Long-Term Mapping of a Greenhouse in a Typical Protected Agricultural Region Using Landsat Imagery and the Google Earth Engine

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.

Sentinel-2 Recognition of Uncovered and Plastic Covered Agricultural Soil

“…The top three ranked acquisition days provided 99.1% of the parcels with NDVI < 0.3 in the considered window, emphasising the reliability of the approach at different acquisition conditions. Our approach sets a foundation for analysing time-series of multiple years and monitoring of the use of artificial covers and their trends [22]. Compositing the data acquired from the three dates while preserving each parcel's spatial and temporal integrity resulted in a database of bare soil parcels ready for further analysis and in-line with the expected RED-NIR line representation (Figure 19).…”

“…Artificial covers, such as greenhouses and temporary soil covers, need to be addressed in the detection of bare soil. Plastic greenhouses have been successfully observed in medium resolution imagery, such as those of Landsat and Sentinel-2 [16][17][18][19][20][21][22]. Spectra of plastic greenhouses are of relatively high reflectance, holding information of the crops underneath the plastic cover [18].…”

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

“…This study is oriented to the provincial area, which covers an area of more than 150,000 km 2 , while AG in this area are concentrated in several developed regions of protected agriculture. According to the conclusion of the previous study [25], the labeled samples are limited to the above-mentioned regions only, which will lead to large misclassification in other regions. In order to avoid this phenomenon, a 10 km grid sampling structure covering the study area was constructed in this study, which ensured that labeled samples are available in each grid (Figure 3).…”

“…Arcidiacono et al proposed a model to manage crop-shelter spatial development and used it in a highly representative study of the Italian protected cultivation during 1994-1999 [24]. Ou et al produced seven greenhouses maps in the 1990-2018 period in a typical protected agricultural region of China [25].…”

Google Earth Engine e sua aplicabilidade na gestão de recursos hídricos

“…Na pesquisa apresentada em [Ou et al 2020], geraram-se mapas de estufa mul-titemporais a partir de imagens Landsat e do GEE da região de preservação natural de Shouguang, China no período de 1990 a 2018. Além disso, avaliaram e quantificaram a dinâmica espaço-temporal o aumento agricultura nesta área de estudo.…”