基于无人机遥感影像的高精度森林资源调查系统设计与试验.

For the sustainable development and the accurate monitoring and information-based management of forestry resources, in this paper, we constructed a forest resource inventory system for forestry investigation and management. This research was based on the unmanned aerial vehicle (UAV) images data with the help of photogrammetric technique, UAV image post-processing technology and geographic information system technology, etc. In the system, we used C# programming language and fully used ArcGIS Engine10.2 embedded component technology. We also used UAV images to achieve efficient functions, such as forest spatial division, area adjustment and forest sub-compartment investigation, vegetation information extraction in high-precision and large-scale. The system can timely update resource database, greatly shorten the traditional investigation period, and achieve the scientific management of forest resources. The extraction of landform factors by software is based on UAV image data, DEM data, and spatial data from spatial division. It can be divided into two steps. The first is to extract the topographical attributes among the UAV image of whole area; the second is to ultimately extract landform factors combined with spatial data to finish attribute tables of each division, which is based on the first step. The goal of the stand factor extraction module is to obtain and store the vegetation information such as sub-compartment volume, stand density, average crown diameter, average tree height, average DBH and canopy coverage. Due to the difficulties for UAV to extract canopy coverage information in the dense forest area, in the study, we divided stand factor extraction module into the sparse forest area and the dense forest area. In the dense forest area, canopy coverage information of each sub-compartment was manually interpreted with the help of pattern spot border, and the input data were directly stored in its corresponding attribute database. In the sparse forest area, based on UAV images and spatial data, the system allowed its users to circle a nearly round sample area within the sub-compartment border, collect information of each single tree in the circle, automatically store the statistical results of all sample information into the corresponding sub-compartment, namely its vegetation information. The empirical study of this forest resource investigation system has been conducted in Lao Tudingzi, Liaoning Province. The results have proven its simple interface, high automation and good interactivity on system operation. The relative error in extraction of slope and elevation were about 5.17% and 5.41% respectively. Before the stand factors were acquired, the relationship between crown diameter and DBH and the relationship between tree height and DBH in test area were fitted by 1stOpt software, then the evaluation indexes, such as coefficient of determination, standard error estimate and total relative error, were introduced to determine the optimal model. The stand factors of the test area have been extracted based on the optimal model with the statistical value of stand density and sub-compartment volume measurement. The results showed the relative error of stand density is 2.68%, and that of sub-compartment volume is 4.01%. [ABSTRACT FROM AUTHOR]