Simple Method to Extract Lake Ice Condition From Landsat Images
Simple Method to Extract Lake Ice Condition From Landsat Images
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DOI：
10.1109/tgrs.2021.3088144
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发表时间：
2022
影响因子：
8.2
通讯作者：
Xiao Yang;T. Pavelsky;Liam Bendezu;Shuai Zhang
中科院分区：
文献类型：
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作者：
Xiao Yang;T. Pavelsky;Liam Bendezu;Shuai Zhang
冰在调节湖泊的水文、生态、生物地球化学和社会经济功能方面起着关键作用。长期的原位湖泊冰物候记录表明，湖泊结冰时间趋于推迟，解冻时间趋于提前，结冰期缩短。与利用原位记录和基于过程的模型研究湖泊冰情并行的是，卫星遥感可以扩大我们对大空间尺度上湖泊冰情变化的了解。然而，大多数遥感研究集中在大型湖泊或短时间段上，这可能无法有力地代表几十年时间跨度内的变化，也无法代表数量多得多的小型湖泊的情况。在此，我们提出一个随机森林模型——灵敏湖泊冰情探测（SLIDE），以便从陆地卫星专题制图仪（TM）、增强型专题制图仪（ETM +）和陆地成像仪（OLI）图像中准确提取冰情状况。我们使用一个人工标注的湖泊冰情数据集（全球995个湖泊的1089个标注区域）对该模型进行了训练。我们的结果显示，我们的模型在冰雪和水之间实现了准确分类（准确率：97.8%，卡帕系数：95.5%）。将陆地卫星得出的冰盖情况与原位冰情状况进行比较，我们发现我们的模型比质量评估波段中的陆地卫星冰雪标识产生的偏差更小、均方根误差（RMSE）更低、卡帕值更高。在原位报告的结冰和融冰日期前后的过渡时期尤其如此（我们的模型平均偏差为 -7.3%，陆地卫星质量波段为 -17.3%）。我们的结果证明了挖掘丰富的陆地卫星档案以研究湖泊冰动力学以及更好地标记受冰影响的湖泊观测结果的可行性。
Ice plays key roles in regulating hydrological, ecological, biogeochemical, and socioeconomic functions of lakes. Long-term in situ lake ice phenological records indicate that lake ice is trending toward later freeze-up, earlier breakup, and a shorter ice duration. Parallel to study of lake ice using in situ records and process-based models, satellite remote sensing can expand our understanding of lake ice change over large spatial scales. However, most remote sensing studies have focused on large lakes or short periods of time, which may not robustly represent changes over multidecadal time periods or in the much more numerous small lakes. Here, we present a random forest model, Sensitive Lake Ice Detection (SLIDE), to accurately extract ice conditions from Landsat TM, ETM+, and OLI images. We trained the model using a manually labeled lake ice dataset (1089 labeled areas over 995 lakes globally). Our results show that our model achieves accurate classification between ice/snow and water (accuracy: 97.8%, kappa coefficient: 95.5%). Comparing Landsat-derived ice cover with in situ ice conditions, we show that our model produces less bias, lower RMSE, and higher kappa than does the Landsat snow/ice flag from the quality assessment band. This is especially true during the transitional period surrounding the ice on and off dates reported from in situ (mean bias −7.3% from our model, −17.3% from the Landsat quality band). Our results demonstrate the feasibility of mining the rich Landsat archive to study lake ice dynamics and of better flagging ice-affected lake observations.