Yang, YY; Wu, TX; Wang, SD; Li, H (2020). Fractional evergreen forest cover mapping by MODIS time-series FEVC-CV methods at sub-pixel scales. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 163, 272-283.

Evergreen forest plays a particular role in biodiversity, carbon sequestration, and soil and water conservation, and the spatial and temporal evolutions of evergreen forest complexly interact with climate change. However, it is difficult for the existing remote sensed data to meet both demands of large scale and high accuracy. To solve this problem, we developed a time-series FEVC-CV method by combining the fractional evergreen forest cover model (FEVC) with the coefficient of variation (CV) at sub-pixel scales. This method using the normalized difference vegetation index (NDVI) dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS) product to meet the needs of large-scale mapping. Considering the severe mixed-pixel problem and spatial heterogeneity of the study area, the improved dimidiate pixel model was used to extract the fractional evergreen forest cover at sub-pixel scales by introducing a new variable the infra-annual NDVI minimum value (NDVIann-min) and dividing grid units. Meanwhile, the CV of the time-series NDVI highlighted the time series fluctuation stability of evergreen forest compared with other vegetation types, such as deciduous forest and continuous crop. Therefore, the infra-annual time-series CV (CVai) and the CV of the continuous crop key phenology (CVkp) period were used to eliminate the interferences from other vegetation types, which were extremely admixed with low-coverage evergreen forest. We then verified the accuracy of the algorithm using 2-m resolution Gaofen-1 images. The results revealed that the overall accuracy of our algorithm exceeded 90%, with a root mean square error (RMSE) of cover fraction of around 10%. In addition, the mean relative error (MRE) indicated that the extraction accuracy of evergreen forest in non-urban areas was superior to the accuracy in urban areas. The results show that the algorithm achieved fairly high accuracy in detecting evergreen forest, including evergreen trees in urban areas, at a large scale.