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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Classification of savanna tree species, in the Greater Kruger National Park region,by integrating hyperspectral and lidar data in a Random Forest data mining environment
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Classification of savanna tree species, in the Greater Kruger National Park region,by integrating hyperspectral and lidar data in a Random Forest data mining environment

机译：通过在随机森林数据挖掘环境中整合高光谱和激光雷达数据，在大克鲁格国家公园地区对大草原树木物种进行分类

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The accurate classification and mapping of individual trees at species level in the savanna ecosystem can provide numerous benefits for the managerial authorities. Such benefits include the mapping of economically useful tree species, which are a key source of food production and fuel wood for the local communities, and of problematic alien invasive and bush encroaching species, which can threaten the integrity of the environment and livelihoods of the local communities. Species level mapping is particularly challenging in African savannas which are complex, heterogeneous, and open environments with high intra-species spectral variability due to differences in geology, topography, rainfall, herbivory and human impacts within relatively short distances. Savanna vegetation are also highly irregular in canopy and crown shape, height and other structural dimensions with a combination of open grassland patches and dense woody thicket - a stark contrast to the more homogeneous forest vegetation. This study classified eight common savanna tree species in the Greater Kruger National Park region, South Africa, using a combination of hyperspectral and Light Detection and Ranging (LiDAR)-derived structural parameters, in the form of seven predictor datasets, in an automated Random Forest modelling approach. The most important predictors, which were found to play an important role in the different classification models and contributed to the success of the hybrid dataset model when combined, were species tree height; NDVI; the chlorophyll b wavelength (466 nm) and a selection of raw, continuum removed and Spectral Angle Mapper (SAM) bands. It was also concluded that the hybrid predictor dataset Random Forest model yielded the highest classification accuracy and prediction success for the eight savanna tree species with an overall classification accuracy of 87.68% and KHAT value of 0.843.
机译：在热带稀树草原生态系统中，在树种级别上对单个树木进行准确的分类和映射可以为管理机构提供许多好处。这些好处包括：对经济有用的树种进行标测，这些树种是当地社区粮食生产和薪柴的主要来源；对有问题的外来入侵物种和灌木入侵物种进行标测，这可能威胁到环境的完整性和当地生计社区。由于在相对短距离内的地质，地形，降雨，食草和人类影响方面的差异，物种稀少的非洲热带稀树草原的物种水平制图尤其具有挑战性，这些稀树草原是复杂，异质和开放环境，具有较高的物种内部光谱变异性。稀树草原植被在冠层和冠状形状，高度和其他结构尺寸上也高度不规则，结合了开阔的草地斑块和茂密的木本灌木丛-与更均匀的森林植被形成鲜明对比。这项研究结合了高光谱和光探测与测距（LiDAR）派生的结构参数，以七个预测变量数据集的形式，在南非的大克鲁格国家公园地区对八种常见的稀树草原树种进行了分类。建模方法。发现最重要的预测因素是物种树高；这些因素在不同的分类模型中起着重要的作用，当混合数据集模型结合使用时，它们的成功也起着重要作用。 NDVI；叶绿素b波长（466 nm）以及精选的未去除的，连续的和光谱角映射器（SAM）谱带。还得出结论，混合预测变量数据集“随机森林”模型对八种热带稀树草原树种具有最高的分类精度和预测成功率，总分类精度为87.68％，KHAT值为0.843。

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