Assessment of different change vector analysis techniques over rugged terrain MODIS sensor satellite imagery

Remote sensed information is a primary source of oversee Land-Use/Land-Cover (LULC) changes for environmental impacts, natural hazard analysis and region planning. Change Vector Analysis (CVA) as change detection technique has level headed capability of extracting and identifying LULC changes in terms of change-magnitude and change-direction from two different temporal satellite imageries. During past decade, many effective CVA based change detection techniques such as Improved Change Vector Analysis (ICVA), Modified Change Vector Analysis (MCVA) and Change Vector Analysis Posterior-probability Space (CVAPS) have been developed. But the assortment of an appropriate CVA technique for particular area is a very essential and difficult process because different CVA techniques have their own features and no one technique is applicable to all case studies. Aforementioned, CVA techniques were rarely examined on snow cover area of rugged terrain. In present paper, different CVA techniques have been investigated over snow cover area of rugged terrain MODIS satellite imagery to evaluate a technique which could more accurately distinguish the “change” and “no-change” pixels, and also accurately perform “from-to” change detection. Experiment outcomes confirm that CVAPS change detection technique has greater potential than MCVA and ICVA techniques to determine the overall transformed information over snow cover area of rugged terrain. It is expected that this paper will effectively guide the natural hazard forecaster's or algorithm engineer's to accurately detect the multi-temporal environment changes over LULC rugged terrain.