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Development of the LCMAP annual land cover product across Hawaiʻi
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-14 , DOI: 10.1016/j.jag.2022.103015
Congcong Li, George Xian, Danika Wellington, Kelcy Smith, Josephine Horton, Qiang Zhou

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 https://doi.org/10.5066/P91E8M23.



中文翻译:

在夏威夷开发 LCMAP 年度土地覆盖产品

在完成美国本土 (CONUS) 的土地覆盖和变化 (LCC) 产品之后,美国地质调查局 (USGS) 的土地变化监测、评估和预测计划扩大了表征整个地区连续历史土地变化的能力夏威夷的陆地卫星记录,分辨率为 30 米。实施 LCMAP 框架处理夏威夷年度土地覆盖图的挑战之一是收集足够的高质量训练数据。尽管夏威夷有多个描述土地覆盖信息的数据集,但它们涵盖了有限的时间范围,并且是从具有不同分类类别、空间分辨率和制图精度的各种遥感来源产生的。没有任何单独的产品适合单独提供 LCMAP 训练数据标签。在本文中,我们专注于增强 LCMAP 训练数据集,以生成 2000 年至 2019 年夏威夷的土地覆盖产品。生成并手动解释了总共 200 个独立的参考数据图,以验证训练数据集产生的映射结果。结果表明,使用多个产品的适当过滤器作为训练数据池提高了分类模型的性能。总结了训练数据集(例如,空间覆盖率、质量)对不同土地覆盖类型准确性的影响。夏威夷的 LCMAP 地表变化产品可在 生成并手动解释了总共 200 个独立的参考数据图,以验证训练数据集产生的映射结果。结果表明,使用多个产品的适当过滤器作为训练数据池提高了分类模型的性能。总结了训练数据集(例如,空间覆盖率、质量)对不同土地覆盖类型准确性的影响。夏威夷的 LCMAP 地表变化产品可在 生成并手动解释了总共 200 个独立的参考数据图,以验证训练数据集产生的映射结果。结果表明,使用多个产品的适当过滤器作为训练数据池提高了分类模型的性能。总结了训练数据集(例如,空间覆盖率、质量)对不同土地覆盖类型准确性的影响。夏威夷的 LCMAP 地表变化产品可在 https://doi.org/10.5066/P91E8M23。

更新日期:2022-09-15
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