title: Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.
abstract: Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge fromremotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using highspatial resolutionimagery and machine learning image classification algorithms for mapping heterogeneouswetland plantcommunities. This study addresses this void by analyzing whether machine learning classifierssuch as decisiontrees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedgecommunities usinghigh resolution aerial imagery and image texture data in the Everglades National Park, Florida.In addition tospectral bands, the normalized difference vegetation index, and first- and second-order texturefeatures derivedfrom the near-infrared band were analyzed. Classifier accuracies were assessed using confusiontablesand the calculated kappa coefficients of the resulting maps. The results indicated that an ANN(multilayerperceptron based on backpropagation) algorithm produced a statistically significantly higheraccuracy(82.04%) than the DT (QUEST) algorithm (80.48%) or the maximum likelihood (80.56%)classifier (α<0.05). Findings show that using multiple window sizes provided the best results. First-ordertexture featuresalso provided computational advantages and results that were not significantly different fromthose usingsecond-order texture features.
authors.affiliation: Land Resource Management Unit, Joint Research Centre, European Commission, Ispra, 21027, VA, Italy, zoltan.szantoi@remote-sensing-biodiversity.org.
authors.forename: Zoltan
authors.initials: Z
authors.lastname: Szantoi
authors.forename: Francisco J
authors.initials: FJ
authors.lastname: Escobedo
authors.forename: Amr
authors.initials: A
authors.lastname: Abd-Elrahman
authors.forename: Leonard
authors.initials: L
authors.lastname: Pearlstine
authors.forename: Bon
authors.initials: B
authors.lastname: Dewitt
authors.forename: Scot
authors.initials: S
authors.lastname: Smith
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004784:Environmental Monitoring / Q000379:methods*; D005431:Florida; D005618:Fresh Water; D016571:Neural Networks, Computer; D053833:Wetlands