GeoPython 2020

Analysis of burnt scar using optical and radar satellite data
2020-09-21, 13:45–14:15, Room 2

To analyze the use of satellite SAR data and its comparison to optical imagery for
identification and classification of burnt and unburnt patches after a forest fire.

This study compares the use of Sentinel 1 (S1) SAR sensor alongside with Sentinel 2 (S2) optical sensor in
detection and mapping of burnt and unburnt scars occurring after a bushfire in Victoria, Australia, and
Spain. The bushfires had recently occurred in the period of 2017-2018. The C-band dual polarized S1 data
have been investigated to assess the backscatter intensity together with polarimetric decomposition
component to determine forest burn severity over the two sites. The backscatter coefficient was also used
in deriving texture measures from local statistics, using grey level co-occurrence matrix (GLCM). This was
because of its sensitivity in the identification of textural variation of burnt and unburnt scars. While for S2
the difference normalized burnt ratio (dNBR) was utilized to determine the magnitude of burnt severity
levels present in both areas. Its analysis was explored using a contextual classifier Support Vector Machine
and Markov Random Field classifier (SVM-MRF). This is because of its integration of spectral
information and spatial context through the optimal smoothing parameter without degrading image
quality. The training and test set datasets consisting of burned and unburned pixels were created from S2
scenes used as reference data. The experimental results showed that a strong correlation exists in both
spectral sensitivity and polarimetric sensitivity of the two defined classes after classification. The
performance of the algorithm was evaluated using the kappa coefficient and f-score measurement. All fire
zones yielded an accuracy of (0.80) except for S1 data in Spain. Also, the performance in users and
producers accuracy provided the highest accuracies in both S1 and S2. The entropy alpha decomposition
helped to classify the target based on their physical properties as presented by the H-α plane. The entropy
and alpha values decreased and formed a pattern after the fire. The sensitivity analysis to the GLCM
features showed that homogeneity, contrast and entropy were the key statistical features that showed clear
separation of burnt and unburnt scars using backscatter intensity. This was after the key parameters such
as number of quantization levels, window size, pixel pair sampling distance which was one and the
orientation were optimized. The use of S1 in discrimination of burnt and unburnt scars was highly
dependent on local incidence angle, acquisition geometry and environmental conditions. In hilly areas, the
low incidence angles showed high discrimination of burnt from unburnt areas compared to high incidence
angles. Also, topography was of high influence as areas facing slopes in hilly areas showed high
discrimination of unburnt areas from burnt compared to areas facing backslopes. The Spain dataset did
not foreshow any changes in vegetation structure after the fire as compared to Australia using S1. This led
to the conclusion that also the intensity of the fire and its effect to vegetation structure is of great
influence to the sensitivity of SAR sensor in the analysis of changes in forest structure after a bushfire.
Also optical data in such cases can be used as a substitute as it showed strong spectral sensitivity to
changes in Spain fire irrespective of the intensity of the fire. Nevertheless, results in both areas verify the
use of satellite SAR sensor and optical in forestry application and their sensitivity highly depends on
vegetation structure, geographical nature of the area of study and fire intensity.