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A Semi-Automated, Multi-Source Data Fusion Update of a Wetland Inventory for East-Central Minnesota, USA

机译：美国东部明尼苏达州的湿地清单的半自动，多源数据融合更新。

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摘要

Comprehensive wetland inventories are an essential tool for wetland management, but developing and maintaining an inventory is expensive and technically challenging. Funding for these efforts has also been problematic. Here we describe a large-area application of a semi-automated process used to update a wetland inventory for east-central Minnesota. The original inventory for this area was the product of a labor-intensive, manual photo-interpretation process. The present application incorporated high resolution, multi-spectral imagery from multiple seasons; high resolution elevation data derived from lidar; satellite radar imagery; and other GIS data. Map production combined image segmentation and random forest classification along with aerial photo-interpretation. More than 1000 validation data points were acquired using both independent photo-interpretation as well as field reconnaissance. Overall accuracy for wetland identification was 90 % compared to field data and 93 % compared to photo-interpretation data. Overall accuracy for wetland type was 72 and 78 % compared to field and photo-interpretation data, respectively. By automating the most time consuming part of the image interpretations, initial delineation of boundaries and identification of broad wetland classes, we were able to allow the image interpreters to focus their efforts on the more difficult components, such as the assignment of detailed wetland classes and modifiers.
机译：全面的湿地清单是湿地管理的重要工具，但是开发和维护清单非常昂贵且技术上具有挑战性。这些努力的资金也存在问题。在这里，我们描述了半自动化过程的大面积应用，该过程用于更新明尼苏达州中东部的湿地清单。该区域的原始库存是劳动密集型手动照片解释过程的产物。本申请结合了来自多个季节的高分辨率，多光谱图像。来自激光雷达的高分辨率高程数据；卫星雷达图像；和其他GIS数据。地图制作结合了图像分割和随机森林分类以及航空照片解释功能。使用独立的照片解释以及现场侦察都获得了1000多个验证数据点。湿地识别的总体准确度与现场数据相比为90％，与照片解释数据相比为93％。与现场和照片解释数据相比，湿地类型的总体准确度分别为72％和78％。通过使图像解释中最耗时的部分自动化，初始划定边界以及确定广泛的湿地类别，我们能够使图像解释者将精力集中在更困难的部分上，例如详细的湿地类别和修饰符。

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