{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:30:28Z","timestamp":1760146228952,"version":"build-2065373602"},"reference-count":64,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T00:00:00Z","timestamp":1728172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Geological Survey National Land Imaging (NLI)","award":["140G0124D0001"],"award-info":[{"award-number":["140G0124D0001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>An accurate and historical land cover monitoring dataset for Alaska could provide fundamental information for a range of studies, such as conservation habitats, biogeochemical cycles, and climate systems, in this distinctive region. This research addresses challenges associated with the extraction of training data for timely and accurate land cover classifications in Alaska over longer time periods (e.g., greater than 10 years). Specifically, we designed the \u201cRegion-Specific Model Adaptation (RSMA)\u201d method for training data. The method integrates land cover information from the National Land Cover Database (NLCD), LANDFIRE\u2019s Existing Vegetation Type (EVT), and the National Wetlands Inventory (NWI) and machine learning techniques to generate robust training samples based on the Anderson Level II classification legend. The assumption of the method is that spectral signatures vary across regions because of diverse land surface compositions; however, despite these variations, there are consistent, collective land cover characteristics that span the entire region. Building upon this assumption, this research utilized the classification power of deep learning algorithms and the generalization ability of RSMA to construct a model for the RSMA method. Additionally, we interpreted existing vegetation plot information for land cover labels as validation data to reduce inconsistency in the human interpretation. Our validation results indicate that the RSMA method improved the quality of the training data derived solely from the NLCD by approximately 30% for the overall accuracy. The validation assessment also demonstrates that the RSMA method can generate reliable training data on large scales in regions that lack sufficient reliable data.<\/jats:p>","DOI":"10.3390\/rs16193717","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T07:30:18Z","timestamp":1728286218000},"page":"3717","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A \u201cRegion-Specific Model Adaptation (RSMA)\u201d-Based Training Data Method for Large-Scale Land Cover Mapping"],"prefix":"10.3390","volume":"16","author":[{"given":"Congcong","family":"Li","sequence":"first","affiliation":[{"name":"ASRC Federal Data Solutions, Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Work Performed under USGS Contract 140G0124D0001, Sioux Falls, SD 57198, USA"}]},{"given":"George","family":"Xian","sequence":"additional","affiliation":[{"name":"U.S. Geological Survey, Earth Resources and Observation Science (EROS) Center, 47914 252nd St., Sioux Falls, SD 57198, USA"}]},{"given":"Suming","family":"Jin","sequence":"additional","affiliation":[{"name":"U.S. Geological Survey, Earth Resources and Observation Science (EROS) Center, 47914 252nd St., Sioux Falls, SD 57198, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1890\/08-2025.1","article-title":"Sensitivity of the carbon cycle in the Arctic to climate change","volume":"79","author":"McGuire","year":"2009","journal-title":"Ecol. 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