Abstract
The greenhouse is the fastest growing food production approach and has become the symbol of protected agriculture with the development of agricultural modernization. Previous studies have verified the effectiveness of remote sensing techniques for mono-temporal greenhouse mapping. In practice, long-term monitoring of greenhouse from remote sensing data is vital for the sustainable management of protected agriculture and existing studies have been limited in understanding its spatiotemporal dynamics. This study aimed to generate multi-temporal greenhouse maps in a typical protected agricultural region (Shouguang region, north China) from 1990 to 2018 using Landsat imagery and the Google Earth Engine and quantify its spatiotemporal dynamics that occur as a consequence of the development of protected agriculture in the study area. The multi-temporal greenhouse maps were produced using random forest supervised classification at seven-time intervals, and the overall accuracy of the results greater than 90%. The total area of greenhouses in the study area expanded by 1061.94 km 2 from 1990 to 2018, with the largest growth occurring in 1995–2010. And a large number of increased greenhouses occurred in 10–35 km northwest and 0–5 km primary roads buffer zones. Differential change trajectories between the total area and number of patches of greenhouses were revealed using global change metrics. Results of five landscape metrics showed that various landscape patterns occurred in both spatial and temporal aspects. According to the value of landscape expansion index in each period, the growth mode of greenhouses was from outlying to edge-expansion and then gradually changed to infilling. Spatial heterogeneity, which measured by Shannon’s entropy, of the increased greenhouses was different between the global and local levels. These results demonstrated the advantage of utilizing Landsat imagery and Google Earth Engine for monitoring the development of greenhouses in a long-term period and provided a more intuitive perspective to understand the process of this special agricultural production approach than relevant social science studies.
Highlights
One of the biggest challenges facing the future world is the need to increase the production of food to feed a growing population, a large part of which needs to rely on protected agriculture to Remote Sens. 2020, 12, 55; doi:10.3390/rs12010055 www.mdpi.com/journal/remotesensingRemote Sens. 2020, 12, 55 increase food supply [1]
With the high-speed image processing tools and online non-commercial remote sensing data service of Google Earth Engine (GEE), we have successfully completed multi-temporal greenhouses classification based on multi-season Landsat images, which is difficult to achieve for desktop processing
According to the literature review, we found that the development model of the greenhouses in the study area has evolved from independent construction to standardization, basement and branding
Summary
One of the biggest challenges facing the future world is the need to increase the production of food to feed a growing population, a large part of which needs to rely on protected agriculture to Remote Sens. 2020, 12, 55; doi:10.3390/rs12010055 www.mdpi.com/journal/remotesensingRemote Sens. 2020, 12, 55 increase food supply [1]. The expansion of greenhouses results in some environmental problems, such as soil degradation after long-term fertilization [4] and agricultural wastes (vegetable, plastic, chemical, etc.) [5]. To alleviate these contradictions and maintain the balance between food supply and environmental security, the region planner should make exact judgments on the extent of greenhouses expansion. In this context, a better understanding of its spatiotemporal dynamics overtime is required in a country or in a given region. As a special food production method on cultivated land, multi-temporal greenhouse mapping can provide basic geomatics information for other agricultural monitoring [6,7,8,9,10]
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