Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest
Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest
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DOI：
10.1016/j.rse.2012.06.024
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发表时间：
2012-09
影响因子：
13.5
通讯作者：
B. Wing;M. Ritchie;K. Boston;W. Cohen;A. Gitelman;M. Olsen
中科院分区：
文献类型：
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作者：
B. Wing;M. Ritchie;K. Boston;W. Cohen;A. Gitelman;M. Olsen
森林林下群落是森林生态系统的重要组成部分，提供野生动物栖息地，并随着时间的推移影响养分循环、燃料负荷、火灾行为和树种组成。最广泛使用的林下组成指标之一是林下植被覆盖度，通常用作植被丰度的衡量标准。迄今为止，事实证明，使用传统的解释变量（例如叶面积指数、断面积、坡度和坡向）来估计和预测林下植被覆盖本身就很困难。我们将机载激光雷达导出的指标引入林下植被覆盖的建模框架中。一种新的机载激光雷达指标，即林下激光雷达覆盖密度，是通过使用强度值过滤林下激光雷达点而创建的，增加了非激光雷达林下植被覆盖估计模型的传统解释力（非激光雷达 R2 值：0.2–0.45 与激光雷达 R2 值：0.7–0.8）。 Beta 回归是针对此类数据的一种相对较新的建模技术，它与使用留一交叉验证程序的传统加权线性回归模型进行了比较。两个模型使用 40.5 平方米圆形图 (n=154) 提供了相似的林下植被覆盖精度 (±22%) 和偏差 (~0%)。本文提出的方法能够在广泛的内陆黄松林类型的林分条件下以精细的空间分辨率准确获取普查林下植被覆盖信息。其他模型增强以及该方法扩展到其他森林类型值得进一步研究。
Forest understory communities are important components in forest ecosystems providing wildlife habitat and influencing nutrient cycling, fuel loadings, fire behavior and tree species composition over time. One of the most widely utilized understory component metrics is understory vegetation cover, often used as a measure of vegetation abundance. To date, understory vegetation cover estimation and prediction has proven to be inherently difficult using traditional explanatory variables such as: leaf area index, basal area, slope, and aspect. We introduce airborne lidar-derived metrics into the modeling framework for understory vegetation cover. A new airborne lidar metric, understory lidar cover density, created by filtering understory lidar points using intensity values increased traditional explanatory power from non-lidar understory vegetation cover estimation models (non-lidar R2-values: 0.2–0.45 vs. lidar R2-values: 0.7–0.8). Beta regression, a relatively new modeling technique for this type of data, was compared with a traditional weighted linear regression model using a leave-one-out cross‐validation procedure. Both models provided similar understory vegetation cover accuracies (±22%) and biases (~0%) using 40.5m2circular plots (n=154). The method presented in this paper provides the ability to accurately obtain census understory vegetation cover information at fine spatial resolutions over a broad range of stand conditions for the interior ponderosa pine forest type. Additional model enhancement and the extension of the method into other forest types warrant further investigation.