Comparative Study on Object-Oriented Identification Methods of Plastic Greenhouses Based on Landsat Operational Land Imager

Yang Yi, Mingchang Shi (), Mengjie Gao, Guimin Zhang, Luqi Xing, Chen Zhang and Jianwu Xie
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Yang Yi: Beijing Engineering Research Center of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
Mingchang Shi: Beijing Engineering Research Center of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
Mengjie Gao: Beijing Engineering Research Center of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
Guimin Zhang: Inner Mongolia Aohan Banner Water Conservancy Bureau, Hohhot 024300, China
Luqi Xing: Key Laboratory of National Forestry and Grassland Administration on Ecological Landscaping of Challenging Urban Sites, National Innovation Alliance of National Forestry and Grassland Administration on Afforestation and Landscaping of Challenging Urban Sites, Shanghai Engineering Research Center of Landscaping on Challenging Urban Sites, Shanghai Academy of Landscape Architecture Science and Planning, Shanghai 200232, China
Chen Zhang: Key Laboratory of National Forestry and Grassland Administration on Ecological Landscaping of Challenging Urban Sites, National Innovation Alliance of National Forestry and Grassland Administration on Afforestation and Landscaping of Challenging Urban Sites, Shanghai Engineering Research Center of Landscaping on Challenging Urban Sites, Shanghai Academy of Landscape Architecture Science and Planning, Shanghai 200232, China
Jianwu Xie: College of Road and Bridge Engineering, Tianjin Vocational College of Communications, Tianjin 300393, China

Land, 2023, vol. 12, issue 11, 1-22

Abstract: The rapid and precise acquisition of the agricultural plastic greenhouse (PG) spatial distribution is essential in understanding PG usage and degradation, ensuring agricultural production, and protecting the ecological environment and human health. It is of great practical significance to realize the effective utilization of remote sensing images in the agricultural field and improve the extraction accuracy of PG remote sensing data. In this study, Landsat operational land imager (OLI) remote sensing images were used as data sources, and Shandong Province, which has the largest PG distribution in China, was selected as the study area. PGs in the study area were identified by means of contour recognition, feature set construction of the spatial structure, and machine learning. The results were as follows. (1) Through an optimal segmentation parameter approach, it was determined that the optimal segmentation scale for size, shape, and compactness should be set at 20, 0.8, and 0.5, respectively, which significantly improved PG contour recognition. (2) Among the 72 feature variables for PG spatial recognition, the number of features and classification accuracy showed a trend of first gradually increasing and then decreasing. Among them, fifteen feature variables, including the mean of bands 2 and 5; six index features (NDWI, GNDVI, SWIR1_NIR, NDVI, and PMLI); two shape features, the density and shape index; and two texture features, the contrast and standard deviation, played an important role. (3) According to the recall rate, accuracy rate, and F-value of three machine learning methods, random forest (RDF), CART decision tree (CART), and support vector machine (SVM), SVM had the best classification effect. The classification method described in this paper can accurately extract continuous plastic greenhouses through remote sensing images and provide a reference for the application of facility agriculture and non-point-source pollution control.

Keywords: plastic greenhouse; geometric space segmentation; machine learning; Landsat OLI; Shandong Province (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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