As an important facet of modern agricultural development, greenhouses satisfy ever-increasing demands for agricultural production and, therefore, constitute a growing proportion of global agriculture. However, just a handful of countries regularly collect statistics on the land cover of greenhouse infrastructure. Even when collected, these data cannot provide the detailed spatial information required for environmental risk assessment. It is, therefore, important to map spatial changes in greenhouse land cover using remote sensing (RS) approaches to determine the underlying factors driving these changes. In this paper, we apply a support vector machine (SVM) algorithm to identify greenhouse land cover in Shouguang City, China. Enhanced thematic mapper (ETM) images were selected as the data source for land use classification in this study as they can be freely acquired and offer the necessary spatial resolution. We then used a binary logistic regression model to quantitatively discern the mechanisms underlying changes in greenhouse land cover. The results of this study show that greenhouse land cover in Shouguang increased by 50.51% between 2000 and 2015, and that 90.39% of this expansion took place between 2010 and 2015. Elevation, slope, precipitation, and the distance to the nearest rural settlements and coastline are all significant factors driving expansion in greenhouse land cover, while distance to the nearest urban areas, rivers, roads, railways, and coastline have contributed to contractions in this land use type. Our research provided a practical approach to allow the detection of changes in greenhouse land cover in the countries with using free or low-cost satellite images.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited