介绍了联邦德国的土地估价信息系统具备的功能和发展现状。设计了适合我国国情的、具有世界先进水平的估价信息系统LAIS(Land Appraisal Information System)。该系统包括数据调查、管理和评价、产品的生产和表达三个部分。最后对估价...介绍了联邦德国的土地估价信息系统具备的功能和发展现状。设计了适合我国国情的、具有世界先进水平的估价信息系统LAIS(Land Appraisal Information System)。该系统包括数据调查、管理和评价、产品的生产和表达三个部分。最后对估价信息系统在中国的实现进行了可行性分析。展开更多
叶面积指数(leaf area index,LAI)是反映植物冠层结构和光能利用的重要指标.随着遥感技术的不断发展,利用遥感数据获取大面积LAI已经成为监测作物生长和估产的重要手段.基于物理模型的LAI遥感反演方法经常假设作物冠层结构是均匀分布,然...叶面积指数(leaf area index,LAI)是反映植物冠层结构和光能利用的重要指标.随着遥感技术的不断发展,利用遥感数据获取大面积LAI已经成为监测作物生长和估产的重要手段.基于物理模型的LAI遥感反演方法经常假设作物冠层结构是均匀分布,然而,作为典型的垄行结构,作物冠层被公认为是介于连续植被与离散植被之间的一种过渡形式,而简单的均匀假设必然会给反演带来偏差.本文以农作物玉米为研究对象,首先重建了玉米三维冠层结构,并定量对比分析了一维辐射传输模型PROSAIL和三维辐射传输模型LESS在玉米冠层不同生长期的反射率差异,确定了玉米冠层的非均匀分布特征是引起PROSAIL模型模拟和反演误差的主要因素;然后,考虑到玉米冠层生长过程中聚集指数的变化特征,利用LESS模型定量计算了不同生育期玉米冠层结构对应的聚集指数,建立了聚集指数和有效叶面积指数(LAI_(e))之间的关系;进而,利用该关系对基于PROSAIL模型反演得到的LAI进行修正.结果表明,修正后的LAI精度有明显提高,R^(2)从0.27提高到了0.55.该方法有望提高中高分辨率遥感数据在农作物LAI反演精度.展开更多
山地森林叶面积指数(Leaf Area Index,LAI)的准确获取对评估森林生态系统的生产力和碳循环至关重要。遥感手段是当前获取面尺度LAI的主要方法,植被指数(Vegetation Indices,VIs)因其简便性和鲁棒性,广泛用于LAI反演。然而,复杂地形会导...山地森林叶面积指数(Leaf Area Index,LAI)的准确获取对评估森林生态系统的生产力和碳循环至关重要。遥感手段是当前获取面尺度LAI的主要方法,植被指数(Vegetation Indices,VIs)因其简便性和鲁棒性,广泛用于LAI反演。然而,复杂地形会导致VIs反演结果存在不确定性。本研究基于地面实测LAI和无人机高光谱数据,选择40种主流VIs,按波段和数学构成分为4类,评估其在坡度、高程和天空可视因子(Sky View Factor,SVF)变化下的反演精度。结果表明:1)红光和近红外波段的严格比值型VIs与LAI具有最优的建模精度,以NDVI为例,不同坡度变化下R^(2)在0.450~0.681之间浮动,不同高程变化下R^(2)在0.507~0.824之间浮动,不同SVF变化下R^(2)在0.311~0.765之间浮动。2)坡度变化对反演精度的影响可通过波段比值部分削弱;高程变化通过影响植被分布影响建模精度;目前无VIs能有效消除SVF变化带来的影响。3)不同季节VIs的适用性不同,GCC适用于初春,R^(2)最优为0.657,SIPI适用于夏季高温期,R^(2)最优为0.558,kNDVI在秋季表现最佳R^(2)最优分别为0.578,NDVI在冬季表现最佳,R^(2)最优为0.708。本研究对VIs在山地森林LAI反演时的地形效应进行了系统评估,可为准确评估森林生态系统碳循环及实现“碳达峰碳中和”做出一定的贡献。展开更多
Crop leaf area index(LAI)and biomass are two major biophysical parameters to measure crop growth and health condition.Measuring LAI and biomass in field experiments is a destructive method.Therefore,we focused on the ...Crop leaf area index(LAI)and biomass are two major biophysical parameters to measure crop growth and health condition.Measuring LAI and biomass in field experiments is a destructive method.Therefore,we focused on the application of unmanned aerial vehicles(UAVs)in agriculture,which is a cost and labor-efficientmethod.Hence,UAV-captured multispectral images were applied to monitor crop growth,identify plant bio-physical conditions,and so on.In this study,we monitored soybean crops using UAV and field experiments.This experiment was conducted at theMAFES(Mississippi Agricultural and Forestry Experiment Station)Pontotoc Ridge-Flatwoods Branch Experiment Station.It followed a randomized block design with five cover crops:Cereal Rye,Vetch,Wheat,MC:mixed Mustard and Cereal Rye,and native vegetation.Planting was made in the fall,and three fertilizer treatments were applied:Synthetic Fertilizer,Poultry Litter,and none,applied before planting the soybean,in a full factorial combination.We monitored soybean reproductive phases at R3(initial pod development),R5(initial seed development),R6(full seed development),and R7(initial maturity)and used UAV multispectral remote sensing for soybean LAI and biomass estimations.The major goal of this study was to assess LAI and biomass estimations from UAV multispectral images in the reproductive stages when the development of leaves and biomass was stabilized.Wemade about fourteen vegetation indices(VIs)fromUAVmultispectral images at these stages to estimate LAI and biomass.Wemodeled LAI and biomass based on these remotely sensed VIs and ground-truth measurements usingmachine learning methods,including linear regression,Random Forest(RF),and support vector regression(SVR).Thereafter,the models were applied to estimate LAI and biomass.According to the model results,LAI was better estimated at the R6 stage and biomass at the R3 stage.Compared to the other models,the RF models showed better estimation,i.e.,an R^(2) of about 0.58–0.68 with an RMSE(rootmean square error)of 0.52–0.60(m^(2)/m^(2))for the LAI and about 0.44–0.64 for R^(2) and 21–26(g dry weight/5 plants)for RMSE of biomass estimation.We performed a leave-one-out cross-validation.Based on cross-validatedmodels with field experiments,we also found that the R6 stage was the best for estimating LAI,and the R3 stage for estimating crop biomass.The cross-validated RF model showed the estimation ability with an R^(2) about 0.25–0.44 and RMSE of 0.65–0.85(m^(2)/m^(2))for LAI estimation;and R^(2) about 0.1–0.31 and an RMSE of about 28–35(g dry weight/5 plants)for crop biomass estimation.This result will be helpful to promote the use of non-destructive remote sensing methods to determine the crop LAI and biomass status,which may bring more efficient crop production and management.展开更多
青藏高原自然资源丰富、生态系统多样,是我国重要的生态安全屏障。叶面积指数(Leaf Area Index,LAI)是表征植被冠层结构的重要参数,高效准确地获取青藏高原LAI数据对于青藏高原植被生长状况动态监测及生态环境变化等研究具有重要的意义...青藏高原自然资源丰富、生态系统多样,是我国重要的生态安全屏障。叶面积指数(Leaf Area Index,LAI)是表征植被冠层结构的重要参数,高效准确地获取青藏高原LAI数据对于青藏高原植被生长状况动态监测及生态环境变化等研究具有重要的意义。本研究以青藏高原为研究区,采用PROSAIL物理机理模型和机器学习(随机森林方法)结合的LAI反演方法,生产了1990-2023年青藏高原长时间序列30米分辨率年度最大有效LAI产品。Google Earth Engine云平台存档的近40年Landsat系列卫星历史影像为LAI产品生产提供了数据保障。数据产品质量评估结果表明,青藏高原30 m分辨率LAI产品在直接验证和交叉验证中均有较好的精度,产品质量可靠,可以为青藏高原植被资源调查、生态环境保护与恢复等研究提供数据产品支撑。展开更多
Editor-in-Chief Yuanming Lai,Academician of Chinese Academy of Sciences,director of Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou,China,Associate Editor of Cold Regions Scien...Editor-in-Chief Yuanming Lai,Academician of Chinese Academy of Sciences,director of Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou,China,Associate Editor of Cold Regions Science and Technology.展开更多
Editor-in-Chief Yuanming Lai,Academician of Chinese Academy of Sciences,director of Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou,China,Associate Editor of Cold Regions Scien...Editor-in-Chief Yuanming Lai,Academician of Chinese Academy of Sciences,director of Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou,China,Associate Editor of Cold Regions Science and Technology.展开更多
文摘叶面积指数(leaf area index,LAI)是反映植物冠层结构和光能利用的重要指标.随着遥感技术的不断发展,利用遥感数据获取大面积LAI已经成为监测作物生长和估产的重要手段.基于物理模型的LAI遥感反演方法经常假设作物冠层结构是均匀分布,然而,作为典型的垄行结构,作物冠层被公认为是介于连续植被与离散植被之间的一种过渡形式,而简单的均匀假设必然会给反演带来偏差.本文以农作物玉米为研究对象,首先重建了玉米三维冠层结构,并定量对比分析了一维辐射传输模型PROSAIL和三维辐射传输模型LESS在玉米冠层不同生长期的反射率差异,确定了玉米冠层的非均匀分布特征是引起PROSAIL模型模拟和反演误差的主要因素;然后,考虑到玉米冠层生长过程中聚集指数的变化特征,利用LESS模型定量计算了不同生育期玉米冠层结构对应的聚集指数,建立了聚集指数和有效叶面积指数(LAI_(e))之间的关系;进而,利用该关系对基于PROSAIL模型反演得到的LAI进行修正.结果表明,修正后的LAI精度有明显提高,R^(2)从0.27提高到了0.55.该方法有望提高中高分辨率遥感数据在农作物LAI反演精度.
基金This research was supported in part by a postdoctoral research fellow appointment to the Agricultural Research Service(ARS)Research Participation Program administered by the Oak Ridge Institute for Science and Education(ORISE)through an interagency agreement between the U.S.Department of Energy(DOE)and the U.S.Department of Agriculture(USDA).
文摘Crop leaf area index(LAI)and biomass are two major biophysical parameters to measure crop growth and health condition.Measuring LAI and biomass in field experiments is a destructive method.Therefore,we focused on the application of unmanned aerial vehicles(UAVs)in agriculture,which is a cost and labor-efficientmethod.Hence,UAV-captured multispectral images were applied to monitor crop growth,identify plant bio-physical conditions,and so on.In this study,we monitored soybean crops using UAV and field experiments.This experiment was conducted at theMAFES(Mississippi Agricultural and Forestry Experiment Station)Pontotoc Ridge-Flatwoods Branch Experiment Station.It followed a randomized block design with five cover crops:Cereal Rye,Vetch,Wheat,MC:mixed Mustard and Cereal Rye,and native vegetation.Planting was made in the fall,and three fertilizer treatments were applied:Synthetic Fertilizer,Poultry Litter,and none,applied before planting the soybean,in a full factorial combination.We monitored soybean reproductive phases at R3(initial pod development),R5(initial seed development),R6(full seed development),and R7(initial maturity)and used UAV multispectral remote sensing for soybean LAI and biomass estimations.The major goal of this study was to assess LAI and biomass estimations from UAV multispectral images in the reproductive stages when the development of leaves and biomass was stabilized.Wemade about fourteen vegetation indices(VIs)fromUAVmultispectral images at these stages to estimate LAI and biomass.Wemodeled LAI and biomass based on these remotely sensed VIs and ground-truth measurements usingmachine learning methods,including linear regression,Random Forest(RF),and support vector regression(SVR).Thereafter,the models were applied to estimate LAI and biomass.According to the model results,LAI was better estimated at the R6 stage and biomass at the R3 stage.Compared to the other models,the RF models showed better estimation,i.e.,an R^(2) of about 0.58–0.68 with an RMSE(rootmean square error)of 0.52–0.60(m^(2)/m^(2))for the LAI and about 0.44–0.64 for R^(2) and 21–26(g dry weight/5 plants)for RMSE of biomass estimation.We performed a leave-one-out cross-validation.Based on cross-validatedmodels with field experiments,we also found that the R6 stage was the best for estimating LAI,and the R3 stage for estimating crop biomass.The cross-validated RF model showed the estimation ability with an R^(2) about 0.25–0.44 and RMSE of 0.65–0.85(m^(2)/m^(2))for LAI estimation;and R^(2) about 0.1–0.31 and an RMSE of about 28–35(g dry weight/5 plants)for crop biomass estimation.This result will be helpful to promote the use of non-destructive remote sensing methods to determine the crop LAI and biomass status,which may bring more efficient crop production and management.
文摘Editor-in-Chief Yuanming Lai,Academician of Chinese Academy of Sciences,director of Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou,China,Associate Editor of Cold Regions Science and Technology.
文摘Editor-in-Chief Yuanming Lai,Academician of Chinese Academy of Sciences,director of Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou,China,Associate Editor of Cold Regions Science and Technology.
