Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data
Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data
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DOI：
10.1016/j.rse.2012.03.013
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发表时间：
2012-08-01
影响因子：
13.5
通讯作者：
Gianelle, Damiano
中科院分区：
文献类型：
--
作者：
Dalponte, Michele;Bruzzone, Lorenzo;Gianelle, Damiano
树种识别是森林经营中的一个重要问题。近年来，许多研究已经探索了这个话题，使用高光谱，多光谱和激光雷达数据。在这项研究中，我们分析了两种多传感器设置：1）机载高空间分辨率超光谱图像与LiDAR数据相结合; 2）高空间分辨率卫星多光谱图像与LiDAR数据相结合。考虑了两次LiDAR采集：低点密度（约10000个像素）。0.48点每米（2））和高点密度（约8.6点/m（2））。这项工作的目的是：i）了解使用高光谱和空间分辨率的多传感器数据设置可以达到什么水平的分类精度（非常高的空间和光谱分辨率的机载超光谱图像与高点密度激光雷达数据相结合），在一个山区，其特点是有许多物种，包括阔叶树和针叶树; ii）了解数据特征降级的影响（在光谱数据的光谱分辨率和激光雷达数据的点密度方面），相对于先前的设置，关于物种可分离性;以及iii）了解高密度和低密度LiDAR采集在树种分类上的差异。研究区域是一个山区，在南阿尔卑斯山的特点是许多树种（7种和一个“非森林”类），无论是针叶或阔叶。对于每个设置，采用特定的处理链，从原始数据的预处理到分类（使用两个分类器：支持向量机和随机森林）。不同的类定义进行了测试，包括一般的宏类，森林类型，最后单一树种。实验结果表明，基于高光谱数据的设置是有效的一般宏类，森林类型，和单一物种，达到高的kappa精度（93.2%，82.1%和76.5%，分别）。多光谱数据的使用降低了分类的准确性，这是尖锐的单一树种，森林类型仍然很高。考虑到一般的宏观类，多光谱设置仍然是非常准确的（85.8%）。关于激光雷达数据，实验分析表明，高密度激光雷达数据提供更多的信息，树种分类相对于低密度数据，无论是高光谱或多光谱数据相结合。(C)2012 Elsevier Inc. All rights reserved.
The identification of tree species is an important issue in forest management. In recent years, many studies have explored this topic using hyperspectral, multispectral, and LiDAR data. In this study we analyzed two multi-sensor set-ups: 1) airborne high spatial resolution hyperspectral images combined with LiDAR data; and 2) high spatial resolution satellite multispectral images combined with LiDAR data. Two LiDAR acquisitions were considered: low point density (approx. 0.48 points per m(2)) and high point density (approx. 8.6 points per m(2)). The aims of this work were: i) to understand what level of classification accuracy can be achieved using a high spectral and spatial resolution multi-sensor data set-up (very high spatial and spectral resolution airborne hyperspectral images integrated with high point density LiDAR data), over a mountain area characterized by many species, both broadleaf and coniferous; ii) to understand the implications of a downgrading of the data characteristics (in terms of spectral resolution of spectral data and point density of LiDAR data), on species separability, with respect to the previous set-up; and iii) to understand the differences between high- and low-point density LiDAR acquisitions on tree species classification. The study region was a mountain area in the Southern Alps characterized by many tree species (7 species and a "non-forest" class), either coniferous or broadleaf. For each set-up a specific processing chain was adopted, from the pre-processing of the raw data to the classification (two classifiers were used: support vector machine and random forest). Different class definitions were tested, including general macro-classes, forest types, and finally single tree species. Experimental results showed that the set-up based on hyperspectral data was effective with general macro-classes, forest types, and single species, reaching high kappa accuracies (93.2%, 82.1% and 76.5%, respectively). The use of multispectral data produced a reduction in the classification accuracy, which was sharp for single tree species, and still high for forest types. Considering general macro-classes, the multispectral set-up was still very accurate (85.8%). Regarding LiDAR data, the experimental analysis showed that high density LiDAR data provided more information for tree species classification with respect to low density data, when combined with either hyperspectral or multispectral data. (C) 2012 Elsevier Inc. All rights reserved.