Evaluation of ALOS PALSAR Data for High-Resolution Mapping of Vegetated Wetlands in Alaska

Abstract

As the largest natural source of methane, wetlands play an important role in the carbon cycle. High-resolution maps of wetland type and extent are required to quantify wetland responses to climate change. Mapping northern wetlands is particularly important because of a disproportionate increase in temperatures at higher latitudes. Synthetic aperture radar data from a spaceborne platform can be used to map wetland types and dynamics over large areas. Following from earlier work by using Japanese Earth Resources Satellite (JERS-1) data, we applied the "random forests" classification algorithm to variables from L-band ALOS PALSAR data for 2007, topographic data (e.g., slope, elevation) and locational information (latitude, longitude) to derive a map of vegetated wetlands in Alaska, with a spatial resolution of 50 m. We used the National Wetlands Inventory and National Land Cover Database (for upland areas) to select training and validation data and further validated classification results with an independent dataset that we created. A number of improvements were made to the method of Whitcomb et al. (2009): (1) more consistent training data in upland areas; (2) better distribution of training data across Remote Sens. 2015, 7 7273 all classes by taking a stratified random sample of all available training pixels; and (3) a more efficient implementation, which allowed classification of the entire state as a single entity (rather than in separate tiles), which eliminated discontinuities at tile boundaries. The overall accuracy for discriminating wetland from upland was 95%, and the accuracy at the level of wetland classes was 85%. The total area of wetlands mapped was 0.59 million km 2 , or 36% of the total land area of the state of Alaska. The map will be made available to download from NASA's wetland monitoring website.

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

“…Random Forest classifications performed well, confirming the widely reported effectiveness of the RF algorithm for land cover classification (e.g., [15,58,64,65]), and particularly for mapping wetlands in tropical environments (e.g., [73,89,92]). Overall, OOB accuracy was above 80% for most models, above 90% for a selection of optimized models, and as high as 99% for model M1, which integrated spectral, SAR and topographic data, and images from multiple years and seasons.…”

“…The contribution of PALSAR L-band HH and HV data to wetland classification has been well documented [33,34,58,100]. For example, large-area mapping of the Pantanal using PALSAR L-band FBD [33], achieved 80% accuracy across the entire area.…”

“…All topographic metrics were calculated using the open source GIS SAGA (System for Automated Geoscientific Analyses, version 2.2.3) [57]. The predictor variables listed in Table 3 also include the coefficient of variation from a 3 × 3-pixel moving window applied to the HH and HV PALSAR images; it was used as a texture metric to evaluate how spatial heterogeneity in backscatter intensity can contribute to improving the discrimination of the wetland classes [58,59]. PALSAR L-band polarization intensity ratio HV/HH was selected for its effectiveness in discriminating flooded from non-flooded vegetation and water [47], while HH/HV demonstrated similar characteristics using SAR C-band data [60].…”

“…RF was selected for this study because it generally outperforms conventional classifiers such as the Gaussian maximum likelihood classifier [61,62], while performing favorably, or equally well, to other non-parametric approaches; e.g., CART [63,64], Support Vector Machines [32,65,66], Artificial Neural Networks [67], and K-Nearest Neighbor [68]. It is a powerful non-linear and non-parametric classifier that allows for fusion and aggregation of high-dimensional data from various sources (e.g., optical, SAR, and topography [30,69,70]; SAR and topography [21,58,71]; and optical and topography [72][73][74]). RF produces independently constructed classification trees, similar to the Classification and Regression (CART) method, using bootstrapped samples of the original data [75,76].…”

Mapping stand age of rubber plantation using ALOS-2 polarimetric SAR data

“…Several supervised classification techniques have been proposed, generally categorized into parametric (including likelihood-based techniques) or non-parametric approaches, as used in decision trees and neural networks. Recent literature, however, tend to focus on the latter, especially SVM and RF [Chabrier et al, 2012;Naidoo et al 2014;Sonobe et al, 2014;Clewley et al 2015]. Both approaches have known being consistently superior than conventional methods such as maximum likelihood classification or decision trees [Rodriguez-Galiano and Chica-Rivas, 2014;He et al, 2015;Low et al 2015].…”