现阶段,大比例尺地形图测绘方式存在生产效率低、劳动强度大、作业周期长等问题。通过分析大比例尺地形图测绘实际情况可知,该工作已经无法通过传统测绘作业得到满足。基于此,首先文章将简单介绍机载Lidar(Light detection and ranging...现阶段,大比例尺地形图测绘方式存在生产效率低、劳动强度大、作业周期长等问题。通过分析大比例尺地形图测绘实际情况可知,该工作已经无法通过传统测绘作业得到满足。基于此,首先文章将简单介绍机载Lidar(Light detection and ranging,激光雷达)系统,然后结合某工程案例探讨在大比例尺地形图测绘中综合运用机载Lidar和无人机航空摄影的方法,最终分析此方法应用效果,希望可以为相关人员实际开展工作提供一定借鉴意义。展开更多
Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from vario...Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing datasets.However,combining the advantages of active and passive data sources to improve estimation accuracy remains challenging.Here,we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral information.Over 60 km^(2) of drone light detection and ranging(LiDAR)data and 1,370 field plot measurements,covering the four major forest types of China(coniferous forest,sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and tropical broadleaf forest),were collected together with Sentinel-2 images to evaluate the proposed approach.The results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum values.Compared with structural attributes used alone,combining structural and spectral information can improve the estimation accuracy of AGB,increasing R^(2) by about 10%and reducing the root mean square error by about 22%;the accuracy of the proposed approach can yield a R^(2) of 0.7 in different forests types.The proposed approach performs the best in coniferous forest,followed by sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and then tropical broadleaf forest.Furthermore,the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression,artificial neural networks,and Random Forest.The proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.展开更多
农田地形、农田结构精度测绘是农作物生产的重要措施之一。不同地区农田海拔、地形地貌与结构高程等存在显著差异,传统农田测绘通常采用RTK-GPS(Real time kinematic GPS)实时动态载波相位差分技术,由GPS卫星定位系统、一个基准站配合...农田地形、农田结构精度测绘是农作物生产的重要措施之一。不同地区农田海拔、地形地貌与结构高程等存在显著差异,传统农田测绘通常采用RTK-GPS(Real time kinematic GPS)实时动态载波相位差分技术,由GPS卫星定位系统、一个基准站配合多个流动站进行地面农田载波相位观测,得到农田海拔高度的精度测绘与检验结果,但相较PPK-GPS(Post Processing Kinematic GPS)动态后处理差分技术而言其时延过大、定位成本过高。基于此,提出一种多旋翼无人机低空摄影、激光雷达成像相结合的测距技术,在无人机设备上安装摄像机模块、激光雷达测距模块,采用无人机倾斜摄影、PPK-GPS三维定位测距技术,实现区域农田地形地块结构、海拔高度分量、其他地理信息等数据的精确测量,提升不同地形地貌结构农田测绘的效率与精确率。展开更多
文摘现阶段,大比例尺地形图测绘方式存在生产效率低、劳动强度大、作业周期长等问题。通过分析大比例尺地形图测绘实际情况可知,该工作已经无法通过传统测绘作业得到满足。基于此,首先文章将简单介绍机载Lidar(Light detection and ranging,激光雷达)系统,然后结合某工程案例探讨在大比例尺地形图测绘中综合运用机载Lidar和无人机航空摄影的方法,最终分析此方法应用效果,希望可以为相关人员实际开展工作提供一定借鉴意义。
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA19050401)the National Natural Science Foundation of China(41871332,31971575,41901358).
文摘Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing datasets.However,combining the advantages of active and passive data sources to improve estimation accuracy remains challenging.Here,we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral information.Over 60 km^(2) of drone light detection and ranging(LiDAR)data and 1,370 field plot measurements,covering the four major forest types of China(coniferous forest,sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and tropical broadleaf forest),were collected together with Sentinel-2 images to evaluate the proposed approach.The results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum values.Compared with structural attributes used alone,combining structural and spectral information can improve the estimation accuracy of AGB,increasing R^(2) by about 10%and reducing the root mean square error by about 22%;the accuracy of the proposed approach can yield a R^(2) of 0.7 in different forests types.The proposed approach performs the best in coniferous forest,followed by sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and then tropical broadleaf forest.Furthermore,the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression,artificial neural networks,and Random Forest.The proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.
