Fusing lidar and digital aerial photography for object-based forest mapping in the Florida Everglades

Abstract

The Florida Everglades has a diverse forest community which needs to be accurately mapped to support the ongoing Comprehensive Everglades Restoration Plan (CERP). In this study, we examined whether a combination of light detection and ranging (lidar) and digital aerial photography can improve the accuracy of forest mapping in the Everglades, compared with using fine spatial resolution digital aerial photographs alone. We extracted lidar elevation and intensity features from original point cloud data at the object level to avoid the errors and uncertainties in the raster-based lidar methods. These features were combined with lidar-derived topographic information, and aerial photograph derived texture measures to map 7 forest communities in a portion of the Everglades. An overall accuracy of 71% and Kappa value of 0.64 were produced. We found that low-posting-density lidar data (i.e., <4 pts/m2) can significantly increase forest classification accuracy by providing important elevation, intensity, and topography information. It is anticipated that the modern lidar remote-sensing techniques can benefit the Everglades mapping to reduce the cost in CERP.