Accurate forest cover maps are the basis for estimating forest biomass and are crucial for climate regulation and biodiversity conservation,especially in sub-humid and semi-arid regions such as Oklahoma,USA.To date,th...Accurate forest cover maps are the basis for estimating forest biomass and are crucial for climate regulation and biodiversity conservation,especially in sub-humid and semi-arid regions such as Oklahoma,USA.To date,there is very limited data and knowledge of the spatial pattern and temporal dynamics of forest cover in Oklahoma,and current forest cover maps have large uncertainties.In this study,multi-sensor datasets,including the Phased Arrayed L-band Synthetic Aperture Radar(PALSAR-2),Landsat,and spaceborne Light Detection and Ranging(LiDAR),were combined to generate annual forest cover maps for the years 2015 to 2021.Specifically,both PALSAR-derived HV,HH-HV,and HH/HV and Landsat-derived Normalized Difference Vegetation Index(NDVI)were used together to generate annual maps of forest cover and three forest types(evergreen,deciduous,and mixed forest)at 30-m spatial resolution for each year.The canopy height and canopy coverage samples from the Global Ecosystem Dynamics Investigation(GEDI)and the Ice,Cloud,and land Elevation Satellite-2(ICESat-2)were used to assess forest cover maps.We also compared the spatial distribution and forested area of several forest products.Our results show that using the forest definition(canopy height>5 m,canopy coverage>10%over an area of 0.5 ha)of the Food and Agriculture Organization of the United Nations(FAO),the accuracy of resultant PALSAR/Landsat forest cover map for 2019 were 77.4%(GEDI)and 95.6%(ICESat-2).The estimated forested area(51,916 km2)was moderately higher(7.2%)than the forested area from the USDA Forest Inventory and Analysis(FIA)statistics dataset(48,202 km2)in 2017.Between 2016 and 2020,Oklahoma’s forested area increased slightly by 1.9%.The PALSAR/Landsat forest maps are more accurate in western Oklahoma compared to other satellite-based forest products.The resultant annual maps of forest cover and three different forest types over Oklahoma can be used to support statewide forest management and conservation.展开更多
Accurate estimation of gross primary production(GPP)of terrestrial vegetation is crucial for comprehending the carbon dynamics.To date,there is still no consensus on the magnitude and seasonality of global GPP among t...Accurate estimation of gross primary production(GPP)of terrestrial vegetation is crucial for comprehending the carbon dynamics.To date,there is still no consensus on the magnitude and seasonality of global GPP among the major global GPP products,underscoring the necessity to improve GPP models for higher accuracy of global GPP estimates.Here,we introduce an improved Vegetation Photosynthesis Model(VPM v3.0),which incorporates site-specific apparent optimum temperature for photosynthesis,leaf-trait-based light absorption(flat leaf vs.needle leaf),and improved water stress estimation.The global VPM simulation is driven by Moderate Resolution Imaging Spectroradiometer images and the ERA5-Land climate dataset.We evaluate VPM v3.0 using GPP from 205 eddy flux tower sites across 11 land cover types(1,658 site-years)(GPPEC),as well as the TROPOspheric monitoring instrument(TROPOMI)solar-induced fluorescence(SIF)product for 2018 to 2021.The slope,R^(2),and root mean square error between GPP from VPM v3.0(GPPVPM-v3)and GPPEC are 0.97,0.78,and 1.46 gC m^(−2) day^(−1),respectively.GPPVPM-v3 shows high temporal consistency with TROPOMI SIF.VPM v3.0 provides higher accuracy of GPP estimates at most evaluated sites than VPM v2.0.Comparisons of global GPP from VPM v3.0 with other major global GPP products reveal both spatial-temporal consistency and discrepancies.These findings clearly indicate the improved accuracy of VPM v3.0 in estimating GPP,making it suitable for generating global GPP datasets.展开更多
基金supported in part by research grants from the US National Science Foundation(OIA-1946093,OIA-1920946).
文摘Accurate forest cover maps are the basis for estimating forest biomass and are crucial for climate regulation and biodiversity conservation,especially in sub-humid and semi-arid regions such as Oklahoma,USA.To date,there is very limited data and knowledge of the spatial pattern and temporal dynamics of forest cover in Oklahoma,and current forest cover maps have large uncertainties.In this study,multi-sensor datasets,including the Phased Arrayed L-band Synthetic Aperture Radar(PALSAR-2),Landsat,and spaceborne Light Detection and Ranging(LiDAR),were combined to generate annual forest cover maps for the years 2015 to 2021.Specifically,both PALSAR-derived HV,HH-HV,and HH/HV and Landsat-derived Normalized Difference Vegetation Index(NDVI)were used together to generate annual maps of forest cover and three forest types(evergreen,deciduous,and mixed forest)at 30-m spatial resolution for each year.The canopy height and canopy coverage samples from the Global Ecosystem Dynamics Investigation(GEDI)and the Ice,Cloud,and land Elevation Satellite-2(ICESat-2)were used to assess forest cover maps.We also compared the spatial distribution and forested area of several forest products.Our results show that using the forest definition(canopy height>5 m,canopy coverage>10%over an area of 0.5 ha)of the Food and Agriculture Organization of the United Nations(FAO),the accuracy of resultant PALSAR/Landsat forest cover map for 2019 were 77.4%(GEDI)and 95.6%(ICESat-2).The estimated forested area(51,916 km2)was moderately higher(7.2%)than the forested area from the USDA Forest Inventory and Analysis(FIA)statistics dataset(48,202 km2)in 2017.Between 2016 and 2020,Oklahoma’s forested area increased slightly by 1.9%.The PALSAR/Landsat forest maps are more accurate in western Oklahoma compared to other satellite-based forest products.The resultant annual maps of forest cover and three different forest types over Oklahoma can be used to support statewide forest management and conservation.
基金supported by research grants from the U.S.National Science Foundation(OIA-1946093 and OIA-1920946)NASA(80NSSC24K0118)USDA National Institute of Food and Agriculture(2020-67014-30935).
文摘Accurate estimation of gross primary production(GPP)of terrestrial vegetation is crucial for comprehending the carbon dynamics.To date,there is still no consensus on the magnitude and seasonality of global GPP among the major global GPP products,underscoring the necessity to improve GPP models for higher accuracy of global GPP estimates.Here,we introduce an improved Vegetation Photosynthesis Model(VPM v3.0),which incorporates site-specific apparent optimum temperature for photosynthesis,leaf-trait-based light absorption(flat leaf vs.needle leaf),and improved water stress estimation.The global VPM simulation is driven by Moderate Resolution Imaging Spectroradiometer images and the ERA5-Land climate dataset.We evaluate VPM v3.0 using GPP from 205 eddy flux tower sites across 11 land cover types(1,658 site-years)(GPPEC),as well as the TROPOspheric monitoring instrument(TROPOMI)solar-induced fluorescence(SIF)product for 2018 to 2021.The slope,R^(2),and root mean square error between GPP from VPM v3.0(GPPVPM-v3)and GPPEC are 0.97,0.78,and 1.46 gC m^(−2) day^(−1),respectively.GPPVPM-v3 shows high temporal consistency with TROPOMI SIF.VPM v3.0 provides higher accuracy of GPP estimates at most evaluated sites than VPM v2.0.Comparisons of global GPP from VPM v3.0 with other major global GPP products reveal both spatial-temporal consistency and discrepancies.These findings clearly indicate the improved accuracy of VPM v3.0 in estimating GPP,making it suitable for generating global GPP datasets.
