Publications

Zhao, X; Wu, TX; Wang, SD; Liu, K; Yang, JY (2023). Cropland abandonment mapping at sub-pixel scales using crop phenological information and MODIS time-series images. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 208, 107763.

Abstract
Cropland abandonment is a common land-use change with mixed impacts on the environment and rural eco-nomic development. Prevalent small family farms and excessive land fragmentation result in cropland aban-donment processes that are often gradual and spatially dispersed. These difficulties limit the ability to apply previous remote sensing mapping methods of cropland abandonment to areas with complex underlying surfaces, where mixed pixels and spectral aliasing problems arise. To meet the demands of broad-scale and high-accuracy abandoned cropland mapping, we developed the PCRRSBS -CV model by combining the phenology-based cropland retirement remote sensing (PCRRS) model with the coefficient of variation (CV) and tilled soil frac-tion (BS). This method combines information regarding crop phenology with Moderate Resolution Imaging Spectroradiometer (MODIS) time-series images. The PCRRS model was used to estimate changes in spectral metrics parameters during the process of cropland abandonment. Additionally, the dead fuel index and normalized difference vegetation index (NDVI) were employed to estimate the BS in mixed pixels (according to crop type). We predicted that use of the BS as the weighting coefficient for the PCRRS model would reduce interference from the mixed pixel problem, while spatial heterogeneity could be reduced by dividing the research area into regional units. Use of the CV of the NDVI time series with phenological information highlights the volatility of crop growth periods and helps to eliminate disturbances associated with woodlands and grasslands. Finally, we demonstrated this method in the Loess Plateau of China and a portion of central Europe; we verified its accuracy using high-resolution images from Google Earth. Our algorithm demonstrated overall accuracy of 82.2 % and can extract cropland abandonment information as little as 20 % of a pixel. The overall root mean square error (RMSE) was controlled below 15 %. In summary, the high accuracy achieved by this method enables the monitoring of cropland abandonment dynamics on a large scale.

DOI:
10.1016/j.compag.2023.107763

ISSN:
1872-7107