Pakistan Gis Research Paper

(2006) Analysis of the Effects of Land Use Change on Protected Areas in the Philippines. [7] Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B. (2004) Digital Change Detection Methods in Ecosystem Monitoring: A Review. CIPEC, Indiana University, Nepal Forestry Resources and Institutions, Kathmandu. (2002) A Comparison of Methods for Monitoring Multi-Temporal Vegetation Change Using Thematic Mapper Imagery. [12] Nagendra, H., Southworth, J., Tucker, C., Karna, B. (2005) Remote Sensing for Policy Evaluation: Monitoring Parks in Nepal and Honduras.

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IOV risk maps were developed through two available methods: kernel density and getis-ord-gi* by giving single parameter “frequency”, and one new indexing method by giving two additional parameters as well “severity” and “probability”.

For each district, IOV frequency and IOV severity values were calculated from PIPS data whereas IOV probability value was derived from the Benazir Income Support Program—Poverty Scorecard Survey (BISP—PSS).

This study has showed that GIS has the incredible capabilities that facilitate us in capturing, analyzing, and visualizing the IOV data. Introduction Nelson Mandela stated that twentieth century will be remembered as a century marked by violence.

Violence is a leading factor to cause death; around one million people aged from fifteen to forty-four years die every year due to violence and many others are injured [1] .

For validation purpose, spatial overlay analysis was conducted between year 2011 IOV data (classified through natural breaks) and year 2010 IOV risk maps (developed through kernel density, getis-ord-gi* and indexing method).

The indexing method has proved as a reliable method to develop an IOV risk map with an accuracy of 93 percent then getis-ord-gi* and kernel density having accuracy of 58 percent and 89 percent respectively.The main objectives of the study were to: 1) identify different classes of land use and land cover, and its spatial distribution in the study area; 2) determine the trend, nature, location and magnitude of forest cover change; and 3) prepare maps of forest-cover change in different time periods in the study area. (2015) Application of Remote Sensing and GIS in Forest Cover Change in Tehsil Barawal, District Dir, Pakistan. To assess the objectives remote sensing and GIS techniques were utilized. (2006) Integrated Applications of Remote Sensing & GIS for Mapping & Monitoring Changes in Forest Cover in Pakistan. Mitigation and Adaptation Strategies for Global Change, 12, 441-453. Mitigation and Adaption Strategies of Global Change.By using our site, you agree to our collection of information through the use of cookies. Google(); req('single_work'); $('.js-splash-single-step-signup-download-button').one('click', function(e){ req_and_ready('single_work', function() ); new c. (2004) Fuel Wood, Timber and Deforestation in the Himalayas: The Case of Basho Valley, Baltistan Region, Pakistan. Proceedings of the International Seminar on Natural Hazard Monitoring, Karachi, 429-435. (2010) Analysis of Forest Cover Change Process, Using Remote Sensing and GIS: A Case Study in Sultan Syarif Hasyim Grand Forest Park, Riau Province, Indonesia. Furthermore, indexing method predicted IOV risk areas more efficiently in terms of spatial distribution.Indexing method highlighted Khuzdar, Zhob, Upper Dir, Khyber Agency, Orakzai Agency, Peshawar and Karachi Districts under high-risk category where actions are needed from the law enforcement agencies and stakeholders to minimize the violent incidents.


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