, the analytic hierarchy process (AHP)-coefficient of variation (CV) method and the self-organizing map (SOM) model are used to comprehensively and quantitatively evaluate the EV of Badong County in 2020. The main factors affecting the EV are explored through GeoDetector. The results show that the EV in Badong County is at a moderate level and above; it is high in the north and south and low in between. According to the analysis of the EV results, the 12 townships in Badong County can be divided into 3 classes. Class I includes Xinling and Dongdukou towns, where the EV is high. Ecological governance measures should be strengthened in these two towns. Based on the GeoDetector results, the spatial distribution of EV in Badong County is mainly related to five factors: the geological hazard index, habitat quality index, proportion of developed land, remote sensing ecological environment index, and soil erosion sensitivity.
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author_variant	x w xw s t st
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allfields	10.1016/j.ecolind.2022.108758 doi (DE-627)ELV007629257 (ELSEVIER)S1470-160X(22)00229-1 DE-627 ger DE-627 rda eng 570 630 DE-600 BIODIV DE-30 fid Wu, Xueling verfasserin aut Comprehensive evaluation of ecological vulnerability based on the AHP-CV method and SOM model: A case study of Badong County, China 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a result of rapid economic development, ecological environmental problems have become increasingly serious. Ecological vulnerability (EV) assessment can scientifically analyse the causal mechanism and change laws of ecosystem vulnerability, providing a basis for decision-making for ecological environmental protection. Based on the pressure-state-response (PSR) model, this paper selects 13 indicators, including the geological hazard index, soil erosion sensitivity, population density, proportion of developed land, remote sensing ecological index, and landscape adaptability index, to construct an EV evaluation index system for Badong County, China. Furthermore, the analytic hierarchy process (AHP)-coefficient of variation (CV) method and the self-organizing map (SOM) model are used to comprehensively and quantitatively evaluate the EV of Badong County in 2020. The main factors affecting the EV are explored through GeoDetector. The results show that the EV in Badong County is at a moderate level and above; it is high in the north and south and low in between. According to the analysis of the EV results, the 12 townships in Badong County can be divided into 3 classes. Class I includes Xinling and Dongdukou towns, where the EV is high. Ecological governance measures should be strengthened in these two towns. Based on the GeoDetector results, the spatial distribution of EV in Badong County is mainly related to five factors: the geological hazard index, habitat quality index, proportion of developed land, remote sensing ecological environment index, and soil erosion sensitivity. Ecological vulnerability PSR model AHP-CV method SOM model GeoDetector Tang, Shiyi verfasserin aut Enthalten in Ecological indicators Amsterdam [u.a.] : Elsevier Science, 2001 137 Online-Ressource (DE-627)338074163 (DE-600)2063587-4 (DE-576)259272388 1872-7034 nnns volume:137 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 137
spelling	10.1016/j.ecolind.2022.108758 doi (DE-627)ELV007629257 (ELSEVIER)S1470-160X(22)00229-1 DE-627 ger DE-627 rda eng 570 630 DE-600 BIODIV DE-30 fid Wu, Xueling verfasserin aut Comprehensive evaluation of ecological vulnerability based on the AHP-CV method and SOM model: A case study of Badong County, China 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a result of rapid economic development, ecological environmental problems have become increasingly serious. Ecological vulnerability (EV) assessment can scientifically analyse the causal mechanism and change laws of ecosystem vulnerability, providing a basis for decision-making for ecological environmental protection. Based on the pressure-state-response (PSR) model, this paper selects 13 indicators, including the geological hazard index, soil erosion sensitivity, population density, proportion of developed land, remote sensing ecological index, and landscape adaptability index, to construct an EV evaluation index system for Badong County, China. Furthermore, the analytic hierarchy process (AHP)-coefficient of variation (CV) method and the self-organizing map (SOM) model are used to comprehensively and quantitatively evaluate the EV of Badong County in 2020. The main factors affecting the EV are explored through GeoDetector. The results show that the EV in Badong County is at a moderate level and above; it is high in the north and south and low in between. According to the analysis of the EV results, the 12 townships in Badong County can be divided into 3 classes. Class I includes Xinling and Dongdukou towns, where the EV is high. Ecological governance measures should be strengthened in these two towns. Based on the GeoDetector results, the spatial distribution of EV in Badong County is mainly related to five factors: the geological hazard index, habitat quality index, proportion of developed land, remote sensing ecological environment index, and soil erosion sensitivity. Ecological vulnerability PSR model AHP-CV method SOM model GeoDetector Tang, Shiyi verfasserin aut Enthalten in Ecological indicators Amsterdam [u.a.] : Elsevier Science, 2001 137 Online-Ressource (DE-627)338074163 (DE-600)2063587-4 (DE-576)259272388 1872-7034 nnns volume:137 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 137
allfields_unstemmed	10.1016/j.ecolind.2022.108758 doi (DE-627)ELV007629257 (ELSEVIER)S1470-160X(22)00229-1 DE-627 ger DE-627 rda eng 570 630 DE-600 BIODIV DE-30 fid Wu, Xueling verfasserin aut Comprehensive evaluation of ecological vulnerability based on the AHP-CV method and SOM model: A case study of Badong County, China 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a result of rapid economic development, ecological environmental problems have become increasingly serious. Ecological vulnerability (EV) assessment can scientifically analyse the causal mechanism and change laws of ecosystem vulnerability, providing a basis for decision-making for ecological environmental protection. Based on the pressure-state-response (PSR) model, this paper selects 13 indicators, including the geological hazard index, soil erosion sensitivity, population density, proportion of developed land, remote sensing ecological index, and landscape adaptability index, to construct an EV evaluation index system for Badong County, China. Furthermore, the analytic hierarchy process (AHP)-coefficient of variation (CV) method and the self-organizing map (SOM) model are used to comprehensively and quantitatively evaluate the EV of Badong County in 2020. The main factors affecting the EV are explored through GeoDetector. The results show that the EV in Badong County is at a moderate level and above; it is high in the north and south and low in between. According to the analysis of the EV results, the 12 townships in Badong County can be divided into 3 classes. Class I includes Xinling and Dongdukou towns, where the EV is high. Ecological governance measures should be strengthened in these two towns. Based on the GeoDetector results, the spatial distribution of EV in Badong County is mainly related to five factors: the geological hazard index, habitat quality index, proportion of developed land, remote sensing ecological environment index, and soil erosion sensitivity. Ecological vulnerability PSR model AHP-CV method SOM model GeoDetector Tang, Shiyi verfasserin aut Enthalten in Ecological indicators Amsterdam [u.a.] : Elsevier Science, 2001 137 Online-Ressource (DE-627)338074163 (DE-600)2063587-4 (DE-576)259272388 1872-7034 nnns volume:137 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 137
allfieldsGer	10.1016/j.ecolind.2022.108758 doi (DE-627)ELV007629257 (ELSEVIER)S1470-160X(22)00229-1 DE-627 ger DE-627 rda eng 570 630 DE-600 BIODIV DE-30 fid Wu, Xueling verfasserin aut Comprehensive evaluation of ecological vulnerability based on the AHP-CV method and SOM model: A case study of Badong County, China 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a result of rapid economic development, ecological environmental problems have become increasingly serious. Ecological vulnerability (EV) assessment can scientifically analyse the causal mechanism and change laws of ecosystem vulnerability, providing a basis for decision-making for ecological environmental protection. Based on the pressure-state-response (PSR) model, this paper selects 13 indicators, including the geological hazard index, soil erosion sensitivity, population density, proportion of developed land, remote sensing ecological index, and landscape adaptability index, to construct an EV evaluation index system for Badong County, China. Furthermore, the analytic hierarchy process (AHP)-coefficient of variation (CV) method and the self-organizing map (SOM) model are used to comprehensively and quantitatively evaluate the EV of Badong County in 2020. The main factors affecting the EV are explored through GeoDetector. The results show that the EV in Badong County is at a moderate level and above; it is high in the north and south and low in between. According to the analysis of the EV results, the 12 townships in Badong County can be divided into 3 classes. Class I includes Xinling and Dongdukou towns, where the EV is high. Ecological governance measures should be strengthened in these two towns. Based on the GeoDetector results, the spatial distribution of EV in Badong County is mainly related to five factors: the geological hazard index, habitat quality index, proportion of developed land, remote sensing ecological environment index, and soil erosion sensitivity. Ecological vulnerability PSR model AHP-CV method SOM model GeoDetector Tang, Shiyi verfasserin aut Enthalten in Ecological indicators Amsterdam [u.a.] : Elsevier Science, 2001 137 Online-Ressource (DE-627)338074163 (DE-600)2063587-4 (DE-576)259272388 1872-7034 nnns volume:137 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 137
allfieldsSound	10.1016/j.ecolind.2022.108758 doi (DE-627)ELV007629257 (ELSEVIER)S1470-160X(22)00229-1 DE-627 ger DE-627 rda eng 570 630 DE-600 BIODIV DE-30 fid Wu, Xueling verfasserin aut Comprehensive evaluation of ecological vulnerability based on the AHP-CV method and SOM model: A case study of Badong County, China 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As a result of rapid economic development, ecological environmental problems have become increasingly serious. Ecological vulnerability (EV) assessment can scientifically analyse the causal mechanism and change laws of ecosystem vulnerability, providing a basis for decision-making for ecological environmental protection. Based on the pressure-state-response (PSR) model, this paper selects 13 indicators, including the geological hazard index, soil erosion sensitivity, population density, proportion of developed land, remote sensing ecological index, and landscape adaptability index, to construct an EV evaluation index system for Badong County, China. Furthermore, the analytic hierarchy process (AHP)-coefficient of variation (CV) method and the self-organizing map (SOM) model are used to comprehensively and quantitatively evaluate the EV of Badong County in 2020. The main factors affecting the EV are explored through GeoDetector. The results show that the EV in Badong County is at a moderate level and above; it is high in the north and south and low in between. According to the analysis of the EV results, the 12 townships in Badong County can be divided into 3 classes. Class I includes Xinling and Dongdukou towns, where the EV is high. Ecological governance measures should be strengthened in these two towns. Based on the GeoDetector results, the spatial distribution of EV in Badong County is mainly related to five factors: the geological hazard index, habitat quality index, proportion of developed land, remote sensing ecological environment index, and soil erosion sensitivity. Ecological vulnerability PSR model AHP-CV method SOM model GeoDetector Tang, Shiyi verfasserin aut Enthalten in Ecological indicators Amsterdam [u.a.] : Elsevier Science, 2001 137 Online-Ressource (DE-627)338074163 (DE-600)2063587-4 (DE-576)259272388 1872-7034 nnns volume:137 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-BIODIV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 137
language	English
source	Enthalten in Ecological indicators 137 volume:137
sourceStr	Enthalten in Ecological indicators 137 volume:137
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institution	findex.gbv.de
topic_facet	Ecological vulnerability PSR model AHP-CV method SOM model GeoDetector
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container_title	Ecological indicators
authorswithroles_txt_mv	Wu, Xueling @@aut@@ Tang, Shiyi @@aut@@
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author	Wu, Xueling
spellingShingle	Wu, Xueling ddc 570 fid BIODIV misc Ecological vulnerability misc PSR model misc AHP-CV method misc SOM model misc GeoDetector Comprehensive evaluation of ecological vulnerability based on the AHP-CV method and SOM model: A case study of Badong County, China
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topic_title	570 630 DE-600 BIODIV DE-30 fid Comprehensive evaluation of ecological vulnerability based on the AHP-CV method and SOM model: A case study of Badong County, China Ecological vulnerability PSR model AHP-CV method SOM model GeoDetector
topic	ddc 570 fid BIODIV misc Ecological vulnerability misc PSR model misc AHP-CV method misc SOM model misc GeoDetector
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title	Comprehensive evaluation of ecological vulnerability based on the AHP-CV method and SOM model: A case study of Badong County, China
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title_full	Comprehensive evaluation of ecological vulnerability based on the AHP-CV method and SOM model: A case study of Badong County, China
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title_sort	comprehensive evaluation of ecological vulnerability based on the ahp-cv method and som model: a case study of badong county, china
title_auth	Comprehensive evaluation of ecological vulnerability based on the AHP-CV method and SOM model: A case study of Badong County, China
abstract	As a result of rapid economic development, ecological environmental problems have become increasingly serious. Ecological vulnerability (EV) assessment can scientifically analyse the causal mechanism and change laws of ecosystem vulnerability, providing a basis for decision-making for ecological environmental protection. Based on the pressure-state-response (PSR) model, this paper selects 13 indicators, including the geological hazard index, soil erosion sensitivity, population density, proportion of developed land, remote sensing ecological index, and landscape adaptability index, to construct an EV evaluation index system for Badong County, China. Furthermore, the analytic hierarchy process (AHP)-coefficient of variation (CV) method and the self-organizing map (SOM) model are used to comprehensively and quantitatively evaluate the EV of Badong County in 2020. The main factors affecting the EV are explored through GeoDetector. The results show that the EV in Badong County is at a moderate level and above; it is high in the north and south and low in between. According to the analysis of the EV results, the 12 townships in Badong County can be divided into 3 classes. Class I includes Xinling and Dongdukou towns, where the EV is high. Ecological governance measures should be strengthened in these two towns. Based on the GeoDetector results, the spatial distribution of EV in Badong County is mainly related to five factors: the geological hazard index, habitat quality index, proportion of developed land, remote sensing ecological environment index, and soil erosion sensitivity.
abstractGer	As a result of rapid economic development, ecological environmental problems have become increasingly serious. Ecological vulnerability (EV) assessment can scientifically analyse the causal mechanism and change laws of ecosystem vulnerability, providing a basis for decision-making for ecological environmental protection. Based on the pressure-state-response (PSR) model, this paper selects 13 indicators, including the geological hazard index, soil erosion sensitivity, population density, proportion of developed land, remote sensing ecological index, and landscape adaptability index, to construct an EV evaluation index system for Badong County, China. Furthermore, the analytic hierarchy process (AHP)-coefficient of variation (CV) method and the self-organizing map (SOM) model are used to comprehensively and quantitatively evaluate the EV of Badong County in 2020. The main factors affecting the EV are explored through GeoDetector. The results show that the EV in Badong County is at a moderate level and above; it is high in the north and south and low in between. According to the analysis of the EV results, the 12 townships in Badong County can be divided into 3 classes. Class I includes Xinling and Dongdukou towns, where the EV is high. Ecological governance measures should be strengthened in these two towns. Based on the GeoDetector results, the spatial distribution of EV in Badong County is mainly related to five factors: the geological hazard index, habitat quality index, proportion of developed land, remote sensing ecological environment index, and soil erosion sensitivity.
abstract_unstemmed	As a result of rapid economic development, ecological environmental problems have become increasingly serious. Ecological vulnerability (EV) assessment can scientifically analyse the causal mechanism and change laws of ecosystem vulnerability, providing a basis for decision-making for ecological environmental protection. Based on the pressure-state-response (PSR) model, this paper selects 13 indicators, including the geological hazard index, soil erosion sensitivity, population density, proportion of developed land, remote sensing ecological index, and landscape adaptability index, to construct an EV evaluation index system for Badong County, China. Furthermore, the analytic hierarchy process (AHP)-coefficient of variation (CV) method and the self-organizing map (SOM) model are used to comprehensively and quantitatively evaluate the EV of Badong County in 2020. The main factors affecting the EV are explored through GeoDetector. The results show that the EV in Badong County is at a moderate level and above; it is high in the north and south and low in between. According to the analysis of the EV results, the 12 townships in Badong County can be divided into 3 classes. Class I includes Xinling and Dongdukou towns, where the EV is high. Ecological governance measures should be strengthened in these two towns. Based on the GeoDetector results, the spatial distribution of EV in Badong County is mainly related to five factors: the geological hazard index, habitat quality index, proportion of developed land, remote sensing ecological environment index, and soil erosion sensitivity.
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title_short	Comprehensive evaluation of ecological vulnerability based on the AHP-CV method and SOM model: A case study of Badong County, China
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up_date	2024-07-06T16:55:24.527Z
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According to the analysis of the EV results, the 12 townships in Badong County can be divided into 3 classes. Class I includes Xinling and Dongdukou towns, where the EV is high. Ecological governance measures should be strengthened in these two towns. 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Comprehensive evaluation of ecological vulnerability based on the AHP-CV method and SOM model: A case study of Badong County, China

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