Background:Mangrove forests are a significant contributor to the global carbon cycle,and the accurate estimation of their gross primary productivity(GPP)is essential for understanding the carbon budget within blue car...Background:Mangrove forests are a significant contributor to the global carbon cycle,and the accurate estimation of their gross primary productivity(GPP)is essential for understanding the carbon budget within blue carbon ecosystems.Little attention has been given to the investigation of spatiotemporal patterns and ecological variations within mangrove ecosystems,as well as the quantitative analysis of the influence of geo-environmental factors on time-series estimations of mangrove GPP.Methods:This study explored the spatiotemporal dynamics of mangrove GPP from 2000 to 2020 in Gaoqiao Mangrove Reserve,China.A leaf area index(LAI)-based light-use efficiency(LUE)model was combined with Landsat data on Google Earth Engine(GEE)to reveal the variations in mangrove GPP using the Mann-Kendall(MK)test and Theil-Sen median trend.Moreover,the spatiotemporal patterns and ecological variations in mangrove ecosystems across regions were explored using four landscape indicators.Furthermore,the effects of six geo-environmental factors(species distribution,offshore distance,elevation,slope,planar curvature and profile curvature)on GPP were investigated using Geodetector and multi-scale geo-weighted regression(MGWR).Results:The results showed that the mangrove forest in the study area experienced an area loss from 766.26 ha in 2000 to 718.29 ha in 2020,mainly due to the conversion to farming,terrestrial forest and aquaculture zones.Landscape patterns indicated high levels of vegetation aggregation near water bodies and aquaculture zones,and low levels of aggregation but high species diversity and distribution density near building zone.The mean value of mangrove GPP continuously increased from 6.35 g C⋅m^(-2)⋅d^(-1) in 2000 to 8.33 g C⋅m^(-2)⋅d^(-1) in 2020,with 23.21%of areas showing a highly and significantly increasing trend(trend value>0.50).The Geodetector and MGWR analyses showed that species distribution,offshore distance and elevation contributed most to the GPP variations.Conclusions:These results provide guidelines for selecting GPP products,and the combination of Geodetector and MGWR based on multiple geo-environmental factors could quantitatively capture the mode,direction,pathway and intensity of the influencing factors on mangrove GPP variation.The findings provide a foundation for understanding the spatiotemporal dynamics of mangrove GPP at the landscape or regional scale.展开更多
对随机效应线性模型（y,X<sub>0</sub>β,Aα,σ<sup>2</sup>V）:y=x<sub>0</sub>β+ε,E（<sub>ε</sub><sup>β</sup>）=（A<sub>α</sub>/0）,Cov（<su...对随机效应线性模型（y,X<sub>0</sub>β,Aα,σ<sup>2</sup>V）:y=x<sub>0</sub>β+ε,E（<sub>ε</sub><sup>β</sup>）=（A<sub>α</sub>/0）,Cov（<sub>ε</sub><sup>β</sup>）（?）给出了下列问题的解:当且仅当 X 满足什么条件时,才能使（y,X<sub>0</sub>β,Aα,σ<sup>2</sup>V）下任一可估函数ω′<sub>1</sub>α（或ω′<sub>2</sub>β或ω′<sub>1</sub>α+ω′<sub>2</sub>β）的所有 BLUE 都是（1）（y,xβ,Aα,σ<sup>2</sup>V）下ω′<sub>1</sub>α（或ω′<sub>2</sub>β或ω′<sub>1</sub>α+ω′<sub>2</sub>β）的线性无偏估计（LUE）或 BLUE（2）（y,Xβ,Aα,σ<sup>2</sup>V）下ω′<sub>1</sub>α（或ω′<sub>2</sub>β或ω′<sub>1</sub>α+ω′<sub>2</sub>β）的线性最小偏差估计（LIMBE）或最佳线性最小偏差估计（BLIMBE）展开更多
光化学植被指数PRI(photochemical reflectance index)为估算植被的光能利用率LUE(light use effi-ciency)提供了一种快速、有效的方法。越来越多的研究关注外界环境对PRI和LUE之间关系的影响,这些因素包括水分含量、CO2浓度等等。文章...光化学植被指数PRI(photochemical reflectance index)为估算植被的光能利用率LUE(light use effi-ciency)提供了一种快速、有效的方法。越来越多的研究关注外界环境对PRI和LUE之间关系的影响,这些因素包括水分含量、CO2浓度等等。文章选择了不同氮、钾施肥量处理的小麦,测量其LUE和PRI,分析不同肥料处理对二者关系的影响。实验表明,氮、钾施肥量的增加将提高冠层光谱的PRI值和叶片内部叶绿素的含量,在此基础上提高小麦的LUE。对于不同氮、钾处理的小麦,PRI和LUE之间都获得了很好的相关关系,总的相关系数R2分别是0.7104和0.8534。随着氮、钾肥量的增加,PRI和LUE之间的相关性也在增加。对1,2,3份的氮施肥量,相关系数R2分别是0.6020,0.6404和0.8014;钾施肥量为1,2,3份时,R2分别为0.3791,0.6404和0.6769。因此,PRI不仅能够获可靠精度的LUE,并且为监测小麦的肥料状况提供了一种间接方法,这将为田间管理和精细农业提供了必要的参考信息。展开更多
基金This work was supported by Guangdong Basic and Applied Basic Research Foundation(2019A1515010741 and 2021A1515110910)Guangdong Regional Joint Fund-Youth Fund(2020A1515111142)Shenzhen Science and Technology Program(JCYJ20210324093210029).
文摘Background:Mangrove forests are a significant contributor to the global carbon cycle,and the accurate estimation of their gross primary productivity(GPP)is essential for understanding the carbon budget within blue carbon ecosystems.Little attention has been given to the investigation of spatiotemporal patterns and ecological variations within mangrove ecosystems,as well as the quantitative analysis of the influence of geo-environmental factors on time-series estimations of mangrove GPP.Methods:This study explored the spatiotemporal dynamics of mangrove GPP from 2000 to 2020 in Gaoqiao Mangrove Reserve,China.A leaf area index(LAI)-based light-use efficiency(LUE)model was combined with Landsat data on Google Earth Engine(GEE)to reveal the variations in mangrove GPP using the Mann-Kendall(MK)test and Theil-Sen median trend.Moreover,the spatiotemporal patterns and ecological variations in mangrove ecosystems across regions were explored using four landscape indicators.Furthermore,the effects of six geo-environmental factors(species distribution,offshore distance,elevation,slope,planar curvature and profile curvature)on GPP were investigated using Geodetector and multi-scale geo-weighted regression(MGWR).Results:The results showed that the mangrove forest in the study area experienced an area loss from 766.26 ha in 2000 to 718.29 ha in 2020,mainly due to the conversion to farming,terrestrial forest and aquaculture zones.Landscape patterns indicated high levels of vegetation aggregation near water bodies and aquaculture zones,and low levels of aggregation but high species diversity and distribution density near building zone.The mean value of mangrove GPP continuously increased from 6.35 g C⋅m^(-2)⋅d^(-1) in 2000 to 8.33 g C⋅m^(-2)⋅d^(-1) in 2020,with 23.21%of areas showing a highly and significantly increasing trend(trend value>0.50).The Geodetector and MGWR analyses showed that species distribution,offshore distance and elevation contributed most to the GPP variations.Conclusions:These results provide guidelines for selecting GPP products,and the combination of Geodetector and MGWR based on multiple geo-environmental factors could quantitatively capture the mode,direction,pathway and intensity of the influencing factors on mangrove GPP variation.The findings provide a foundation for understanding the spatiotemporal dynamics of mangrove GPP at the landscape or regional scale.
文摘对随机效应线性模型（y,X<sub>0</sub>β,Aα,σ<sup>2</sup>V）:y=x<sub>0</sub>β+ε,E（<sub>ε</sub><sup>β</sup>）=（A<sub>α</sub>/0）,Cov（<sub>ε</sub><sup>β</sup>）（?）给出了下列问题的解:当且仅当 X 满足什么条件时,才能使（y,X<sub>0</sub>β,Aα,σ<sup>2</sup>V）下任一可估函数ω′<sub>1</sub>α（或ω′<sub>2</sub>β或ω′<sub>1</sub>α+ω′<sub>2</sub>β）的所有 BLUE 都是（1）（y,xβ,Aα,σ<sup>2</sup>V）下ω′<sub>1</sub>α（或ω′<sub>2</sub>β或ω′<sub>1</sub>α+ω′<sub>2</sub>β）的线性无偏估计（LUE）或 BLUE（2）（y,Xβ,Aα,σ<sup>2</sup>V）下ω′<sub>1</sub>α（或ω′<sub>2</sub>β或ω′<sub>1</sub>α+ω′<sub>2</sub>β）的线性最小偏差估计（LIMBE）或最佳线性最小偏差估计（BLIMBE）
文摘光化学植被指数PRI(photochemical reflectance index)为估算植被的光能利用率LUE(light use effi-ciency)提供了一种快速、有效的方法。越来越多的研究关注外界环境对PRI和LUE之间关系的影响,这些因素包括水分含量、CO2浓度等等。文章选择了不同氮、钾施肥量处理的小麦,测量其LUE和PRI,分析不同肥料处理对二者关系的影响。实验表明,氮、钾施肥量的增加将提高冠层光谱的PRI值和叶片内部叶绿素的含量,在此基础上提高小麦的LUE。对于不同氮、钾处理的小麦,PRI和LUE之间都获得了很好的相关关系,总的相关系数R2分别是0.7104和0.8534。随着氮、钾肥量的增加,PRI和LUE之间的相关性也在增加。对1,2,3份的氮施肥量,相关系数R2分别是0.6020,0.6404和0.8014;钾施肥量为1,2,3份时,R2分别为0.3791,0.6404和0.6769。因此,PRI不仅能够获可靠精度的LUE,并且为监测小麦的肥料状况提供了一种间接方法,这将为田间管理和精细农业提供了必要的参考信息。
