Landsat-Derived Annual Maps of Agricultural Greenhouse in Shandong Province, China from 1989 to 2018

作者：Cong Ou, Jianyu Yang, Zhenrong Du, Tingting Zhang, Bowen Niu, Quanlong Feng, Y. Liu, Dehai Zhu · 发表于：Remote Sensing · 年份：2021 · DOI：10.3390/rs13234830 · 被引用次数：24 · 研究领域：Remote Sensing in Agriculture、Land Use and Ecosystem Services、Species Distribution and Climate Change

Agricultural greenhouse (AG), one of the fastest-growing technology-based approaches worldwide in terms of controlling the environmental conditions of crops, plays an essential role in food production, resource conservation and the rural economy, but has also caused environmental and socio-economic problems due to policy promotion and market demand. Therefore, long-term monitoring of AG is of utmost importance for the sustainable management of protected agriculture, and previous efforts have verified the effectiveness of remote sensing-based techniques for mono-temporal AG mapping in a relatively small area. However, currently, a continuous annual AG remote sensing-based dataset at large-scale is generally unavailable. In this study, an annual AG mapping method oriented to the provincial area and long-term period was developed to produce the first Landsat-derived annual AG dataset in Shandong province, China from 1989 to 2018 on the Google Earth Engine (GEE) platform. The mapping window for each year was selected based on the vegetation growth and the phenological information, which was critical in distinguishing AG from other misclassified categories. Classification for each year was carried out initially based on the random forest classifier after the feature optimization. A temporal consistency correction algorithm based on classification probability was then proposed to the classified AG maps for further improvement. Finally, the average User’s Accuracy, Producer’s Accura...