Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region

A full understanding of the patterns, trends, and strategies for long-term ecosystem changes helps decision-makers evaluate the effectiveness of ecological restoration projects. This study identified the ecological restoration approaches on planted forest, natural forest, and natural grassland protection during 2000–2022 based on a developed object-oriented continuous change detection and classification (OO-CCDC) method. Taking the Loess hilly region in the southern Ningxia Hui Autonomous Region, China as a case study, we assessed the ecological effects after protecting forest or grassland automatically and continuously by highlighting the location and change time of positive or negative effects. The results showed that the accuracy of ecological restoration approaches extraction was 90.73%, and the accuracies of the ecological restoration effects were 86.1% in time and 84.4% in space. A detailed evaluation from 2000 to 2022 demonstrated that positive effects peaked in 2013 (1262.69 km<sup<2</sup<), while the highest negative effects were observed in 2017 (54.54 km<sup<2</sup<). In total, 94.39% of the planted forests, 99.56% of the natural forest protection, and 62.36% of the grassland protection were in a stable pattern, and 35.37% of the natural grassland displayed positive effects, indicating a proactive role for forest management and ecological restoration in an ecologically fragile region. The negative effects accounted for a small proportion, only 2.41% of the planted forests concentrated in Pengyang County and 2.62% of the natural grassland protection mainly distributed around the farmland in the central-eastern part of the study area. By highlighting regions with positive effects as acceptable references and regions with negative effects as essential conservation objects, this study provides valuable insights for evaluating the effectiveness of the integrated ecological restoration pattern and determining the configuration of ecological restoration measures. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Caiyong Wei [verfasserIn] Xiaojing Xue [verfasserIn] Lingwen Tian [verfasserIn] Qin Yang [verfasserIn] Bowen Hou [verfasserIn] Wenlong Wang [verfasserIn] Dawei Ma [verfasserIn] Yuanyuan Meng [verfasserIn] Xiangnan Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	restoration approaches OO-CCDC algorithm segmentation ecologically fragile areas

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 15(2023), 16, p 4023
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:16, p 4023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs15164023

Katalog-ID:	DOAJ093557701

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allfields	10.3390/rs15164023 doi (DE-627)DOAJ093557701 (DE-599)DOAJ62f8f8f921e54aeca730ef2293733753 DE-627 ger DE-627 rakwb eng Caiyong Wei verfasserin aut Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A full understanding of the patterns, trends, and strategies for long-term ecosystem changes helps decision-makers evaluate the effectiveness of ecological restoration projects. This study identified the ecological restoration approaches on planted forest, natural forest, and natural grassland protection during 2000–2022 based on a developed object-oriented continuous change detection and classification (OO-CCDC) method. Taking the Loess hilly region in the southern Ningxia Hui Autonomous Region, China as a case study, we assessed the ecological effects after protecting forest or grassland automatically and continuously by highlighting the location and change time of positive or negative effects. The results showed that the accuracy of ecological restoration approaches extraction was 90.73%, and the accuracies of the ecological restoration effects were 86.1% in time and 84.4% in space. A detailed evaluation from 2000 to 2022 demonstrated that positive effects peaked in 2013 (1262.69 km<sup<2</sup<), while the highest negative effects were observed in 2017 (54.54 km<sup<2</sup<). In total, 94.39% of the planted forests, 99.56% of the natural forest protection, and 62.36% of the grassland protection were in a stable pattern, and 35.37% of the natural grassland displayed positive effects, indicating a proactive role for forest management and ecological restoration in an ecologically fragile region. The negative effects accounted for a small proportion, only 2.41% of the planted forests concentrated in Pengyang County and 2.62% of the natural grassland protection mainly distributed around the farmland in the central-eastern part of the study area. By highlighting regions with positive effects as acceptable references and regions with negative effects as essential conservation objects, this study provides valuable insights for evaluating the effectiveness of the integrated ecological restoration pattern and determining the configuration of ecological restoration measures. restoration approaches OO-CCDC algorithm segmentation ecologically fragile areas Science Q Xiaojing Xue verfasserin aut Lingwen Tian verfasserin aut Qin Yang verfasserin aut Bowen Hou verfasserin aut Wenlong Wang verfasserin aut Dawei Ma verfasserin aut Yuanyuan Meng verfasserin aut Xiangnan Liu verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 16, p 4023 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:16, p 4023 https://doi.org/10.3390/rs15164023 kostenfrei https://doaj.org/article/62f8f8f921e54aeca730ef2293733753 kostenfrei https://www.mdpi.com/2072-4292/15/16/4023 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 15 2023 16, p 4023
spelling	10.3390/rs15164023 doi (DE-627)DOAJ093557701 (DE-599)DOAJ62f8f8f921e54aeca730ef2293733753 DE-627 ger DE-627 rakwb eng Caiyong Wei verfasserin aut Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A full understanding of the patterns, trends, and strategies for long-term ecosystem changes helps decision-makers evaluate the effectiveness of ecological restoration projects. This study identified the ecological restoration approaches on planted forest, natural forest, and natural grassland protection during 2000–2022 based on a developed object-oriented continuous change detection and classification (OO-CCDC) method. Taking the Loess hilly region in the southern Ningxia Hui Autonomous Region, China as a case study, we assessed the ecological effects after protecting forest or grassland automatically and continuously by highlighting the location and change time of positive or negative effects. The results showed that the accuracy of ecological restoration approaches extraction was 90.73%, and the accuracies of the ecological restoration effects were 86.1% in time and 84.4% in space. A detailed evaluation from 2000 to 2022 demonstrated that positive effects peaked in 2013 (1262.69 km<sup<2</sup<), while the highest negative effects were observed in 2017 (54.54 km<sup<2</sup<). In total, 94.39% of the planted forests, 99.56% of the natural forest protection, and 62.36% of the grassland protection were in a stable pattern, and 35.37% of the natural grassland displayed positive effects, indicating a proactive role for forest management and ecological restoration in an ecologically fragile region. The negative effects accounted for a small proportion, only 2.41% of the planted forests concentrated in Pengyang County and 2.62% of the natural grassland protection mainly distributed around the farmland in the central-eastern part of the study area. By highlighting regions with positive effects as acceptable references and regions with negative effects as essential conservation objects, this study provides valuable insights for evaluating the effectiveness of the integrated ecological restoration pattern and determining the configuration of ecological restoration measures. restoration approaches OO-CCDC algorithm segmentation ecologically fragile areas Science Q Xiaojing Xue verfasserin aut Lingwen Tian verfasserin aut Qin Yang verfasserin aut Bowen Hou verfasserin aut Wenlong Wang verfasserin aut Dawei Ma verfasserin aut Yuanyuan Meng verfasserin aut Xiangnan Liu verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 16, p 4023 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:16, p 4023 https://doi.org/10.3390/rs15164023 kostenfrei https://doaj.org/article/62f8f8f921e54aeca730ef2293733753 kostenfrei https://www.mdpi.com/2072-4292/15/16/4023 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 15 2023 16, p 4023
allfields_unstemmed	10.3390/rs15164023 doi (DE-627)DOAJ093557701 (DE-599)DOAJ62f8f8f921e54aeca730ef2293733753 DE-627 ger DE-627 rakwb eng Caiyong Wei verfasserin aut Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A full understanding of the patterns, trends, and strategies for long-term ecosystem changes helps decision-makers evaluate the effectiveness of ecological restoration projects. This study identified the ecological restoration approaches on planted forest, natural forest, and natural grassland protection during 2000–2022 based on a developed object-oriented continuous change detection and classification (OO-CCDC) method. Taking the Loess hilly region in the southern Ningxia Hui Autonomous Region, China as a case study, we assessed the ecological effects after protecting forest or grassland automatically and continuously by highlighting the location and change time of positive or negative effects. The results showed that the accuracy of ecological restoration approaches extraction was 90.73%, and the accuracies of the ecological restoration effects were 86.1% in time and 84.4% in space. A detailed evaluation from 2000 to 2022 demonstrated that positive effects peaked in 2013 (1262.69 km<sup<2</sup<), while the highest negative effects were observed in 2017 (54.54 km<sup<2</sup<). In total, 94.39% of the planted forests, 99.56% of the natural forest protection, and 62.36% of the grassland protection were in a stable pattern, and 35.37% of the natural grassland displayed positive effects, indicating a proactive role for forest management and ecological restoration in an ecologically fragile region. The negative effects accounted for a small proportion, only 2.41% of the planted forests concentrated in Pengyang County and 2.62% of the natural grassland protection mainly distributed around the farmland in the central-eastern part of the study area. By highlighting regions with positive effects as acceptable references and regions with negative effects as essential conservation objects, this study provides valuable insights for evaluating the effectiveness of the integrated ecological restoration pattern and determining the configuration of ecological restoration measures. restoration approaches OO-CCDC algorithm segmentation ecologically fragile areas Science Q Xiaojing Xue verfasserin aut Lingwen Tian verfasserin aut Qin Yang verfasserin aut Bowen Hou verfasserin aut Wenlong Wang verfasserin aut Dawei Ma verfasserin aut Yuanyuan Meng verfasserin aut Xiangnan Liu verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 16, p 4023 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:16, p 4023 https://doi.org/10.3390/rs15164023 kostenfrei https://doaj.org/article/62f8f8f921e54aeca730ef2293733753 kostenfrei https://www.mdpi.com/2072-4292/15/16/4023 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 15 2023 16, p 4023
allfieldsGer	10.3390/rs15164023 doi (DE-627)DOAJ093557701 (DE-599)DOAJ62f8f8f921e54aeca730ef2293733753 DE-627 ger DE-627 rakwb eng Caiyong Wei verfasserin aut Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A full understanding of the patterns, trends, and strategies for long-term ecosystem changes helps decision-makers evaluate the effectiveness of ecological restoration projects. This study identified the ecological restoration approaches on planted forest, natural forest, and natural grassland protection during 2000–2022 based on a developed object-oriented continuous change detection and classification (OO-CCDC) method. Taking the Loess hilly region in the southern Ningxia Hui Autonomous Region, China as a case study, we assessed the ecological effects after protecting forest or grassland automatically and continuously by highlighting the location and change time of positive or negative effects. The results showed that the accuracy of ecological restoration approaches extraction was 90.73%, and the accuracies of the ecological restoration effects were 86.1% in time and 84.4% in space. A detailed evaluation from 2000 to 2022 demonstrated that positive effects peaked in 2013 (1262.69 km<sup<2</sup<), while the highest negative effects were observed in 2017 (54.54 km<sup<2</sup<). In total, 94.39% of the planted forests, 99.56% of the natural forest protection, and 62.36% of the grassland protection were in a stable pattern, and 35.37% of the natural grassland displayed positive effects, indicating a proactive role for forest management and ecological restoration in an ecologically fragile region. The negative effects accounted for a small proportion, only 2.41% of the planted forests concentrated in Pengyang County and 2.62% of the natural grassland protection mainly distributed around the farmland in the central-eastern part of the study area. By highlighting regions with positive effects as acceptable references and regions with negative effects as essential conservation objects, this study provides valuable insights for evaluating the effectiveness of the integrated ecological restoration pattern and determining the configuration of ecological restoration measures. restoration approaches OO-CCDC algorithm segmentation ecologically fragile areas Science Q Xiaojing Xue verfasserin aut Lingwen Tian verfasserin aut Qin Yang verfasserin aut Bowen Hou verfasserin aut Wenlong Wang verfasserin aut Dawei Ma verfasserin aut Yuanyuan Meng verfasserin aut Xiangnan Liu verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 16, p 4023 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:16, p 4023 https://doi.org/10.3390/rs15164023 kostenfrei https://doaj.org/article/62f8f8f921e54aeca730ef2293733753 kostenfrei https://www.mdpi.com/2072-4292/15/16/4023 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 15 2023 16, p 4023
allfieldsSound	10.3390/rs15164023 doi (DE-627)DOAJ093557701 (DE-599)DOAJ62f8f8f921e54aeca730ef2293733753 DE-627 ger DE-627 rakwb eng Caiyong Wei verfasserin aut Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A full understanding of the patterns, trends, and strategies for long-term ecosystem changes helps decision-makers evaluate the effectiveness of ecological restoration projects. This study identified the ecological restoration approaches on planted forest, natural forest, and natural grassland protection during 2000–2022 based on a developed object-oriented continuous change detection and classification (OO-CCDC) method. Taking the Loess hilly region in the southern Ningxia Hui Autonomous Region, China as a case study, we assessed the ecological effects after protecting forest or grassland automatically and continuously by highlighting the location and change time of positive or negative effects. The results showed that the accuracy of ecological restoration approaches extraction was 90.73%, and the accuracies of the ecological restoration effects were 86.1% in time and 84.4% in space. A detailed evaluation from 2000 to 2022 demonstrated that positive effects peaked in 2013 (1262.69 km<sup<2</sup<), while the highest negative effects were observed in 2017 (54.54 km<sup<2</sup<). In total, 94.39% of the planted forests, 99.56% of the natural forest protection, and 62.36% of the grassland protection were in a stable pattern, and 35.37% of the natural grassland displayed positive effects, indicating a proactive role for forest management and ecological restoration in an ecologically fragile region. The negative effects accounted for a small proportion, only 2.41% of the planted forests concentrated in Pengyang County and 2.62% of the natural grassland protection mainly distributed around the farmland in the central-eastern part of the study area. By highlighting regions with positive effects as acceptable references and regions with negative effects as essential conservation objects, this study provides valuable insights for evaluating the effectiveness of the integrated ecological restoration pattern and determining the configuration of ecological restoration measures. restoration approaches OO-CCDC algorithm segmentation ecologically fragile areas Science Q Xiaojing Xue verfasserin aut Lingwen Tian verfasserin aut Qin Yang verfasserin aut Bowen Hou verfasserin aut Wenlong Wang verfasserin aut Dawei Ma verfasserin aut Yuanyuan Meng verfasserin aut Xiangnan Liu verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 16, p 4023 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:16, p 4023 https://doi.org/10.3390/rs15164023 kostenfrei https://doaj.org/article/62f8f8f921e54aeca730ef2293733753 kostenfrei https://www.mdpi.com/2072-4292/15/16/4023 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 15 2023 16, p 4023
language	English
source	In Remote Sensing 15(2023), 16, p 4023 volume:15 year:2023 number:16, p 4023
sourceStr	In Remote Sensing 15(2023), 16, p 4023 volume:15 year:2023 number:16, p 4023
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	restoration approaches OO-CCDC algorithm segmentation ecologically fragile areas Science Q
isfreeaccess_bool	true
container_title	Remote Sensing
authorswithroles_txt_mv	Caiyong Wei @@aut@@ Xiaojing Xue @@aut@@ Lingwen Tian @@aut@@ Qin Yang @@aut@@ Bowen Hou @@aut@@ Wenlong Wang @@aut@@ Dawei Ma @@aut@@ Yuanyuan Meng @@aut@@ Xiangnan Liu @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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id	DOAJ093557701
language_de	englisch
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author	Caiyong Wei
spellingShingle	Caiyong Wei misc restoration approaches misc OO-CCDC algorithm misc segmentation misc ecologically fragile areas misc Science misc Q Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region
authorStr	Caiyong Wei
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topic_title	Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region restoration approaches OO-CCDC algorithm segmentation ecologically fragile areas
topic	misc restoration approaches misc OO-CCDC algorithm misc segmentation misc ecologically fragile areas misc Science misc Q
topic_unstemmed	misc restoration approaches misc OO-CCDC algorithm misc segmentation misc ecologically fragile areas misc Science misc Q
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title	Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region
ctrlnum	(DE-627)DOAJ093557701 (DE-599)DOAJ62f8f8f921e54aeca730ef2293733753
title_full	Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region
author_sort	Caiyong Wei
journal	Remote Sensing
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lang_code	eng
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author_browse	Caiyong Wei Xiaojing Xue Lingwen Tian Qin Yang Bowen Hou Wenlong Wang Dawei Ma Yuanyuan Meng Xiangnan Liu
container_volume	15
format_se	Elektronische Aufsätze
author-letter	Caiyong Wei
doi_str_mv	10.3390/rs15164023
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title_sort	identification of ecological restoration approaches and effects based on the oo-ccdc algorithm in an ecologically fragile region
title_auth	Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region
abstract	A full understanding of the patterns, trends, and strategies for long-term ecosystem changes helps decision-makers evaluate the effectiveness of ecological restoration projects. This study identified the ecological restoration approaches on planted forest, natural forest, and natural grassland protection during 2000–2022 based on a developed object-oriented continuous change detection and classification (OO-CCDC) method. Taking the Loess hilly region in the southern Ningxia Hui Autonomous Region, China as a case study, we assessed the ecological effects after protecting forest or grassland automatically and continuously by highlighting the location and change time of positive or negative effects. The results showed that the accuracy of ecological restoration approaches extraction was 90.73%, and the accuracies of the ecological restoration effects were 86.1% in time and 84.4% in space. A detailed evaluation from 2000 to 2022 demonstrated that positive effects peaked in 2013 (1262.69 km<sup<2</sup<), while the highest negative effects were observed in 2017 (54.54 km<sup<2</sup<). In total, 94.39% of the planted forests, 99.56% of the natural forest protection, and 62.36% of the grassland protection were in a stable pattern, and 35.37% of the natural grassland displayed positive effects, indicating a proactive role for forest management and ecological restoration in an ecologically fragile region. The negative effects accounted for a small proportion, only 2.41% of the planted forests concentrated in Pengyang County and 2.62% of the natural grassland protection mainly distributed around the farmland in the central-eastern part of the study area. By highlighting regions with positive effects as acceptable references and regions with negative effects as essential conservation objects, this study provides valuable insights for evaluating the effectiveness of the integrated ecological restoration pattern and determining the configuration of ecological restoration measures.
abstractGer	A full understanding of the patterns, trends, and strategies for long-term ecosystem changes helps decision-makers evaluate the effectiveness of ecological restoration projects. This study identified the ecological restoration approaches on planted forest, natural forest, and natural grassland protection during 2000–2022 based on a developed object-oriented continuous change detection and classification (OO-CCDC) method. Taking the Loess hilly region in the southern Ningxia Hui Autonomous Region, China as a case study, we assessed the ecological effects after protecting forest or grassland automatically and continuously by highlighting the location and change time of positive or negative effects. The results showed that the accuracy of ecological restoration approaches extraction was 90.73%, and the accuracies of the ecological restoration effects were 86.1% in time and 84.4% in space. A detailed evaluation from 2000 to 2022 demonstrated that positive effects peaked in 2013 (1262.69 km<sup<2</sup<), while the highest negative effects were observed in 2017 (54.54 km<sup<2</sup<). In total, 94.39% of the planted forests, 99.56% of the natural forest protection, and 62.36% of the grassland protection were in a stable pattern, and 35.37% of the natural grassland displayed positive effects, indicating a proactive role for forest management and ecological restoration in an ecologically fragile region. The negative effects accounted for a small proportion, only 2.41% of the planted forests concentrated in Pengyang County and 2.62% of the natural grassland protection mainly distributed around the farmland in the central-eastern part of the study area. By highlighting regions with positive effects as acceptable references and regions with negative effects as essential conservation objects, this study provides valuable insights for evaluating the effectiveness of the integrated ecological restoration pattern and determining the configuration of ecological restoration measures.
abstract_unstemmed	A full understanding of the patterns, trends, and strategies for long-term ecosystem changes helps decision-makers evaluate the effectiveness of ecological restoration projects. This study identified the ecological restoration approaches on planted forest, natural forest, and natural grassland protection during 2000–2022 based on a developed object-oriented continuous change detection and classification (OO-CCDC) method. Taking the Loess hilly region in the southern Ningxia Hui Autonomous Region, China as a case study, we assessed the ecological effects after protecting forest or grassland automatically and continuously by highlighting the location and change time of positive or negative effects. The results showed that the accuracy of ecological restoration approaches extraction was 90.73%, and the accuracies of the ecological restoration effects were 86.1% in time and 84.4% in space. A detailed evaluation from 2000 to 2022 demonstrated that positive effects peaked in 2013 (1262.69 km<sup<2</sup<), while the highest negative effects were observed in 2017 (54.54 km<sup<2</sup<). In total, 94.39% of the planted forests, 99.56% of the natural forest protection, and 62.36% of the grassland protection were in a stable pattern, and 35.37% of the natural grassland displayed positive effects, indicating a proactive role for forest management and ecological restoration in an ecologically fragile region. The negative effects accounted for a small proportion, only 2.41% of the planted forests concentrated in Pengyang County and 2.62% of the natural grassland protection mainly distributed around the farmland in the central-eastern part of the study area. By highlighting regions with positive effects as acceptable references and regions with negative effects as essential conservation objects, this study provides valuable insights for evaluating the effectiveness of the integrated ecological restoration pattern and determining the configuration of ecological restoration measures.
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container_issue	16, p 4023
title_short	Identification of Ecological Restoration Approaches and Effects Based on the OO-CCDC Algorithm in an Ecologically Fragile Region
url	https://doi.org/10.3390/rs15164023 https://doaj.org/article/62f8f8f921e54aeca730ef2293733753 https://www.mdpi.com/2072-4292/15/16/4023 https://doaj.org/toc/2072-4292
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