This study presents vegetation change detecting in Halabja city, Iraq by using Landsat-5Thematic Mapper images. This city was shelled with chemical weapons on 16 March, 1988. The Normalized Difference Vegetation Index (NDVI) image differencing and postclassification techniques were applied. The NDVI was derived first then classified to produce vegetation maps followed by quantifying the changes.The results indicated a drastic decrease in the dense, sparse and moderate vegetation by55%, 7% and 9% respectively. In contrast, the non-vegetation class increased by 5%. This means that, the field and planted areas were at risk of losing vegetation.

Abstract-Since

Halabja city in Iraq has faced drastic landscape change since the Iraq-Iran war, especially when this city and the surrounding areas were attacked with chemical bombs in 1988. This paper illustrates the results of land use/cover change in Halabja obtained by using multitemporal remotely sensed data from 1986 to 1990. The support vector machine supervised classification technique was used to extract information from satellite data, and postclassification change detection method was employed to detect and monitor land use/cover change. Derived land use/cover maps were further validated by using high resolution images derived from Google earth. The results from this research indicate that the overall accuracy of land cover maps generated from Landsat Thematic Mapper (TM) data were more than 89%. The urban areas and vegetation classes decreased approximately 58.7% to 40.7% between 1986 and 1990, while bare land increased 25.4%. Also, some changes in urban areas were detected that have already been identified as bombed areas particularly around the main roads of Halabja city.

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Detection of land cover (LC) changes allows policymakers to recognize the complexities of environmental modification and change to achieve sustainability of economic growth. As a result, recognition of LC features has appeared as an essential research dimension and, consequently, an appropriate and reliable methodology for classifying LC is occasionally required. In this research, Landsat 8 satellite data captured by Operational Land Imager (OLI) and Thermal Infrared Scanner (TIRS) were utilized for the LC classification using the Support Vector Machine (SVM) classifier algorithm. The aim of the study is to enhance classification accuracy by integrating the use of data from satellite thermal and spectral imaging. Land Surface Temperature (LST) is sensitive to the soil surface characteristics, therefore, it may be used to gather LC feature information. The classification accuracy was designed to enhance the integration of thermal information from Landsat 8’s thermal band TIRS and Landsat 8 OLI’s spectral data. In this study, Advanced Thermal Integrated Vegetation Index (ATLIVI) and Thermal Integrated Vegetation Index (TLIVI) established and revealed fairly strong correlations with the related surface temperature (Ts) by R2=0,7 and 0,65 respectively. The relationship between Ts and the other vegetation indices based on the empirical parameterization demonstrate that these two indices showed an improvement of almost 6% in the overall accuracy of the LC classification results compared to the Landsat 8 Standard False Colour Composite image as an input data using SVM algorithm.