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首页> 外文期刊>Remote Sensing >Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota
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Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

机译：多源，多时相遥感和辅助数据对明尼苏达州北部湿地随机森林分类精度的影响

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Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.
机译：使用遥感数据在景观尺度上进行湿地制图既需要负担得起的数据，又需要有效的准确分类方法。相对于传统的土地覆盖分类技术，随机森林分类具有若干优势，包括自举技术，可生成训练数据中异常值的可靠估计，以及测量分类置信度的能力。尽管随机森林分类器可以生成具有大量输入数据的复杂决策树，并且仍然不存在过度拟合的高风险，但是仍然非常需要通过仅包含关键输入数据集而又不牺牲大量数据来降低计算和运营成本准确性。我们在明尼苏达州北部的研究地点的主要问题是：（1）如何使用不同的遥感平台和输入数据集比较湿地的分类准确性和置信度？ （2）准确区分高地，水和湿地（包括湿地类型）的关键输入变量是什么？ （3）哪些数据集和季节性图像对湿地分类的准确性最高。我们的结果表明，关键的输入变量包括地形（海拔和曲率）和土壤描述符（水文），以及春季收集的各种遥感数据（卫星可见，近红外和热带;卫星归一化植被指数和流苏帽绿度和湿度;使用L波段卫星雷达的水平（HH）和水平垂直（HV）极化）。我们进行了这项探索性分析，以告知负责监视湿地生态系统的自然资源管理者的决定，并帮助设计一个系统，以在与明尼苏达州北部类似的景观中对湿地进行一致的操作制图。

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