Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

Remote Sensing
Tedros M BerhaneHongxing Liu

Abstract

Efforts are increasingly being made to classify the world's wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree (DT), rule-based (RB), and random forest (RF). High-resolution satellite imagery can provide more specificity to the classified end product, and ancillary data layers such as the Normalized Difference Vegetation Index, and hydrogeomorphic layers such as distance-to-a-stream can be coupled to improve overall accuracy (OA) in wetland studies. In this paper, we contrast three nonparametric machine-learning algorithms (DT, RB, and RF) using a large field-based dataset (n = 228) from the Selenga River Delta of Lake Baikal, Russia. We also explore the use of ancillary data layers selected to improve OA, with a goal of providing end users with a recommended classifier to use and the most parsimonious suite of input parameters for classifying wetland-dominated landscapes. Though all classifiers appeared suitable, the RF classification outperformed both the DT and RB methods, achieving OA >81%. Including a texture metric (homogeneity) substantially improved the class...Continue Reading

References

Sep 15, 2012·Journal of Environmental Monitoring : JEM·Josefin ThorslundEkaterina V Belozerova
Apr 6, 2017·Sensors·Meng GuoLi Wu
Jan 1, 2017·Nature Geoscience·Irena F CreedLora Smith

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Citations

Aug 23, 2019·Current Pharmaceutical Biotechnology·Deeksha SaxenaRajnish Kumar
Nov 29, 2020·Environmental Science and Pollution Research International·Shuang HaoYuhuan Cui
Dec 10, 2020·Future Medicinal Chemistry·Fernando de Moura GattiGustavo Henrique Goulart Trossini
Sep 6, 2018·The Science of the Total Environment·Sean A WoznickiAnne Neale
Aug 17, 2021·The Science of the Total Environment·Haowei ZhouXingguo Mo

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Software Mentioned

ISODATA
R
ENVI
C5
randomForest
Thematic Mapper

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