Desertification can be defined as the land degradation in arid, semi-arid, and dry sub-humid regions caused by climatic changes and human activities leading to serious ecological, environmental, and socio-economic threat 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 estimated by the interaction of those two systems. Desertification indicators are indicators showing the level of risk to desertification in order to follow a plan to limit 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.
2.1-Data required
In order to valuate 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.
Three indices should be studied in monitoring desertification through remote sensing which are 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.
-NDVI: The normalized difference vegetation index(NDVI) is the most common form of vegetation indices. 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.
-Crust index: 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 covered in this study, the crust index, was tested for topsoil cover variation.
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.
-Topsoil 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
.