Abstract
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.
Highlights
In the past 20 years, with the continuous innovation of agricultural production technology, the yield of crops has been greatly improved, and the application of greenhouse technology is one of the typical representatives [1,2]
Based on the Google Earth Engine, this research used classification and regression trees model (CART), randomForest, gmoMaxEnt, support vector machines model (SVM), and naiveBayes to extract the spatial distribution of greenhouses
It can be seen that the greenhouse classification accuracies of CART, randomForest and gmoMaxEnt were high and can be used to construct the greenhouse extraction and classification algorithm in Jiangsu Province, while the greenhouse classification accuracies of SVM and naiveBayes were low, so these two algorithms were not considered for the construction of the greenhouse integration algorithm
Summary
In the past 20 years, with the continuous innovation of agricultural production technology, the yield of crops has been greatly improved, and the application of greenhouse technology is one of the typical representatives [1,2]. As an extremely important land-use type in current agricultural production, the large number of applications of greenhouses enable regional agricultural production to overcome native natural conditions that are not conducive to crop growth, providing good greenhouse conditions for the growth and development of crops, greatly improving the yield of crops [3,4,5]. Rapid and accurate large-scale remote-sensing mapping of greenhouses has important practical significance for analyzing the characteristics of land-use transition and guiding regional agricultural production [7,8]
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