Application of Landsat High Spatial Resolution Phenological Synthesized Data in Mountainous Land Cover Classification - Zhengzheng Hu; Fei Xiao; Yun Du; Zhou Wang; Jiahuan Luo; Qi Feng; Miaomiao Chen (Remote Sensing)
Classifying land cover in mountainous areas has always been challenging due to the high diversity of ecosystems and the complexity of the spectral–temporal–spatial relationships caused by the rugged terrain. This paper introduces multi-year synthesized phenology data to improve land cover classification in these regions. Using the Shennongjia Forestry District in Hubei Province, China, as a case study, we investigate how incorporating multi-year synthesized phenology data enhances the accuracy of land cover classification with single-temporal and multi-temporal remote sensing imagery, as well as how it aids in identifying different vegetation types in shaded areas of the mountains. The research results indicate that incorporating multi-year synthesized phenology data significantly improves the accuracy of land cover classification for single summer imagery, single autumn imagery, multi-temporal summer–autumn imagery, and mountain shadow areas. The Kappa coefficient (Kappa) increased by 1.57% to 9.93%, while overall accuracy (OA) improved by 1.4% to 8.75%. Notably, the improvement in classification accuracy was most pronounced for single summer imagery. Furthermore, the results demonstrate that, in the absence of terrain data, multi-year synthesized phenology data provide even greater enhancements in land cover classification accuracy using remote sensing imagery.