CAJ | 학술논문

目前获取森林特征参数的主要方法是外业测量,工作量大、效率低.该文以中国自主研发的轻小型航空遥感系统为数据获取工具,以油松人工林为研究对象,通过对获取森林的激光雷达(light detection and ranging, LIDAR)点云数据去噪,分类,提取等过程获得单木的树高数据,对获取的航空影像数据进行预处理,匹配,拼接,分割及冠幅提取获得单木的冠幅数据,再与外业抽样调查的单木的树高、胸径建立回归模型,同时验证模型精度.试验结果表明:通过LIDAR点云数据提取的树高与实测的树高具有极显著的相关性,所建立的模型预测精度达97.5%,通过影像提取的冠幅与实测的胸径也具有极显著的相关性,预测精度达91.6%,基本上能够满足林业生产的要求.
목전획취삼림특정삼수적주요방법시외업측량,공작량대、효솔저.해문이중국자주연발적경소형항공요감계통위수거획취공구,이유송인공림위연구대상,통과대획취삼림적격광뢰체(light detection and ranging, LIDAR)점운수거거조,분류,제취등과정획득단목적수고수거,대획취적항공영상수거진행예처리,필배,병접,분할급관폭제취획득단목적관폭수거,재여외업추양조사적단목적수고、흉경건립회귀모형,동시험증모형정도.시험결과표명:통과LIDAR점운수거제취적수고여실측적수고구유겁현저적상관성,소건립적모형예측정도체97.5%,통과영상제취적관폭여실측적흉경야구유겁현저적상관성,예측정도체91.6%,기본상능구만족임업생산적요구.
@@@@Airborne lidar and digital aerial photography, in a light small aerial remote sensing system, can obtain three-dimensional coordinates to the quantitative estimate of forest parameters, and in particular have unique advantages in terms of tree height and forest spatial structure estimation. Even though China mainly uses foreign aerial photography system, this study, based on Chinese self-developed high-precision small aerial remote sensing system, established a model between remote sensing data and the ground forestry stand value, and evaluated the accuracy of the model and the feasibility of the aviation system in forestry. The Chinese pine plantation in Shangcheng City, Henan Province was chosen for the study area, and a standard single tree was chosen in the 40 sample plots. The tree height and tree diameter at breast height (DBH) measured by traditional methods were treated as the reference values. A photographic image obtained by the aerial digital photography system was transformed to an orthophoto through mosaic, matching, and stitching processes. With the adjacent pixel-comparison method, the tree crown width was extracted from the orthophoto based on the object-oriented fuzzy algorithm. After noise removal, point cloud data obtained by airborne LIDAR (light detection and ranging) generated a digital elevation model (DEM) and digital surface model (DSM) through an interpolation algorithm. Thus the tree height model is obtained by subtraction. In this paper, based on the 30 sample trees, the linear regression model for tree height was built between LIDAR data and field survey data with model correlation coefficient R2 of 0.895. The relationship is remarkable. The linear regression model for DBH was built by the average tree crown width extracted by aerial images and field survey DBH data, and R is 0.876, also a remarkable result. Based on the other 10 sample trees, the accuracies of tree height model and DBH model were estimated. The height model’s overall relative error RS was 0.8%, the average relative error E was 0.71%, and the estimated precision P was 97.5%. Therefore, the forecast accuracy is high and can achieve the forestry production requirement standard error of less than 5%. The DBH model’s overall relative error RS is -1.9%, the average relative error is E -2.0%, and forecast precision P is 91.6%.