Information on distribution of forest types and land cover classes is essential for decision making and significant in climate regulation, biodiversity conservation, and societal issues. An approach for the combination of advanced polarimetric decompositions and textures of Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar full polarimetric data for the purpose of forest type classification is proposed. Using a support vector machine (SVM) classifier, we classified forest types over a selected Indian region. Further, we tested the classification performance of the Wishart method for the same forest types. The classified results were assessed with confusion matrix-based statistics. The results suggest that incorporation of various polarimetric decompositions features into gray-level co-occurrence matrix textures refines the SVM classification overall accuracy (OA) from 73.82% (k=0.69) to 76.34% (k=0.72). The Wishart supervised classification algorithm has the OA of 73.38% (kappa=0.68). We observed that integration of polarimetric information with textures can give complimentary information in forest type discrimination and produce high accuracy maps. Further, this approach overcomes the limitations of optical remote sensing data in continuous cloud coverage areas.
In the present work, the potential of synthetic aperture radar (SAR) interferometric coherence in land cover classification is studied over forested areas of Bilaspur, Chattisgarh, India using Environmental Satellite-Advanced Synthetic Aperture Radar (ENVISAT-ASAR) C-band data. Single look complex (SLC) interferometric pair ASAR data of 24th September 2006 (SLC-1) and 29th October 2006 (SLC-2) covering the study area were acquired and processed to generate backscatter and interferometric coherence images. A false colored composite of coherence, backscatter difference, and mean backscatter was generated and subjected to maximum likelihood classification to delineate major land cover classes of the study area viz., water, barren, agriculture, moist deciduous forest, and sal mixed forests. Accuracy assessment of the classified map is carried out using kappa statistics. Results of the study suggested potential use of ENVISAT-ASAR C-band data in land cover classification of the study area with an overall classification accuracy of 82.5%, average producer's accuracy of 83.69%, and average user's accuracy of 81%. The present study gives a unique scope of SAR data application in land cover classification over the tropical deciduous forest systems of India, which is still waiting for its indigenous SAR system.
This paper presents experimental results obtained with ENVISAT-ASAR space borne data of VV, VH and HH
polarizations over the parts of Karnataka in the Western Ghats, India. The aim is to quantify various levels of above
ground forest Biomass, Volume and Basal area with the help of empirical relationship of the backscattering coefficients
with the ground inventory. Extensive field data were collected to characterize forest vegetation parameters in the plots.
Ground inventory data such as GBH, tree height were collected. The above ground tree biomass (ABG) is estimated
from the collected ground data using the existing allometric equations at stand level in study area.
Dual polarized ENVISAT C-band Advanced Synthetic Aperture Radar (SAR) data of IS2 beam mode i.e., incidence
angle ranges from 18.8°-26.1° were analyzed. The DN values of the ENVISAT-ASAR data are converted to
backscattering coefficients. The forest in the study area holds 129-203 Mg ha-1 (1Mg=103 kg) of Biomass. Scatter plots
were drawn between backscattering coefficients and Volume, Basal Area and Biomass for VV, VH and HV
polarizations. Results show that with increase in the biomass levels, backscattering coefficient also increases. The
backscattering coefficient of HH has significant correlation with the biophysical parameters whereas VH and VV are
not well correlated. This relationship can be utilized to estimate the forest biophysical parameters of the study area.
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