Application of Remote Sensing
in Studying Desertification
SURV 510- Remote Sensing
Term Paper #1
Jihad Yatim, ID: 11431325
School of Engineering
Supervisor: Dr. Chadi Abdallah
Desertification is a phenomenon defined by the degradation of productivity of a land; due to different causes, leading to serious impact on the environment and human health. This research highlights on how the usage of remote sensing technology plays a pivotal role in limiting desertification by studying the main data needed to notify desertification sensitivity index.
Table of Contents
TABLE OF CONTENTSIII
LIST OF TABLESIV
LIST OF FIGURESV
CHAPTER 1: INTRODUCTION1
1.2 Aim of project1
CHAPTER 2: Materials2
2.1 Data required2
2.2 Satellites used2
CHAPTER 3: Post Processing4
3.1 Normalized difference vegetation index4
3.2 Crust index4
3.3 Topsoil Grain size index5
3.4 Surface roughness5
3.6 Surface Temperature6
3.7 Soil moisture7
CHAPTER 4: Mapping Indexes9
List of Tables
Table 1: Landsat 4-5 TM bands, Wl, Resolution2
Table 2: Landsat 7 ETM bands, Wl, Resolution3
List of Figures
Figure 1: albedo ranges6
Figure 2: soil moisture index7
Figure 3: Vegetation quality index of Basrah Province9
Figure 4: Soil quality index of Basrah Province10
Figure 5: climate quality index of Basrah Province10
Figure 6: LUC sand drift comparison of Basrah Province11
Figure 7: desertification sensitivity index of Basrah Province12
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
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 index (CI)). These indices are extracted from topographic data, geologic maps, and satellite images( through satellite sensors Landsat TM and Landsat ETM).
In our investigation to detect environmentally sensitive areas to desertification, two landsat satellites are significant; 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.
Table 1: Landsat 4-5 TM Bands,Wl, Resolution.
*Note that TM Band 6 is obtained at 120-meter resolution, but products are re-sampled 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.
Table 2: Landsat 7 ETM Bands,Wl, Resolution.
*Note that ETM+ Band 6 is obtained at 60-meter resolution, but products are re-sampled 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 multi-spectral sensors of the Landsat, we can monitor desertification through their images.
Chapter 3 Post Processing
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:
where R and NIR are the red and near infrared bands respectively.
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.
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:
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.
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).
Figure 1: albedo ranges
3.6- Surface Temperature
Surface temperature variation is the result of energy exchange taking place above and below this surface. Thus it is related to albedo, air temperatue and the efficiency of thermal exchanges.
Surface temperature is estimated by quantifying the emitted thermal infrared radiation( passive remote sensing with a wavelength between 10.5 and 12.5 µm).
The temperature of a surface depends on the land’s nature and the area it covers(under the same conditions, temperature of a sandy land differ than that of a rocky one; all other features being equal). The same standard applies to vegetated or bare lands. Water near the surface may also adjust surface temperature as well as the time the land is being observed(morning temperature less than the afternoon temperature). Geostationary satellites are mostly used to indicate thermal changes of the studied region.
3.7- Soil Moisture
Active remote sensing makes it possible to detect the level of water in the soil withing the top 10cm of the earth’s surface. Soil moisture is actively correlated with the surface temperature and it may be estimated by radar. It also strengthen the vegetation cover(seed germination, root striking…). Consequently, soil moisture parameter is a major warning for desertification.
Figure 2: soil moisture index
Figure 2 represents soil moisture map of europe performed by european remote sensing satellite in January 2000, 0% indicates dry lands and 100% shows very wet lands.
Identifying sensitive area to desertification is based according to the hypotheses of desertification and land use project model which applies a geometrical average the quality indices to provide sensitivity diagnosis. The model assumes that each index has a limited influence to the final value of environmentally sensitive area index. The more the parameters have high score, the more the area is assigned to high sensitivity class. Landsat TM and Landsat ETM imagery are the main input for calculating these indices; and image processing software such as ERDAS IMAGINE and GIS are the main tools to compute and map environmentally sensitive areas.
Chapter 4: Mapping indexes
To better understand the importance of remote sensing in monitoring desertification, and the art of mapping that allows us to visualize sensitive areas to desertification, Al Basrah province was taken as an example to detect sensitive areas to desertification. The following are the maps of each index that were integrated to perform our analysis.
Vegetation Quality index is evaluated according to three conditions: erosion protection to the soil, dehydration resistance, and vegetation land cover. Vegetation plays a major role in mitigating the effects of desertification and land degradation process. Areas with good vegetation capacity have an index value<1.2, average quality vegetation have an index value between 1.2 and 1.4, week vegetation indexes value is between 1.4 and 1.6, and areas with very week vegetation quality have an index greater than 1.6. Low density of vegetation cover provides weak indexes.
Figure 3: vegetation quality index of Basrah Province
Soil is the prevailing influence of the land ecosystem in dry and semi-dry regions. Specifically, through its impact on biomass production. Soil quality index depends on three main factors: soil texture, soil depth, and parent material. Areas with moderate quality of soil have an index between 1.2 and 1.4, low soil quality index ranges between 1.4 and 1.6, and areas with very low soil quality index has a value greater than 1.6. low quality indexes indicate regions with poor moisture, low depth, and sandy texture.
Figure 4: soil quality index of Basrah Province
The main purpose of measuring climate quality index is to indicate the availability of water to vegetation. Climate quality index also depends on three factors: rainfall, dryness, and air temperature in normal conditions. Indexes between 1.5 and 1.6 indicates semi-dry regions whereas indexes between 1.7 and 1.8 shows dry regions.
Figure 5: climate quality index of Basrah Province
For further investigation ERDAS imaging with integration with GIS were used to quantify the Land cover of sand movement in Basrah. GIS data can aid our search and awareness of desertification by comparing LC/LU data observed during different times. Comparison of data of sand drifting in Basrah between 1990 and 2003 showed drifting of sand with an expansion rate of 33.8 km2 year?1. Accordingly, sandy desertification has worsened in the study area.
Figure 6: LUC sand drift comparison of Basrah Province
The integration of the four previous indices for the evaluation of dry environmentally sensitive area to desertification, is based on the standards of measured desertification sensitive index. The investigations show the portion of land that are in danger of land degradation where soil, climate, vegetation, and land management quality indexes are low. Consequently, the land in the studied area is threatened to degradation and requires a plan to mitigate desertification and avoid its consequences.
Figure 7: desertification sensitivity index of Basrah Province
In conclusion, Remote sensing plays a major role in converting satellite imagery into information. Data being remotely sensed should be then standardized and transformed into derived variables, that are used to estimate desertification indicators.
the integration of indices and thermal properties can assess the variation in desertification process. Combination of parameters extracted from distinct portion of the spectrum or various sensors provide additional data of various aspect of land degradation, thus provide better mapping accuracy. Consequently, remote sensing art and technology aided researches in monitoring desertification with less time, more accurate, and reliable data. In other words remote sensing can mitigate desertification and protect us and our environment from such serious hazards.
– A. S. Hadeel, Mushtak T. Jabbar, Application of remote sensing and GIS in the study of environmental sensitivity to desertification: a case study in Basrah Province, southern part of Iraq. Retrieved 19 June 2010 from:
https://link.springer.com/article/10.1007%2Fs12518-010-0024-y- Gérard Begni, Richard Escadafal, Delphine Fontannaz, and Anne-Thérèse Hong-Nga Nguyen. Remote sensing: a tool to monitor and assess desertification.
– Hoang Viet Anh, Meredith Williams, David Manning. Remote sensing for desertification mapping: a case study in the coastal area of Vietnam
– A. Karnieli, Development and implementation of spectral crust index over dune sands. Retrieved 25 Nov 2010 from: