Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China

Exploring the spatial and temporal dynamic characteristics of regional forest net primary productivity (NPP) in the context of global climate change can not only provide a theoretical basis for terrestrial carbon cycle studies, but also provide data support for medium- and long-term sustainable management planning of regional forests. In this study, we took Shaoguan City, Guangdong Province, China as the study area, and used Landsat images and National Forest Continuous Inventory (NFCI) data in the corresponding years as the main data sources. Random forest (RF), multiple linear regression (MLR), and BP neural network were the three models applied to estimate forest NPP in the study area. Theil–Sen estimation, Mann–Kendall trend analysis and the standard deviation ellipse (SDE) were chosen to analyze the spatial and temporal dynamic characteristics of NPP, whereas structural equation modeling (SEM) was used to analyze the driving factors of NPP changes. The results show that the performance of the RF model is better than the MLR and BP neural network models. The NPP in the study area showed an increasing trend, as the NPP was 5.66 t·hm<sup<−2</sup<·a<sup<−1</sup<, 7.68 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.17 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.25 t·hm<sup<−2</sup<·a<sup<−1</sup<, and 10.52 t·hm<sup<−2</sup<·a<sup<−1</sup< in 1997, 2002, 2007, 2012, and 2017, respectively. Spatial aggregation of NPP was increased in the period of 1997–2017, and the center shifted from the mid-west to the southwest. In addition, the forest stand factors had the greatest effect on NPP in the study area. The forest stand factors and environmental factors had a positive effect on NPP, and understory factors had a negative effect. Overall, although forest NPP has fluctuated due to the changes of forestry policies and human activities, forest NPP in Shaoguan has been increasing. In the future, the growth potential of NPP in Shaoguan City can be further increased by continuously expanding the area proportion of mixed forests and rationalizing the forest age group structure. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Tao Li [verfasserIn] Mingyang Li [verfasserIn] Fang Ren [verfasserIn] Lei Tian [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	net primary productivity remote sensing inversion dynamic change driving factors Shaoguan City

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 14(2022), 11, p 2541
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:11, p 2541

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs14112541

Katalog-ID:	DOAJ021490260

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520			\|a Exploring the spatial and temporal dynamic characteristics of regional forest net primary productivity (NPP) in the context of global climate change can not only provide a theoretical basis for terrestrial carbon cycle studies, but also provide data support for medium- and long-term sustainable management planning of regional forests. In this study, we took Shaoguan City, Guangdong Province, China as the study area, and used Landsat images and National Forest Continuous Inventory (NFCI) data in the corresponding years as the main data sources. Random forest (RF), multiple linear regression (MLR), and BP neural network were the three models applied to estimate forest NPP in the study area. Theil–Sen estimation, Mann–Kendall trend analysis and the standard deviation ellipse (SDE) were chosen to analyze the spatial and temporal dynamic characteristics of NPP, whereas structural equation modeling (SEM) was used to analyze the driving factors of NPP changes. The results show that the performance of the RF model is better than the MLR and BP neural network models. The NPP in the study area showed an increasing trend, as the NPP was 5.66 t·hm<sup<−2</sup<·a<sup<−1</sup<, 7.68 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.17 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.25 t·hm<sup<−2</sup<·a<sup<−1</sup<, and 10.52 t·hm<sup<−2</sup<·a<sup<−1</sup< in 1997, 2002, 2007, 2012, and 2017, respectively. Spatial aggregation of NPP was increased in the period of 1997–2017, and the center shifted from the mid-west to the southwest. In addition, the forest stand factors had the greatest effect on NPP in the study area. The forest stand factors and environmental factors had a positive effect on NPP, and understory factors had a negative effect. Overall, although forest NPP has fluctuated due to the changes of forestry policies and human activities, forest NPP in Shaoguan has been increasing. In the future, the growth potential of NPP in Shaoguan City can be further increased by continuously expanding the area proportion of mixed forests and rationalizing the forest age group structure.
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allfields	10.3390/rs14112541 doi (DE-627)DOAJ021490260 (DE-599)DOAJe5e95b9b43bd4d90bef588e352474770 DE-627 ger DE-627 rakwb eng Tao Li verfasserin aut Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Exploring the spatial and temporal dynamic characteristics of regional forest net primary productivity (NPP) in the context of global climate change can not only provide a theoretical basis for terrestrial carbon cycle studies, but also provide data support for medium- and long-term sustainable management planning of regional forests. In this study, we took Shaoguan City, Guangdong Province, China as the study area, and used Landsat images and National Forest Continuous Inventory (NFCI) data in the corresponding years as the main data sources. Random forest (RF), multiple linear regression (MLR), and BP neural network were the three models applied to estimate forest NPP in the study area. Theil–Sen estimation, Mann–Kendall trend analysis and the standard deviation ellipse (SDE) were chosen to analyze the spatial and temporal dynamic characteristics of NPP, whereas structural equation modeling (SEM) was used to analyze the driving factors of NPP changes. The results show that the performance of the RF model is better than the MLR and BP neural network models. The NPP in the study area showed an increasing trend, as the NPP was 5.66 t·hm<sup<−2</sup<·a<sup<−1</sup<, 7.68 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.17 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.25 t·hm<sup<−2</sup<·a<sup<−1</sup<, and 10.52 t·hm<sup<−2</sup<·a<sup<−1</sup< in 1997, 2002, 2007, 2012, and 2017, respectively. Spatial aggregation of NPP was increased in the period of 1997–2017, and the center shifted from the mid-west to the southwest. In addition, the forest stand factors had the greatest effect on NPP in the study area. The forest stand factors and environmental factors had a positive effect on NPP, and understory factors had a negative effect. Overall, although forest NPP has fluctuated due to the changes of forestry policies and human activities, forest NPP in Shaoguan has been increasing. In the future, the growth potential of NPP in Shaoguan City can be further increased by continuously expanding the area proportion of mixed forests and rationalizing the forest age group structure. net primary productivity remote sensing inversion dynamic change driving factors Shaoguan City Science Q Mingyang Li verfasserin aut Fang Ren verfasserin aut Lei Tian verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 11, p 2541 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:11, p 2541 https://doi.org/10.3390/rs14112541 kostenfrei https://doaj.org/article/e5e95b9b43bd4d90bef588e352474770 kostenfrei https://www.mdpi.com/2072-4292/14/11/2541 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 11, p 2541
spelling	10.3390/rs14112541 doi (DE-627)DOAJ021490260 (DE-599)DOAJe5e95b9b43bd4d90bef588e352474770 DE-627 ger DE-627 rakwb eng Tao Li verfasserin aut Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Exploring the spatial and temporal dynamic characteristics of regional forest net primary productivity (NPP) in the context of global climate change can not only provide a theoretical basis for terrestrial carbon cycle studies, but also provide data support for medium- and long-term sustainable management planning of regional forests. In this study, we took Shaoguan City, Guangdong Province, China as the study area, and used Landsat images and National Forest Continuous Inventory (NFCI) data in the corresponding years as the main data sources. Random forest (RF), multiple linear regression (MLR), and BP neural network were the three models applied to estimate forest NPP in the study area. Theil–Sen estimation, Mann–Kendall trend analysis and the standard deviation ellipse (SDE) were chosen to analyze the spatial and temporal dynamic characteristics of NPP, whereas structural equation modeling (SEM) was used to analyze the driving factors of NPP changes. The results show that the performance of the RF model is better than the MLR and BP neural network models. The NPP in the study area showed an increasing trend, as the NPP was 5.66 t·hm<sup<−2</sup<·a<sup<−1</sup<, 7.68 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.17 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.25 t·hm<sup<−2</sup<·a<sup<−1</sup<, and 10.52 t·hm<sup<−2</sup<·a<sup<−1</sup< in 1997, 2002, 2007, 2012, and 2017, respectively. Spatial aggregation of NPP was increased in the period of 1997–2017, and the center shifted from the mid-west to the southwest. In addition, the forest stand factors had the greatest effect on NPP in the study area. The forest stand factors and environmental factors had a positive effect on NPP, and understory factors had a negative effect. Overall, although forest NPP has fluctuated due to the changes of forestry policies and human activities, forest NPP in Shaoguan has been increasing. In the future, the growth potential of NPP in Shaoguan City can be further increased by continuously expanding the area proportion of mixed forests and rationalizing the forest age group structure. net primary productivity remote sensing inversion dynamic change driving factors Shaoguan City Science Q Mingyang Li verfasserin aut Fang Ren verfasserin aut Lei Tian verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 11, p 2541 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:11, p 2541 https://doi.org/10.3390/rs14112541 kostenfrei https://doaj.org/article/e5e95b9b43bd4d90bef588e352474770 kostenfrei https://www.mdpi.com/2072-4292/14/11/2541 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 11, p 2541
allfields_unstemmed	10.3390/rs14112541 doi (DE-627)DOAJ021490260 (DE-599)DOAJe5e95b9b43bd4d90bef588e352474770 DE-627 ger DE-627 rakwb eng Tao Li verfasserin aut Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Exploring the spatial and temporal dynamic characteristics of regional forest net primary productivity (NPP) in the context of global climate change can not only provide a theoretical basis for terrestrial carbon cycle studies, but also provide data support for medium- and long-term sustainable management planning of regional forests. In this study, we took Shaoguan City, Guangdong Province, China as the study area, and used Landsat images and National Forest Continuous Inventory (NFCI) data in the corresponding years as the main data sources. Random forest (RF), multiple linear regression (MLR), and BP neural network were the three models applied to estimate forest NPP in the study area. Theil–Sen estimation, Mann–Kendall trend analysis and the standard deviation ellipse (SDE) were chosen to analyze the spatial and temporal dynamic characteristics of NPP, whereas structural equation modeling (SEM) was used to analyze the driving factors of NPP changes. The results show that the performance of the RF model is better than the MLR and BP neural network models. The NPP in the study area showed an increasing trend, as the NPP was 5.66 t·hm<sup<−2</sup<·a<sup<−1</sup<, 7.68 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.17 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.25 t·hm<sup<−2</sup<·a<sup<−1</sup<, and 10.52 t·hm<sup<−2</sup<·a<sup<−1</sup< in 1997, 2002, 2007, 2012, and 2017, respectively. Spatial aggregation of NPP was increased in the period of 1997–2017, and the center shifted from the mid-west to the southwest. In addition, the forest stand factors had the greatest effect on NPP in the study area. The forest stand factors and environmental factors had a positive effect on NPP, and understory factors had a negative effect. Overall, although forest NPP has fluctuated due to the changes of forestry policies and human activities, forest NPP in Shaoguan has been increasing. In the future, the growth potential of NPP in Shaoguan City can be further increased by continuously expanding the area proportion of mixed forests and rationalizing the forest age group structure. net primary productivity remote sensing inversion dynamic change driving factors Shaoguan City Science Q Mingyang Li verfasserin aut Fang Ren verfasserin aut Lei Tian verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 11, p 2541 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:11, p 2541 https://doi.org/10.3390/rs14112541 kostenfrei https://doaj.org/article/e5e95b9b43bd4d90bef588e352474770 kostenfrei https://www.mdpi.com/2072-4292/14/11/2541 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 11, p 2541
allfieldsGer	10.3390/rs14112541 doi (DE-627)DOAJ021490260 (DE-599)DOAJe5e95b9b43bd4d90bef588e352474770 DE-627 ger DE-627 rakwb eng Tao Li verfasserin aut Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Exploring the spatial and temporal dynamic characteristics of regional forest net primary productivity (NPP) in the context of global climate change can not only provide a theoretical basis for terrestrial carbon cycle studies, but also provide data support for medium- and long-term sustainable management planning of regional forests. In this study, we took Shaoguan City, Guangdong Province, China as the study area, and used Landsat images and National Forest Continuous Inventory (NFCI) data in the corresponding years as the main data sources. Random forest (RF), multiple linear regression (MLR), and BP neural network were the three models applied to estimate forest NPP in the study area. Theil–Sen estimation, Mann–Kendall trend analysis and the standard deviation ellipse (SDE) were chosen to analyze the spatial and temporal dynamic characteristics of NPP, whereas structural equation modeling (SEM) was used to analyze the driving factors of NPP changes. The results show that the performance of the RF model is better than the MLR and BP neural network models. The NPP in the study area showed an increasing trend, as the NPP was 5.66 t·hm<sup<−2</sup<·a<sup<−1</sup<, 7.68 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.17 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.25 t·hm<sup<−2</sup<·a<sup<−1</sup<, and 10.52 t·hm<sup<−2</sup<·a<sup<−1</sup< in 1997, 2002, 2007, 2012, and 2017, respectively. Spatial aggregation of NPP was increased in the period of 1997–2017, and the center shifted from the mid-west to the southwest. In addition, the forest stand factors had the greatest effect on NPP in the study area. The forest stand factors and environmental factors had a positive effect on NPP, and understory factors had a negative effect. Overall, although forest NPP has fluctuated due to the changes of forestry policies and human activities, forest NPP in Shaoguan has been increasing. In the future, the growth potential of NPP in Shaoguan City can be further increased by continuously expanding the area proportion of mixed forests and rationalizing the forest age group structure. net primary productivity remote sensing inversion dynamic change driving factors Shaoguan City Science Q Mingyang Li verfasserin aut Fang Ren verfasserin aut Lei Tian verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 11, p 2541 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:11, p 2541 https://doi.org/10.3390/rs14112541 kostenfrei https://doaj.org/article/e5e95b9b43bd4d90bef588e352474770 kostenfrei https://www.mdpi.com/2072-4292/14/11/2541 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 11, p 2541
allfieldsSound	10.3390/rs14112541 doi (DE-627)DOAJ021490260 (DE-599)DOAJe5e95b9b43bd4d90bef588e352474770 DE-627 ger DE-627 rakwb eng Tao Li verfasserin aut Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Exploring the spatial and temporal dynamic characteristics of regional forest net primary productivity (NPP) in the context of global climate change can not only provide a theoretical basis for terrestrial carbon cycle studies, but also provide data support for medium- and long-term sustainable management planning of regional forests. In this study, we took Shaoguan City, Guangdong Province, China as the study area, and used Landsat images and National Forest Continuous Inventory (NFCI) data in the corresponding years as the main data sources. Random forest (RF), multiple linear regression (MLR), and BP neural network were the three models applied to estimate forest NPP in the study area. Theil–Sen estimation, Mann–Kendall trend analysis and the standard deviation ellipse (SDE) were chosen to analyze the spatial and temporal dynamic characteristics of NPP, whereas structural equation modeling (SEM) was used to analyze the driving factors of NPP changes. The results show that the performance of the RF model is better than the MLR and BP neural network models. The NPP in the study area showed an increasing trend, as the NPP was 5.66 t·hm<sup<−2</sup<·a<sup<−1</sup<, 7.68 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.17 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.25 t·hm<sup<−2</sup<·a<sup<−1</sup<, and 10.52 t·hm<sup<−2</sup<·a<sup<−1</sup< in 1997, 2002, 2007, 2012, and 2017, respectively. Spatial aggregation of NPP was increased in the period of 1997–2017, and the center shifted from the mid-west to the southwest. In addition, the forest stand factors had the greatest effect on NPP in the study area. The forest stand factors and environmental factors had a positive effect on NPP, and understory factors had a negative effect. Overall, although forest NPP has fluctuated due to the changes of forestry policies and human activities, forest NPP in Shaoguan has been increasing. In the future, the growth potential of NPP in Shaoguan City can be further increased by continuously expanding the area proportion of mixed forests and rationalizing the forest age group structure. net primary productivity remote sensing inversion dynamic change driving factors Shaoguan City Science Q Mingyang Li verfasserin aut Fang Ren verfasserin aut Lei Tian verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 11, p 2541 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:11, p 2541 https://doi.org/10.3390/rs14112541 kostenfrei https://doaj.org/article/e5e95b9b43bd4d90bef588e352474770 kostenfrei https://www.mdpi.com/2072-4292/14/11/2541 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 11, p 2541
language	English
source	In Remote Sensing 14(2022), 11, p 2541 volume:14 year:2022 number:11, p 2541
sourceStr	In Remote Sensing 14(2022), 11, p 2541 volume:14 year:2022 number:11, p 2541
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	net primary productivity remote sensing inversion dynamic change driving factors Shaoguan City Science Q
isfreeaccess_bool	true
container_title	Remote Sensing
authorswithroles_txt_mv	Tao Li @@aut@@ Mingyang Li @@aut@@ Fang Ren @@aut@@ Lei Tian @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	608937916
id	DOAJ021490260
language_de	englisch
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author	Tao Li
spellingShingle	Tao Li misc net primary productivity misc remote sensing inversion misc dynamic change misc driving factors misc Shaoguan City misc Science misc Q Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China
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topic_title	Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China net primary productivity remote sensing inversion dynamic change driving factors Shaoguan City
topic	misc net primary productivity misc remote sensing inversion misc dynamic change misc driving factors misc Shaoguan City misc Science misc Q
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title	Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China
ctrlnum	(DE-627)DOAJ021490260 (DE-599)DOAJe5e95b9b43bd4d90bef588e352474770
title_full	Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China
author_sort	Tao Li
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author_browse	Tao Li Mingyang Li Fang Ren Lei Tian
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title_sort	estimation and spatio-temporal change analysis of npp in subtropical forests: a case study of shaoguan, guangdong, china
title_auth	Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China
abstract	Exploring the spatial and temporal dynamic characteristics of regional forest net primary productivity (NPP) in the context of global climate change can not only provide a theoretical basis for terrestrial carbon cycle studies, but also provide data support for medium- and long-term sustainable management planning of regional forests. In this study, we took Shaoguan City, Guangdong Province, China as the study area, and used Landsat images and National Forest Continuous Inventory (NFCI) data in the corresponding years as the main data sources. Random forest (RF), multiple linear regression (MLR), and BP neural network were the three models applied to estimate forest NPP in the study area. Theil–Sen estimation, Mann–Kendall trend analysis and the standard deviation ellipse (SDE) were chosen to analyze the spatial and temporal dynamic characteristics of NPP, whereas structural equation modeling (SEM) was used to analyze the driving factors of NPP changes. The results show that the performance of the RF model is better than the MLR and BP neural network models. The NPP in the study area showed an increasing trend, as the NPP was 5.66 t·hm<sup<−2</sup<·a<sup<−1</sup<, 7.68 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.17 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.25 t·hm<sup<−2</sup<·a<sup<−1</sup<, and 10.52 t·hm<sup<−2</sup<·a<sup<−1</sup< in 1997, 2002, 2007, 2012, and 2017, respectively. Spatial aggregation of NPP was increased in the period of 1997–2017, and the center shifted from the mid-west to the southwest. In addition, the forest stand factors had the greatest effect on NPP in the study area. The forest stand factors and environmental factors had a positive effect on NPP, and understory factors had a negative effect. Overall, although forest NPP has fluctuated due to the changes of forestry policies and human activities, forest NPP in Shaoguan has been increasing. In the future, the growth potential of NPP in Shaoguan City can be further increased by continuously expanding the area proportion of mixed forests and rationalizing the forest age group structure.
abstractGer	Exploring the spatial and temporal dynamic characteristics of regional forest net primary productivity (NPP) in the context of global climate change can not only provide a theoretical basis for terrestrial carbon cycle studies, but also provide data support for medium- and long-term sustainable management planning of regional forests. In this study, we took Shaoguan City, Guangdong Province, China as the study area, and used Landsat images and National Forest Continuous Inventory (NFCI) data in the corresponding years as the main data sources. Random forest (RF), multiple linear regression (MLR), and BP neural network were the three models applied to estimate forest NPP in the study area. Theil–Sen estimation, Mann–Kendall trend analysis and the standard deviation ellipse (SDE) were chosen to analyze the spatial and temporal dynamic characteristics of NPP, whereas structural equation modeling (SEM) was used to analyze the driving factors of NPP changes. The results show that the performance of the RF model is better than the MLR and BP neural network models. The NPP in the study area showed an increasing trend, as the NPP was 5.66 t·hm<sup<−2</sup<·a<sup<−1</sup<, 7.68 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.17 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.25 t·hm<sup<−2</sup<·a<sup<−1</sup<, and 10.52 t·hm<sup<−2</sup<·a<sup<−1</sup< in 1997, 2002, 2007, 2012, and 2017, respectively. Spatial aggregation of NPP was increased in the period of 1997–2017, and the center shifted from the mid-west to the southwest. In addition, the forest stand factors had the greatest effect on NPP in the study area. The forest stand factors and environmental factors had a positive effect on NPP, and understory factors had a negative effect. Overall, although forest NPP has fluctuated due to the changes of forestry policies and human activities, forest NPP in Shaoguan has been increasing. In the future, the growth potential of NPP in Shaoguan City can be further increased by continuously expanding the area proportion of mixed forests and rationalizing the forest age group structure.
abstract_unstemmed	Exploring the spatial and temporal dynamic characteristics of regional forest net primary productivity (NPP) in the context of global climate change can not only provide a theoretical basis for terrestrial carbon cycle studies, but also provide data support for medium- and long-term sustainable management planning of regional forests. In this study, we took Shaoguan City, Guangdong Province, China as the study area, and used Landsat images and National Forest Continuous Inventory (NFCI) data in the corresponding years as the main data sources. Random forest (RF), multiple linear regression (MLR), and BP neural network were the three models applied to estimate forest NPP in the study area. Theil–Sen estimation, Mann–Kendall trend analysis and the standard deviation ellipse (SDE) were chosen to analyze the spatial and temporal dynamic characteristics of NPP, whereas structural equation modeling (SEM) was used to analyze the driving factors of NPP changes. The results show that the performance of the RF model is better than the MLR and BP neural network models. The NPP in the study area showed an increasing trend, as the NPP was 5.66 t·hm<sup<−2</sup<·a<sup<−1</sup<, 7.68 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.17 t·hm<sup<−2</sup<·a<sup<−1</sup<, 8.25 t·hm<sup<−2</sup<·a<sup<−1</sup<, and 10.52 t·hm<sup<−2</sup<·a<sup<−1</sup< in 1997, 2002, 2007, 2012, and 2017, respectively. Spatial aggregation of NPP was increased in the period of 1997–2017, and the center shifted from the mid-west to the southwest. In addition, the forest stand factors had the greatest effect on NPP in the study area. The forest stand factors and environmental factors had a positive effect on NPP, and understory factors had a negative effect. Overall, although forest NPP has fluctuated due to the changes of forestry policies and human activities, forest NPP in Shaoguan has been increasing. In the future, the growth potential of NPP in Shaoguan City can be further increased by continuously expanding the area proportion of mixed forests and rationalizing the forest age group structure.
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title_short	Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China
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