Monitoring Land Cover Changes in Halabja City (original) (raw)
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Monitoring Land Cover Changes in Halabja City, Iraq
This paper presents land use / land cover changes of the Halabja city in the north part of Iraq over 1986 to 1990 by utilizing multi-temporal remote sensing imagery. Halabja city has been facing severe land use/land cover changes following a series of wars beginning with Iraq-Iran war (1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988) to the just concluded invasion of Iraq (March 19, 2003 -2011). In this study, multi-temporal Landsat images (TM) between the years of 1986 and 1990 were used. All images are rectified and registered to Universal Transverse Mercator (UTM), zone 38N and WGS_84 datum. Hybrid classification as a combine of k-Means and Maximum Likelihood Classification (MLC) algorithms were applied to classify the images in five different land cover categories: water body, cultivated area, shrub land, urban area and bare land. Quantitative analysis was conducted by using post-classification change detection technique. The results show an overall accuracy for 1986 and 1990 images are 92.2% and 96.8% respectively. During 1986 to 1990 land use / land cover changes a lot with a huge decrease about 40.8% in cultivated area whereas, urban area, Shrub Land and bare land classes increased by 57.9 %, 67.1 % and14 % respectively.
Advances in Remote Sensing and Geo Informatics Applications, 2018
Rapid growth in urbanized areas is a worldwide phenomenon. The rate of urban growth is very fast in developing countries like Iraq. This study illustrated urbanized area development in Sulaimaniyah Governorate from 2001 to 2017 using different Landsat imagery, Landsat Thematic Mapper (TM) and Landsat Operational Land Imager (OLI). The Environment for visualizing images ENVI 5.3 and GIS software was utilized for image pre-processing, calibration and classification. The Maximum likelihood method was used in the accurately extracted solution information from geospatial Landsat satellite imagery of different periods. The Landsat images from the study area were categorized into six different classes. These are: forest, vegetation, rock, soil, built up and water body. Land cover variation and land use change detection in the area were calculated for over a 17 year period. The Change detection Analysis shows an explosive demographic shift in the urban area with a record of +8.99% which is equivalent to 51.80 km 2 over a 17 years period and the vegetation area increased with 214 km 2. On the other hand, soil area was reduced by 257.87 km 2. This work will help urban planners in the future development of the city.
Journal of Geographic Information System
Land use & land cover change detection in rapid growth urbanized area have been studied by many researchers and there are many works on this topic. Commonly, settlement sprawl in area depends on many factors such as economic prosperity and population growth. Iraq is one of the countries which witnessed rapid development in the settlement area. Remote sensing and geographic information system (GIS) are analytical software technologies to evaluate this familiar worldwide phenomenon. This study illustrates settlement development in Sulaimaniyah Governorate from 2001 to 2017 using Landsat satellite imageries of different periods. All images had been classified using remote sensing software in order to proceed powerful mapping of land use classification. Maximum likelihood method is used in the accurately extracted solution information from geospatial imagery. Landsat images from the study area were categorized into four different classes. These are: forest, vegetation, soil, and settlement. Change detection analysis results illustrate that in the face of an explosive demographic shift in the settlement area where the record + 8.99 percent which is equivalent to 51.80 Km 2 over a 16-year period and settlement area increasing from 3.87 percent in 2001 to 12.86 percent in 2017. Accuracy assessment model was used to evaluate (LULC) classified images. Accuracy results show an overall accuracy of 78.83% to 90.09% from 2001 to 2017 respectively while convincing results of Kappa coefficient given between substantial and almost perfect agreements. This study will help decision-makers in urban plan for future city development.
Journal of Geographic Information System, 2018
Land use & land cover change detection in rapid growth urbanized area have been studied by many researchers and there are many works on this topic. Commonly, settlement sprawl in area depends on many factors such as economic prosperity and population growth. Iraq is one of the countries which witnessed rapid development in the settlement area. Remote sensing and geographic information system (GIS) are analytical software technologies to evaluate this familiar worldwide phenomenon. This study illustrates settlement development in Sulaimaniyah Governorate from 2001 to 2017 using Landsat satellite imageries of different periods. All images had been classified using remote sensing software in order to proceed powerful mapping of land use classification. Maximum likelihood method is used in the accurately extracted solution information from geospatial imagery. Landsat images from the study area were categorized into four different classes. These are: forest, vegetation, soil, and settlement. Change detection analysis results illustrate that in the face of an explosive demographic shift in the settlement area where the record + 8.99 percent which is equivalent to 51.80 Km 2 over a 16-year period and settlement area increasing from 3.87 percent in 2001 to 12.86 percent in 2017. Accuracy assessment model was used to evaluate (LULC) classified images. Accuracy results show an overall accuracy of 78.83% to 90.09% from 2001 to 2017 respectively while convincing results of Kappa coefficient given between substantial and almost perfect agreements. This study will help decision-makers in urban plan for future city development.
Journal of Fundamental and Applied Sciences, 2018
This study examines the spatial and temporal changes of land use and land cover in South Ghor, Jordan. Satellite images for the years 1972, 1989, 1999 and 2016 were used for LULC supervised classification techniques, four LULC classes were decided: built-up areas, pastures and bare land, agricultural land and water bodies. For the accuracy of assessment classifications, matrix error and KAPPA analysis have been used in this paper. The analysis detected by supervised classification techniques show that agricultural land and built-up land have increased, while barren land and water bodies have decreased. It is predicted that the study would significantly contribute to better policy making, sustainable development and developing useful change detection planes for the south ghor regions and similar regions of the country.
American Journal of Environmental Sciences, 2014
Knowledge of Land Use and Land Cover (LULC) changes is important for many planning and management activities. It is thought to be an essential element for modeling and understanding the major land forms, especially in arid regions like Saudi Arabia. This study investigates the LULC changes in Dirab region of Saudi Arabia between 1980 and 2010, using Landsat TM/ETM+images. After the geometric correction and radiometric normalization, multi-temporal image data sets were spectrally enhanced separately using Principal Component Analysis (PCA) and Tasseled Cap Transformation (TCT). Each image was then separately subjected to supervised classification and processed to identify and quantify LULC changes (vegetation, barren land and built-up area). Post Classification Comparison (PCC) method was adopted for LULC change detection. Change trajectories ("from-to" classes) and accuracy assessments were made by comparing the detected land use change layers with medium/high resolution images of Google Earth data base. The TCT enhanced procedure gave better identification of the changed areas than PCA based method. The overall accuracy of PCA based change detection was 64.58, 62.68 and 62.12% for 1980-1990, 1990-2000 and 2000-2010 images, respectively. However, the TCT based change detection resulted in higher accuracy of 77.78, 75.62 and 77.92% for
Support Vector Machine Cl assification to Detect Land Cover Changes in Halabja City, Iraq
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 multi-temporal remotely sensed data from 1986 to 1990. The support vector machine supervised classification technique was used to extract information from satellite data, and post-classification 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.
Eng. &Tech. Journal,, 2013
This study was conducted to determine the land cover changes between year 1976 and year 2011 in Karbala Governorate by using an integrated approach of remote sensing data and GIS applications for investigation of the spatial and temporal changes. A part of Karbala Governorate, whose Area is 768 km2 was selected as study area. Four cloud free Landsat MSS, TM, ETM+, and SPOT scenes covering the study area were selected for analysis. Images were acquired in years 1976, 1990, 2001, and 2011 respectively. All images which mentioned above are rectified and registered in Universal Transverse Mercator (UTM) projection zone 38 N and supervised image classification system has been observed to classify the images in different land cover categories. Six land cover classes have been identified and used to determine the change in land cover in study area and these classes are: Agricultural land, Water bodies, Urban Area, Sand dunes, Bare soil, and Waterlogged Area. According to the results obtained from statistics of classification, it was observed that most changes occurred in heterogeneous agricultural areas. It is thought that the main reasons of this change are increasing population pressure, increasing sand dunes, appearance and increasing waterlogged area and changing economic activities. Those reasons have been led to the decrease of the agricultural areas in study area during period from year 2001 to 2011. Keywords: Land cover, Remote Sensing, GIS, Landsat, SPOT.
2010
Land use data are needed in the analysis of environmental processes and problems that must be understood if living conditions and standards are to be improved or maintained at current levels. Information on land use/land cover in the form of maps and statistical data is very vital for spatial planning, management and utilization of land. In the study, Remote Sensing and geographic information system (GIS) were used in order to study land cover changes. The study area locate in Baghdad city, two satellite images were used in order to monitor the different in land cover Mss for years 1976 and Ikonos for the year 2003 images. There was supervised classification for the categories urban, vegetation, and water just for mss image. All raster layers were converted to vector in order to calculate all the areas of land cover (urban, vegetation, water). And that was by isolated every feature alone. The results were for mss image to urban, vegetation, and water 762, 194, 18 Km² respectively while the results for Ikonos were 862, 103, and 12 Km² respectively.
2019
Nowadays, land use and land cover (LULC) changes due to both human beings and natural environment. Consequently, LULC changes impact on water resources such as forestry, water bodies, agriculture land, wetland, urbanization, industrialization and so on. The aim of this research is to detect LULC changes in Belin Township. ERDAS IMAGINE 2015 and ArcGIS 10.4.1 have been used to analyze the images processing and classification. LULC conditions of this area for the time periods 1999, 2009 and 2018 have been considered and downloaded from Landsat ETM+ satellites images. Maximum likelihood method has been conducted in supervised image classification technique. The ground truth data or reference points are used to classify the image classification applying Google Earth Pro. Moreover, forest, settlement, water bodies, agriculture and bare land of five LULC classes are identified in this study. Bare land and Settlement are significantly unchanged during two decades. Further, forest area was increased approximately 22.36% between 1999 and 2018. However, the water bodies of this study area were decreased slightly. LULC by agriculture land was decreased between 1999 and 2018. The finding results of this research paper can contribute effectively about LULC change detection and help decision makers to develop plan in this study area.