| Autor: |
Zhou, Zhong-fa, Wang, Ling-yu, Chen, Quan, Luo, Jian-cheng, Zhao, Xin, Zhang, Shu, Zhang, Wen-hui, Liao, Juan, Lyu, Zhi-jun |
| Předmět: |
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| Zdroj: |
Journal of Mountain Science; Mar2023, Vol. 20 Issue 3, p792-809, 18p |
| Abstrakt: |
Mapping abandoned land is very important for accurate agricultural management. However, in karst mountainous areas, continuous high-resolution optical images are difficult to obtain in rainy weather, and the land is fragmented, which poses a great challenge for remote sensing monitoring of agriculture activities. In this study, a new method for identifying abandoned land is proposed: firstly, a few Google Earth images are used to transform arable land into accurate vectorized geo-parcels; secondly, a time-series data set was constructed using Sentinel-1A Alpha parameters for 2020 on each farmland geoparcel; thirdly, the semi-variation function (SVF) was used to analyze the spatial-temporal characteristics, then identify abandoned land. The results show: (1) On the basis of accurate spatial information and boundary of farmland land, the SAR time-series dataset reflects the structure and time-series response. The method eventually extracted abandoned land with an accuracy of 80.25%. The problem of remote sensing monitoring in rainy regions and complex surface areas is well-resolved. (2) The spatial heterogeneity of abandoned land is more obvious than that of cultivated land within geo-parcels. The step size for significant changes in the SVF of abandoned land is shorter than that of cultivated land. (3) The SVF time sequence curve presented a strong peak feature when farmland was abandoned. This reveals that the internal spatial structure of abandoned land is more disordered and complex. It showed that time-series variations of spatial structure within cultivated land have broader applications in remote sensing monitoring of agriculture in complex imaging environments. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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