Mapping Inter-Annual Land Cover Variations Automatically Based on a Novel Sample Transfer Method
AbstractMost land cover mapping methods require the collection of ground reference data at the time when the remotely sensed data are acquired. Due to the high cost of repetitive collection of reference data, however, it limits the production of annual land cover maps to a short time span. In order to reduce the mapping cost and to improve the timeliness, an object-based sample transfer (OBST) method was presented in this study. The object-based analysis with strict constrains in area, shape and index values is expected to reduce the accident errors in selecting and transferring samples. The presented method was tested and compared with same-year mapping (SY), cross-year mapping (CY) and multi-index automatic classification (MI). For the study years of 2001–2016, both the overall accuracies (above 90%) and detailed accuracy indicators of the presented method were very close to the SY accuracy and higher than accuracies of CY and MI. With the presented method, the times-series land cover map of Guangzhou, China were derived and analyzed. The results reveal that the city has undergone rapid urban expansion and the pressure on natural resources and environment has increased. These results indicate the proposed method could save considerable cost and time for mapping the spatial-temporal changes of urban development. This suggests great potential for future applications as more satellite observations have become available all over the globe. View Full-Text
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Zhong, C.; Wang, C.; Li, H.; Chen, W.; Hou, Y. Mapping Inter-Annual Land Cover Variations Automatically Based on a Novel Sample Transfer Method. Remote Sens. 2018, 10, 1457.
Zhong C, Wang C, Li H, Chen W, Hou Y. Mapping Inter-Annual Land Cover Variations Automatically Based on a Novel Sample Transfer Method. Remote Sensing. 2018; 10(9):1457.Chicago/Turabian Style
Zhong, Cheng; Wang, Cuizhen; Li, Hui; Chen, Wenlong; Hou, Yong. 2018. "Mapping Inter-Annual Land Cover Variations Automatically Based on a Novel Sample Transfer Method." Remote Sens. 10, no. 9: 1457.