Document Type
Article
Publication Date
2-2023
Department
College of Forest Resources and Environmental Science
Abstract
To map and manage forest vegetation including wetland communities, remote sensing technology has been shown to be a valid and widely employed technology. In this paper, two ecologically different study areas were evaluated using free and widely available high-resolution multispectral National Agriculture Imagery Program (NAIP) and ultra-high-resolution multispectral unmanned aerial vehicle (UAV) imagery located in the Upper Great Lakes Laurentian Mixed Forest. Three different machine learning algorithms, random forest (RF), support vector machine (SVM), and averaged neural network (avNNet), were evaluated to classify complex natural habitat communities as defined by the Michigan Natural Features Inventory. Accurate training sets were developed using both spectral enhancement and transformation techniques, field collected data, soil data, texture, spectral indices, and expert knowledge. The utility of the various ancillary datasets significantly improved classification results. Using the RF classifier, overall accuracies (OA) between 83.8% and 87.7% with kappa (k) values between 0.79 and 0.85 for the NAIP imagery and between 87.3% and 93.7% OA with k values between 0.83 and 0.92 for the UAV dataset were achieved. Based on the results, we concluded RF to be a robust choice for classifying complex forest vegetation including surrounding wetland communities.
Publication Title
GIScience and Remote Sensing
Recommended Citation
Bhatt, P.,
&
Maclean, A.
(2023).
Comparison of high-resolution NAIP and unmanned aerial vehicle (UAV) imagery for natural vegetation communities classification using machine learning approaches.
GIScience and Remote Sensing,
60(1).
http://doi.org/10.1080/15481603.2023.2177448
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/17052
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Version
Publisher's PDF
Publisher's Statement
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Publisher’s version of record: https://doi.org/10.1080/15481603.2023.2177448