Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest

Abstract

Forest environment classification in mountain regions based on single-sensor remote sensing approaches is hindered by forest complexity and topographic effects. Temperate broadleaf forests in western Asia such as the Hyrcanian forest in northern Iran have already suffered from intense anthropogenic activities. In those regions, forests mainly extend in rough terrain and comprise different stand structures, which are difficult to discriminate. This paper explores the joint analysis of Landsat7/ETM+, L-band SAR and their derived parameters and the effect of terrain corrections to overcome the challenges of discriminating forest stand age classes in mountain regions. We also verified the performances of three machine learning methods which have recently shown promising results using multisource data; support vector machines (SVM), neural networks (NN), random forest (RF) and one traditional classifier (i.e., maximum likelihood classification (MLC)) as a benchmark. The non-topographically corrected ETM+ data failed to differentiate among different forest stand age classes (average classification accuracy (OA) = 65%). This confirms the need to reduce relief effects prior data classification in mountain regions. SAR backscattering alone cannot properly differentiate among different forest stand age classes (OA = 62%). However, textures and PolSAR features are very efficient for the separation of forest classes (OA = 82%). The highest classification accuracy was achieved by the joint usage of SAR and ETM+ (OA = 86%). However, this shows a slight improvement compared to the ETM+ classification (OA = 84%). The machine learning OPEN ACCESSRemote Sens. 2014, 6 3625 classifiers proved t o be more robust and accurate compared to MLC. SVM and RF statistically produced better classification results than NN in the exploitation of the considered multi-source data.

Contributions of Actual and Simulated Satellite SAR Data for Substrate Type Differentiation and Shoreline Mapping in the Canadian Arctic

“…It is also relatively simple to implement, requiring little user-intervention; an advantage that is frequently noted [34][35][36]. This is of relevance to this research, since compared to other non-parametric approaches, including Neural Networks, Support Vector Machines, and Classification and Regression Trees, that often require more user-intervention, Random Forests tends to yield similar classification accuracies [30,31,33,[36][37][38][39][40]. For these reasons, and because [16] demonstrated its efficacy for classifying shorelines, Random Forests are also evaluated in this research.…”

“…Multiple authors have demonstrated that Random Forests tends to perform better than Maximum Likelihood and other conventional parametric methods [30][31][32][33]. It is also relatively simple to implement, requiring little user-intervention; an advantage that is frequently noted [34][35][36].…”

Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

“…It has proven effective for classifying highly dimensional data (i.e., many input variables) from a variety of sensors [23][24][25][26], and has been shown to outperform conventional parametric classifiers, including Maximum Likelihood [23,[27][28][29]. This is particularly relevant with respect to the classification of SAR data, since backscatter values are not typically normally distributed when represented in linear power format [11].…”

“…A number of authors have also achieved comparable or improved results with Random Forest, compared to other non-parametric approaches, including Classification and Regression Trees (CARTs) [23,27,30,34], Support Vector Machines [29,[35][36][37], and Neural Networks [29]. These approaches typically require more user-interference with classifier settings whereas Random Forest only requires that users define: (1) the number of trees that are generated; and (2) the number of variables tested during each iteration of node splitting (described subsequently); a benefit that is commonly noted in the literature [24,38,39].…”

Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps

“…From the data availability aspect, multi-sensor/source data including optical, SAR, and GIS data have been used as inputs for classification [6][7][8][9]. To properly treat input data for classification, advanced classification methodologies such as machine learning approaches and object-based classification Remote Sens.…”