In recent years, non-grain production of cultivated land (NGPCL) has become increasingly prominent in China, seriously affecting food production and threatening the country’s food security. However, there is a lack of large-scale and high-precision methods for remote sensing identification of NGPCL. From the perspective of effective management of cultivated land resources, the characteristics of the spatial patterns of NGPCL, both on a large scale and at a patch scale, need to be further studied. For solving this problem, this paper uses the Google Earth engine (GEE) cloud computing platform and multi-source remote sensing data with a machine learning algorithm to determine the occurrence of NGPCL in Anhui province in 2019, and then uses nine selected landscape pattern indexes to analyze the spatial patterns of NGPCL from two aspects, specifically, economic development level and topography. The results show that: (1) terrain features, radar features, and texture features are beneficial to the extraction of NGPCL; (2) the degree of separation obtained by using an importance evaluation approach shows that spectral features have the highest importance, followed by index features with red edges, texture features, index features without red edges, radar features, and terrain features; and (3) the cultivated land in Anhui province in 2019 is mainly planted with food crops, and the phenomenon of NGPCL is more likely to occur in areas with high economic development levels and flat terrain. Aided by the GEE cloud platform, multi-source remote sensing data, and machine learning algorithm, the remote sensing monitoring approach utilized in this study could accurately, quickly, and efficiently determine NGPCL on a regional scale.

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Particulate matter (PM) is a common air pollutant linked to many human deaths and illnesses worldwide. Once emitted in the air, PM is most effectively removed by plants in phytoremediation. While the physical mechanics and factors of phytoremediation have been well-studied, there has been little attention regarding its biological mechanisms. In this empirical study, we measure the net effect of biological processes in \snakeplant{} on its ability to remove PM from an enclosed space by comparing the time taken for PM to decrease in enclosures containing living specimens versus dried and pressed specimens. Our results show that live specimens are in fact worse at removing PM from their enclosures as compared to dried and pressed specimens. This suggests that biological processes can be detrimental to PM removal for some plants species.