**2.9. Land use and land cover (LU/LC)**

Land-use and land-cover change being one of the major driving forces of global ecological change, is vital to the sustainable development discussion. One of the most accurate methods


**2.10. Landsat index**

*2.10.1. Salinity index (SI)*

describes salinity index equation [69].

classified salt affected land to give better results [68].

Source: [57]. Band 8 (ETM+ only) is higher resolution visible light data. Planned wavelength (PW), Actual wavelength (AW), Resolution (R).

**Table 3.** Band Wavelength (urn) and resolution (m) for Landsat 7.

SI = \_\_\_\_\_\_\_\_\_\_\_ *Band* <sup>3</sup>

*2.10.2. Normalized differential salinity index (NDSI)*

Soil and green vegetation have different methods of reflectance characteristics. The mixture of soil, green vegetation and shade in the pixels make remote sensing of land cover a challenge. Stewar and Rogerson [67] used vegetation indices to minimize the impacts of soil background and biological aging materials. Vegetation indices such as salinity index (SI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Green Vegetation Index (GVI), Transformed Soil Adjusted Vegetation Index (TSAVI), Simple Ratio (SR), Normalized Salinity Differential Index (NSDI), and Normalized Differential Vegetation Index (NDVI) are used to

Band 1: Blue 0.45–0.52 0.452–0.514 30 Band 2: Green 0.52–0.60 0.519–0.601 30 Band 3: Red 0.63–0.69 0.631–0.692 30 Band 4: Near Infrared (NIR) 0.77–0.90 0.772–0.898 30 Band 5: Middle Infrared (MIR) 1.55–1.75 1.547–1.748 30 Band 6: Thermal Infrared (TIR) 10.40–12.50 10.31–12.36 60 Band 7: Middle Infrared (SWIR) 2.09–2.35 2.097–2.346 30 Band 8: Panchromatic 0.52–0.90 0.515–0.896 15

Salinity index is the fraction of red band to near infrared band (NIR). The equation below

Lhissou et al. [70] cited that Al-khaier [71] reported the usefulness of the salinity index using ASTER (Advance Space borne Thermal Emission and Reflection Radiometer) sensor data in mapping salinity of irrigated farmland in Syria. Salinity Index (SI), and Normalized Differential Salinity Index (NDSI) give good results in detecting salt-affected lands; the spectral reflectance

NDSI is the ratio of the difference between the red band and NIR to the summation of the red band and NIR. Chandana et al. [73] used NDSI for identification of salt affected soils in

of NIR, which radioed with red hand, gives very spectral values for vegetation [72].

*Band* <sup>4</sup> (1)

**PW AW R**

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**Table 2.** Band Wavelength (urn) and resolution (m) for Landsat 5.

to comprehend how land was used in the past, the types of changes to be expected in future, and also the forces and processes behind the changes is LU/LC analysis [45]. Increase in population, which lead people to clear forest for agricultural purposes in conjunction with anthropogenic activities accounts for the changes in LU/LC [60]. Messay [61] used sequential satellite images and GIS technologies, in combination with field observations, to investigate the LU/LC changes in the district of Nonno. The author stated that the overall consequence of conversion and modification processes of the LU/LC is the severe decrease in quality of the natural environment in the area. Changes in land use and land-cover provide a wide range of effect on environmental and landscape qualities including quality water, land and air resources, processes of ecosystem and functions, and the climate system itself through greenhouse gas fluxes [62]. Land cover is a significant element in change studies, affecting many aspects of the environmental system. Accurate and updated change in land cover information is necessary to understand the main factor causing changes and its environmental consequences [3]. Significant land-cover and land-use variation occurred in areas where irrigation is being practice in response to the increase of saline soils from time to time affecting crop cultivation leading to change in land-use [63]. The baseline data required for adequate and good understanding on the land-use patterns of previous years and its impacts can be extracted from Land-cover analysis. It also helps to figure out the percentage of the past land-cover changes and the physical factors behind [64]. Changes in land-use that occurs especially through deforestation and improper cultivation practice may rapidly degrade the quality of soil, as ecologically sensitive constituents of the habitats are not able to buffer the adverse effects. As a result, severe deterioration of the soil quality may result, leading to a permanent degradation of land productivity, and land degradation increases agricultural costs to maintain soil [65]. One of the major impacts of Land-use and Landcover changes in arid and semi-arid region is soil salinization, this occur mostly wherever irrigation is being practiced. The decrease in soil quality due to accumulation of salts and sodicity keeps increasing at an alarming rate of endangering agricultural ecosystem and its environment [66].



Planned wavelength (PW), Actual wavelength (AW), Resolution (R).

**Table 3.** Band Wavelength (urn) and resolution (m) for Landsat 7.

#### **2.10. Landsat index**

to comprehend how land was used in the past, the types of changes to be expected in future, and also the forces and processes behind the changes is LU/LC analysis [45]. Increase in population, which lead people to clear forest for agricultural purposes in conjunction with anthropogenic activities accounts for the changes in LU/LC [60]. Messay [61] used sequential satellite images and GIS technologies, in combination with field observations, to investigate the LU/LC changes in the district of Nonno. The author stated that the overall consequence of conversion and modification processes of the LU/LC is the severe decrease in quality of the natural environment in the area. Changes in land use and land-cover provide a wide range of effect on environmental and landscape qualities including quality water, land and air resources, processes of ecosystem and functions, and the climate system itself through greenhouse gas fluxes [62]. Land cover is a significant element in change studies, affecting many aspects of the environmental system. Accurate and updated change in land cover information is necessary to understand the main factor causing changes and its environmental consequences [3]. Significant land-cover and land-use variation occurred in areas where irrigation is being practice in response to the increase of saline soils from time to time affecting crop cultivation leading to change in land-use [63]. The baseline data required for adequate and good understanding on the land-use patterns of previous years and its impacts can be extracted from Land-cover analysis. It also helps to figure out the percentage of the past land-cover changes and the physical factors behind [64]. Changes in land-use that occurs especially through deforestation and improper cultivation practice may rapidly degrade the quality of soil, as ecologically sensitive constituents of the habitats are not able to buffer the adverse effects. As a result, severe deterioration of the soil quality may result, leading to a permanent degradation of land productivity, and land degradation increases agricultural costs to maintain soil [65]. One of the major impacts of Land-use and Landcover changes in arid and semi-arid region is soil salinization, this occur mostly wherever irrigation is being practiced. The decrease in soil quality due to accumulation of salts and sodicity keeps increasing at an alarming rate of endangering agricultural ecosystem and its

Band 1: Blue 0.45–0.52 0.452–0.518 30 Band 2: Green 0.52–0.60 0.528–0.609 30 Band 3: Red 0.63–0.69 0.626–0.693 30 Band 4: Near Infrared (NIR) 0.76–0.90 0.776–0.94 30 Band 5: Middle Infrared (MIR) 1.55–1.75 1.567–1.784 30 Band 6: Thermal Infrared (TIR) 10.40–12.50 10.45–12.42 120 Band 7: Middle Infrared (SWIR) 2.08–2.35 2.097–2.349 30

Source: [57]. Planned wavelength (PW), Actual wavelength (AW), Resolution (R).

**Table 2.** Band Wavelength (urn) and resolution (m) for Landsat 5.

72 Multi-purposeful Application of Geospatial Data

**PW AW R**

environment [66].

Soil and green vegetation have different methods of reflectance characteristics. The mixture of soil, green vegetation and shade in the pixels make remote sensing of land cover a challenge. Stewar and Rogerson [67] used vegetation indices to minimize the impacts of soil background and biological aging materials. Vegetation indices such as salinity index (SI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Green Vegetation Index (GVI), Transformed Soil Adjusted Vegetation Index (TSAVI), Simple Ratio (SR), Normalized Salinity Differential Index (NSDI), and Normalized Differential Vegetation Index (NDVI) are used to classified salt affected land to give better results [68].

#### *2.10.1. Salinity index (SI)*

Salinity index is the fraction of red band to near infrared band (NIR). The equation below describes salinity index equation [69].

$$\text{SI} = \frac{\text{Band 3}}{\text{Band 4}} \tag{1}$$

Lhissou et al. [70] cited that Al-khaier [71] reported the usefulness of the salinity index using ASTER (Advance Space borne Thermal Emission and Reflection Radiometer) sensor data in mapping salinity of irrigated farmland in Syria. Salinity Index (SI), and Normalized Differential Salinity Index (NDSI) give good results in detecting salt-affected lands; the spectral reflectance of NIR, which radioed with red hand, gives very spectral values for vegetation [72].

#### *2.10.2. Normalized differential salinity index (NDSI)*

NDSI is the ratio of the difference between the red band and NIR to the summation of the red band and NIR. Chandana et al. [73] used NDSI for identification of salt affected soils in assessment of soil salinity level of Pambantota district, Southern Sri Lanka, based on remote sensing information of TM sensor of Landsat 7 satellite. The equation for calculating NDSI is given in Eq. (2) [74].

$$\text{NDSI} = \frac{\text{Band} \, 3-\text{Band} \, 4}{\text{Band} \, 3+\text{Band} \, 4} \tag{2}$$

#### *2.10.3. Normalized different vegetation index (NDVI)*

The vegetation cover of a place can be examined using Normalized different vegetation index (NDVI) method [75]. NDVI is expressed as the fractional difference between NIR and red band to the addition of the two. It may be calculated from reflectance measured in the visible and near infrared channels from satellite based remote sensing. According to [75] NDVI shows spatial and temporal change of vegetation cover. The use of NDVI helps to create better and visual interpretation of healthy vegetation in contrast to other features [73]. The amount of salt present in the soil can be measured using NDVI through stressed vegetation; Aldakheel et al. [73, 76] gave the mathematical expression of NDVI as:

$$\text{NDVI} = \frac{\text{Band} \, 4-\text{Band } 3}{\text{Band } 3+\text{Band } 4} \tag{3}$$

end-members. The PPI values are calculated by projecting n-dimensional scatterplots onto a random unit vector repeatedly. The pure pixels in each projection are recorded and the total number of period at which each pure pixel was marked is noted [71]. The digital number (DN) of each PPI generated corresponds to the number of pixel occurrence, which is recorded as extreme. The PPI normally run on a Minimum Noise Fraction (MNF) transform result apart from the noise bands. Results of the unmixing model and conventional classification technique are then compared for identification of land quality reduction in region [82]. Number of iterations with different threshold limit is carried out interactively to separate the position of most pure pixels in the image. A threshold of two are fixed for the identification of pure pixels in the image which will be explained as, all the pixels having 2 DN values (maximum limit) greater than the extreme pixel is thought to be pure. Two different sets of iterations 1000 and 5000 is carried out on the data set while keeping the threshold at two. The more the number of iterations, the more the number of extreme pixels found with more variability in the data set [40]. The value in the PPI image indicates the number of times each pixel as extreme in some projection while PPI image with higher values indicate pixels that are closer "corners" to the n-dimensional data cloud, and are hence relatively purer than the pixels with lower value. Lastly, Region of interest (ROI) is generated for the PPI image keeping the minimum threshold limit at 50, after comparing the PPI image with calibrated image to get a better idea

**Band Combination Classification Total Accuracy Total Accuracy (Medium** 

1,3,4,7 Hybrid 50% 80.5%

1,3,4,7 Hybrid (No salinity and High salinity) 62% \*\*\*

**Table 4.** Total Accuracy of soil classification based on different Band Combination.

1,2,3,4,5,7 Supervised (Maximum likelihood Classification)

1,3,7 Supervised (Maximum likelihood Classification)

1,3,4,7 Supervised (Maximum likelihood Classification)

1,2,3 Supervised (Maximum likelihood Classification)

Source: [59]. \*\*\* No figure. **and High Salinity)**

75

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57% 66.20%

Geospatial Analysis for Irrigated Land Assessment, Modeling and Mapping

51% 56.50%

54% 71.3%

40% \*\*\*

Vegetation index is a spectral index that detects the presence of chlorophyll [84]. Various crop indices have been derived using the fact that chlorophyll strongly absorbs the light energy in the red part and highly reflects in the near-infrared part [85]. Several researches for specific analyses have proposed a number of vegetation indices. Many papers have explained the

about the position of the pure pixels [83].

*2.10.5. Digital analysis using surface vegetation index*

NDVI has been used many researches to work mask vegetation from non-vegetation, and to detect the spatio-temporal change in vegetation biomass. Panah and Goossens [23] used TM based NDVI as an indicator of vegetation cover to separate bare soil from vegetation cover 1990 and the MSS based NDVI was taken to separate bare soil from vegetation cover in 1975. In Egypt, Masoud and Koike [77] used vegetation indices to examine and monitored salinization from changes in surface characteristics and radiometric thermal temperature for specific years. Darvishsefat et al. [78] classified salt affected soils based on ETM+ images acquired for Hoze Soltan Ghom area using image proportional and principal component analysis method. They claimed that the methods used were not suitable for image classification of saline soils. Saha et al. [47] employed band 3, 4, 5, and 7 of TM images to classifying salt affected land of moorland in India with an accuracy of 95%. Landsat ETM+ was applied in preparation soil salinity assessment for Texaco in Mexico [79]. Combined spectral response index (COSRI) and an exponential model was used to derive a high correlation coefficient between soil characteristics and spectral values of the multiband index. They reported values between −0.885 and 0.857 for EC and sodium adsorption ratio SAR as a correlation coefficient respectively with derived variance of 82.6 and 75.1% for EC and SAR as respectively. Unsupervised image classification technique is largely automated while supervised classification method requires considerable human input in the classification process [80]. Classification of soil using different Band Combination is presented in **Table 4**.

#### *2.10.4. Pixel purity index (PPI)*

Pixel Purity Index (PPI) is a way of finding the most spectrally pure pixels in hyper-spectral and multispectral images [81]. The most spectrally pure pixels typically correspond to mixing


**Table 4.** Total Accuracy of soil classification based on different Band Combination.

assessment of soil salinity level of Pambantota district, Southern Sri Lanka, based on remote sensing information of TM sensor of Landsat 7 satellite. The equation for calculating NDSI is

The vegetation cover of a place can be examined using Normalized different vegetation index (NDVI) method [75]. NDVI is expressed as the fractional difference between NIR and red band to the addition of the two. It may be calculated from reflectance measured in the visible and near infrared channels from satellite based remote sensing. According to [75] NDVI shows spatial and temporal change of vegetation cover. The use of NDVI helps to create better and visual interpretation of healthy vegetation in contrast to other features [73]. The amount of salt present in the soil can be measured using NDVI through stressed vegetation; Aldakheel

NDVI has been used many researches to work mask vegetation from non-vegetation, and to detect the spatio-temporal change in vegetation biomass. Panah and Goossens [23] used TM based NDVI as an indicator of vegetation cover to separate bare soil from vegetation cover 1990 and the MSS based NDVI was taken to separate bare soil from vegetation cover in 1975. In Egypt, Masoud and Koike [77] used vegetation indices to examine and monitored salinization from changes in surface characteristics and radiometric thermal temperature for specific years. Darvishsefat et al. [78] classified salt affected soils based on ETM+ images acquired for Hoze Soltan Ghom area using image proportional and principal component analysis method. They claimed that the methods used were not suitable for image classification of saline soils. Saha et al. [47] employed band 3, 4, 5, and 7 of TM images to classifying salt affected land of moorland in India with an accuracy of 95%. Landsat ETM+ was applied in preparation soil salinity assessment for Texaco in Mexico [79]. Combined spectral response index (COSRI) and an exponential model was used to derive a high correlation coefficient between soil characteristics and spectral values of the multiband index. They reported values between −0.885 and 0.857 for EC and sodium adsorption ratio SAR as a correlation coefficient respectively with derived variance of 82.6 and 75.1% for EC and SAR as respectively. Unsupervised image classification technique is largely automated while supervised classification method requires considerable human input in the classification process [80]. Classification of soil using differ-

Pixel Purity Index (PPI) is a way of finding the most spectrally pure pixels in hyper-spectral and multispectral images [81]. The most spectrally pure pixels typically correspond to mixing

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ *Band* <sup>3</sup> <sup>+</sup> *Band* <sup>4</sup> (2)

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ *Band* <sup>3</sup> <sup>+</sup> *Band* <sup>4</sup> (3)

given in Eq. (2) [74].

74 Multi-purposeful Application of Geospatial Data

NDSI = *Band* <sup>3</sup> <sup>−</sup> *Band* <sup>4</sup>

et al. [73, 76] gave the mathematical expression of NDVI as:

NDVI = *Band* <sup>4</sup> <sup>−</sup> *Band* <sup>3</sup>

ent Band Combination is presented in **Table 4**.

*2.10.4. Pixel purity index (PPI)*

*2.10.3. Normalized different vegetation index (NDVI)*

end-members. The PPI values are calculated by projecting n-dimensional scatterplots onto a random unit vector repeatedly. The pure pixels in each projection are recorded and the total number of period at which each pure pixel was marked is noted [71]. The digital number (DN) of each PPI generated corresponds to the number of pixel occurrence, which is recorded as extreme. The PPI normally run on a Minimum Noise Fraction (MNF) transform result apart from the noise bands. Results of the unmixing model and conventional classification technique are then compared for identification of land quality reduction in region [82]. Number of iterations with different threshold limit is carried out interactively to separate the position of most pure pixels in the image. A threshold of two are fixed for the identification of pure pixels in the image which will be explained as, all the pixels having 2 DN values (maximum limit) greater than the extreme pixel is thought to be pure. Two different sets of iterations 1000 and 5000 is carried out on the data set while keeping the threshold at two. The more the number of iterations, the more the number of extreme pixels found with more variability in the data set [40]. The value in the PPI image indicates the number of times each pixel as extreme in some projection while PPI image with higher values indicate pixels that are closer "corners" to the n-dimensional data cloud, and are hence relatively purer than the pixels with lower value. Lastly, Region of interest (ROI) is generated for the PPI image keeping the minimum threshold limit at 50, after comparing the PPI image with calibrated image to get a better idea about the position of the pure pixels [83].

#### *2.10.5. Digital analysis using surface vegetation index*

Vegetation index is a spectral index that detects the presence of chlorophyll [84]. Various crop indices have been derived using the fact that chlorophyll strongly absorbs the light energy in the red part and highly reflects in the near-infrared part [85]. Several researches for specific analyses have proposed a number of vegetation indices. Many papers have explained the detection of salinity through its effect on the vegetation. Richardson et al. [86] specified that an inverse relationship is observed between reflectance and salinity, as salt content induces less plant cover (decreasing of density, LAI and height) and sometimes slight salt deposition on surface associated with vegetation have similar reflectance as that of normal cropped area. Salt tolerant plants are good references of salinity level on salt marshes but necessitate good calibration [30]. Contrasted associations of vegetation and bare soils can be more useful for salinity detection than individual surface types. Remotely sensed imagery cannot be used to classify and assess soil profile. Spectral characteristics of the earth surface features that are indicative of subsurface conditions can be analyzed. Satellite multi-spectral data denote changes that aid in locating mapping units; they hold great promise for soil surveys and landuse planning [87]. Some relationships have been established to relate soil properties and spectral data while most of these properties have been from the surface soil, subsurface properties that influence some surface characteristics were considered. Satellite sensors observe only the ground surface, actually both subsurface and surface soil conditions are influenced by common genetic factors [88]. Both subsurface conditions and surface conditions are plant canopy. Therefore, when satellite imagery depicts a pattern based on a different spectral response, it is not unreasonable to attempt some inferences about subsurface soil patterns [70].

area on the earth's surface was stored in each element emitted from the energy. The spatial arrangement of the measurements defines the image or image space, depending on the sen-

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The chapter demonstrates possibility of use of high technology in particular remote sensing and GIS technology in land cover/land use and soil salinity monitoring with demonstration of advantages of use such technology in similar problem solving. Detection of soil salinity by conventional means of soil survey requires a great deal of time, but the application of geospatial analysis using remote sensing and GIS techniques minimize time consuming and offer the possibility assessment, modeling and mapping of irrigated land. The chapter also worked on general subjects with reflection of the cycle of satellite data use with a variety of application, indexes for registration and data processing stage. The fact is that the use of space technology advances in land classification are commonly used instrument for soil monitoring which is one of the suitable and flexible instrument from a wide point of view. The instrument makes it possible to perform results conveniently for users. In addition, the application of these indexes is a good indicator of soil salinity in irrigated lands, which may influence decision on reclamation of soil salinity and used as an input for agricultural land management. Irrigation managers, planners, farmers and government agencies for smart agriculture can use models

sor; data are recorded in n bands.

and maps generated through geospatial analysis.

Olumuyiwa Idowu Ojo\* and Masengo Francois Ilunga

\*Address all correspondence to: ojooi@unisa.ac.za

The authors would like to thank UNISA for providing their literature datasets.

The authors declared no potential conflicts of interest with respect to the research, authorship,

Department of Civil and Chemical Engineering, University of South Africa, Pretoria,

**Acknowledgements**

**Conflict of interest**

**Author details**

South Africa

and/or publication of this article.

**3. Conclusion**

#### *2.10.6. Image classification*

There are many procedures commonly used for the classification of remote sensing images and this depends on the radiometric information in the image bands. The traditionally used classification method is a pixel-based approach and is one of the procedures based on conventional statistical techniques and it performs well. Pixel based approach is based on conventional statistical techniques, such as parallelepiped, maximum likelihood and minimum distance procedures [57]. In pixel-based classification, two kinds of traditional classification methods-unsupervised classification and supervised classification are used. Ideally, pixels are expected to be to a degree, more or less grouped in the multispectral space in clusters corresponding to different land cover types [89]. It is a classic classification approach that classifies an image pixel by pixel and one pixel can only be classified into one class, thus produces is a hard classification [67].
