Characterizing vertical forest structure using small-footprint airborne LiDAR
Characterizing vertical forest structure using small-footprint airborne LiDAR
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DOI：
10.1016/s0034-4257(03)00139-1
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发表时间：
2003-10
影响因子：
13.5
通讯作者：
D. Zimble;David L. Evans;G. C. Carlson;Robert C. Parker;S. Grado;P. Gerard
中科院分区：
文献类型：
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作者：
D. Zimble;David L. Evans;G. C. Carlson;Robert C. Parker;S. Grado;P. Gerard
为了以尽可能接近自然生态条件的方式管理陆地资源，有必要在精细尺度上确定森林属性的特征。在森林生态系统中，管理决策是由森林组成、森林结构（垂直和水平）和其他辅助数据（即，地形、土壤、坡度、坡向和扰动动态）。垂直森林结构难以量化，但却是决策过程中的一个重要组成部分。本研究调查了使用光探测和测距（LiDAR）数据在景观尺度上对该属性进行分类，以便纳入决策支持系统。对实测树高方差的分析表明，该指标可以区分两类垂直森林结构。LiDAR衍生树高方差的分析表明，单层和多层垂直结构类之间的差异可以检测到。景观尺度分类的两个结构类是97%的准确率。这项研究表明，在美国西部山区的森林类型，激光雷达衍生的树高可以是有用的检测的差异，在连续的，非专题性质的垂直结构森林可接受的精度。
Characterization of forest attributes at fine scales is necessary to manage terrestrial resources in a manner that replicates, as closely as possible, natural ecological conditions. In forested ecosystems, management decisions are driven by variables such as forest composition, forest structure (both vertical and horizontal), and other ancillary data (i.e., topography, soils, slope, aspect, and disturbance regime dynamics). Vertical forest structure is difficult to quantify and yet is an important component in the decision-making process. This study investigated the use of light detection and ranging (LiDAR) data for classifying this attribute at landscape scales for inclusion into decision-support systems. Analysis of field-derived tree height variance demonstrated that this metric could distinguish between two classes of vertical forest structure. Analysis of LiDAR-derived tree height variance demonstrated that differences between single-story and multistory vertical structural classes could be detected. Landscape-scale classification of the two structure classes was 97% accurate. This study suggested that within forest types of the Intermountain West region of the United States, LiDAR-derived tree heights could be useful in the detection of differences in the continuous, nonthematic nature of vertical structure forest with acceptable accuracies.