Department of Forestry, Faculty of Natural Resources, Tarbiat Modares University, Jalal Ale Ahmad Highway, P.O. Box, 14115-111, Tehran, Iran.
Environ Monit Assess. 2021 Dec 27;194(1):45. doi: 10.1007/s10661-021-09695-7.
Canopy cover is an important structural trait that is frequently used in forest inventories to assess sustainability as well as many other important aspects of forest stands. Remote sensing data is more effective and suitable for canopy cover estimating than traditional field measurements such as sample plots, especially at broad scales. Measurement and mapping this attribute in fine-scale is a difficult task. Aerial imagery using unmanned aerial vehicle (UAV) has been recognized as an excellent tool to estimate canopy attributes. In this study, we compared the potential of using digital hemispherical photography (DHP), digital cover photography (DCP), UAV RGB data, and canopy height model (CHM) for estimation of canopy cover of mix broad-leaved forest on seven different stands. The canopy cover was measured from two digital canopy photographic methods, including DHP (as the reference method) and DCP. The stand orthophotos were segmented using a multi-resolution image segmentation method. Afterward, the classification in two classes of the canopy cover and the non-canopy cover was conducted using minimum distance classification to estimate canopy cover. The CHM layer was generated based on the SfM algorithm and utilized in the canopy cover estimation in each stand as auxiliary data. The results showed a slight improvement when we used the CHM as auxiliary data. Overall, the results showed that the efficiency of the ground digital canopy photographic methods (zenith view) in multi-storied and dense forests is the lowest. In return, our method for digital aerial canopy photography (object-based canopy segmentation and classification) is simple, quick, efficient, and cost-effective.
林冠郁闭度是一种重要的结构特征，常用于评估森林的可持续性以及许多其他重要方面。与传统的样地等实地测量相比，遥感数据在估算林冠郁闭度方面更加有效和适用，尤其是在大尺度上。在小尺度上测量和绘制这个属性是一项困难的任务。使用无人机 (UAV) 的航空影像已被认为是估算林冠属性的一种极好工具。在本研究中，我们比较了使用数字半球摄影 (DHP)、数字覆盖摄影 (DCP)、UAV RGB 数据和树冠高度模型 (CHM) 估算七种不同林分混交阔叶林林冠郁闭度的潜力。从两种数字树冠摄影方法（包括 DHP（作为参考方法）和 DCP）测量林冠郁闭度。使用多分辨率图像分割方法对林分正射影像进行分割。之后，使用最小距离分类对树冠覆盖和非树冠覆盖进行分类，以估算树冠覆盖度。基于 SfM 算法生成 CHM 层，并在每个林分中作为辅助数据用于估算树冠覆盖度。结果表明，使用 CHM 作为辅助数据时，估算精度略有提高。总体而言，在多层和茂密森林中，地面数字树冠摄影方法（天顶角视角）的效率最低。相比之下，我们的数字航空树冠摄影方法（基于对象的树冠分割和分类）简单、快速、高效且具有成本效益。
