Abstract
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.
Highlights
Characterizing changes in forested areas is of particular importance to the study of terrestrial and atmospheric carbon circle, as they play a key role in modulating carbon flux between the biosphere and the atmosphere [1,2,3,4]
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
Superpixel based refinements is performed to remove the salt-and-pepper effects resulted from per-pixel detection by taking into account that eucalyptus plantations are usually managed parcel by parcel
Summary
Characterizing changes in forested areas is of particular importance to the study of terrestrial and atmospheric carbon circle, as they play a key role in modulating carbon flux between the biosphere and the atmosphere [1,2,3,4]. As reported by the Food and Agriculture Organization of the United Nations (FAO), the area of planted forest has increased notably in all climatic domains over the last 25 years, i.e., it increased respectively by 67% and 51% in the tropical and temperate zones [7] Tree plantations, especially those established on what was formerly natural forest land, may cause various environmental and social impacts if not managed properly [8,9,10]. Sensed data from Landsat 8 Operational Land Imager (OLI), MODIS, and Sentinel-2 MultiSpectral Instrument (MSI) are employed to identify short-rotation eucalyptus plantations automatically on the GEE Platform By studying both the pattern of vegetation indices due to tree crop rotation and spectral characteristics of eucalyptus in the red-edge region, we could produce high resolution maps using a short observation history, i.e., five or six years. The proposed method allows gaps in the time series, long and continuous observations over the study area is not required as a result
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