I. Some research results related to LULC classification:
A number of studies has been conducted related to Land cover and land use impact on water regimes of the study area. These studies have also shown that in hydrological modelling LULC plays a vital role as it directly influence the flow pattern.
In the research “Quantification of impact of changes in land use-land cover on hydrology in the upper Indus Basin, Pakistan” The study emphases on the impacts of land use land cover change for a 10?year period (2000–2010) on the stream discharge in a sub-watershed of Indus River using lumped model HEC-HMS. The research concluded that Soil and built up area in this sub-watershed has been increased from 69%, 8.2% in 2000 to 78%, 13.76% in 2010 respectively, whereas the corresponding river discharge increased by 33.61% from year 2000–2010, proving solid positive correlation on a local scale between the LULC and the river discharge.
Study on “The effect of land use/cover change on surface runoff in Shenzhen region, China” has been conducted in 2007. The results showed that urbanization played an important factor intensifying the flood process. Urbanization led to obvious increase in the maximum flood discharge and decrease in runoff confluence time. At 1%, 2% and 5% rainfall probability, the increase of the maximum flood discharge was 20.2%, 23.0% and 28.9% respectively, under relatively dry soil moisture condition. The corresponding value was 1.3%, 1.6% and 2.6% respectively under relatively wet soil moisture condition. Due to urbanization in the past 20 years, runoff coefficient is increased 13.4% and the maximum flood discharge is increased 12.9% on average. (Shi et al., 2007)
The research “Impact of land cover and land use change on runoff characteristics” conducted by N. Sajikumar and R.S. Remya, Department of Civil Engineering, Government Engineering College, Trichur, Thrissur, Kerala, 680009, India, has shown that Change in Land Cover and Land Use (LCLU) influences the runoff characteristics of a drainage basin to a large extent, which in turn, affects the surface and groundwater availability of the area, and hence leads to further change in LCLU. This forms a vicious circle. Hence it becomes essential to assess the effect of change in LCLU on the runoff characteristics of a region in general and of small watershed levels (subbasin levels) in particular.
Therefore, keeping in view all these facts, I have proposed the research “Spatial Analysis and Land use and land cover (LULC) classification in the irrigated Indus Basin for water management analysis using intelligent pixel information”.
II. Research Goals:
· Classification of Land use and land cover in the Indus Basin
· Spatial analysis of land use and land cover to support water management
· Research some good models to study the impact of Land cover and Land use on surface water runoff and surface water characteristics.
· Propose solutions for water management in context with land cover and land use for future scenarios.
III. Design and Research Methods
The research will apply both description and explanation research design. Description research will need to answer questions such as “what”, “where”, “when”, and explanation research will answer the question “why”. Furthermore, this research is a combination of two qualitative and quantitative research methods by collecting data for both. So that in the course of the study will allow describing a certain phenomenon, then verified by the results, data of quantitative research methods.
The international Indus Basin is located in four countries. The basin lies in between24° 38? to 37° 03? N latitude and 66° 18? to 82° 28? E longitudes. The total size of the basin is 116.2 million hectares (mha). The vast area of the basin is located in Pakistan (53% of total). The area in India is 33% followed by China and Afghanistan with 8% and 6%, respectively.
· Desk study: Collecting secondary data on the Internet, reports, statistics data, etc. related to the content of research topics.
· Satellite data: The SPOT VGT crop mapping sensor has four spectral bands. The red and NIR bands will be used to characterize vegetation. The SPOT vegetation data is originally available in Digital Numbers (DN) that has to be then converted into NDVI using the following equation.
· Unsupervised classification: Unsupervised classification will be performed to identify clusters by their spectral similarities and allows the feature space to segment into similar spectral clusters.
· Accuracy assessment: selection of proper accuracy assessment technique as the usability of the LULC classification for water management analysis depends on the reliability of the developed LULC map.
· Ground truthing: selection of appropriate approach to ensure the validation of results obtained through the analysis of satellite imaging and mapping. A ground truthing survey will be conducted during September-October to capture peak kharif cropping season and in January to correspond with the rabi cropping season conditions. Due to the vast dimension of the Indus Basin, ground truthing will be focused on the middle and lower reaches that have different agro-ecological regions.