Crops planting structure and karst rocky desertification analysis by Sentinel-1 data
نویسندگان
چکیده
Abstract Accurate crop planting structure (CPS) information and its relationship with the surrounding special environment can provide strong support for adjustment of agricultural in areas limited cultivated land resources, it will help regional food security, social economy, ecological balance adjustment. However, due to perennial cloudy, rainy, scattered arable Karst mountainous areas, monitoring by traditional remote sensing methods is greatly limited. In this regard, we focus on synthetic aperture radar (SAR) sensing, which penetrate clouds rain, without light constraints image. article, based parcel-based temporal sequence SAR, CPS South China karst area was extracted deep learning technology, spatial coupling between rocky desertification (KRD) analyzed. The results showed that: (a) overall accuracy classification 75.98%, proved that geo-parcel-based time series SAR has a good effect mapping areas; (b) Through analysis KRD, found lower KRD level caused simpler higher grade more complex richer landscape types. variation trend indicates process water shortage deepening farmland; (c) connectivity (Contagion Index, CI 0.52–1.73) (CI 0.83–2.05) level, shows degree fragmentation connection positively proportional KRD. study, extraction crops under imaging realized using farmland geo-parcels-based Sentinel-1 data, This study provides new idea method cloudy rainy Southwest China. have certain guiding significance development.
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ژورنال
عنوان ژورنال: Open Geosciences
سال: 2021
ISSN: ['2391-5447']
DOI: https://doi.org/10.1515/geo-2020-0272