Abstract
Plastic greenhouses are an important hallmark of agricultural progress. To meet the growing demand for vegetable and food, the amount of plastic greenhouses has increased significantly over the past few decades. Remote sensing is considered as a promising data source for taking inventory and monitoring plastic greenhouses for managing modern agriculture. However, a systematic catalog of number and spatial distribution of plastic greenhouses is mostly inexistent. This is primarily due to the complex land surface characteristics and seasonal changes, which make automated classification based on EO data challenging. Current approaches generally suffer from the susceptibility of approaches toward thresholds and changes in the phenological stage. Besides, they often require an extensive training of models, however, often the necessary amount of training data is inexistent. To address these issues, we suggest an adaptable and universal plastic greenhouse mapping method based on very high spatial resolution optical satellite data (GaoFen-2 image) with a three-step procedure. A plastic greenhouse gathering area (100 km2) is selected for the development of the initial method. We receive a very competitive mapping accuracy 97.34% and the likelihood of plastic greenhouses being mapped correctly reaches to 95.20%. Subsequently, we transfer it to a much larger area (2025 km2) featuring a different phenological stage and different surrounding patterns. The stable mapping accuracy proves the validity of our approach.
Highlights
A S GLOBAL population is increasing, the demand for vegetables and food is growing [1]
We propose a new method for mapping plastic greenhouses using very high spatial resolution (VHR) satellite images; in our case, we use data from the GaoFen-2 (GF-2) satellite
We propose a three-step hierarchical procedure: first, we develop a new metric titled “Double Coefficient Vegetation Sieving Index” (DCVSI) to explicitly distinguish plastic greenhouses and vegetation from other land surfaces by enhancing vegetation information; second, we develop a new metric titled “High-Density Vegetation Inhibition Index” (HDVII) to explicitly eliminate high-density vegetation by inhibiting its spectral signature; and third, the commonly used Normalized Difference Vegetation Index (NDVI) is adopted to distinguish plastic greenhouses
Summary
A S GLOBAL population is increasing, the demand for vegetables and food is growing [1]. Cultivated land on which vegetable and food production depends gets more and more occupied by ever more dynamic settlement expansion during the past few decades [2]–[4]. This phenomenon is happening in developing countries [5]–[7]. Manuscript received May 6, 2019; revised August 10, 2019; accepted October 25, 2019. Date of publication November 17, 2019; date of current version February 12, 2020.
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