Rapid Mapping of Large-Scale Greenhouse Based on Integrated Learning Algorithm and Google Earth Engine

A greenhouse is an important land-use type, which can effectively improve agricultural production conditions and increase crop yields. It of great significance to obtain the spatial distribution data greenhouses quickly accurately for regional food security. Based on Google Earth Engine cloud platform Landsat 8 images, this study selected a total 18 indicators from three aspects spectral features, texture features terrain construct identification features. From variety classification algorithms remote-sensing recognition greenhouses, classifiers with higher accuracy (classification regression trees (CART), random forest model (randomForest) maximum entropy (gmoMaxEnt)) integrated algorithm, then extracted in Jiangsu Province. The results show that: (1) its own massive computing capabilities, combined algorithms, achieve rapid mapping large-scale under complex terrain, than that single algorithm. (2) combination different spectral, has greater impact extraction all highest accuracy. Spectral are key factors mapping, but also enhance (3) Province significant differentiation agglomeration characteristics. most widely distributed mainly concentrated agriculturally developed areas such as Dongtai City, Hai’an County, Rudong County Pizhou City.