Mapping the annual dynamics of cultivated land in typical area of the Middle-lower Yangtze plain using long time-series of Landsat images based on Google Earth Engine

Abstract

Cultivated land in Middle-lower Yangtze Plain has been greatly reduced over the last few decades due to rapid urban expansion and massive urban construction. Accurate and timely monitoring of cultivated land changes has significant for regional food security and the impact of national land policy on cultivated land dynamics. However, generating high-resolution spatial-temporal records of cultivated land dynamics in complex areas remains difficult due to the limitations of computing resources and the diversity of land cover over a complex region. In our study, the annual dynamics of cultivated land in Middle-lower Yangtze Plain were first produced at 30 m resolution with a one-year interval in 1990-2010.Changes of vegetation and cultivated land are examined with the breakpoints inter-annual Normalized Difference Vegetation Index (NDVI) trajectories and synthetic NDVI derived by the enhanced spatial and temporal adaptive reflectance fusion model (ESTRAFM), respectively. Last, cultivated land dynamics is extracted with a one-year interval by detecting phenological characteristic. The results indicate that the rate of reduction in cultivated land area has accelerated over the past two decades, and has intensified since 1997.The dynamics of cultivated land mainly occurred in the mountains, hills, lakes and around towns, and the change frequency of these area was mainly one or two times. In particular, the changes in cultivated land in Nanjing have been most intense, perhaps attributed to urban greening and infrastructure construction.

Assessing the Effects of Land Cover Land Use Change on Precipitation Dynamics in Guangdong–Hong Kong–Macao Greater Bay Area from 2001 to 2019

“…Studies have shown that LCLUC has a certain impact on climate and regional precipitation, and many scholars have carried out related studies in this regard [1][2][3]10,12]. Some scholars have analyzed the influence of cropland, forest, and urban change on precipitation in specific regions [13,14,17,19,20]. However, there have been no reports on the spatialtemporal effects of LCLUC on precipitation in the GBA, especially construction land and cropland change on regional precipitation.…”

“…Most of the studies focused on the impact of urbanization on urban precipitation and the impact of forest or farmland on regional precipitation, while there were few studies on the underlying surface structure and the change of regional water resources in the water-air cycle [17][18][19]. It was found that the change of urban construction land has an important influence on the change of regional precipitation, vegetation, and land surface temperature, but it failed to quantitatively express the influence among them [20][21][22].…”

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

“…More importantly, Google Earth Engine (GEE) provides an outstanding cloud computing platform with open access to a variety of EO data. Thanks to the potential of big data processing of these platforms, researchers have completed extensive studies at greater extent, for example, on urban change monitoring 22 , cultivated land mapping 23 , and forest disturbance detection 24 . Multi-category land cover products were also produced such as a 13-category land cover map of South-east Asia covering 11 nations 25 .…”

Urban Fire Dynamics and Its Association with Urban Growth: Evidence from Nanjing, China

“…The training samples used by the random forest classifier are extracted from Google Earth high-resolution images (1 m × 1 m) by visual inspection, 500 for each year. The primary variables adopted in the classification include spectral bands, normalized difference vegetation index, grey level co-occurrence matrix with respect to imagery texture and topographic variables (e.g., slope and aspect; see [22] for technical details).…”