Response of Different Band Combinations in Gaofen-6 WFV for Estimating of Regional Maize Straw Resources Based on Random Forest Classification

Huawei Mou, Huan Li, Yuguang Zhou and Renjie Dong
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Huawei Mou: Bioenergy and Environment Science & Technology Laboratory, College of Engineering, China Agricultural University, Beijing 100083, China
Huan Li: Bioenergy and Environment Science & Technology Laboratory, College of Engineering, China Agricultural University, Beijing 100083, China
Yuguang Zhou: Bioenergy and Environment Science & Technology Laboratory, College of Engineering, China Agricultural University, Beijing 100083, China
Renjie Dong: Bioenergy and Environment Science & Technology Laboratory, College of Engineering, China Agricultural University, Beijing 100083, China

Sustainability, 2021, vol. 13, issue 9, 1-16

Abstract: Maize straw is a valuable renewable energy source. The rapid and accurate determination of its yield and spatial distribution can promote improved utilization. At present, traditional straw estimation methods primarily rely on statistical analysis that may be inaccurate. In this study, the Gaofen 6 (GF-6) satellite, which combines high resolution and wide field of view (WFV) imaging characteristics, was used as the information source, and the quantity of maize straw resources and spatial distribution characteristics in Qihe County were analyzed. According to the phenological characteristics of the study area, seven classification classes were determined, including maize, buildings, woodlands, wastelands, water, roads, and other crops, to explore the influence of sample separation and test the responsiveness to different land cover types with different waveband combinations. Two supervised classification methods, support vector machine (SVM) and random forest (RF), were used to classify the study area, and the influence of the newly added band of GF-6 WFV on the classification accuracy of the study area was analyzed. Furthermore, combined with field surveys and agricultural census data, a method for estimating the quantity of maize straw and analyzing the spatial distribution based on a single-temporal remote sensing image and random forests was proposed. Finally, the accuracy of the measurement results is evaluated at the county level. The results showed that the RF model made better use of the newly added bands of GF-6 WFV and improved the accuracy of classification, compared with the SVM model; the two red-edge bands improved the accuracy of crop classification and recognition; the purple and yellow bands identified non-vegetation more effectively than vegetation, thus minimizing the “salt-and-pepper noise” of classification results. However, the changes to total classification accuracy were not obvious; the theoretical quantity of maize straw in Qihe County in 2018 was 586.08 kt, which reflects an error of only 2.42% compared to the statistical result. Hence, the RF model based on single-temporal GF-6 WFV can effectively estimate regional maize straw yield and spatial distribution, which lays a theoretical foundation for straw recycling.

Keywords: GF-6; maize; straw; support vector machine; random forest; red-edge wavelength (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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