Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative

• Published in: ISPRS journal of photogrammetry and remote sensing 2016-12, Vol.122, p.206-221
• Main Authors: Zhu, Zhe, Gallant, Alisa L., Woodcock, Curtis E., Pengra, Bruce, Olofsson, Pontus, Loveland, Thomas R., Jin, Suming, Dahal, Devendra, Yang, Limin, Auch, Roger F.
• Format: Article
• Language: English
• Subjects: Auxiliary data; Continuous Change Detection and Classification (CCDC); Land cover classification; Landsat; Training strategy
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2016.11.004

The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a new end-to-end capability to continuously track and characterize changes in land cover, use, and condition to better support research and applications relevant to resource management and environmental change. Among the LCMAP product suite are annual land cover maps that will be available to the public. This paper describes an approach to optimize the selection of training and auxiliary data for deriving the thematic land cover maps based on all available clear observations from Landsats 4–8. Training data were selected from map products of the U.S. Geological Survey’s Land Cover Trends project. The Random Forest classifier was applied for different classification scenarios based on the Continuous Change Detection and Classification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence of land cover classes was superior to an equal distribution of training data per class, and suggest using a total of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced training data was alleviated by extracting a minimum of 600 training pixels and a maximum of 8000 training pixels per class. We additionally explored removing outliers contained within the training data based on their spectral and spatial criteria, but observed no significant improvement in classification results. We also tested the importance of different types of auxiliary data that were available for the conterminous United States, including: (a) five variables used by the National Land Cover Database, (b) three variables from the cloud screening “Function of mask” (Fmask) statistics, and (c) two variables from the change detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and its derivatives (aspect, position index, and slope), potential wetland index, water probability, snow probability, and cloud probability improved the accuracy of land cover classification. Compared to the original strategy of the CCDC algorithm (500 pixels per class), the use of the optimal strategy improved the classification accuracies substantially (15-percentage point increase in overall accuracy and 4-percentage point increase in minimum accuracy).