Liang, W.; Abidi, M.; Carrasco, L.; McNelis, J.; Tran, L.; Li, Y.; Grant, J. Mapping Vegetation at Species Level With High-Resolution Multispectral And Lidar Data Over A Large Spatial Area: A Case Study With Kudzu. Remote Sens.2020, 12, 609.
Liang, W.; Abidi, M.; Carrasco, L.; McNelis, J.; Tran, L.; Li, Y.; Grant, J. Mapping Vegetation at Species Level With High-Resolution Multispectral And Lidar Data Over A Large Spatial Area: A Case Study With Kudzu. Remote Sens. 2020, 12, 609.
Liang, W.; Abidi, M.; Carrasco, L.; McNelis, J.; Tran, L.; Li, Y.; Grant, J. Mapping Vegetation at Species Level With High-Resolution Multispectral And Lidar Data Over A Large Spatial Area: A Case Study With Kudzu. Remote Sens.2020, 12, 609.
Liang, W.; Abidi, M.; Carrasco, L.; McNelis, J.; Tran, L.; Li, Y.; Grant, J. Mapping Vegetation at Species Level With High-Resolution Multispectral And Lidar Data Over A Large Spatial Area: A Case Study With Kudzu. Remote Sens. 2020, 12, 609.
Abstract
Mapping vegetation species is critical to facilitate related quantitative assessment, and for invasive plants mapping their distribution is important to enhance monitoring and controlling activities. Integrating high resolution multispectral remote sensing (RS) image and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple source of high-resolution RS data for vegetation mapping at large spatial scale can be both computationally and sampling intensive. Here we designed a two-step classification workflow to decrease computational cost and sampling effort, and to increase classification accuracy by integrating multispectral and lidar data to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1,362 km2) of Tennessee, the United States. Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the Producer’s Accuracy, User’s Accuracy, and Kappa for the SVM model on kudzu was 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions or other vegetation species.
Environmental and Earth Sciences, Environmental Science
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
