Environmental Change Monitoring by Geoinformation Technology for Baghdad and its Neighboring Areas2. Study Area The study area extends between latitude N 32° 13' to N 34° 07', longitude E 42° 43' to 44° 41'. It covers an area of 26,943 km2, accounting 6.193% of the total area of Iraq. The study area is located in The Alluvial plain which the hot desert climate prevails in the sedimentary plain and the western plateau. It extends among parts of five governorates in the middle part of Iraq, which form Baghdad city (capital of Iraq) the eastern side of it, with a population of 7.5 million. Annual rainfall ranges between 50-200 mm, most of the rain comes between October and April .It is characterized by great temperature variation between the day and night, summer and winter, the maximum of which reaches 45-50o c (Ministry of Planning, 2002). Figure 1 shows the location map of the study area in the middle part of Iraq and the Landsat ETM+ satellite image for the year of 2001.  Figure.1 Location Map of the Study Area in Baghdad and its Neighboring Areas and the ETM+ satellite image for the year of 2001. 3. Materials and Methods 3.1 Remote Sensing Data Multi-temporal Landsat (WRS2: 169/37) TM (dated March 04, 1990) and ETM+ (dated March 18, 2001) imageries remotely sensed dataset were assembled and analysed for environmental changes analysis in the study area. The spatial resolution of one pixel of TM and ETM images were 28.5m by 28.5m. 3.2 Preprocessing 3.2.1. Radiometric Correction and Image Normalisation The Landsat images were calibrated for sensor differences, converted into spectral radiance and normalized for illumination properties through differences in sun-elevation angle and sun–earth distance by recalculating the pixel values into at-satellite reflectance. Rectification and registration of TM and ETM+ imageries were based on control points collected from vector files for the big and small rivers of the study area. Fifty control points were selected from the study areas. 3.2.2 Image to image registration The remotely sensed data (TM 1990 and ETM 2001) were geometrically corrected in the datum WGS84 and projection UTM N38 using the first order (linear) of polynomial function and Nearest Neighbor rectification re-sampling. The RMS error of the image-to-image rectification comes between 0.35 and 0.41 pixels. 3.3 The Normalized Difference Vegetation Index (NDVI) Rouse et al. (1974) initially proposed the Normalized Difference Vegetation Index (NDVI). The NDVI derived from the ratio of band 3 and band 4 in Landsat TM and ETM images data was applied for monitoring vegetation changes in the study area within the years of 1990 and 2001.  3.4 Bare Soil Index (BSI) Bare Soil Index (BSI) was computed to identify the bare soil which includes bare areas (houses, roads, urban and rural built up areas, and eroded areas. The bare soil areas are enhanced using the BSI index (Jamalabad and Abkar, 2004).  3.5 The Normalized Differential Water Index (NDWI) The Normalized Differential Water Index was used to oversee the situation of water in the study area. The ratio between Red and SWIR spectral region clearly enhanced water bodies to the brighter pixels (CPM, 2003).  3.6 Tasseled Cap transformation Wetness indicator (TCW) Tasseled Cap transformation (Crist et al. 1984 and 1986) is one of the available methods for enhancing spectral information content of Landsat TM and ETM data. Tasseled Cap transformation especially optimizes data viewing for vegetation studies. Tasseled Cap index was calculated from data of the related six TM and ETM bands. One of the six tasseled cap transformation bands (wetness indicator "TCW") which used as an indicator for the soil moisture was used in this study. Reflectance-based Tasseled Cap features range from -0.5 to 1.4. To facilitate the calculation, it was normalised to the extent from 0 to 255. 3.7 Change Discrimination In order to detect, assess, and mapping the environmental changes of the study area during the period from 1990 to 2001, Landsat TM and ETM imageries dataset were used. Change detection involves the use of multi-temporal image data sets to discriminate the changes between dates of imaging. There are two ways were adopted in this research to detect the changes in case of using satellite image data set; 1) comparison of two independent enhanced images (two dates), 2) image differencing; in the image differencing procedure for change detection, the corresponding pixel values (DNs) from one date (t1) are simply subtracted from those of the other (t2). That produced three levels of information: negative change, positive change, and no change (Jensen et al., 1982; Lambin, 1994 and 1997). Thresholding and masking (fig.2) produced the vegetation and environmental information maps for the years 1990 and 2001. Each type of change was quantified to county-level by GIS technique.  Figure 2. Differencing image shows the dryness of Al-Habbaniya Lake during the study period from 1990 to 2001. 3.8 Ancillary Data and Software Packages County-level socio-economic, meteorological data, and software such as ERMapper for image processing, ArcView GIS for analysing and presenting the results, Statistical Graphics, NCSS, and Microsoft Excel were utilised in this research. 4. Results and Discussion NDVI, BSI, NDWI, and TCW indices were computed in multi-temporal Landsat images and tried to analyze the environmental changes in respect of vegetation, agriculture, water, meteorological data, and people activities. The studied indices have produced relative results based on electromagnetic spectrum recorded in the images. Principally the NDVI shows brighter in healthy vegetation areas whereas BSI seems brighter in bare land areas. Water can be seen as brighter in NDWI index where TCW areas more highlight the soil moisture. 4.1 NDVI The results of the NDVI were presented in table 1 and figures 3, 4. The results showed that vegetation cover in the entire study area was 1,701 km2 in the year 1990, while it increased to 2,636 km2 in the year 2001; it forms 0.06 and 0.10% respectively. Kadhumiya County (Baghdad governorate) recorded the highest percentage in vegetation increase during the study period, when the vegetation cover percentage was 37.315 in 1990; it reached to 42.525% in the year of 2001. The smallest increase values in the vegetation cover was with Heet County (Anbar governorate), it was 0.594 and 1.174% respectively. The highest vegetation increase rate in the study area was in Balad County (Salah-Alddin governorate), while Abu Ghraib County (Baghdad governorate) had the lowest rate (1.154 km2. year-1). The overall average of the vegetation cover increase rate in the study area was 7.079 km2. year-1. The statistical analysis showed this index has a significant correlation with (TCW_p) tasseled cap wetness positive change (0.903).   Figure 3. County-level NDVI Map of the Study Area for the Year 1990.  Figure 4. County-level NDVI Map of the Study Area for the Year 2001. 4.2 BSI The results (tab.2 and fig.5) showed a general increase in the bare soils in the study area, it was 406.328 km2 accounting 1.508% of the total study area. The highest increase rate in the bare soils during the study area was 66.768 km2.Year-1in Falluja County (Salah Alddin governorate), while the biggest decrease rate in the bare soils area was (-26.309) km2.Year-1 in Heet County. The highest increase in the bare soils was in Falluja and Ramadi counties 734.450 and 308.744 km2 respectively. Baghdad' counties values had appeared a decrease in the bare soils area despite of its increase in the built up area, which can refer to the increase of the vegetation area in the district during the study period. The statistical data of the census showed a general increase in the population of the whole study area. The results of the statistical analysis showed that this index has a significant correlation with (TCW_n) tasseled cap wetness negative change (0.901).   Figure 5. The bare soils area values in the study period from 1990 to 2001. |