title: Accuracy assessment of land cover/land use classifiers in dry and humid areas of Iran.
abstract: Land cover/land use (LCLU) maps are essential inputs for environmental analysis. Remote sensing provides an opportunity to construct LCLU maps of large geographic areas in a timely fashion. Knowing the most accurate classification method to produce LCLU maps based on site characteristics is necessary for the environment managers. The aim of this research is to examine the performance of various classification algorithms for LCLU mapping in dry and humid climates (from June to August). Testing is performed in three case studies from each of the two climates in Iran. The reference dataset of each image was randomly selected from the entire images and was randomly divided into training and validation set. Training sets included 400 pixels, and validation sets included 200 pixels of each LCLU. Results indicate that the support vector machine (SVM) and neural network methods can achieve higher overall accuracy (86.7 and 86.6%) than other examined algorithms, with a slight advantage for the SVM. Dry areas exhibit higher classification difficulty as man-made features often have overlapping spectral responses to soil. A further observation is that spatial segregation and lower mixture of LCLU classes can increase classification overall accuracy.
authors.affiliation: Watershed Science Department, Faculty of Natural Resources, Tarbiat Modares University (TMU), Mazandaran, Iran.
authors.forename: Saleh
authors.initials: S
authors.lastname: Yousefi
authors.affiliation: Department of Environmental Resources Engineering, State University of New York College of Environmental Science and Forestry, One Forestry Drive, Syracuse, NY, 13210, USA.
authors.forename: Reza
authors.initials: R
authors.lastname: Khatami
authors.affiliation: Department of Environmental Resources Engineering, State University of New York College of Environmental Science and Forestry, One Forestry Drive, Syracuse, NY, 13210, USA.
authors.forename: Giorgos
authors.initials: G
authors.lastname: Mountrakis
authors.affiliation: College of Natural Resources, Lorestan University, Lorestan, Iran.
authors.forename: Somayeh
authors.initials: S
authors.lastname: Mirzaee
authors.affiliation: Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran. hamidreza.pourghasemi@yahoo.com.
authors.forename: Hamid Reza
authors.initials: HR
authors.lastname: Pourghasemi
authors.affiliation: College of Natural Resources, Ardekan University, Ardekan, Iran.
authors.forename: Mehdi
authors.initials: M
authors.lastname: Tazeh
mesh_terms: D000383:Agriculture; D000465:Algorithms; D003889:Desert Climate; D004777:Environment; D004784:Environmental Monitoring / Q000379:methods*; D062305:Geographic Mapping; D006813:Humidity; D007492:Iran; D016571:Neural Networks, Computer; D012621:Seasons; D060388:Support Vector Machine; D014329:Tropical Climate
keywords: Classification accuracy; Humid and dry areas; Land cover/land use; Neural networks; SVM