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Development of the LCMAP annual land cover product across Hawai'i

September 14, 2022

Following the completion of land cover and change (LCC) products for the conterminous United States (CONUS), the U.S. Geological Survey's (USGS’s) Land Change Monitoring, Assessment, and Projection initiative has broadened the capability of characterizing continuous historical land change across the full Landsat records for Hawaiʻi at 30-meter resolution. One of the challenges of implementing the LCMAP framework to process annual land cover maps in Hawaiʻi is to collect sufficient high-quality training data. Although multiple datasets depicting land cover information are available in Hawaiʻi, they covered limited time frames and were produced from various remote sensing sources with different, classification categories, spatial resolution, and mapping accuracies. No solo product is suitable to provide LCMAP training data labels on its own. In this paper, we focused on enhancing the LCMAP training datasets to generate land cover products from 2000 to 2019 in Hawaiʻi. A total of 200 independent reference data plots were generated and manually interpreted for validating the mapping results produced by the training datasets. The results revealed that using the appropriate filter of multiple products as training data pools improved the classification model performance. The effect of training datasets (e.g., spatial coverage, quality) on accuracies for different land cover types were summarized. The LCMAP land surface change products for Hawaiʻi are available at

Publication Year 2022
Title Development of the LCMAP annual land cover product across Hawai'i
DOI 10.1016/j.jag.2022.103015
Authors Congcong Li, George Z. Xian, Danika F. Wellington, Kelcy Smith, Josephine Horton, Qiang Zhou
Publication Type Article
Publication Subtype Journal Article
Series Title International Journal of Applied Earth Observation and Geoinformation
Index ID 70236690
Record Source USGS Publications Warehouse
USGS Organization Earth Resources Observation and Science (EROS) Center; Advanced Research Computing (ARC)