Abstract
Wetlands are dynamic in space and time, providing varying ecosystem services. Field reference data for both training and assessment of wetland inventories in the State of Minnesota are typically collected as GPS points over wide geographical areas and at infrequent intervals. This status-quo makes it difficult to keep updated maps of wetlands with adequate accuracy, efficiency, and consistency to monitor change. Furthermore, point reference data may not be representative of the prevailing land cover type for an area, due to point location or heterogeneity within the ecosystem of interest. In this research, we present techniques for training a land cover classification for two study sites in different ecoregions by implementing the RandomForest classifier in three ways: (1) field and photo interpreted points; (2) fixed window surrounding the points; and (3) image objects that intersect the points. Additional assessments are made to identify the key input variables. We conclude that the image object area training method is the most accurate and the most important variables include: compound topographic index, summer season green and blue bands, and grid statistics from LiDAR point cloud data, especially those that relate to the height of the return.
Highlights
Wetlands are dynamic in both space and time, providing important ecosystem services, which vary depending upon location
Other than normalized difference vegetation index (NDVI), optical data did not rank very highly in importance for a Level 2 classification, but normalized digital surface models (nDSMs) related attributes did rank highly in all three training methods. This result shows the high importance of topographical derivatives corroborates what we found for the Level 1 classification: land cover classes in an agricultural area are more often distinguishable by topographical data, alone
Given the increased availability of the input datasets used in this research for other regions throughout the United States, including comparable resolution satellite imagery and the lowering costs of collecting LiDAR data, the methods developed in this research are applicable to many other regions for updating land cover classifications more frequently
Summary
Wetlands are dynamic in both space and time, providing important ecosystem services, which vary depending upon location. These valuable ecosystems help mitigate flooding, provide filtration of polluted waters from waste and run-off, recharge groundwater supply, and provide habitat for many aquatic organisms [1,2,3,4,5]. Wetland maps have been conventionally made using manual photo interpretation and heads-up (i.e., on-screen) digitizing and are not frequently updated [8]. Traditional wetland inventories, such as the US
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
