Citation:
Abstract:
The objective of this study is to test a cost-effective, physically based Light Detection and Ranging (LiDAR) classification methodology for wetland and upland land cover types within an area exceeding 1,000 km2 in the Boreal Plains, Alberta, Canada. Decision criteria are based on physical attributes of the landscape that influence maintenance of land cover types. Results are compared with 38 geolocated measurement plots at land cover boundaries and transition zones, manual delineation of 2,337 wetlands using photogrammetric methods and publicly available land cover classifications.
Results suggest that 57% of LiDAR-based wetland classes correspond with delineated wetlands, whereas 37% occur as errors of commission due to excluded wetlands in the manual delineation and confusion with harvested areas. Comparison of classified edges with plot shows that all classifications underestimate wetland area. Residual differences of the LiDAR-based classification are −0.3 m, on average (compared with measured), and have reduced range of error compared with other methods. Multispectral classifications misclassify up to 2/3 of wetland boundaries as a result of lower-resolution mixed pixels. Therefore, high-resolution maps of terrain morphology and vegetation structure provide an accurate, cost-effective means for characterizing wetland vs. upland forest in areas where LiDAR data are available.