8 research outputs found
Route alignment planning for a new highway between two cities using Geoinformatics techniques
No full textAn attempt has been made to delineate and identify the alignment of a new route between two important cities of north India, Haridwar & Roorkee using Geoinformatics techniques. Geo-engineering parameters like slope, aspect, geology, land use, drainage and soil along with some techno-economical parameters have been used for this purpose. Multi-criteria weight method has been applied. Five weighting methods (AHP - Analytical Hierarchy Process, Rank Sum, Rank Reciprocal, Rank Exponent and Ratio Estimation) were applied simultaneously to eliminate biasness in weight assignment to the input parameters. The results show that AHP method is the best and ratio estimation method is the second best method for identification of optimum route alignment. Few more parameters were used for final selection of optimum route viz., minimum construction cost; minimum number of bridges and culverts on that route; maximum number of settlement within 5 km buffers on both sides of route; maximum number of tourist locations like temples, waterfalls, springs etc. within 5 km buffer zone on both side of route. The proposed route between Roorkee and Haridwar towns is only 29.22 km long (includes a 17.10 km long part of the existing road), the new road required is 12.12 km, while the existing longer route between Roorkee and Haridwar is 33 km (instead of 29.22 km). By using multi-criteria weighted methods of route alignment, a length of approximately 3.78 km can be avoided. It was also observed that slope, land use and drainage parameters are more sensitive for route alignment
CA MARKOV MODELING OF LAND USE LAND COVER DYNAMICS AND SENSITIVITY ANALYSIS TO IDENTIFY SENSITIVE PARAMETER(S)
No full textAbstract. An attempt has been made to explore, evaluate and identify the sensitive parameter(s) of Cellular Automata Markov chain modeling to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India. For this purpose, land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov model. Sensitivity analysis has been carried out to identify the land use land cover parameter(s), which have the highest, lowest or intermediate influence on predicted results. The validity of the Cellular Automata Markov process for projecting future land use and cover changes in the study area calculates various Kappa Indices of Agreement (Kstandard) which indicate how well the comparison map agrees and disagrees with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The results shows that the land with or without scrub appeared to be most sensitive parameter as it has highest influences on predicted results of land use land cover of 2007. The second most sensitive parameter was lakes / reservoirs / ponds to predict land use land cover of 2007, followed by river, agricultural crop land, plantation, open land, marshy / swampy, sandy area, aquatic vegetation, built up land, dense forest, degraded forest, waterlogged area and agricultural fallow land. The least sensitive parameter is agricultural fallow land, which has minimum influence on predicted results of land use land cover of 2007. The validation of CA Markov land use land cover prediction results shows Kstandard is 0.7928
Critical Assessment of Land Use Land Cover Dynamics Using Multi-Temporal Satellite Images
No full textAn attempt has been made to assess the dynamics of land use land cover change (LULCC) in the study area. LANDSAT-5 TM, IRS-1C LISS III, IRS-P6 LISS III images of 1987, 1997 and 2007, respectively, were digitally classified for land use land cover (LULC) mapping. The dynamics of LULCC critically analyzed for the two time periods 1987–1997 and 1997–2007. The LULCC analyzed in terms of quantity of change and allocation of change. Relative changes; gross gains, gross losses and persistence; net change and swap changes of LULC of the study area examined carefully. The study provided a better understanding of the LULCC pattern. The total change during (1987–1997) was 68.40% and during (1997–2007) was 80.12%. Major exchanges of areas are in between degraded forest and built up land followed by dense forest and degraded forest. Others dominant systematic transitions are: degraded forest to built up land; dense forest to degraded forest; agricultural land to built up; degraded forest to land with or without scrub; land with or without scrub to built up; and in between river and sandy area. The transformation from forest to built up land especially built-up area constitutes a large percentage of the total landscape. The direct beneficiaries of this research will include resource managers and regional planners as well as others scientific community
CA Markov modeling of dynamics of land use land cover and sensitivity analysis to identify sensitive parameter(s)
No full textStatistical independence test and validation of CA Markov land use land cover (LULC) prediction results
No full textModeling of spatio-temporal dynamics of land use and land cover in a part of Brahmaputra River basin using Geoinformatic techniques
No full textAn attempt has been made to explore and evaluate the Cellular Automata (CA) Markov modelling to monitor and predict the future land use and land cover (LULC) scenario in a part of Brahmaputra River basin using LULC maps derived from multi-temporal satellite images. CA Markov is a combined cellular automata/Markov chain/multi-criteria/multi-objective land allocation (MOLA) LULC prediction procedure that adds an element of spatial contiguity as well as knowledge base of the likely spatial distribution of transitions to Markov chain analysis. Evidence likelihood map was used for as knowledge base of the likely spatial procedure in CA Markov model. The predicting quantity and predicting location change have been analysed and statistically evaluated. The validation statistics indicated how well the comparison map agreed and disagreed with the reference map. Predicted results accuracy is slightly higher when compare to others studies of LULC change using CA Markov approaches
CA Markov modeling of dynamics of land use land cover and sensitivity analysis to identify sensitive parameter(s)
No full textStatistical independence test and validation of CA Markov land use land cover (LULC) prediction results
No full textAbstractStatistical independence test and validity of the CA (Cellular Automata) Markov process for projecting future land use and land cover (LULC) changes were carried out in this study. Predicting quantity and location changes have been analyzed, and statistically evaluated. Validity of the CA Markov process has been examined using various Kappa Index of Agreement (KIA or Kstandard) and related statistical variations on the KIA. Statistical test of independence (K2) was performed and markovian suitability has been checked using hypothesis of goodness of fit (Xc2). Hypothesis of statistical independence was rejected, which proved that land use land cover change trends are similar like previous development of land. With acceptance of the hypothesis of goodness of fit (Xc2) proved that actual transition probability of matrix is fitted with expected transition probability prepared using Markov chain method. Statistics indicates Kno, Klocation, Klocation Strata and Kstandard are 0.8347, 0.859, 0.8591 and 0.7928, respectively
