Chapter 2: Review of Literature……………………………………………………... 9-52
2.6 Land use/land cover change detection study
2.6.1 Application of remote sensing and GIS on land use/land
Remote sensing and GIS plays an important role in detection of land use/land cover changes (Yuan et al., 2005; Brondizio et al., 1994). Remote sensing collects multi-spectral, multi- resolution, multi-temporal data, and turns them into useful information. Remote sensing and GIS techniques are widely used in the determination of spatial distribution of the catchment ecosystem characteristics and their impacts on catchment hydrology (Sharma et al., 2001;
Zhan and Huang, 2004; Tsou and Zhan, 2004). Since the invention of remote sensing and GIS, scientists around the world have been using this technology to prepare land use land cover maps (Khoram and John, 1991). The conventional methods of land use mapping are both labor intensive and time consuming. With passage of time, such maps along with the technology have become outdated. According to Olorunfemi (1983), monitoring changes and time series analysis is quite difficult with traditional method of surveying. The rapid development of satellite remote sensing techniques helps in preparation of accurate land use/land cover maps which in turn helps in monitoring changes at regular intervals of time.
The accurate mapping of a region is an efficient way to improve the selection of areas designed to agriculture, urban and/or industrial areas of a region (Selcuk et al., 2003). One advantage of remotely sensed data is that, it made possible to study the changes in land cover in less time, at low cost and with better accuracy (Kachhwala, 1985) in association with GIS that provides suitable platform for data analysis, update and retrieval (Chilar, 2000). As a result, the land use/land cover mapping has become one of the most important applications of remote sensing (Lo and Choi, 2004). Analysis of satellite data in conjunction with drainage, lithology and land use/land cover data, assist evaluation of geomorphological conditions and status of degraded land in a particular region. As GIS provide an excellent means of spatial data analysis and interpretation, the datasets in GIS would provide a powerful mechanism to monitor degraded lands and other environmental changes (Burrough, 1986; Reddy et al., 2002).
Land use/land cover studies have been carried out in an extensive way ever since the launch of the first remote sensing satellite (Landsat-1) in 1972. For instance, NRSA used 1980-82 Landsat multispectral scanner data for waste land mapping of India on 1:1 million scales.
About 16.2% of waste lands were estimated based on the study.
Shoshany (1994) investigated the advantages of remote sensing techniques over field surveys providing a regional description of vegetation cover. The results of their research were then used to produce four vegetation cover maps that provided new information on spatial and temporal distribution of vegetation and also allowed quantitative assessment of vegetation cover.
Adeniyi and Omojola (1999), used aerial photographs, Landsat MSS, SPOT XS/Panchromatic images to study land use/land cover changes in two dams (Sokoto and Guronyo) between 1962 and 1986 in the Sokoto-Rima basin of North-Western Nigeria. Their work revealed that before the construction of the dams, the land use/land cover in the basin was unchanged and settlement alone covered most part of the area. However, during the post- dam era, land use/land cover classes changed but with settlement still remaining the largest.
Daniel et al., (2002) compared five different methods to study the land use/land cover changes. The methods they employed were, traditional post-classification cross tabulation, cross correlation analysis, neural networks, knowledge-based expert systems and image segmentation and object-oriented classification. Nine land use/land cover classes were selected for the analysis. They observed that there are merits to each of the five methods examined and that, at the point of their research, no single approach can solve the land use change detection problem.
Pandy and Nathawat (2006) carried out a study on land use/land cover mapping of Panchkula, Ambala and Yamunanger districts, Haryana state in India. They observed that the development of land use and land cover in these districts is mainly due to heterogeneous climate and physiographic conditions. By analysis of satellite data they also found that majority of areas in these three districts are used for agricultural purpose.
Bhagawat (2011) presented the change analysis based on the statistics extracted from four land use/land cover maps of the Kathmandu Metropolitan area using GIS. According to him, land use statistics and transition matrices are important information to analyze the changes of land use.
El-Asmar et al., (2013) applied two remote sensing indices, i.e., normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) in the Burullus Lagoon, North of the Nile Delta, Egypt for quantifying the change in the water body area of the lagoon in 1973 and 2011.
Landsat-TM images represent valuable and continuous records of the earth’s surface and are also a wealth of information for identifying and monitoring changes in manmade and physical environments (USGS, 2014; Chander et al., 2009; El Bastawesy, 2014). To map and monitor land cover changes in the seven-country Twin Cities of Metropolitan Area of Minnesota for 1986, 1991, 1998 and 2002, Yuan et al., (2005) developed a methodology using multi-temporal Landsat TM data. They found from the analysis that between 1986 and 2002 the amount of urban land increased from 23.7% to 32.8% of the total area, while agriculture, forest and wetland cover decreased from 69.6% to 60.5%. A post-classification method with maximum likelihood classifier algorithm was adopted by Adepoju et al., (2006) to examine the land use/land cover changes that have taken place in Lagos for the last two decades due to rapid urbanization. Nori et al., (2008) applied two different change detection techniques namely, comparison of classification and multivariate alteration detection(MAD) to assess land cover changes in El Rawshda forest, Sudan by using Landsat ETM+ and ASTER imageries. They found increase in area in both close forest and open forest and decrease in area covered by grasslands within the period 2003-2006.
The freely available MODIS data can also be used efficiently to study land use/land cover with greater thematic, spatial and temporal detail. Zhan et al., (2002) used the MODIS 250m Level 1B radiance data for five different cases viz., Idaho-Montana wildfires, the Cerro Grande prescribed fire in New Maxico, flood in Combodia, Thailand-Laos flood retreat and deforestation in southern Brazil to test the Vegetative Cover Conversion (VCC) change detection algorithm for the year 2000. VCC product is designed to serve as a global alarm for land cover change caused by anthropogenic activities and extreme natural events. They found that four of the five change detection methods for the MODIS VCC product worked satisfactorily for the detection of flooded area, burned area and deforestation.
The MODIS 500m resolution data are particularly relevant for regional and continental applications. Giri and Jenkins (2005) prepared land cover database of Greater Mesoamerica using MODIS 500m resolution satellite data. These land cover data were an improvement over traditional AVHRR based land cover data in terms of both spatial and thematic detail.
Their study revealed that MODIS 500 m data are quite useful for broad-scale land cover mapping.
Kaishan et al., (2011) used MODIS 250m Normalized Difference Vegetation Index (NDVI), Land Surface Vegetation Index (LSVI) and reflectance time series data for 2001 and 2007 to map land use/land cover in the Amur River basin. They found that MODIS 250m NDVI
datasets provide sufficient spatial, spectral and temporal resolution to detect major land cover types compared to the existing land use/land cover data as derived from Landsat TM data.
Hu and Zhang (2013) detected temporal and spatial changes of land use/land cover over the Pearl River Delta region, China from March 2008 to December 2009 using multi-temporal MODIS images. They used post-classification change detection method and maximum likelihood classifier algorithm to detect the changes in five classes (cropland, water, bare land, urban and woodland). The study provides an example of seasonal change detection of land use/land cover types using MODIS data in regions of rapid urbanization.
Usman et al., (2015) used MODIS data of 250m spatial resolution for preparation of land use maps of the Lower Chenab canal irrigated region of Pakistan from 2005 to 2012. They concluded that MODIS products are quite useful to discriminate different land use/land cover classes.
Like in other parts of the world, in India also, various scholars have done researches on land use/land cover analysis. Sarma and Kushwaha (2005) worked on the impact of coal mining on land use/land cover in Jaintia hills districtof Meghalaya, India using Landsat data of 1975, 1987, 1999 and 2005. Visual interpretation technique has been used to prepare the land use/land cover maps. They concluded that there was fourfold increase in mining area from 1975 to 2005 accompanied by three fold decrease in forest area. Kuldeep and Kamlesh (2011) studied the land use/land cover change detection in the Dehradun valley, India between 2000 and 2009 using Landsat (ETM+, TM, MSS), LISSIII, SRTM and digital SOI topographic maps. They found that during this period, forest area has decreased by 3.75%
and water has decreased by 9.5%. Also built up area during this period has remarkably increased by 112.4%. Pooja et al., (2012) studied the land use/land cover changes of Gagas watershed of Almora district over a period of 43 years using Survey of India toposheet of the year 1965 and LISS III satellite data for the year 2008. Amin et al., (2012) carried out a study on land use land cover mapping of Srinagar city in Kashmir valley. Significant changes have been observed in the city during 1990 to 2007 including loss of forest area, open spaces etc.
Mehta et al., (2012) presented an integrated approach of remote sensing and GIS for land use/land cover study of arid environment of Kutch region in Gujarat from 1999 to 2009.
Sharma et al., (2012) introduced land consumption rate (LCR) and land absorption coefficient (LAC) for quantitative assessment of changes in Bhagalpur city of Bihar, India between the years 1976 and 2008. Rawat et al., (2013a-d, 2014) carried out land use land cover of Ramnagar, Nainital, Bhimtal, Almora and Haldwani towns of Kumaun Himalaya in
Uttarakhand, India.Based on satellite data from 1990 to 2010 of land use/land cover change, they found that built up area has sharply increased due to the construction of new buildings in agricultural and vegetation lands. Singh et al., (2014) used Landsat-8 data to assess the land use pattern and their spatial variation in Orr watershed of Ashok Nagar district, Madhya Pradesh, India. Bora and Goswami (2016) used Survey of India toposheets for the year 1967- 68 and Landsat (ETM+) for the year 2014 to study the changes in land use/land cover in the Kolong river basin of Assam, India. They found that agricultural land has decreased from 2216.96 km2 to 1449.39 km2 during the period from 1967-68 to 2014. The built up area has increased from 1069.05 km2 to 1838.84 km2 during the study period.