Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

Abstract

Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r 2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r 2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r 2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r 2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling OPEN ACCESSRemote Sens. 2014, 6 2135 above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation.

Scattering Characteristics of X-, C- and L-Band PolSAR Data Examined for the Tundra Environment of the Tuktoyaktuk Peninsula, Canada

“…In contrast, use of short wavelengths (X-and C-Band) is beneficial for characterizing tundra and wetland vegetation. This observation is in accordance with other studies [9,12,15].…”

Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada

“…There was no direct linear correlation between any of the PolSAR features and the NDVI nor the Greenness. This is in line with the findings of [23].…”

“…The final classification has five classes: NBG, VLD, VMD, VSD and VWT, and the masked water bodies. The supervised classification of tundra land cover has been demonstrated with other techniques such as the Neural Network Classification [23], Minimum Distance Classification [4] or Decision-Tree Classification [7].…”

“…Few research projects have addressed the use of PolSAR data for land cover characterization and classification of tundra environments. Collingwood et al [23] showed the usage of quad-polarized Radarsat-2 data for the modeling of above-ground phytomass for high arctic environment. The findings indicate that the data are sensitive for specific cover types of the area of investigation.…”

Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network

“…Efforts have been devoted to improve oil spill detection and classification. Nowadays, there is a general consensus that the extra information provided by the polarimetric SAR (Pol-SAR) data enhances the capabilities of identifying and classifying the A 12 High Low Skrunes, Brekke, and Eltoft (2014) [8] α Low High Minchew, Jones, and Holt (2012) [25] ν High Low Skrunes, Brekke, and Eltoft (2014) [8] [15], Skrunes Brekke, and Eltoft (2014) [8] Over the past several decades, artificial neural network algorithms have been widely used in remote sensing image classification [27][28][29], due to their good self-organization [30][31][32], self-learning [33,34], and self-adaptive abilities [35]. Among these, BP (Back Propagation) neural network is one of the first widely used network models due to its simple and easy to implement training in initially stage.…”