Abstract:

As larch is the major part for forest management,the rapid and accurate extraction of the distribution of the larch plantation is of great significance to the operation and management of the Saihanba forest farm,which is a large State-owned forest farm in China.Remote sensing image classification based on traditional stand-alone mode is time-consuming and inefficient,while with the advance of geographic information big data and cloud computing era,Google Earth Engine (GEE),the pioneer of geospatial analysis platform,brings new opportunities for remote sensing image classification.The research is based on the GEE platform and uses Sentinel-2 data to realize the image classification of main tree species of the Saihanba Mechanical Forest Farm.By preprocessing the Sentinel-2 image data of 309 sceneries of the Saihanba Mechanical Forest Farm in 2019,the ratio vegetation index,texture features and topographic features are calculated,and the selection is optimized to construct a multi-feature classification data set.Then,the study compares the classification accuracy under the minimum distance method,decision tree and random forest classifier to obtain the tree species classification map of the forest farm with the best classification accuracy.The results show that the GEE has significant advantages compared with the single-machine image classification mode; the classification accuracy under the minimum distance,decision tree and random forest classifier are 80%,83% and 92%,respectively.Random forest classifier is more suitable for complex remote sensing classification tasks.

Key words: Sentinel-2, artificial larch forest, GEE cloud calculation, forest classification, random forest method