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

Abstract

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.