Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images
The Karst Plateau in eastern Yunnan, China (KPEYC) is densely populated. In this area, rocks are widely exposed, and soil erosion and rock desertification are serious issues. Furthermore, the conflict between humans and land is prominent. It is important to monitor abandoned croplands to ensure regi...
Ausführliche Beschreibung
Autor*in: |
Zhe Zhao [verfasserIn] Jiasheng Wang [verfasserIn] Limeng Wang [verfasserIn] Xun Rao [verfasserIn] Wenjing Ran [verfasserIn] Chunxiao Xu [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2023 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: Ecological Indicators - Elsevier, 2021, 146(2023), Seite 109828- |
---|---|
Übergeordnetes Werk: |
volume:146 ; year:2023 ; pages:109828- |
Links: |
---|
DOI / URN: |
10.1016/j.ecolind.2022.109828 |
---|
Katalog-ID: |
DOAJ020801599 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ020801599 | ||
003 | DE-627 | ||
005 | 20230502100155.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230226s2023 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.ecolind.2022.109828 |2 doi | |
035 | |a (DE-627)DOAJ020801599 | ||
035 | |a (DE-599)DOAJe1d67cf4576b4525851291c3427bb1c9 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a QH540-549.5 | |
100 | 0 | |a Zhe Zhao |e verfasserin |4 aut | |
245 | 1 | 0 | |a Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images |
264 | 1 | |c 2023 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a The Karst Plateau in eastern Yunnan, China (KPEYC) is densely populated. In this area, rocks are widely exposed, and soil erosion and rock desertification are serious issues. Furthermore, the conflict between humans and land is prominent. It is important to monitor abandoned croplands to ensure regional food security, preserve the ecological environment, and promote sustainable regional development. Regarding the lack of historical data and inaccurate identification of abandoned cropland, this study proposed a monitoring method for abandoned croplands based on Landsat time series remote sensing images. First, a decision tree was used to generate constant samples from 2000 to 2020. Thereafter, land use in KPEYC was classified year-by-year based on the random forest method and abandoned cropland pixels were identified based on land use data. Finally, the spatial and temporal characteristics of abandoned cropland in the KPEYC were analysed. Based on the results, the overall accuracy of the method was 0.75. Cropland abandonment in the eastern Yunnan Karst Plateau displayed a rapid growth trend and a slow decrease during the study period, with a significant growth rate from 2001 to 2010. The abandoned cropland was mainly concentrated in southwestern Zhaotong City, the entire territory of Qujing City, and northwestern Wenshan Prefecture, among which Fuyuan County, Luoping County, and Huize County repeatedly experienced more serious cropland abandonment. In addition to social factors, topography had a greater influence on cropland abandonment in the KPEYC, and the greater the slope or altitude, the more severe the cropland abandonment. | ||
650 | 4 | |a Landsat time series | |
650 | 4 | |a Cropland abandonment | |
650 | 4 | |a Remote sensing classification | |
650 | 4 | |a Karst Plateau | |
653 | 0 | |a Ecology | |
700 | 0 | |a Jiasheng Wang |e verfasserin |4 aut | |
700 | 0 | |a Limeng Wang |e verfasserin |4 aut | |
700 | 0 | |a Xun Rao |e verfasserin |4 aut | |
700 | 0 | |a Wenjing Ran |e verfasserin |4 aut | |
700 | 0 | |a Chunxiao Xu |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Ecological Indicators |d Elsevier, 2021 |g 146(2023), Seite 109828- |w (DE-627)338074163 |w (DE-600)2063587-4 |x 18727034 |7 nnns |
773 | 1 | 8 | |g volume:146 |g year:2023 |g pages:109828- |
856 | 4 | 0 | |u https://doi.org/10.1016/j.ecolind.2022.109828 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/e1d67cf4576b4525851291c3427bb1c9 |z kostenfrei |
856 | 4 | 0 | |u http://www.sciencedirect.com/science/article/pii/S1470160X22013012 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/1470-160X |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a SSG-OLC-PHA | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
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_74 | ||
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_213 | ||
912 | |a GBV_ILN_224 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2004 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2008 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2034 | ||
912 | |a GBV_ILN_2044 | ||
912 | |a GBV_ILN_2048 | ||
912 | |a GBV_ILN_2064 | ||
912 | |a GBV_ILN_2106 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_2112 | ||
912 | |a GBV_ILN_2122 | ||
912 | |a GBV_ILN_2143 | ||
912 | |a GBV_ILN_2152 | ||
912 | |a GBV_ILN_2153 | ||
912 | |a GBV_ILN_2232 | ||
912 | |a GBV_ILN_2336 | ||
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_4251 | ||
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_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 146 |j 2023 |h 109828- |
author_variant |
z z zz j w jw l w lw x r xr w r wr c x cx |
---|---|
matchkey_str |
article:18727034:2023----::oioignaayioaadndrpadnhkrtltaoesenunnhnb |
hierarchy_sort_str |
2023 |
callnumber-subject-code |
QH |
publishDate |
2023 |
allfields |
10.1016/j.ecolind.2022.109828 doi (DE-627)DOAJ020801599 (DE-599)DOAJe1d67cf4576b4525851291c3427bb1c9 DE-627 ger DE-627 rakwb eng QH540-549.5 Zhe Zhao verfasserin aut Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Karst Plateau in eastern Yunnan, China (KPEYC) is densely populated. In this area, rocks are widely exposed, and soil erosion and rock desertification are serious issues. Furthermore, the conflict between humans and land is prominent. It is important to monitor abandoned croplands to ensure regional food security, preserve the ecological environment, and promote sustainable regional development. Regarding the lack of historical data and inaccurate identification of abandoned cropland, this study proposed a monitoring method for abandoned croplands based on Landsat time series remote sensing images. First, a decision tree was used to generate constant samples from 2000 to 2020. Thereafter, land use in KPEYC was classified year-by-year based on the random forest method and abandoned cropland pixels were identified based on land use data. Finally, the spatial and temporal characteristics of abandoned cropland in the KPEYC were analysed. Based on the results, the overall accuracy of the method was 0.75. Cropland abandonment in the eastern Yunnan Karst Plateau displayed a rapid growth trend and a slow decrease during the study period, with a significant growth rate from 2001 to 2010. The abandoned cropland was mainly concentrated in southwestern Zhaotong City, the entire territory of Qujing City, and northwestern Wenshan Prefecture, among which Fuyuan County, Luoping County, and Huize County repeatedly experienced more serious cropland abandonment. In addition to social factors, topography had a greater influence on cropland abandonment in the KPEYC, and the greater the slope or altitude, the more severe the cropland abandonment. Landsat time series Cropland abandonment Remote sensing classification Karst Plateau Ecology Jiasheng Wang verfasserin aut Limeng Wang verfasserin aut Xun Rao verfasserin aut Wenjing Ran verfasserin aut Chunxiao Xu verfasserin aut In Ecological Indicators Elsevier, 2021 146(2023), Seite 109828- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:146 year:2023 pages:109828- https://doi.org/10.1016/j.ecolind.2022.109828 kostenfrei https://doaj.org/article/e1d67cf4576b4525851291c3427bb1c9 kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22013012 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 146 2023 109828- |
spelling |
10.1016/j.ecolind.2022.109828 doi (DE-627)DOAJ020801599 (DE-599)DOAJe1d67cf4576b4525851291c3427bb1c9 DE-627 ger DE-627 rakwb eng QH540-549.5 Zhe Zhao verfasserin aut Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Karst Plateau in eastern Yunnan, China (KPEYC) is densely populated. In this area, rocks are widely exposed, and soil erosion and rock desertification are serious issues. Furthermore, the conflict between humans and land is prominent. It is important to monitor abandoned croplands to ensure regional food security, preserve the ecological environment, and promote sustainable regional development. Regarding the lack of historical data and inaccurate identification of abandoned cropland, this study proposed a monitoring method for abandoned croplands based on Landsat time series remote sensing images. First, a decision tree was used to generate constant samples from 2000 to 2020. Thereafter, land use in KPEYC was classified year-by-year based on the random forest method and abandoned cropland pixels were identified based on land use data. Finally, the spatial and temporal characteristics of abandoned cropland in the KPEYC were analysed. Based on the results, the overall accuracy of the method was 0.75. Cropland abandonment in the eastern Yunnan Karst Plateau displayed a rapid growth trend and a slow decrease during the study period, with a significant growth rate from 2001 to 2010. The abandoned cropland was mainly concentrated in southwestern Zhaotong City, the entire territory of Qujing City, and northwestern Wenshan Prefecture, among which Fuyuan County, Luoping County, and Huize County repeatedly experienced more serious cropland abandonment. In addition to social factors, topography had a greater influence on cropland abandonment in the KPEYC, and the greater the slope or altitude, the more severe the cropland abandonment. Landsat time series Cropland abandonment Remote sensing classification Karst Plateau Ecology Jiasheng Wang verfasserin aut Limeng Wang verfasserin aut Xun Rao verfasserin aut Wenjing Ran verfasserin aut Chunxiao Xu verfasserin aut In Ecological Indicators Elsevier, 2021 146(2023), Seite 109828- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:146 year:2023 pages:109828- https://doi.org/10.1016/j.ecolind.2022.109828 kostenfrei https://doaj.org/article/e1d67cf4576b4525851291c3427bb1c9 kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22013012 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 146 2023 109828- |
allfields_unstemmed |
10.1016/j.ecolind.2022.109828 doi (DE-627)DOAJ020801599 (DE-599)DOAJe1d67cf4576b4525851291c3427bb1c9 DE-627 ger DE-627 rakwb eng QH540-549.5 Zhe Zhao verfasserin aut Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Karst Plateau in eastern Yunnan, China (KPEYC) is densely populated. In this area, rocks are widely exposed, and soil erosion and rock desertification are serious issues. Furthermore, the conflict between humans and land is prominent. It is important to monitor abandoned croplands to ensure regional food security, preserve the ecological environment, and promote sustainable regional development. Regarding the lack of historical data and inaccurate identification of abandoned cropland, this study proposed a monitoring method for abandoned croplands based on Landsat time series remote sensing images. First, a decision tree was used to generate constant samples from 2000 to 2020. Thereafter, land use in KPEYC was classified year-by-year based on the random forest method and abandoned cropland pixels were identified based on land use data. Finally, the spatial and temporal characteristics of abandoned cropland in the KPEYC were analysed. Based on the results, the overall accuracy of the method was 0.75. Cropland abandonment in the eastern Yunnan Karst Plateau displayed a rapid growth trend and a slow decrease during the study period, with a significant growth rate from 2001 to 2010. The abandoned cropland was mainly concentrated in southwestern Zhaotong City, the entire territory of Qujing City, and northwestern Wenshan Prefecture, among which Fuyuan County, Luoping County, and Huize County repeatedly experienced more serious cropland abandonment. In addition to social factors, topography had a greater influence on cropland abandonment in the KPEYC, and the greater the slope or altitude, the more severe the cropland abandonment. Landsat time series Cropland abandonment Remote sensing classification Karst Plateau Ecology Jiasheng Wang verfasserin aut Limeng Wang verfasserin aut Xun Rao verfasserin aut Wenjing Ran verfasserin aut Chunxiao Xu verfasserin aut In Ecological Indicators Elsevier, 2021 146(2023), Seite 109828- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:146 year:2023 pages:109828- https://doi.org/10.1016/j.ecolind.2022.109828 kostenfrei https://doaj.org/article/e1d67cf4576b4525851291c3427bb1c9 kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22013012 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 146 2023 109828- |
allfieldsGer |
10.1016/j.ecolind.2022.109828 doi (DE-627)DOAJ020801599 (DE-599)DOAJe1d67cf4576b4525851291c3427bb1c9 DE-627 ger DE-627 rakwb eng QH540-549.5 Zhe Zhao verfasserin aut Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Karst Plateau in eastern Yunnan, China (KPEYC) is densely populated. In this area, rocks are widely exposed, and soil erosion and rock desertification are serious issues. Furthermore, the conflict between humans and land is prominent. It is important to monitor abandoned croplands to ensure regional food security, preserve the ecological environment, and promote sustainable regional development. Regarding the lack of historical data and inaccurate identification of abandoned cropland, this study proposed a monitoring method for abandoned croplands based on Landsat time series remote sensing images. First, a decision tree was used to generate constant samples from 2000 to 2020. Thereafter, land use in KPEYC was classified year-by-year based on the random forest method and abandoned cropland pixels were identified based on land use data. Finally, the spatial and temporal characteristics of abandoned cropland in the KPEYC were analysed. Based on the results, the overall accuracy of the method was 0.75. Cropland abandonment in the eastern Yunnan Karst Plateau displayed a rapid growth trend and a slow decrease during the study period, with a significant growth rate from 2001 to 2010. The abandoned cropland was mainly concentrated in southwestern Zhaotong City, the entire territory of Qujing City, and northwestern Wenshan Prefecture, among which Fuyuan County, Luoping County, and Huize County repeatedly experienced more serious cropland abandonment. In addition to social factors, topography had a greater influence on cropland abandonment in the KPEYC, and the greater the slope or altitude, the more severe the cropland abandonment. Landsat time series Cropland abandonment Remote sensing classification Karst Plateau Ecology Jiasheng Wang verfasserin aut Limeng Wang verfasserin aut Xun Rao verfasserin aut Wenjing Ran verfasserin aut Chunxiao Xu verfasserin aut In Ecological Indicators Elsevier, 2021 146(2023), Seite 109828- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:146 year:2023 pages:109828- https://doi.org/10.1016/j.ecolind.2022.109828 kostenfrei https://doaj.org/article/e1d67cf4576b4525851291c3427bb1c9 kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22013012 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 146 2023 109828- |
allfieldsSound |
10.1016/j.ecolind.2022.109828 doi (DE-627)DOAJ020801599 (DE-599)DOAJe1d67cf4576b4525851291c3427bb1c9 DE-627 ger DE-627 rakwb eng QH540-549.5 Zhe Zhao verfasserin aut Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Karst Plateau in eastern Yunnan, China (KPEYC) is densely populated. In this area, rocks are widely exposed, and soil erosion and rock desertification are serious issues. Furthermore, the conflict between humans and land is prominent. It is important to monitor abandoned croplands to ensure regional food security, preserve the ecological environment, and promote sustainable regional development. Regarding the lack of historical data and inaccurate identification of abandoned cropland, this study proposed a monitoring method for abandoned croplands based on Landsat time series remote sensing images. First, a decision tree was used to generate constant samples from 2000 to 2020. Thereafter, land use in KPEYC was classified year-by-year based on the random forest method and abandoned cropland pixels were identified based on land use data. Finally, the spatial and temporal characteristics of abandoned cropland in the KPEYC were analysed. Based on the results, the overall accuracy of the method was 0.75. Cropland abandonment in the eastern Yunnan Karst Plateau displayed a rapid growth trend and a slow decrease during the study period, with a significant growth rate from 2001 to 2010. The abandoned cropland was mainly concentrated in southwestern Zhaotong City, the entire territory of Qujing City, and northwestern Wenshan Prefecture, among which Fuyuan County, Luoping County, and Huize County repeatedly experienced more serious cropland abandonment. In addition to social factors, topography had a greater influence on cropland abandonment in the KPEYC, and the greater the slope or altitude, the more severe the cropland abandonment. Landsat time series Cropland abandonment Remote sensing classification Karst Plateau Ecology Jiasheng Wang verfasserin aut Limeng Wang verfasserin aut Xun Rao verfasserin aut Wenjing Ran verfasserin aut Chunxiao Xu verfasserin aut In Ecological Indicators Elsevier, 2021 146(2023), Seite 109828- (DE-627)338074163 (DE-600)2063587-4 18727034 nnns volume:146 year:2023 pages:109828- https://doi.org/10.1016/j.ecolind.2022.109828 kostenfrei https://doaj.org/article/e1d67cf4576b4525851291c3427bb1c9 kostenfrei http://www.sciencedirect.com/science/article/pii/S1470160X22013012 kostenfrei https://doaj.org/toc/1470-160X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 146 2023 109828- |
language |
English |
source |
In Ecological Indicators 146(2023), Seite 109828- volume:146 year:2023 pages:109828- |
sourceStr |
In Ecological Indicators 146(2023), Seite 109828- volume:146 year:2023 pages:109828- |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Landsat time series Cropland abandonment Remote sensing classification Karst Plateau Ecology |
isfreeaccess_bool |
true |
container_title |
Ecological Indicators |
authorswithroles_txt_mv |
Zhe Zhao @@aut@@ Jiasheng Wang @@aut@@ Limeng Wang @@aut@@ Xun Rao @@aut@@ Wenjing Ran @@aut@@ Chunxiao Xu @@aut@@ |
publishDateDaySort_date |
2023-01-01T00:00:00Z |
hierarchy_top_id |
338074163 |
id |
DOAJ020801599 |
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">DOAJ020801599</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502100155.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ecolind.2022.109828</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ020801599</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJe1d67cf4576b4525851291c3427bb1c9</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="050" ind1=" " ind2="0"><subfield code="a">QH540-549.5</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Zhe Zhao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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">The Karst Plateau in eastern Yunnan, China (KPEYC) is densely populated. In this area, rocks are widely exposed, and soil erosion and rock desertification are serious issues. Furthermore, the conflict between humans and land is prominent. It is important to monitor abandoned croplands to ensure regional food security, preserve the ecological environment, and promote sustainable regional development. Regarding the lack of historical data and inaccurate identification of abandoned cropland, this study proposed a monitoring method for abandoned croplands based on Landsat time series remote sensing images. First, a decision tree was used to generate constant samples from 2000 to 2020. Thereafter, land use in KPEYC was classified year-by-year based on the random forest method and abandoned cropland pixels were identified based on land use data. Finally, the spatial and temporal characteristics of abandoned cropland in the KPEYC were analysed. Based on the results, the overall accuracy of the method was 0.75. Cropland abandonment in the eastern Yunnan Karst Plateau displayed a rapid growth trend and a slow decrease during the study period, with a significant growth rate from 2001 to 2010. The abandoned cropland was mainly concentrated in southwestern Zhaotong City, the entire territory of Qujing City, and northwestern Wenshan Prefecture, among which Fuyuan County, Luoping County, and Huize County repeatedly experienced more serious cropland abandonment. In addition to social factors, topography had a greater influence on cropland abandonment in the KPEYC, and the greater the slope or altitude, the more severe the cropland abandonment.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Landsat time series</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cropland abandonment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Remote sensing classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Karst Plateau</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Ecology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jiasheng Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Limeng Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xun Rao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wenjing Ran</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Chunxiao Xu</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">Ecological Indicators</subfield><subfield code="d">Elsevier, 2021</subfield><subfield code="g">146(2023), Seite 109828-</subfield><subfield code="w">(DE-627)338074163</subfield><subfield code="w">(DE-600)2063587-4</subfield><subfield code="x">18727034</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:146</subfield><subfield code="g">year:2023</subfield><subfield code="g">pages:109828-</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.ecolind.2022.109828</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/e1d67cf4576b4525851291c3427bb1c9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.sciencedirect.com/science/article/pii/S1470160X22013012</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1470-160X</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">SSG-OLC-PHA</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_31</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_74</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_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</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_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</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_2008</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_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</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_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</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_4251</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_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_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">146</subfield><subfield code="j">2023</subfield><subfield code="h">109828-</subfield></datafield></record></collection>
|
callnumber-first |
Q - Science |
author |
Zhe Zhao |
spellingShingle |
Zhe Zhao misc QH540-549.5 misc Landsat time series misc Cropland abandonment misc Remote sensing classification misc Karst Plateau misc Ecology Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images |
authorStr |
Zhe Zhao |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)338074163 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
QH540-549 |
illustrated |
Not Illustrated |
issn |
18727034 |
topic_title |
QH540-549.5 Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images Landsat time series Cropland abandonment Remote sensing classification Karst Plateau |
topic |
misc QH540-549.5 misc Landsat time series misc Cropland abandonment misc Remote sensing classification misc Karst Plateau misc Ecology |
topic_unstemmed |
misc QH540-549.5 misc Landsat time series misc Cropland abandonment misc Remote sensing classification misc Karst Plateau misc Ecology |
topic_browse |
misc QH540-549.5 misc Landsat time series misc Cropland abandonment misc Remote sensing classification misc Karst Plateau misc Ecology |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Ecological Indicators |
hierarchy_parent_id |
338074163 |
hierarchy_top_title |
Ecological Indicators |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)338074163 (DE-600)2063587-4 |
title |
Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images |
ctrlnum |
(DE-627)DOAJ020801599 (DE-599)DOAJe1d67cf4576b4525851291c3427bb1c9 |
title_full |
Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images |
author_sort |
Zhe Zhao |
journal |
Ecological Indicators |
journalStr |
Ecological Indicators |
callnumber-first-code |
Q |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2023 |
contenttype_str_mv |
txt |
container_start_page |
109828 |
author_browse |
Zhe Zhao Jiasheng Wang Limeng Wang Xun Rao Wenjing Ran Chunxiao Xu |
container_volume |
146 |
class |
QH540-549.5 |
format_se |
Elektronische Aufsätze |
author-letter |
Zhe Zhao |
doi_str_mv |
10.1016/j.ecolind.2022.109828 |
author2-role |
verfasserin |
title_sort |
monitoring and analysis of abandoned cropland in the karst plateau of eastern yunnan, china based on landsat time series images |
callnumber |
QH540-549.5 |
title_auth |
Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images |
abstract |
The Karst Plateau in eastern Yunnan, China (KPEYC) is densely populated. In this area, rocks are widely exposed, and soil erosion and rock desertification are serious issues. Furthermore, the conflict between humans and land is prominent. It is important to monitor abandoned croplands to ensure regional food security, preserve the ecological environment, and promote sustainable regional development. Regarding the lack of historical data and inaccurate identification of abandoned cropland, this study proposed a monitoring method for abandoned croplands based on Landsat time series remote sensing images. First, a decision tree was used to generate constant samples from 2000 to 2020. Thereafter, land use in KPEYC was classified year-by-year based on the random forest method and abandoned cropland pixels were identified based on land use data. Finally, the spatial and temporal characteristics of abandoned cropland in the KPEYC were analysed. Based on the results, the overall accuracy of the method was 0.75. Cropland abandonment in the eastern Yunnan Karst Plateau displayed a rapid growth trend and a slow decrease during the study period, with a significant growth rate from 2001 to 2010. The abandoned cropland was mainly concentrated in southwestern Zhaotong City, the entire territory of Qujing City, and northwestern Wenshan Prefecture, among which Fuyuan County, Luoping County, and Huize County repeatedly experienced more serious cropland abandonment. In addition to social factors, topography had a greater influence on cropland abandonment in the KPEYC, and the greater the slope or altitude, the more severe the cropland abandonment. |
abstractGer |
The Karst Plateau in eastern Yunnan, China (KPEYC) is densely populated. In this area, rocks are widely exposed, and soil erosion and rock desertification are serious issues. Furthermore, the conflict between humans and land is prominent. It is important to monitor abandoned croplands to ensure regional food security, preserve the ecological environment, and promote sustainable regional development. Regarding the lack of historical data and inaccurate identification of abandoned cropland, this study proposed a monitoring method for abandoned croplands based on Landsat time series remote sensing images. First, a decision tree was used to generate constant samples from 2000 to 2020. Thereafter, land use in KPEYC was classified year-by-year based on the random forest method and abandoned cropland pixels were identified based on land use data. Finally, the spatial and temporal characteristics of abandoned cropland in the KPEYC were analysed. Based on the results, the overall accuracy of the method was 0.75. Cropland abandonment in the eastern Yunnan Karst Plateau displayed a rapid growth trend and a slow decrease during the study period, with a significant growth rate from 2001 to 2010. The abandoned cropland was mainly concentrated in southwestern Zhaotong City, the entire territory of Qujing City, and northwestern Wenshan Prefecture, among which Fuyuan County, Luoping County, and Huize County repeatedly experienced more serious cropland abandonment. In addition to social factors, topography had a greater influence on cropland abandonment in the KPEYC, and the greater the slope or altitude, the more severe the cropland abandonment. |
abstract_unstemmed |
The Karst Plateau in eastern Yunnan, China (KPEYC) is densely populated. In this area, rocks are widely exposed, and soil erosion and rock desertification are serious issues. Furthermore, the conflict between humans and land is prominent. It is important to monitor abandoned croplands to ensure regional food security, preserve the ecological environment, and promote sustainable regional development. Regarding the lack of historical data and inaccurate identification of abandoned cropland, this study proposed a monitoring method for abandoned croplands based on Landsat time series remote sensing images. First, a decision tree was used to generate constant samples from 2000 to 2020. Thereafter, land use in KPEYC was classified year-by-year based on the random forest method and abandoned cropland pixels were identified based on land use data. Finally, the spatial and temporal characteristics of abandoned cropland in the KPEYC were analysed. Based on the results, the overall accuracy of the method was 0.75. Cropland abandonment in the eastern Yunnan Karst Plateau displayed a rapid growth trend and a slow decrease during the study period, with a significant growth rate from 2001 to 2010. The abandoned cropland was mainly concentrated in southwestern Zhaotong City, the entire territory of Qujing City, and northwestern Wenshan Prefecture, among which Fuyuan County, Luoping County, and Huize County repeatedly experienced more serious cropland abandonment. In addition to social factors, topography had a greater influence on cropland abandonment in the KPEYC, and the greater the slope or altitude, the more severe the cropland abandonment. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
title_short |
Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images |
url |
https://doi.org/10.1016/j.ecolind.2022.109828 https://doaj.org/article/e1d67cf4576b4525851291c3427bb1c9 http://www.sciencedirect.com/science/article/pii/S1470160X22013012 https://doaj.org/toc/1470-160X |
remote_bool |
true |
author2 |
Jiasheng Wang Limeng Wang Xun Rao Wenjing Ran Chunxiao Xu |
author2Str |
Jiasheng Wang Limeng Wang Xun Rao Wenjing Ran Chunxiao Xu |
ppnlink |
338074163 |
callnumber-subject |
QH - Natural History and Biology |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1016/j.ecolind.2022.109828 |
callnumber-a |
QH540-549.5 |
up_date |
2024-07-03T17:05:13.353Z |
_version_ |
1803578311552532480 |
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">DOAJ020801599</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502100155.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ecolind.2022.109828</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ020801599</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJe1d67cf4576b4525851291c3427bb1c9</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="050" ind1=" " ind2="0"><subfield code="a">QH540-549.5</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Zhe Zhao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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">The Karst Plateau in eastern Yunnan, China (KPEYC) is densely populated. In this area, rocks are widely exposed, and soil erosion and rock desertification are serious issues. Furthermore, the conflict between humans and land is prominent. It is important to monitor abandoned croplands to ensure regional food security, preserve the ecological environment, and promote sustainable regional development. Regarding the lack of historical data and inaccurate identification of abandoned cropland, this study proposed a monitoring method for abandoned croplands based on Landsat time series remote sensing images. First, a decision tree was used to generate constant samples from 2000 to 2020. Thereafter, land use in KPEYC was classified year-by-year based on the random forest method and abandoned cropland pixels were identified based on land use data. Finally, the spatial and temporal characteristics of abandoned cropland in the KPEYC were analysed. Based on the results, the overall accuracy of the method was 0.75. Cropland abandonment in the eastern Yunnan Karst Plateau displayed a rapid growth trend and a slow decrease during the study period, with a significant growth rate from 2001 to 2010. The abandoned cropland was mainly concentrated in southwestern Zhaotong City, the entire territory of Qujing City, and northwestern Wenshan Prefecture, among which Fuyuan County, Luoping County, and Huize County repeatedly experienced more serious cropland abandonment. In addition to social factors, topography had a greater influence on cropland abandonment in the KPEYC, and the greater the slope or altitude, the more severe the cropland abandonment.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Landsat time series</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cropland abandonment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Remote sensing classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Karst Plateau</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Ecology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jiasheng Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Limeng Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xun Rao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wenjing Ran</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Chunxiao Xu</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">Ecological Indicators</subfield><subfield code="d">Elsevier, 2021</subfield><subfield code="g">146(2023), Seite 109828-</subfield><subfield code="w">(DE-627)338074163</subfield><subfield code="w">(DE-600)2063587-4</subfield><subfield code="x">18727034</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:146</subfield><subfield code="g">year:2023</subfield><subfield code="g">pages:109828-</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.ecolind.2022.109828</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/e1d67cf4576b4525851291c3427bb1c9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.sciencedirect.com/science/article/pii/S1470160X22013012</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1470-160X</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">SSG-OLC-PHA</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_31</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_74</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_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</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_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</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_2008</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_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</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_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</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_4251</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_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_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">146</subfield><subfield code="j">2023</subfield><subfield code="h">109828-</subfield></datafield></record></collection>
|
score |
7.4004526 |