The main purpose of this research is to determine the anomalies regarding with the coal mining operations in an
abandoned coal mine site in central Anatolia by multi-temporal image analysis of Landsat 4-5 surface reflectance data. A
well-known anomaly detection algorithm, Reed-Xioli (RX), which calculates square of Mahalanobis metrics to calculate
the likelihood ratios by normalizing the difference between the test pixel and the background to allocate anomaly pixels,
is implemented across the time series. The experimental results reveal especially the profound land use – land cover
change in time series, pointing out critically abandoned regions that need immediate rehabilitation action. The rate of
anomaly scores together with their relation to mine development over the focused time spectrum discloses a linearity
trend as of the operations are ceased at the end of 1990s, which is indicative of the capacity of the applied method. The
performance of the algorithm is also quantified with Receiver Operating Characteristics (ROC) curves and precisionrecall
graphs to quantify its capability on Landsat Thematic Mapper (TM) multispectral image series. The resulting plots
show the increasing capability of the hyperspectral anomaly detection technique in multi-temporal data set, with a steady
and slight increase in performance between 2000 and 2012 after the end of the mining activities, which substantiates the
success of global RX algorithm to identify the mining-induced land use and land cover anomalies.
In this paper, we compare the conventional methods in hydrocarbon seepage anomalies with the signature based detection algorithms. The Crosta technique  is selected as a basement in the experimental comparisons for the conventional approach. The Crosta technique utilizes the characteristic bands of the searched target for principal component transformation in order to determine the components characterizing the target in interest. Desired Target Detection and Classification Algorithm (DTDCA), Spectral Matched Filter (SMF), and Normalized Correlation (NC) are employed for signature based target detection. Signature based target detection algorithms are applied to the whole spectrum benefiting from the information stored in all spectral bands. The selected methods are applied to a multispectral Advanced SpaceBorne Thermal Emission and Radiometer (ASTER) image of the study region, with an atmospheric correction prior to the realization of the algorithms. ASTER provides multispectral bands covering visible, short wave, and thermal infrared region, which serves as a useful tool for the interpretation of the areas with hydrocarbon anomalies. The exploration area is selected as Gemrik Anticline which is located in South East Anatolia, Adıyaman, Bozova Oil Field, where microseeps can be observed with almost no vegetation cover. The spectral signatures collected with Analytical Spectral Devices Inc. (ASD) spectrometer from the reference valley  have been utilized as an input to the signature based detection algorithms. The experiments have indicated that DTDCA and MF outperforms the Crosta technique by locating the microseepage patterns along the mitigation pathways with a better contrast. On the other hand, NC has not been able to map the searched target with a visible distinction. It is concluded that the signature based algorithms can be more effective than the conventional methods for the detection of microseepage induced anomalies.