Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carb...Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB).Different levels of detail are needed to estimate FAGB at local,regional and national scales.Multi-scale remote sensing analysis from high,medium and coarse spatial resolution data,along with field sampling,is one approach often used.However,the methods developed are still time consuming,expensive,and inconvenient for systematic monitoring,especially for developing countries,as they require vast numbers of field samples for upscaling.Here,we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites.The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m),Landsat(30 m)and Google Earth very high resolution(GEVHR)Quickbird(0.65 m)images.For the local scale(Kayerkhola watershed),tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images.An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot.A TCC vs.FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots.A coefficient of determination(R2)of 0.76 was obtained in the modelling,and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district,open source GEVHR images were used as virtual field plots.We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model.Using the multivariate adaptive regression splines machine learning algorithm,we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices.The model was then used to extrapolate FAGB to the entire district.This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m)and accuracy(R2=0.76 and 0.7)with minimal error(RMSE=64 and 38 tons ha-1)at local and regional scales.This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time.The method is especially applicable for developing countries that have low budgets for carbon estimations,and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD?)monitoring reporting and verification processes.展开更多
在城市建设用地日益紧张的背景下,低效用地再开发已成为城市发展建设的重要选择。本文融合地理信息数据资源和自然资源管理相关数据,在地块尺度将建设用地节约集约评价与低效用地识别结合,从低效用地主导因素出发,运用单因素集成评价方...在城市建设用地日益紧张的背景下,低效用地再开发已成为城市发展建设的重要选择。本文融合地理信息数据资源和自然资源管理相关数据,在地块尺度将建设用地节约集约评价与低效用地识别结合,从低效用地主导因素出发,运用单因素集成评价方法,从规划符合度、建筑特征、建筑强度、利用效益四个递推层级逐项判别,快速识别出低效用地再利用POI数据测算低效用地再开发潜力。通过ArcGIS Model Builder工具搭建快速评价应用模型,并以呼和浩特赛罕区、新城区中心城区为研究区进行应用,共识别出该区域各类低效住宅用地共10.65km^(2),经过卫星遥感影像以及百度街景验证发现整体识别效果较好。该方法可以快速初步识别地块尺度低效用地,可以辅助低效用地外业调查,管理部门衡量当前建设用地利用现状,并为再开发工作提供支持。展开更多
基金supported by the CAS Strategic Priority Research Program(No.XDA19030402)the National Key Research and Development Program of China(No.2016YFD0300101)+2 种基金the Natural Science Foundation of China(Nos.31571565,31671585)the Key Basic Research Project of the Shandong Natural Science Foundation of China(No.ZR2017ZB0422)Research Funding of Qingdao University(No.41117010153)
文摘Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB).Different levels of detail are needed to estimate FAGB at local,regional and national scales.Multi-scale remote sensing analysis from high,medium and coarse spatial resolution data,along with field sampling,is one approach often used.However,the methods developed are still time consuming,expensive,and inconvenient for systematic monitoring,especially for developing countries,as they require vast numbers of field samples for upscaling.Here,we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites.The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m),Landsat(30 m)and Google Earth very high resolution(GEVHR)Quickbird(0.65 m)images.For the local scale(Kayerkhola watershed),tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images.An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot.A TCC vs.FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots.A coefficient of determination(R2)of 0.76 was obtained in the modelling,and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district,open source GEVHR images were used as virtual field plots.We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model.Using the multivariate adaptive regression splines machine learning algorithm,we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices.The model was then used to extrapolate FAGB to the entire district.This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m)and accuracy(R2=0.76 and 0.7)with minimal error(RMSE=64 and 38 tons ha-1)at local and regional scales.This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time.The method is especially applicable for developing countries that have low budgets for carbon estimations,and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD?)monitoring reporting and verification processes.
文摘在城市建设用地日益紧张的背景下,低效用地再开发已成为城市发展建设的重要选择。本文融合地理信息数据资源和自然资源管理相关数据,在地块尺度将建设用地节约集约评价与低效用地识别结合,从低效用地主导因素出发,运用单因素集成评价方法,从规划符合度、建筑特征、建筑强度、利用效益四个递推层级逐项判别,快速识别出低效用地再利用POI数据测算低效用地再开发潜力。通过ArcGIS Model Builder工具搭建快速评价应用模型,并以呼和浩特赛罕区、新城区中心城区为研究区进行应用,共识别出该区域各类低效住宅用地共10.65km^(2),经过卫星遥感影像以及百度街景验证发现整体识别效果较好。该方法可以快速初步识别地块尺度低效用地,可以辅助低效用地外业调查,管理部门衡量当前建设用地利用现状,并为再开发工作提供支持。
