A new method to identify short-rotation eucalyptus plantations by exploring both the changing pattern of vegetation indices due to tree crop rotation and spectral characteristics of eucalyptus in the red-edge region is presented. It can be adopted to produce eucalyptus maps of high spatial resolution (30 m) at large scales, with the use of open remote sensing images from Landsat 8 Operational Land Imager (OLI), MODerate resolution Imaging Spectroradiometer (MODIS), and Sentinel-2 MultiSpectral Instrument (MSI), as well as a free cloud computing platform, Google Earth Engine (GEE). The method is composed of three main steps. First, a time series of Enhanced Vegetation Index (EVI) is constructed from Landsat data for each pixel, and a statistical hypothesis testing is followed to determine whether the pixel belongs to a tree plantation or not based on the idea that tree crops should be harvested in a specific period. Then, a broadleaf/needleleaf classification is applied to distinguish eucalyptus from coniferous trees such as pine and fir using the red-edge bands of Sentinel-2 data. Refinements based on superpixel are performed at last to remove the salt-and-pepper effects resulted from per-pixel detection. The proposed method allows gaps in the time series that are very common in tropical and subtropical regions by employing time series segmentation and statistical hypothesis testing, and could capture forest disturbances such as conversion of natural forest or agricultural lands to eucalyptus plantations emerged in recent years by using a short observing time. The experiment in Guangxi province of China demonstrated that the method had an overall accuracy of 87.97%, with producer’s accuracy of 63.85% and user’s accuracy of 66.89% for eucalyptus plantations.

中文翻译：

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利用多卫星图像和云计算平台大规模识别短轮桉树人工林

提出了一种新的识别短轮伐桉树人工林的方法，该方法既可以探索由于轮作引起的植被指数变化模式，又可以探索红边地区桉树的光谱特征。通过使用Landsat 8 Operational Land Imager（OLI），MODerate Resolution Imaging Spectroradiometer（MODIS）和Sentinel-2的开放式遥感图像，可将其大规模生成高分辨率的桉树地图（30 m）。 MultiSpectral Instrument（MSI），以及免​​费的云计算平台Google Earth Engine（GEE）。该方法包括三个主要步骤。首先，根据Landsat数据为每个像素构建增强植被指数（EVI）的时间序列，然后根据假设应该在特定时期内收获树木的想法，进行统计假设检验以确定像素是否属于人工林。然后，使用Sentinel-2数据的红边带，将阔叶/针叶分类应用于区分桉树和针叶树（例如松树和冷杉）。最后执行基于超像素的细化，以消除因每个像素检测而产生的盐和胡椒的影响。通过采用时间序列分割和统计假设检验，该方法可以解决热带和亚热带地区非常普遍的时间序列空白，并且可以捕获近年来由自然森林或农业用地向桉树人工林转化等森林干扰。用很短的观察时间。