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 intra-annual NDVI minimum value (NDVI_ann-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 intra-annual time-series CV (CV_ai) and the CV of the continuous crop key phenology (CV_kp) 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.

常绿森林在生物多样性，碳固存以及水土保持方面发挥着特殊作用，而常绿森林的时空演变与气候变化有着复杂的相互作用。但是，现有的遥感数据很难同时满足大规模和高精度的需求。为了解决这个问题，我们通过结合分数常绿森林覆盖模型（FEVC）和亚像素尺度下的变异系数（CV），开发了时间序列FEVC-CV方法。该方法使用中等分辨率成像光谱仪（MODIS）产品的归一化差异植被指数（NDVI）数据集来满足大规模制图的需求。考虑到研究区域的严重混合像素问题和空间异质性，_ann-min）和划分网格单位。同时，时间序列NDVI的CV强调了常绿森林与落叶林和连作作物等其他植被类型相比的时间序列波动稳定性。因此，年内时间序列CV（CV _ai）和连续作物关键物候（CV _kp）时期被用来消除来自其他植被类型的干扰，这些干扰与低覆盖率的常绿森林极为混杂。然后，我们使用2米分辨率的Gaofen-1图像验证了该算法的准确性。结果表明，我们算法的整体准确性超过90％，覆盖率的均方根误差（RMSE）约为10％。另外，平均相对误差（MRE）表明，非城市地区常绿森林的提取精度优于城市地区。结果表明，该算法在大规模检测常绿森林（包括城市常绿乔木）方面取得了较高的精度。