Abstract:
The aim of the dissertation is to elaborate the remote sensing methods for monitoring subsurface
lire (hotspots) in Jharia Region, (Jharkhand) India; as the Jharia coal field contains almost half of
subsurface mine fires within the Indian coal fields [1]. Thus, detecting and monitoring such
hotspots are mandatory. Since ground based monitoring are quite expensive and difficult task,
exploiting the potential of satellite images have been tried as an alternative solution. For this
purpose, freely available satellite images (e.g., MODIS, NOAA/AVHRR, and LANDSAT) are
being used for our study. This study involves the application of most renowned soft computing
techniques such as: supervised classification (parallelepiped, minimum distance) and
unsupervised classification (ISODATA, K-means) over optical data: MODIS, NOAA/AVI-IRR,
- and LAN DSA'I'. NE)VI plays an important role for the detection of hotspot due to the fact that
hotspot region usually has bare ground such that neither bushes nor grasses grows over hotspot
region. Thus, NDVI classified image into hotspot and non-hotspot regions is used. The accuracy
of the classified image is assessed using the metrics: hotspot detection accuracy (HDA) and false
alarm rate (FAR). The assessed value indicates that there is room for improvement. Thus, an
attempt based on heuristic method- genetic algorithm (GA) have been carried out, since it has
higher chances to result in an optimal classification of hotspot and non-hotspot pixels due to its
ability to search for the optimal hypothesis over a larger search space. Therefore, the attempt of
GA based KMI (K-Means Index) indicates that the detection of hotspot with an accuracy of
81%-I 11)A and 11%-FAR over MODIS dataset.
Such high HDA and low FAR over detection of hotspot and an attainment of good
temporal resolution recommends use of MODIS dataset for area estimation over hotspot
coverage in Jharia region. But due to fragment size of hotspot in comparison to spatial resolution
of MODIS, major amount of hotspot are present partially within a pixel (i.e., mixed pixel issues).
In order to perform hotspot area estimation over such coarse resolution image, subpixel analysis
is performed; by refining the per-pixel spectral-based detected hotspot from MODIS image by
proposing a method that uses a subpixel spectral detection method called GEM (a target
- constraint approach). Constrained energy minimization (CEM) is very efficient in the detection
of small hotspots very effectively as well as it requires only a prior knowledge of target spectral
signature. Due to the requirement of hotspot pure spectral signature, we have used LANDSAT-
5TM image for the endmember selection using PH. With such refined detected hotspots, the
estimated area coverage of hotspot were found to be of 11.09 Km2 (on I 4-Mar-20 15) and when
validated with week and yearly variation; it is observed that hotspot of 0.165 Km2 of variation
been observed within two weeks interval and 2.647 Km2 of increased I-Iotspot coverage is
observed over a period of two years.
