desertification can be defined as the land degradation in arid semi-arid and dry sub-humid regions caused by climatic changes and human activities that lead to serious ecological environmental and socio-economic threats to the universe

desertification can be defined as the land degradation in arid semi-arid and dry sub-humid regions caused by climatic changes and human activities that lead to serious ecological environmental and socio-economic threats to the universe. the effects of desertification include a set of important operations which are dynamic in arid and sub-arid regions where water is the essential limiting factor of land use execution in such ecological system sandy deserts being one of the most dangerous ecological problems in the world many countries in the arid and semi-arid areas including iraq are witnessing such desertification problems. to assess desertification problems different methods were proposed. in europe mediterranean areas the soil loss caused by water erosion correlating with loss of soil nutrients status was the most serious problem in those areas. salinization and wind erosion were more often to occur in arid mediterranean regions. environmentally sensitive areas to desertification shows various sensitivity status to desertification for different reasons. for instance some regions present high sensitivity to low rainfall and extreme events due to low coverage of vegetation low durability of vegetation due to dryness sharp slopes and excessive man-made damage. loss of land capacity falls into two overlapping systems: human social system and the natural ecological system. the degree of land degradation can be evaluated according to those two systems. desertification indicators are indicators showing the level of risk to desertification in order to follow a plan to mitigate desertification. those indicators should be based on remotely sensed images soil climate geology and topographic data at a scale of 1/25000 the effect of socio-economic system is uttered through land use pattern. different types of environmentally sensitive areas to desertification can be featured and mapped using different symbols for evaluating land capacity to resist and further degradation or support different land uses. 1.2- aim of project the aim of this report is to show how the advanced usage of remote sensing can limit and detect desertification by analyzing data based on different parameters; which should be based on four categories defining the quality of soil vegetation climate and land management. chapter 2: materials 2.1-data required in order to evaluate the desertification sensitivity index dsi three main indices are required which are the thematic layer of soil quality index sqi climate quality index cqi and the range of sand movement crust indext ci these indices are extracted from topographic data geologic maps and satellite images through satellite sensors landsat tm and landsat etm 2.2-satellites used in our investigation to detect environmentally sensetive areas to desertification two landsat satellites are significat; the landsat tm and landsat etm. these satellites consist of detectors which produce signals relative to the mean amount of light reflected from a specific region which correlates to the resolution of their sensors. the landsat thematic mapper tm sensor was mounted on landsat 4-5 has images made of six spectral channels bands with a spatial resolution of 30 meters for bands 1 to 5 and 7 and one thermal band band 6 the scene size is about 170 km north-south by 183 km east-west. *note that tm band 6 is obtained at 120-meter resolution but products are resampled to 30-meter resolution. the landsat enhanced thematic mapper plus etm+ sensor is mounted on landsat 7 has images made of seven spectral channels bands with a spatial resolution of 30 meters for bands 1-5 and 7 and a resolution of 15 meters for band 8 panchromatic the gain settings for all bands can be collected high or low for increased radiometric sensitivity and dynamic range however band 6 collects both high and low gain for all scenes bands 61 and 62 the scene size is about 170 km north-south by 183 km east-west. *note that etm+ band 6 is obtained at 60-meter resolution but products are resampled to 30-meter resolution. *note the panchromatic band works similarly as the black and white film it combines the visible spectrum into one bands allowing the sensor to observe more light and provide the sharpest image among the others. due to the multispectral sensors of the landsat we can monitor desertification through their images.
There are many indices that should be studied in monitoring desertification through remote sensing most notably the study of vegetation variation (normalized difference vegetation index (NDVI)), range of sand movement (CI), and specifying the type of topsoil grain size index (GSI), which should be calculated respectively according to the equation of each index.
3.1-Normalized difference vegetation index(NDVI)
Tracking vegetation cover variations plays a fundemental part in detecting degradation of land and is considered an obvious warning to desertification.
The normalized difference vegetation index is the most common form of vegetation indices to help us identify vegetation and provide a measure of its health and vitality. Vegetation contains chlorophyll which absorbs light mostly in the red region and reflects mostly Near infrared light. In fact, healthy and dense vegetation reflects a lot of infrared light and a bit of red light, as the chlorophyll gets weaker it has more tendency to reflect red and lower infrared radiation. The normalized difference vegetation index is basically the difference between the red and near infrared band combination divided by the sum of the red and near infrared band combination or:
NDVI=(NIR-R)/(NIR+R)
where R and NIR are the red and near infrared bands respectively.
3.2-Crust Index(CI)
In order to study a practicable indicator (fine sand content in topsoil) for monitoring the variation of surface soil using remote sensing technology, soil index is covered in this study, the crust index, should be tested for topsoil cover variation.
Cyanobacteria (microorganisms related to bacteria), are found in almost every terrestrial habitat which acts as an organic fertilizer to maintain the productivity of land. It has been shown that cyanobacteria contribute to higher reflectance of blue light than the same type of substrate without biogenic crust.
The crest index algorithm was run and a new dataset was produced. A spectral crest index is developed based on the normalized difference between the red and the blue spectral weight. Applying the index to a sand soil region, it has been known that the crest index can be used to detect and to map, from remote sensing imagery, different lithological/morphological units such as active crusted sand regions, which are expressed in the topography. As a mapping tool, the crest index image is much more sensitive to ground features than the original image.
CI=1-(R-B)/(R+B)
The allocation of soil crust is a vital information for vegetation degradation and climate variation investigations. They are also important information tools for increasing agricultural regions and/or infrastructures in location studies since soil crusts is related to soil stability, soil build-up, and soil fertility. Applying the suggested crust index can be performed with imagery gathered by any sensor which has the blue band. Nowadays, Landsat TM and Landsat ETM are the most common data sources for colored images.
3.3-Topsoil Grain size index(GSI)
Topsoil grain size index (GSI) is developed according to field survey of soil surface spectral reflectance and laboratory interpretation of soil grain composition. The grain size index found has close correlation to the fine sand or clay–silt-sized grain content of the topsoil in sparsely vegetated arid land. High grain size index values correspond to the region with high content of fine sand in topsoil or low content of clay–silt grains. The GSI can be simply calculated by:
GSI=(R-B)/(R+B+G)
where R, B, and G are the red, blue, and green bands respectively.
Grain size index value is approximately to zero in the vegetated regions, and a negative value for water body.
3.4- Surface Roughness
Roughness is a parameter that permits us to quantify the variability of a surface. Radar remote sensing is mostly used to determine the roughness of a surface; Radar sends microwaves and measure the power of which the surface reflects them back. Greater backscattering indicates high level of unevenness of a surface. The more the surface is uneven, the higher the roughness parameter is.
3.5- Albedo
Albedo is the portion of light that is reflected by an object compared to the value of light that hits the object; it’s the ratio of reflected light to the incident light. Measuring albedo ranges between 0(none of the incident light is reflected) and 1(all the amount of light is reflected). It is also expressed by percentage.
In the same geographic area, the percentage of albedo may change within the year due to physical phenomena or biases such as clouds in low resolution images. Analyzing this parameter with its tentative and special variations and relating them with other predictable variables provide information on desertification operations. For instance, the albedo of a bare soil decreases (less light reflection) as its water content rises. The albedo of vegetation depends on its land coverage and its chlorophyll’s activity.
Several works require to study the relationship between albedo and desertification (mostly the relation between albedo and variation of vegetation land coverage in arid regions, and the relationship between albedo and climate changes).