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

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2021-04, Vol.13 (7), p.1245
• Main Authors: Lin, Jinhuang, Jin, Xiaobin, Ren, Jie, Liu, Jingping, Liang, Xinyuan, Zhou, Yinkang
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural production; Algorithms; Classification; Cloud computing; Crop yield; Crops; Earth; Efficiency; Entropy; Farm buildings; Feature extraction; Food security; Google Earth Engine; greenhouse; Greenhouses; integrated learning; Jiangsu Province; Land use; Landsat; Landsat 8; Landsat satellites; Machine learning; Mapping; Maximum entropy; Regression analysis; Remote sensing; Satellite imagery; Spatial data; Spatial distribution; Spectra; Terrain; Texture; Topography
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13071245

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