The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging(LiDAR) data.A digi...The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging(LiDAR) data.A digital canopy model(DCM),generated from the LiDAR data,was combined with aerial photography for segmenting crowns of individual trees.To eliminate errors in over and under-segmentation,the combined image was smoothed using a Gaussian filtering method.The processed image was then segmented into individual trees using a marker-controlled watershed segmentation method.After measuring the crown area from the segmented individual trees,the individual tree diameter at breast height(DBH) was estimated using a regression function developed from the relationship observed between the field-measured DBH and crown area.The above ground biomass of individual trees could be calculated by an image-derived DBH using a regression function developed by the Korea Forest Research Institute.The carbon storage,based on individual trees,was estimated by simple multiplication using the carbon conversion index(0.5),as suggested in guidelines from the Intergovernmental Panel on Climate Change.The mean carbon storage per individual tree was estimated and then compared with the field-measured value.This study suggested that the biomass and carbon storage in a large forest area can be effectively estimated using aerial photographs and LiDAR data.展开更多
单位面积穗数是小麦产量构成的重要因素,利用图像信息处理技术快速、准确地估测田间小麦穗数,可以为小麦长势监测和产量估测提供直接依据。利用无人机路径规划和控制系统(fragmentation monitoring and analysis with aerial photograph...单位面积穗数是小麦产量构成的重要因素,利用图像信息处理技术快速、准确地估测田间小麦穗数,可以为小麦长势监测和产量估测提供直接依据。利用无人机路径规划和控制系统(fragmentation monitoring and analysis with aerial photography,FragMAP)获取标准统一、高分辨率的田间小麦RGB航拍影像,通过高效的目标检测手段(YOLOv3)获得训练模型并自动识别麦穗,通过分析该方法(FY方法)与传统方法测定麦穗数量的关系来构建单位面积麦穗估测模型。结果表明,FY方法的样本获取效率和观测面积显著高于传统方法(P <0.001);YOLOv3训练模型识别麦穗的准确率随着训练样本数量和迭代次数增加而增加,500个训练样本迭代6 250次,获得模型识别麦穗的准确率超过90%;FY方法和与传统方法测定的田间小麦穗数量呈显著的线性相关关系,据此构建估测田间小麦穗数的模型为:y=0.816x-14.863(R;=0.790,P<0.001)。上述结果表明,结合标准统一、高分辨率的无人机航拍影像和深度学习方法估测田间小麦穗数精度高、实时性强,可为小麦长势监测和产量估测提供重要的数据和技术支撑。展开更多
基金the support of the ‘Public Applications Research of Satellite Data Project’ (Grant No. FR09662). provided by the Korea Aerospace Research Institutesupported by a research grant from the Korea Science and Engineering Foundation (KOSEF) (Grant No. A307-K001)
文摘The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging(LiDAR) data.A digital canopy model(DCM),generated from the LiDAR data,was combined with aerial photography for segmenting crowns of individual trees.To eliminate errors in over and under-segmentation,the combined image was smoothed using a Gaussian filtering method.The processed image was then segmented into individual trees using a marker-controlled watershed segmentation method.After measuring the crown area from the segmented individual trees,the individual tree diameter at breast height(DBH) was estimated using a regression function developed from the relationship observed between the field-measured DBH and crown area.The above ground biomass of individual trees could be calculated by an image-derived DBH using a regression function developed by the Korea Forest Research Institute.The carbon storage,based on individual trees,was estimated by simple multiplication using the carbon conversion index(0.5),as suggested in guidelines from the Intergovernmental Panel on Climate Change.The mean carbon storage per individual tree was estimated and then compared with the field-measured value.This study suggested that the biomass and carbon storage in a large forest area can be effectively estimated using aerial photographs and LiDAR data.
文摘海洋动物是南极气候变化的＂生物指示剂＂,其排泄物中丰富的碳（C）和氮（N）等营养物质为土壤中温室气体的产生与排放提供了有利条件,企鹅作为一种重要的海洋动物,因此其聚居区成为甲烷（CH4）和氧化亚氮（N2O）等温室气体排放的潜在＂热点＂区域.然而,受企鹅数量遥感资料的限制,区域尺度上企鹅源温室气体排放总量尚缺乏精确估算.以南极维多利亚地难言岛企鹅聚集区为研究对象,基于0.1 m分辨率航拍照片发展了面向像元的RGB颜色模型法（pixel-oriented RGB color model）识别企鹅数量,通过企鹅粪便CH4和N2O排放通量、企鹅排便量等数据建立了企鹅源温室气体估算模型.结果显示,航拍照片中企鹅像元在RGB彩色空间模型中的R值（17~104）与其他背景像元（〉110）存在显著差异,该差异可以作为将企鹅与背景像元有效分离的理论依据;南极维多利亚地难言岛企鹅总数为19150只,企鹅源CH4和N2O排放总量分别约为275和2.99 kg.
文摘单位面积穗数是小麦产量构成的重要因素,利用图像信息处理技术快速、准确地估测田间小麦穗数,可以为小麦长势监测和产量估测提供直接依据。利用无人机路径规划和控制系统(fragmentation monitoring and analysis with aerial photography,FragMAP)获取标准统一、高分辨率的田间小麦RGB航拍影像,通过高效的目标检测手段(YOLOv3)获得训练模型并自动识别麦穗,通过分析该方法(FY方法)与传统方法测定麦穗数量的关系来构建单位面积麦穗估测模型。结果表明,FY方法的样本获取效率和观测面积显著高于传统方法(P <0.001);YOLOv3训练模型识别麦穗的准确率随着训练样本数量和迭代次数增加而增加,500个训练样本迭代6 250次,获得模型识别麦穗的准确率超过90%;FY方法和与传统方法测定的田间小麦穗数量呈显著的线性相关关系,据此构建估测田间小麦穗数的模型为:y=0.816x-14.863(R;=0.790,P<0.001)。上述结果表明,结合标准统一、高分辨率的无人机航拍影像和深度学习方法估测田间小麦穗数精度高、实时性强,可为小麦长势监测和产量估测提供重要的数据和技术支撑。
