Comparison of Pixel- and Object-Based Approaches in Phenology-Based Rubber Plantation Mapping in Fragmented Landscapes

The increasing expansion of rubber plantations throughout East and Southeast Asia urgently requires improved methods for effective mapping and monitoring. The phenological information from rubber plantations was found effective in rubber mapping. Previous studies have mostly applied rule-pixel-based phenology approaches for rubber plantations mapping, which might result in broken patches in fragmented landscapes. This study introduces a new paradigm by combining phenology information with object-based classification to map fragmented patches of rubber plantations in Xishuangbanna. This research first delineated the time windows of the defoliation and foliation phases of rubber plantations by acquiring the temporal profile and phenological features of rubber plantations and natural forests through the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data. To investigate the ability of finer resolution images at capturing the temporal profile or phenological information, 30 m resolution Landsat image data were used to capture the temporal profile, and a phenology algorithm to separate rubber plantations and natural forests was then defined. The derived phenology algorithm was used by both the object-based and pixel-based classification to investigate whether the object-based approach could improve the mapping accuracy. Whether adding the phenology information to the object-based classification could improve rubber plantation mapping accuracy in mountainous Xishuangbanna was also investigated. This resulted in three approaches: rule-pixel-based phenology, rule-object-based phenology, and nearest-neighbor-object-based phenology. The results showed that the rule-object-based phenology approaches (with overall accuracy 77.5% and Kappa Coefficients of 0.66) and nearest-neighbor-object-based phenology approach (91.0% and 0.86) achieved a higher accuracy than that of the rule-pixel-based phenology approach (72.7% and 0.59). The results proved Remote Sens. 2018, 10, 44; doi:10.3390/rs10010044 www.mdpi.com/journal/remotesensing Remote Sens. 2018, 10, 44 2 of 20 that (1) object-based approaches could improve the accuracy of rubber plantation mapping compared to the pixel-based approach and (2) incorporating the phenological information from vegetation improved the overall accuracy of the thematic map.