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
青藏高原是陆地碳循环研究中的热点地区。在全球气候变化背景下,其总初级生产力(gross primary production,GPP)在区域碳循环过程中发挥着重要作用。结合遥感数据使用模型模拟有助于了解青藏高原区域尺度上生态系统生产力的变化过程,以...青藏高原是陆地碳循环研究中的热点地区。在全球气候变化背景下,其总初级生产力(gross primary production,GPP)在区域碳循环过程中发挥着重要作用。结合遥感数据使用模型模拟有助于了解青藏高原区域尺度上生态系统生产力的变化过程,以及预测其未来的变化趋势。本研究使用6种常见的遥感GPP产品(GLASS、MODIS MOD17A2、FLUXCOM、VODCA2、改进的EC-LUE数据及VPM数据),结合涡度协方差通量观测数据(海北灌丛、海北湿地和当雄)进行验证后,对青藏高原2001—2015年生态系统GPP空间分布格局及时间变化趋势进行分析。结果表明:不同生态遥感产品得到的青藏高原年平均GPP、区域年均GPP时空分布格局与变化趋势存在较大差异,6套产品得到的2001—2015年变化趋势分别-0.77 g C·m^(-2)·a^(-1)(GLASS)、3.63 g C·m^(-2)·a^(-1)(MOD17A2)、-1.21 g C·m^(-2)·a^(-1)(FLUXCOM)、1.53 g C·m^(-2)·a^(-1)(VODCA2)、4.73 g C·m^(-2)·a^(-1)(VPM)和-0.81 g C·m^(-2)·a^(-1)(改进的EC-LUE);在空间分布上多年平均GPP总体呈现“东南高、西北低”的特点,区域差异较大;在青藏高原生态系统中,GLASS产品区域平均年GPP最高(827.78 Tg C·a^(-1)),MOD17A2产品最低(484.04 Tg C·a^(-1)),2001—2015年青藏高原生态系统GPP变化程度分布区域基本相同,东南部最剧烈,而西部最为稳定;经过站点数据验证,MOD17A2在8天尺度上结果相对更好,而FLUXCOM数据集在月尺度上结果相对更好,结合在区域尺度上的表现,MOD17A2数据集更加适用于青藏高原地区。展开更多
The unique Bayes linear unbiased estimator (Bayes LUE) of estimable functions is derived for the singular linear model. The superiority of Bayes LUE over ordinary best linear unbiased estimator is investigated under m...The unique Bayes linear unbiased estimator (Bayes LUE) of estimable functions is derived for the singular linear model. The superiority of Bayes LUE over ordinary best linear unbiased estimator is investigated under mean square error matrix (MSEM)criterion.展开更多
Vegetation gross primary production(GPP)is an important variable for the carbon cycle on the Qinghai-Tibetan Plateau(QTP).Based on the measurements from 12 eddy covariance flux sites,we validated a light use efficienc...Vegetation gross primary production(GPP)is an important variable for the carbon cycle on the Qinghai-Tibetan Plateau(QTP).Based on the measurements from 12 eddy covariance flux sites,we validated a light use efficiency model(i.e.EC-LUE)to evaluate the spatial-temporal patterns of GPP and the effect of environmental variables on QTP.In general,EC-LUE model performed well in predicting GPP at different time scale over QTP.Annual GPP over the entire QTP ranged from 575 to 703 Tg C,and showed a significantly increasing trend from 1982 to 2013.However,there were large spatial heterogeneities in long-term trends of GPP.Throughout the entire QTP,air temperature increase had a greater influence than solar radiation and precipitation(PREC)changes on productivity.Moreover,our results highlight the large uncertainties of previous GPP estimates due to insufficient parameterization and validations.When compared with GPP estimates of the EC-LUE model,most Coupled Model Intercomparison Project(CMIP5)GPP products overestimate the magnitude and increasing trends of regional GPP,which potentially impact the feedback of ecosystems to regional climate changes.展开更多
陆地生态系统总初级生产力(GPP)反映了植物吸收固定大气中CO2的能力,是碳循环过程中的重要环节。光能利用率(LUE)模型被广泛应用于GPP模拟。叶面积指数(LAI)数据是LUE模型的重要输入数据,不同的LAI数据差异较大,从而导致GPP模拟存在很...陆地生态系统总初级生产力(GPP)反映了植物吸收固定大气中CO2的能力,是碳循环过程中的重要环节。光能利用率(LUE)模型被广泛应用于GPP模拟。叶面积指数(LAI)数据是LUE模型的重要输入数据,不同的LAI数据差异较大,从而导致GPP模拟存在很大差异。利用3种常用的卫星遥感LAI数据(MCD15、GLASS和GlobMap)和气象数据模拟中国2003~2017年的GPP,比较了3种LAI数据在中国区域的时空差异,分析不同LAI数据模拟的中国GPP的时空差异。研究结果表明:3种LAI数据在中国区域的年平均值和LAI变化趋势的空间分布格局存在明显差异,森林区域的差异较大;2003~2017年间,中国区域3种LAI年平均值均呈显著增加趋势(p<0.01),但不同LAI数据年平均值的年际变化差异明显;站点尺度GLASS LAI模拟的GPP与观测值相关性较好;不同LAI数据模拟的中国GPP总量多年平均值差异明显,最大值为7.46 Pg C a-1(GLASS),最小值为6.39 Pg C a-1(GlobMap);3种LAI数据模拟的中国GPP总量在2003~2017年呈显著增加趋势(p<0.05),但不同的LAI数据模拟的中国GPP年总量的年际变化差异明显;不同LAI数据模拟的年均GPP和GPP变化趋势的空间分布格局存在明显差异,森林和农田区域的差异较大。研究结果有助于评估由于LAI数据造成的区域GPP模拟结果的不确定性。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.
文摘青藏高原是陆地碳循环研究中的热点地区。在全球气候变化背景下,其总初级生产力(gross primary production,GPP)在区域碳循环过程中发挥着重要作用。结合遥感数据使用模型模拟有助于了解青藏高原区域尺度上生态系统生产力的变化过程,以及预测其未来的变化趋势。本研究使用6种常见的遥感GPP产品(GLASS、MODIS MOD17A2、FLUXCOM、VODCA2、改进的EC-LUE数据及VPM数据),结合涡度协方差通量观测数据(海北灌丛、海北湿地和当雄)进行验证后,对青藏高原2001—2015年生态系统GPP空间分布格局及时间变化趋势进行分析。结果表明:不同生态遥感产品得到的青藏高原年平均GPP、区域年均GPP时空分布格局与变化趋势存在较大差异,6套产品得到的2001—2015年变化趋势分别-0.77 g C·m^(-2)·a^(-1)(GLASS)、3.63 g C·m^(-2)·a^(-1)(MOD17A2)、-1.21 g C·m^(-2)·a^(-1)(FLUXCOM)、1.53 g C·m^(-2)·a^(-1)(VODCA2)、4.73 g C·m^(-2)·a^(-1)(VPM)和-0.81 g C·m^(-2)·a^(-1)(改进的EC-LUE);在空间分布上多年平均GPP总体呈现“东南高、西北低”的特点,区域差异较大;在青藏高原生态系统中,GLASS产品区域平均年GPP最高(827.78 Tg C·a^(-1)),MOD17A2产品最低(484.04 Tg C·a^(-1)),2001—2015年青藏高原生态系统GPP变化程度分布区域基本相同,东南部最剧烈,而西部最为稳定;经过站点数据验证,MOD17A2在8天尺度上结果相对更好,而FLUXCOM数据集在月尺度上结果相对更好,结合在区域尺度上的表现,MOD17A2数据集更加适用于青藏高原地区。
基金This work was supported by the Doctoral Program Foundation of the Institute of High Educationthe Special Foundation of Chinese Academy of Sciences.
文摘The unique Bayes linear unbiased estimator (Bayes LUE) of estimable functions is derived for the singular linear model. The superiority of Bayes LUE over ordinary best linear unbiased estimator is investigated under mean square error matrix (MSEM)criterion.
基金Key Project of Chinese Academy of Sciences(CAS)[grant number KJZD-EW-G03-04]National Key R&D Program of China[grant number 2017YFA0604801]+2 种基金One Hundred Person Project of CAS[grant number Y329k71002]National Science Foundation for Excellent Young Scholars of China[grant number 41322005]the CAS Interdisciplinary Innovation Team of the Chinese Academy of Sciences.
文摘Vegetation gross primary production(GPP)is an important variable for the carbon cycle on the Qinghai-Tibetan Plateau(QTP).Based on the measurements from 12 eddy covariance flux sites,we validated a light use efficiency model(i.e.EC-LUE)to evaluate the spatial-temporal patterns of GPP and the effect of environmental variables on QTP.In general,EC-LUE model performed well in predicting GPP at different time scale over QTP.Annual GPP over the entire QTP ranged from 575 to 703 Tg C,and showed a significantly increasing trend from 1982 to 2013.However,there were large spatial heterogeneities in long-term trends of GPP.Throughout the entire QTP,air temperature increase had a greater influence than solar radiation and precipitation(PREC)changes on productivity.Moreover,our results highlight the large uncertainties of previous GPP estimates due to insufficient parameterization and validations.When compared with GPP estimates of the EC-LUE model,most Coupled Model Intercomparison Project(CMIP5)GPP products overestimate the magnitude and increasing trends of regional GPP,which potentially impact the feedback of ecosystems to regional climate changes.
文摘陆地生态系统总初级生产力(GPP)反映了植物吸收固定大气中CO2的能力,是碳循环过程中的重要环节。光能利用率(LUE)模型被广泛应用于GPP模拟。叶面积指数(LAI)数据是LUE模型的重要输入数据,不同的LAI数据差异较大,从而导致GPP模拟存在很大差异。利用3种常用的卫星遥感LAI数据(MCD15、GLASS和GlobMap)和气象数据模拟中国2003~2017年的GPP,比较了3种LAI数据在中国区域的时空差异,分析不同LAI数据模拟的中国GPP的时空差异。研究结果表明:3种LAI数据在中国区域的年平均值和LAI变化趋势的空间分布格局存在明显差异,森林区域的差异较大;2003~2017年间,中国区域3种LAI年平均值均呈显著增加趋势(p<0.01),但不同LAI数据年平均值的年际变化差异明显;站点尺度GLASS LAI模拟的GPP与观测值相关性较好;不同LAI数据模拟的中国GPP总量多年平均值差异明显,最大值为7.46 Pg C a-1(GLASS),最小值为6.39 Pg C a-1(GlobMap);3种LAI数据模拟的中国GPP总量在2003~2017年呈显著增加趋势(p<0.05),但不同的LAI数据模拟的中国GPP年总量的年际变化差异明显;不同LAI数据模拟的年均GPP和GPP变化趋势的空间分布格局存在明显差异,森林和农田区域的差异较大。研究结果有助于评估由于LAI数据造成的区域GPP模拟结果的不确定性。
