Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle

Landscape ecological security is an environmental requirement for social and economic development. Understanding the dynamic mechanisms of landscape change and the associated ecological risks in regional socioecological systems is necessary for promoting regional sustainable development. Using the Minjiang River Basin as the research area, the Google Earth Engine platform, random forest (RF) model, and FLUS model were employed for land use classification and future multi-scenario prediction. Multisource remote sensing data were used to establish a three-dimensional evaluation index system for an adaptive cycle. Additionally, the “potential-connection-resilience” framework was adopted to explore the spatial and temporal variations in landscape ecological risk in the basin from 2001 to 2035 under different administrative scales and development scenarios. The results showed that from 2001 to 2020, the building and forest areas increased significantly, whereas grassland and plowland areas decreased significantly. Moreover, the spatial fragmentation of the watershed improved significantly with the transformation of large amounts of grassland into forests. The construction area continued to expand in 2035 under different scenarios. Under the economic development scenario, the grassland and plowland areas decreased considerably, but the forest area increased slowly. Under the ecological protection scenario, the expansion of land use was restrained, and the reduction rate of grassland and cultivated land was moderated. From 2001 to 2020, the overall ecological risk was at a medium-low level and showed a decreasing trend, and the fragmentation degree of the forest had a significant impact on ecological risk. By 2035, landscape ecological risks increased under different development scenarios, and construction land expansion had become the dominant factor affecting the risk level. By evaluating the distribution and development trend of ecologically high-risk areas in the Minjiang River Basin, the results of this study provide basic support for the rational planning of land resources in the basin and decision making for future sustainable development efforts. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Tiantian Bao [verfasserIn] Ruifan Wang [verfasserIn] Linghan Song [verfasserIn] Xiaojie Liu [verfasserIn] Shuangwen Zhong [verfasserIn] Jian Liu [verfasserIn] Kunyong Yu [verfasserIn] Fan Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	landscape ecological risk Google Earth Engine random forest FLUS multi-scenario simulation adaptive cycle

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 14(2022), 21, p 5540
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:21, p 5540

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs14215540

Katalog-ID:	DOAJ086447300

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ086447300
003	DE-627
005	20240414171301.0
007	cr uuu---uuuuu
008	230311s2022 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3390/rs14215540 \|2 doi
035			\|a (DE-627)DOAJ086447300
035			\|a (DE-599)DOAJd7802e6e31114fb9880e1845511db1e3
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	0		\|a Tiantian Bao \|e verfasserin \|4 aut
245	1	0	\|a Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle
264		1	\|c 2022
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Landscape ecological security is an environmental requirement for social and economic development. Understanding the dynamic mechanisms of landscape change and the associated ecological risks in regional socioecological systems is necessary for promoting regional sustainable development. Using the Minjiang River Basin as the research area, the Google Earth Engine platform, random forest (RF) model, and FLUS model were employed for land use classification and future multi-scenario prediction. Multisource remote sensing data were used to establish a three-dimensional evaluation index system for an adaptive cycle. Additionally, the “potential-connection-resilience” framework was adopted to explore the spatial and temporal variations in landscape ecological risk in the basin from 2001 to 2035 under different administrative scales and development scenarios. The results showed that from 2001 to 2020, the building and forest areas increased significantly, whereas grassland and plowland areas decreased significantly. Moreover, the spatial fragmentation of the watershed improved significantly with the transformation of large amounts of grassland into forests. The construction area continued to expand in 2035 under different scenarios. Under the economic development scenario, the grassland and plowland areas decreased considerably, but the forest area increased slowly. Under the ecological protection scenario, the expansion of land use was restrained, and the reduction rate of grassland and cultivated land was moderated. From 2001 to 2020, the overall ecological risk was at a medium-low level and showed a decreasing trend, and the fragmentation degree of the forest had a significant impact on ecological risk. By 2035, landscape ecological risks increased under different development scenarios, and construction land expansion had become the dominant factor affecting the risk level. By evaluating the distribution and development trend of ecologically high-risk areas in the Minjiang River Basin, the results of this study provide basic support for the rational planning of land resources in the basin and decision making for future sustainable development efforts.
650		4	\|a landscape ecological risk
650		4	\|a Google Earth Engine
650		4	\|a random forest
650		4	\|a FLUS
650		4	\|a multi-scenario simulation
650		4	\|a adaptive cycle
653		0	\|a Science
653		0	\|a Q
700	0		\|a Ruifan Wang \|e verfasserin \|4 aut
700	0		\|a Linghan Song \|e verfasserin \|4 aut
700	0		\|a Xiaojie Liu \|e verfasserin \|4 aut
700	0		\|a Shuangwen Zhong \|e verfasserin \|4 aut
700	0		\|a Jian Liu \|e verfasserin \|4 aut
700	0		\|a Kunyong Yu \|e verfasserin \|4 aut
700	0		\|a Fan Wang \|e verfasserin \|4 aut
773	0	8	\|i In \|t Remote Sensing \|d MDPI AG, 2009 \|g 14(2022), 21, p 5540 \|w (DE-627)608937916 \|w (DE-600)2513863-7 \|x 20724292 \|7 nnns
773	1	8	\|g volume:14 \|g year:2022 \|g number:21, p 5540
856	4	0	\|u https://doi.org/10.3390/rs14215540 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/d7802e6e31114fb9880e1845511db1e3 \|z kostenfrei
856	4	0	\|u https://www.mdpi.com/2072-4292/14/21/5540 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2072-4292 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4392
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 14 \|j 2022 \|e 21, p 5540

Indexfelder

author_variant	t b tb r w rw l s ls x l xl s z sz j l jl k y ky f w fw
matchkey_str	article:20724292:2022----::ptoeprlutsaenlssfadcpeooiarsimninrvr
hierarchy_sort_str	2022
publishDate	2022
allfields	10.3390/rs14215540 doi (DE-627)DOAJ086447300 (DE-599)DOAJd7802e6e31114fb9880e1845511db1e3 DE-627 ger DE-627 rakwb eng Tiantian Bao verfasserin aut Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Landscape ecological security is an environmental requirement for social and economic development. Understanding the dynamic mechanisms of landscape change and the associated ecological risks in regional socioecological systems is necessary for promoting regional sustainable development. Using the Minjiang River Basin as the research area, the Google Earth Engine platform, random forest (RF) model, and FLUS model were employed for land use classification and future multi-scenario prediction. Multisource remote sensing data were used to establish a three-dimensional evaluation index system for an adaptive cycle. Additionally, the “potential-connection-resilience” framework was adopted to explore the spatial and temporal variations in landscape ecological risk in the basin from 2001 to 2035 under different administrative scales and development scenarios. The results showed that from 2001 to 2020, the building and forest areas increased significantly, whereas grassland and plowland areas decreased significantly. Moreover, the spatial fragmentation of the watershed improved significantly with the transformation of large amounts of grassland into forests. The construction area continued to expand in 2035 under different scenarios. Under the economic development scenario, the grassland and plowland areas decreased considerably, but the forest area increased slowly. Under the ecological protection scenario, the expansion of land use was restrained, and the reduction rate of grassland and cultivated land was moderated. From 2001 to 2020, the overall ecological risk was at a medium-low level and showed a decreasing trend, and the fragmentation degree of the forest had a significant impact on ecological risk. By 2035, landscape ecological risks increased under different development scenarios, and construction land expansion had become the dominant factor affecting the risk level. By evaluating the distribution and development trend of ecologically high-risk areas in the Minjiang River Basin, the results of this study provide basic support for the rational planning of land resources in the basin and decision making for future sustainable development efforts. landscape ecological risk Google Earth Engine random forest FLUS multi-scenario simulation adaptive cycle Science Q Ruifan Wang verfasserin aut Linghan Song verfasserin aut Xiaojie Liu verfasserin aut Shuangwen Zhong verfasserin aut Jian Liu verfasserin aut Kunyong Yu verfasserin aut Fan Wang verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 21, p 5540 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:21, p 5540 https://doi.org/10.3390/rs14215540 kostenfrei https://doaj.org/article/d7802e6e31114fb9880e1845511db1e3 kostenfrei https://www.mdpi.com/2072-4292/14/21/5540 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 21, p 5540
spelling	10.3390/rs14215540 doi (DE-627)DOAJ086447300 (DE-599)DOAJd7802e6e31114fb9880e1845511db1e3 DE-627 ger DE-627 rakwb eng Tiantian Bao verfasserin aut Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Landscape ecological security is an environmental requirement for social and economic development. Understanding the dynamic mechanisms of landscape change and the associated ecological risks in regional socioecological systems is necessary for promoting regional sustainable development. Using the Minjiang River Basin as the research area, the Google Earth Engine platform, random forest (RF) model, and FLUS model were employed for land use classification and future multi-scenario prediction. Multisource remote sensing data were used to establish a three-dimensional evaluation index system for an adaptive cycle. Additionally, the “potential-connection-resilience” framework was adopted to explore the spatial and temporal variations in landscape ecological risk in the basin from 2001 to 2035 under different administrative scales and development scenarios. The results showed that from 2001 to 2020, the building and forest areas increased significantly, whereas grassland and plowland areas decreased significantly. Moreover, the spatial fragmentation of the watershed improved significantly with the transformation of large amounts of grassland into forests. The construction area continued to expand in 2035 under different scenarios. Under the economic development scenario, the grassland and plowland areas decreased considerably, but the forest area increased slowly. Under the ecological protection scenario, the expansion of land use was restrained, and the reduction rate of grassland and cultivated land was moderated. From 2001 to 2020, the overall ecological risk was at a medium-low level and showed a decreasing trend, and the fragmentation degree of the forest had a significant impact on ecological risk. By 2035, landscape ecological risks increased under different development scenarios, and construction land expansion had become the dominant factor affecting the risk level. By evaluating the distribution and development trend of ecologically high-risk areas in the Minjiang River Basin, the results of this study provide basic support for the rational planning of land resources in the basin and decision making for future sustainable development efforts. landscape ecological risk Google Earth Engine random forest FLUS multi-scenario simulation adaptive cycle Science Q Ruifan Wang verfasserin aut Linghan Song verfasserin aut Xiaojie Liu verfasserin aut Shuangwen Zhong verfasserin aut Jian Liu verfasserin aut Kunyong Yu verfasserin aut Fan Wang verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 21, p 5540 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:21, p 5540 https://doi.org/10.3390/rs14215540 kostenfrei https://doaj.org/article/d7802e6e31114fb9880e1845511db1e3 kostenfrei https://www.mdpi.com/2072-4292/14/21/5540 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 21, p 5540
allfields_unstemmed	10.3390/rs14215540 doi (DE-627)DOAJ086447300 (DE-599)DOAJd7802e6e31114fb9880e1845511db1e3 DE-627 ger DE-627 rakwb eng Tiantian Bao verfasserin aut Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Landscape ecological security is an environmental requirement for social and economic development. Understanding the dynamic mechanisms of landscape change and the associated ecological risks in regional socioecological systems is necessary for promoting regional sustainable development. Using the Minjiang River Basin as the research area, the Google Earth Engine platform, random forest (RF) model, and FLUS model were employed for land use classification and future multi-scenario prediction. Multisource remote sensing data were used to establish a three-dimensional evaluation index system for an adaptive cycle. Additionally, the “potential-connection-resilience” framework was adopted to explore the spatial and temporal variations in landscape ecological risk in the basin from 2001 to 2035 under different administrative scales and development scenarios. The results showed that from 2001 to 2020, the building and forest areas increased significantly, whereas grassland and plowland areas decreased significantly. Moreover, the spatial fragmentation of the watershed improved significantly with the transformation of large amounts of grassland into forests. The construction area continued to expand in 2035 under different scenarios. Under the economic development scenario, the grassland and plowland areas decreased considerably, but the forest area increased slowly. Under the ecological protection scenario, the expansion of land use was restrained, and the reduction rate of grassland and cultivated land was moderated. From 2001 to 2020, the overall ecological risk was at a medium-low level and showed a decreasing trend, and the fragmentation degree of the forest had a significant impact on ecological risk. By 2035, landscape ecological risks increased under different development scenarios, and construction land expansion had become the dominant factor affecting the risk level. By evaluating the distribution and development trend of ecologically high-risk areas in the Minjiang River Basin, the results of this study provide basic support for the rational planning of land resources in the basin and decision making for future sustainable development efforts. landscape ecological risk Google Earth Engine random forest FLUS multi-scenario simulation adaptive cycle Science Q Ruifan Wang verfasserin aut Linghan Song verfasserin aut Xiaojie Liu verfasserin aut Shuangwen Zhong verfasserin aut Jian Liu verfasserin aut Kunyong Yu verfasserin aut Fan Wang verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 21, p 5540 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:21, p 5540 https://doi.org/10.3390/rs14215540 kostenfrei https://doaj.org/article/d7802e6e31114fb9880e1845511db1e3 kostenfrei https://www.mdpi.com/2072-4292/14/21/5540 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 21, p 5540
allfieldsGer	10.3390/rs14215540 doi (DE-627)DOAJ086447300 (DE-599)DOAJd7802e6e31114fb9880e1845511db1e3 DE-627 ger DE-627 rakwb eng Tiantian Bao verfasserin aut Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Landscape ecological security is an environmental requirement for social and economic development. Understanding the dynamic mechanisms of landscape change and the associated ecological risks in regional socioecological systems is necessary for promoting regional sustainable development. Using the Minjiang River Basin as the research area, the Google Earth Engine platform, random forest (RF) model, and FLUS model were employed for land use classification and future multi-scenario prediction. Multisource remote sensing data were used to establish a three-dimensional evaluation index system for an adaptive cycle. Additionally, the “potential-connection-resilience” framework was adopted to explore the spatial and temporal variations in landscape ecological risk in the basin from 2001 to 2035 under different administrative scales and development scenarios. The results showed that from 2001 to 2020, the building and forest areas increased significantly, whereas grassland and plowland areas decreased significantly. Moreover, the spatial fragmentation of the watershed improved significantly with the transformation of large amounts of grassland into forests. The construction area continued to expand in 2035 under different scenarios. Under the economic development scenario, the grassland and plowland areas decreased considerably, but the forest area increased slowly. Under the ecological protection scenario, the expansion of land use was restrained, and the reduction rate of grassland and cultivated land was moderated. From 2001 to 2020, the overall ecological risk was at a medium-low level and showed a decreasing trend, and the fragmentation degree of the forest had a significant impact on ecological risk. By 2035, landscape ecological risks increased under different development scenarios, and construction land expansion had become the dominant factor affecting the risk level. By evaluating the distribution and development trend of ecologically high-risk areas in the Minjiang River Basin, the results of this study provide basic support for the rational planning of land resources in the basin and decision making for future sustainable development efforts. landscape ecological risk Google Earth Engine random forest FLUS multi-scenario simulation adaptive cycle Science Q Ruifan Wang verfasserin aut Linghan Song verfasserin aut Xiaojie Liu verfasserin aut Shuangwen Zhong verfasserin aut Jian Liu verfasserin aut Kunyong Yu verfasserin aut Fan Wang verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 21, p 5540 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:21, p 5540 https://doi.org/10.3390/rs14215540 kostenfrei https://doaj.org/article/d7802e6e31114fb9880e1845511db1e3 kostenfrei https://www.mdpi.com/2072-4292/14/21/5540 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 21, p 5540
allfieldsSound	10.3390/rs14215540 doi (DE-627)DOAJ086447300 (DE-599)DOAJd7802e6e31114fb9880e1845511db1e3 DE-627 ger DE-627 rakwb eng Tiantian Bao verfasserin aut Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Landscape ecological security is an environmental requirement for social and economic development. Understanding the dynamic mechanisms of landscape change and the associated ecological risks in regional socioecological systems is necessary for promoting regional sustainable development. Using the Minjiang River Basin as the research area, the Google Earth Engine platform, random forest (RF) model, and FLUS model were employed for land use classification and future multi-scenario prediction. Multisource remote sensing data were used to establish a three-dimensional evaluation index system for an adaptive cycle. Additionally, the “potential-connection-resilience” framework was adopted to explore the spatial and temporal variations in landscape ecological risk in the basin from 2001 to 2035 under different administrative scales and development scenarios. The results showed that from 2001 to 2020, the building and forest areas increased significantly, whereas grassland and plowland areas decreased significantly. Moreover, the spatial fragmentation of the watershed improved significantly with the transformation of large amounts of grassland into forests. The construction area continued to expand in 2035 under different scenarios. Under the economic development scenario, the grassland and plowland areas decreased considerably, but the forest area increased slowly. Under the ecological protection scenario, the expansion of land use was restrained, and the reduction rate of grassland and cultivated land was moderated. From 2001 to 2020, the overall ecological risk was at a medium-low level and showed a decreasing trend, and the fragmentation degree of the forest had a significant impact on ecological risk. By 2035, landscape ecological risks increased under different development scenarios, and construction land expansion had become the dominant factor affecting the risk level. By evaluating the distribution and development trend of ecologically high-risk areas in the Minjiang River Basin, the results of this study provide basic support for the rational planning of land resources in the basin and decision making for future sustainable development efforts. landscape ecological risk Google Earth Engine random forest FLUS multi-scenario simulation adaptive cycle Science Q Ruifan Wang verfasserin aut Linghan Song verfasserin aut Xiaojie Liu verfasserin aut Shuangwen Zhong verfasserin aut Jian Liu verfasserin aut Kunyong Yu verfasserin aut Fan Wang verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 21, p 5540 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:21, p 5540 https://doi.org/10.3390/rs14215540 kostenfrei https://doaj.org/article/d7802e6e31114fb9880e1845511db1e3 kostenfrei https://www.mdpi.com/2072-4292/14/21/5540 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 21, p 5540
language	English
source	In Remote Sensing 14(2022), 21, p 5540 volume:14 year:2022 number:21, p 5540
sourceStr	In Remote Sensing 14(2022), 21, p 5540 volume:14 year:2022 number:21, p 5540
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	landscape ecological risk Google Earth Engine random forest FLUS multi-scenario simulation adaptive cycle Science Q
isfreeaccess_bool	true
container_title	Remote Sensing
authorswithroles_txt_mv	Tiantian Bao @@aut@@ Ruifan Wang @@aut@@ Linghan Song @@aut@@ Xiaojie Liu @@aut@@ Shuangwen Zhong @@aut@@ Jian Liu @@aut@@ Kunyong Yu @@aut@@ Fan Wang @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	608937916
id	DOAJ086447300
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ086447300</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414171301.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230311s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/rs14215540</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ086447300</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJd7802e6e31114fb9880e1845511db1e3</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Tiantian Bao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Landscape ecological security is an environmental requirement for social and economic development. Understanding the dynamic mechanisms of landscape change and the associated ecological risks in regional socioecological systems is necessary for promoting regional sustainable development. Using the Minjiang River Basin as the research area, the Google Earth Engine platform, random forest (RF) model, and FLUS model were employed for land use classification and future multi-scenario prediction. Multisource remote sensing data were used to establish a three-dimensional evaluation index system for an adaptive cycle. Additionally, the “potential-connection-resilience” framework was adopted to explore the spatial and temporal variations in landscape ecological risk in the basin from 2001 to 2035 under different administrative scales and development scenarios. The results showed that from 2001 to 2020, the building and forest areas increased significantly, whereas grassland and plowland areas decreased significantly. Moreover, the spatial fragmentation of the watershed improved significantly with the transformation of large amounts of grassland into forests. The construction area continued to expand in 2035 under different scenarios. Under the economic development scenario, the grassland and plowland areas decreased considerably, but the forest area increased slowly. Under the ecological protection scenario, the expansion of land use was restrained, and the reduction rate of grassland and cultivated land was moderated. From 2001 to 2020, the overall ecological risk was at a medium-low level and showed a decreasing trend, and the fragmentation degree of the forest had a significant impact on ecological risk. By 2035, landscape ecological risks increased under different development scenarios, and construction land expansion had become the dominant factor affecting the risk level. By evaluating the distribution and development trend of ecologically high-risk areas in the Minjiang River Basin, the results of this study provide basic support for the rational planning of land resources in the basin and decision making for future sustainable development efforts.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">landscape ecological risk</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Google Earth Engine</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">random forest</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">FLUS</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-scenario simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">adaptive cycle</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Science</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Q</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ruifan Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Linghan Song</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiaojie Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shuangwen Zhong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jian Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kunyong Yu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Fan Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Remote Sensing</subfield><subfield code="d">MDPI AG, 2009</subfield><subfield code="g">14(2022), 21, p 5540</subfield><subfield code="w">(DE-627)608937916</subfield><subfield code="w">(DE-600)2513863-7</subfield><subfield code="x">20724292</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:21, p 5540</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/rs14215540</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/d7802e6e31114fb9880e1845511db1e3</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2072-4292/14/21/5540</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2072-4292</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4392</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">14</subfield><subfield code="j">2022</subfield><subfield code="e">21, p 5540</subfield></datafield></record></collection>
author	Tiantian Bao
spellingShingle	Tiantian Bao misc landscape ecological risk misc Google Earth Engine misc random forest misc FLUS misc multi-scenario simulation misc adaptive cycle misc Science misc Q Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle
authorStr	Tiantian Bao
ppnlink_with_tag_str_mv	@@773@@(DE-627)608937916
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut aut aut aut
collection	DOAJ
remote_str	true
illustrated	Not Illustrated
issn	20724292
topic_title	Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle landscape ecological risk Google Earth Engine random forest FLUS multi-scenario simulation adaptive cycle
topic	misc landscape ecological risk misc Google Earth Engine misc random forest misc FLUS misc multi-scenario simulation misc adaptive cycle misc Science misc Q
topic_unstemmed	misc landscape ecological risk misc Google Earth Engine misc random forest misc FLUS misc multi-scenario simulation misc adaptive cycle misc Science misc Q
topic_browse	misc landscape ecological risk misc Google Earth Engine misc random forest misc FLUS misc multi-scenario simulation misc adaptive cycle misc Science misc Q
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Remote Sensing
hierarchy_parent_id	608937916
hierarchy_top_title	Remote Sensing
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)608937916 (DE-600)2513863-7
title	Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle
ctrlnum	(DE-627)DOAJ086447300 (DE-599)DOAJd7802e6e31114fb9880e1845511db1e3
title_full	Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle
author_sort	Tiantian Bao
journal	Remote Sensing
journalStr	Remote Sensing
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2022
contenttype_str_mv	txt
author_browse	Tiantian Bao Ruifan Wang Linghan Song Xiaojie Liu Shuangwen Zhong Jian Liu Kunyong Yu Fan Wang
container_volume	14
format_se	Elektronische Aufsätze
author-letter	Tiantian Bao
doi_str_mv	10.3390/rs14215540
author2-role	verfasserin
title_sort	spatio-temporal multi-scale analysis of landscape ecological risk in minjiang river basin based on adaptive cycle
title_auth	Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle
abstract	Landscape ecological security is an environmental requirement for social and economic development. Understanding the dynamic mechanisms of landscape change and the associated ecological risks in regional socioecological systems is necessary for promoting regional sustainable development. Using the Minjiang River Basin as the research area, the Google Earth Engine platform, random forest (RF) model, and FLUS model were employed for land use classification and future multi-scenario prediction. Multisource remote sensing data were used to establish a three-dimensional evaluation index system for an adaptive cycle. Additionally, the “potential-connection-resilience” framework was adopted to explore the spatial and temporal variations in landscape ecological risk in the basin from 2001 to 2035 under different administrative scales and development scenarios. The results showed that from 2001 to 2020, the building and forest areas increased significantly, whereas grassland and plowland areas decreased significantly. Moreover, the spatial fragmentation of the watershed improved significantly with the transformation of large amounts of grassland into forests. The construction area continued to expand in 2035 under different scenarios. Under the economic development scenario, the grassland and plowland areas decreased considerably, but the forest area increased slowly. Under the ecological protection scenario, the expansion of land use was restrained, and the reduction rate of grassland and cultivated land was moderated. From 2001 to 2020, the overall ecological risk was at a medium-low level and showed a decreasing trend, and the fragmentation degree of the forest had a significant impact on ecological risk. By 2035, landscape ecological risks increased under different development scenarios, and construction land expansion had become the dominant factor affecting the risk level. By evaluating the distribution and development trend of ecologically high-risk areas in the Minjiang River Basin, the results of this study provide basic support for the rational planning of land resources in the basin and decision making for future sustainable development efforts.
abstractGer	Landscape ecological security is an environmental requirement for social and economic development. Understanding the dynamic mechanisms of landscape change and the associated ecological risks in regional socioecological systems is necessary for promoting regional sustainable development. Using the Minjiang River Basin as the research area, the Google Earth Engine platform, random forest (RF) model, and FLUS model were employed for land use classification and future multi-scenario prediction. Multisource remote sensing data were used to establish a three-dimensional evaluation index system for an adaptive cycle. Additionally, the “potential-connection-resilience” framework was adopted to explore the spatial and temporal variations in landscape ecological risk in the basin from 2001 to 2035 under different administrative scales and development scenarios. The results showed that from 2001 to 2020, the building and forest areas increased significantly, whereas grassland and plowland areas decreased significantly. Moreover, the spatial fragmentation of the watershed improved significantly with the transformation of large amounts of grassland into forests. The construction area continued to expand in 2035 under different scenarios. Under the economic development scenario, the grassland and plowland areas decreased considerably, but the forest area increased slowly. Under the ecological protection scenario, the expansion of land use was restrained, and the reduction rate of grassland and cultivated land was moderated. From 2001 to 2020, the overall ecological risk was at a medium-low level and showed a decreasing trend, and the fragmentation degree of the forest had a significant impact on ecological risk. By 2035, landscape ecological risks increased under different development scenarios, and construction land expansion had become the dominant factor affecting the risk level. By evaluating the distribution and development trend of ecologically high-risk areas in the Minjiang River Basin, the results of this study provide basic support for the rational planning of land resources in the basin and decision making for future sustainable development efforts.
abstract_unstemmed	Landscape ecological security is an environmental requirement for social and economic development. Understanding the dynamic mechanisms of landscape change and the associated ecological risks in regional socioecological systems is necessary for promoting regional sustainable development. Using the Minjiang River Basin as the research area, the Google Earth Engine platform, random forest (RF) model, and FLUS model were employed for land use classification and future multi-scenario prediction. Multisource remote sensing data were used to establish a three-dimensional evaluation index system for an adaptive cycle. Additionally, the “potential-connection-resilience” framework was adopted to explore the spatial and temporal variations in landscape ecological risk in the basin from 2001 to 2035 under different administrative scales and development scenarios. The results showed that from 2001 to 2020, the building and forest areas increased significantly, whereas grassland and plowland areas decreased significantly. Moreover, the spatial fragmentation of the watershed improved significantly with the transformation of large amounts of grassland into forests. The construction area continued to expand in 2035 under different scenarios. Under the economic development scenario, the grassland and plowland areas decreased considerably, but the forest area increased slowly. Under the ecological protection scenario, the expansion of land use was restrained, and the reduction rate of grassland and cultivated land was moderated. From 2001 to 2020, the overall ecological risk was at a medium-low level and showed a decreasing trend, and the fragmentation degree of the forest had a significant impact on ecological risk. By 2035, landscape ecological risks increased under different development scenarios, and construction land expansion had become the dominant factor affecting the risk level. By evaluating the distribution and development trend of ecologically high-risk areas in the Minjiang River Basin, the results of this study provide basic support for the rational planning of land resources in the basin and decision making for future sustainable development efforts.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700
container_issue	21, p 5540
title_short	Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle
url	https://doi.org/10.3390/rs14215540 https://doaj.org/article/d7802e6e31114fb9880e1845511db1e3 https://www.mdpi.com/2072-4292/14/21/5540 https://doaj.org/toc/2072-4292
remote_bool	true
author2	Ruifan Wang Linghan Song Xiaojie Liu Shuangwen Zhong Jian Liu Kunyong Yu Fan Wang
author2Str	Ruifan Wang Linghan Song Xiaojie Liu Shuangwen Zhong Jian Liu Kunyong Yu Fan Wang
ppnlink	608937916
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3390/rs14215540
up_date	2024-07-03T20:44:22.534Z
_version_	1803592099468148736
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ086447300</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414171301.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230311s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/rs14215540</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ086447300</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJd7802e6e31114fb9880e1845511db1e3</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Tiantian Bao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Landscape ecological security is an environmental requirement for social and economic development. Understanding the dynamic mechanisms of landscape change and the associated ecological risks in regional socioecological systems is necessary for promoting regional sustainable development. Using the Minjiang River Basin as the research area, the Google Earth Engine platform, random forest (RF) model, and FLUS model were employed for land use classification and future multi-scenario prediction. Multisource remote sensing data were used to establish a three-dimensional evaluation index system for an adaptive cycle. Additionally, the “potential-connection-resilience” framework was adopted to explore the spatial and temporal variations in landscape ecological risk in the basin from 2001 to 2035 under different administrative scales and development scenarios. The results showed that from 2001 to 2020, the building and forest areas increased significantly, whereas grassland and plowland areas decreased significantly. Moreover, the spatial fragmentation of the watershed improved significantly with the transformation of large amounts of grassland into forests. The construction area continued to expand in 2035 under different scenarios. Under the economic development scenario, the grassland and plowland areas decreased considerably, but the forest area increased slowly. Under the ecological protection scenario, the expansion of land use was restrained, and the reduction rate of grassland and cultivated land was moderated. From 2001 to 2020, the overall ecological risk was at a medium-low level and showed a decreasing trend, and the fragmentation degree of the forest had a significant impact on ecological risk. By 2035, landscape ecological risks increased under different development scenarios, and construction land expansion had become the dominant factor affecting the risk level. By evaluating the distribution and development trend of ecologically high-risk areas in the Minjiang River Basin, the results of this study provide basic support for the rational planning of land resources in the basin and decision making for future sustainable development efforts.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">landscape ecological risk</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Google Earth Engine</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">random forest</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">FLUS</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-scenario simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">adaptive cycle</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Science</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Q</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ruifan Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Linghan Song</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiaojie Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shuangwen Zhong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jian Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kunyong Yu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Fan Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Remote Sensing</subfield><subfield code="d">MDPI AG, 2009</subfield><subfield code="g">14(2022), 21, p 5540</subfield><subfield code="w">(DE-627)608937916</subfield><subfield code="w">(DE-600)2513863-7</subfield><subfield code="x">20724292</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:21, p 5540</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/rs14215540</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/d7802e6e31114fb9880e1845511db1e3</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2072-4292/14/21/5540</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2072-4292</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4392</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">14</subfield><subfield code="j">2022</subfield><subfield code="e">21, p 5540</subfield></datafield></record></collection>
score	7.400692

Nicht das Richtige dabei?

Schreiben Sie uns!

Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?