Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau

In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (<i<NPP</i<) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest < meadow steppe < coniferous forest < cropland < shrub < typical steppe < sandy land < alpine steppe < desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) < precipitation (0.33) < soil moisture (0.16) < temperature (0.14) < solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Chaohua Yin [verfasserIn] Xiaoqi Chen [verfasserIn] Min Luo [verfasserIn] Fanhao Meng [verfasserIn] Chula Sa [verfasserIn] Shanhu Bao [verfasserIn] Zhihui Yuan [verfasserIn] Xiang Zhang [verfasserIn] Yuhai Bao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	net primary productivity geographic detector model structural equation model modified CASA model Mongolian Plateau

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 15(2023), 8, p 1986
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:8, p 1986

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs15081986

Katalog-ID:	DOAJ089779959

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520			\|a In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (<i<NPP</i<) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest < meadow steppe < coniferous forest < cropland < shrub < typical steppe < sandy land < alpine steppe < desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) < precipitation (0.33) < soil moisture (0.16) < temperature (0.14) < solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management.
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allfields	10.3390/rs15081986 doi (DE-627)DOAJ089779959 (DE-599)DOAJd702a3d78ab142db82d29458f690b619 DE-627 ger DE-627 rakwb eng Chaohua Yin verfasserin aut Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (<i<NPP</i<) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest < meadow steppe < coniferous forest < cropland < shrub < typical steppe < sandy land < alpine steppe < desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) < precipitation (0.33) < soil moisture (0.16) < temperature (0.14) < solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management. net primary productivity geographic detector model structural equation model modified CASA model Mongolian Plateau Science Q Xiaoqi Chen verfasserin aut Min Luo verfasserin aut Fanhao Meng verfasserin aut Chula Sa verfasserin aut Shanhu Bao verfasserin aut Zhihui Yuan verfasserin aut Xiang Zhang verfasserin aut Yuhai Bao verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 8, p 1986 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:8, p 1986 https://doi.org/10.3390/rs15081986 kostenfrei https://doaj.org/article/d702a3d78ab142db82d29458f690b619 kostenfrei https://www.mdpi.com/2072-4292/15/8/1986 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 8, p 1986
spelling	10.3390/rs15081986 doi (DE-627)DOAJ089779959 (DE-599)DOAJd702a3d78ab142db82d29458f690b619 DE-627 ger DE-627 rakwb eng Chaohua Yin verfasserin aut Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (<i<NPP</i<) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest < meadow steppe < coniferous forest < cropland < shrub < typical steppe < sandy land < alpine steppe < desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) < precipitation (0.33) < soil moisture (0.16) < temperature (0.14) < solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management. net primary productivity geographic detector model structural equation model modified CASA model Mongolian Plateau Science Q Xiaoqi Chen verfasserin aut Min Luo verfasserin aut Fanhao Meng verfasserin aut Chula Sa verfasserin aut Shanhu Bao verfasserin aut Zhihui Yuan verfasserin aut Xiang Zhang verfasserin aut Yuhai Bao verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 8, p 1986 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:8, p 1986 https://doi.org/10.3390/rs15081986 kostenfrei https://doaj.org/article/d702a3d78ab142db82d29458f690b619 kostenfrei https://www.mdpi.com/2072-4292/15/8/1986 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 8, p 1986
allfields_unstemmed	10.3390/rs15081986 doi (DE-627)DOAJ089779959 (DE-599)DOAJd702a3d78ab142db82d29458f690b619 DE-627 ger DE-627 rakwb eng Chaohua Yin verfasserin aut Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (<i<NPP</i<) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest < meadow steppe < coniferous forest < cropland < shrub < typical steppe < sandy land < alpine steppe < desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) < precipitation (0.33) < soil moisture (0.16) < temperature (0.14) < solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management. net primary productivity geographic detector model structural equation model modified CASA model Mongolian Plateau Science Q Xiaoqi Chen verfasserin aut Min Luo verfasserin aut Fanhao Meng verfasserin aut Chula Sa verfasserin aut Shanhu Bao verfasserin aut Zhihui Yuan verfasserin aut Xiang Zhang verfasserin aut Yuhai Bao verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 8, p 1986 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:8, p 1986 https://doi.org/10.3390/rs15081986 kostenfrei https://doaj.org/article/d702a3d78ab142db82d29458f690b619 kostenfrei https://www.mdpi.com/2072-4292/15/8/1986 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 8, p 1986
allfieldsGer	10.3390/rs15081986 doi (DE-627)DOAJ089779959 (DE-599)DOAJd702a3d78ab142db82d29458f690b619 DE-627 ger DE-627 rakwb eng Chaohua Yin verfasserin aut Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (<i<NPP</i<) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest < meadow steppe < coniferous forest < cropland < shrub < typical steppe < sandy land < alpine steppe < desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) < precipitation (0.33) < soil moisture (0.16) < temperature (0.14) < solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management. net primary productivity geographic detector model structural equation model modified CASA model Mongolian Plateau Science Q Xiaoqi Chen verfasserin aut Min Luo verfasserin aut Fanhao Meng verfasserin aut Chula Sa verfasserin aut Shanhu Bao verfasserin aut Zhihui Yuan verfasserin aut Xiang Zhang verfasserin aut Yuhai Bao verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 8, p 1986 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:8, p 1986 https://doi.org/10.3390/rs15081986 kostenfrei https://doaj.org/article/d702a3d78ab142db82d29458f690b619 kostenfrei https://www.mdpi.com/2072-4292/15/8/1986 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 8, p 1986
allfieldsSound	10.3390/rs15081986 doi (DE-627)DOAJ089779959 (DE-599)DOAJd702a3d78ab142db82d29458f690b619 DE-627 ger DE-627 rakwb eng Chaohua Yin verfasserin aut Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (<i<NPP</i<) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest < meadow steppe < coniferous forest < cropland < shrub < typical steppe < sandy land < alpine steppe < desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) < precipitation (0.33) < soil moisture (0.16) < temperature (0.14) < solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management. net primary productivity geographic detector model structural equation model modified CASA model Mongolian Plateau Science Q Xiaoqi Chen verfasserin aut Min Luo verfasserin aut Fanhao Meng verfasserin aut Chula Sa verfasserin aut Shanhu Bao verfasserin aut Zhihui Yuan verfasserin aut Xiang Zhang verfasserin aut Yuhai Bao verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 8, p 1986 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:8, p 1986 https://doi.org/10.3390/rs15081986 kostenfrei https://doaj.org/article/d702a3d78ab142db82d29458f690b619 kostenfrei https://www.mdpi.com/2072-4292/15/8/1986 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 8, p 1986
language	English
source	In Remote Sensing 15(2023), 8, p 1986 volume:15 year:2023 number:8, p 1986
sourceStr	In Remote Sensing 15(2023), 8, p 1986 volume:15 year:2023 number:8, p 1986
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	net primary productivity geographic detector model structural equation model modified CASA model Mongolian Plateau Science Q
isfreeaccess_bool	true
container_title	Remote Sensing
authorswithroles_txt_mv	Chaohua Yin @@aut@@ Xiaoqi Chen @@aut@@ Min Luo @@aut@@ Fanhao Meng @@aut@@ Chula Sa @@aut@@ Shanhu Bao @@aut@@ Zhihui Yuan @@aut@@ Xiang Zhang @@aut@@ Yuhai Bao @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	608937916
id	DOAJ089779959
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">DOAJ089779959</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413040405.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230505s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/rs15081986</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ089779959</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJd702a3d78ab142db82d29458f690b619</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Chaohua Yin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau</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">In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (<i<NPP</i<) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest < meadow steppe < coniferous forest < cropland < shrub < typical steppe < sandy land < alpine steppe < desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) < precipitation (0.33) < soil moisture (0.16) < temperature (0.14) < solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. 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author	Chaohua Yin
spellingShingle	Chaohua Yin misc net primary productivity misc geographic detector model misc structural equation model misc modified CASA model misc Mongolian Plateau misc Science misc Q Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau
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topic_title	Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau net primary productivity geographic detector model structural equation model modified CASA model Mongolian Plateau
topic	misc net primary productivity misc geographic detector model misc structural equation model misc modified CASA model misc Mongolian Plateau misc Science misc Q
topic_unstemmed	misc net primary productivity misc geographic detector model misc structural equation model misc modified CASA model misc Mongolian Plateau misc Science misc Q
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title	Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau
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title_full	Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau
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title_sort	quantifying the contribution of driving factors on distribution and change of net primary productivity of vegetation in the mongolian plateau
title_auth	Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau
abstract	In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (<i<NPP</i<) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest < meadow steppe < coniferous forest < cropland < shrub < typical steppe < sandy land < alpine steppe < desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) < precipitation (0.33) < soil moisture (0.16) < temperature (0.14) < solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management.
abstractGer	In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (<i<NPP</i<) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest < meadow steppe < coniferous forest < cropland < shrub < typical steppe < sandy land < alpine steppe < desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) < precipitation (0.33) < soil moisture (0.16) < temperature (0.14) < solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management.
abstract_unstemmed	In recent years, multiple disturbances have significantly altered terrestrial ecosystems in arid and semi-arid regions, particularly on the Mongolian Plateau (MP). Net primary productivity (<i<NPP</i<) of vegetation is an essential component of the surface carbon cycle. As such, it characterizes the state of variation in terrestrial ecosystems and reflects the productive capacity of natural vegetation. This study revealed the complex relationship between the natural environment and NPP in the ecologically fragile and sensitive MP. The modified Carnegie–Ames–Stanford Approach (CASA) model was used to simulate vegetation NPP. Further, the contributions of topography, vegetation, soils, and climate to NPP’s distribution and spatiotemporal variation were explored using the geographic detector model (GDM) and structural equation model (SEM). The study’s findings indicate the following: (1) NPPs for different vegetation types in the MP were in the order of broad-leaved forest < meadow steppe < coniferous forest < cropland < shrub < typical steppe < sandy land < alpine steppe < desert steppe. (2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) < precipitation (0.33) < soil moisture (0.16) < temperature (0.14) < solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management.
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container_issue	8, p 1986
title_short	Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau
url	https://doi.org/10.3390/rs15081986 https://doaj.org/article/d702a3d78ab142db82d29458f690b619 https://www.mdpi.com/2072-4292/15/8/1986 https://doaj.org/toc/2072-4292
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(2) NPP showed an increasing trend during the growing seasons from 2000 to 2019, with forests providing larger vegetation carbon stocks. It also maintained a more stable level of productivity. (3) Vegetation cover, precipitation, soil moisture, and solar radiation were the key factors affecting NPP’s spatial distribution. NPP’s spatial distribution was primarily explained by the normalized difference vegetation index, solar radiation, precipitation, vegetation type, soil moisture, and soil type (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.86, 0.71, 0.67, 0.67, 0.57, and 0.57, respectively); the contribution of temperature was small (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mi<q</mi<</semantics<</math<</inline-formula<-statistics = 0.26), and topographic factors had the least influence on NPP’s distribution, as their contribution amounted to less than 0.20. (4) A SEM constructed based on the normalized difference vegetation index (NDVI), solar radiation, precipitation, temperature, and soil moisture explained 17% to 65% of the MP’s NPP variations. The total effects of the MP’s NPP variations in absolute values were in the order of NDVI (0.47) < precipitation (0.33) < soil moisture (0.16) < temperature (0.14) < solar radiation (0.02), and the mechanisms responsible for NPP variations differed slightly among the relevant vegetation types. Overall, this study can help understand the mechanisms responsible for the MP’s NPP variations and offer a new perspective for regional vegetation ecosystem management.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">net primary productivity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">geographic detector model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">structural equation model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">modified CASA model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mongolian Plateau</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Science</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Q</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiaoqi Chen</subfield><subfield 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Quantifying the Contribution of Driving Factors on Distribution and Change of Net Primary Productivity of Vegetation in the Mongolian Plateau

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