Detection of change in vegetation cover using multi-spectral and multi-temporal information for district Sargodha, Pakistan

Abstract

Detection of change is the measure of the distinct data framework and thematic change information that can direct to more tangible insights into underlying process involving land cover and landuse changes. Monitoring the locations and distributions of land cover changes is important for establishing links between policy decisions, regulatory actions and subsequent landuse activities. Change detection is the process that helps in determining the changes associated with landuse and land cover properties with reference to geo-registered multi-temporal remote sensing information. It assists in identifying change between two or more dates that is uncharacterized of normal variation. After image to image registrations, the normalized difference vegetation index (NDVI), the transformed normalized difference vegetation index (TNDVI), the enhanced vegetation index (EVI) and the soil-adjusted vegetation index (SAVI) values were derived from Landsat ETM+ dataset and an image differencing algorithm was applied to detect changes. This paper presents an application of the use of multi-temporal Landsat ETM+ images and multi-spectral MODIS (Terra) EVI/NDVI time-series vegetation phenology metrics for the District Sargodha. The results can be utilized as a temporal land use change model for Punjab province of Pakistan to quantify the extent and nature of change and assist in future prediction studies. This will support environmental planning to develop sustainable landuse practices.

Land use change mapping and analysis using Remote Sensing and GIS: A case study of Simly watershed, Islamabad, Pakistan

“…Analysis of detected change is the measure of the distinct data framework and thematic change information that can lead to more tangible discernment to underlying process involved in upbringing of land cover and land use changes (Ahmad, 2012). Change analysis of features of Earth's surface is essential for better understanding of interactions and relationships between human activities and natural phenomena.…”

“…This understanding is necessary for improved resource management and improved decision making (Lu et al, 2004;Seif and Mokarram, 2012). Change detection involves applying multi-temporal Remote Sensing information to analyze the historical effects of an occurrence quantitatively and thus helps in determining the changes associated with land cover and land use properties with reference to the multi-temporal datasets (Ahmad, 2012;Seif and Mokarram, 2012;Zoran, 2006).…”

Remote Sensing Based Vegetation Indices for Estimating Above Ground Carbon Sequestration in Orchards

“…The value of TNDVI was added by 0.5 to avoid the negative value together with applying the square root with the value (Solaimani et al, 2011;Farooq, 2012;Malini and Somashekar, 2013). The researchers adopted the equation 4 to analyze the TNDVI value in this study.…”

Evaluation of Marsh Terraces for Wetland Restoration: A Remote Sensing Approach

“…Hence, change detection can be used to improve the management of natural resources and to help in decision-making processes [26][27][28]. Change detection is performed to assess and quantify the historical changes of Earth's features due human or natural phenomena over time with reference to multi-temporal datasets [26,29,30]. Different change detection algorithms include: write function memory insertion, multi-date composite image, image algebra (e.g., band or image differencing), post-classification comparison, binary mask applied to date 2, ancillary data source used as date 1, spectral change vector analysis, chi-square transformation, cross-relation, visual on-screen digitization, and knowledge-based vision systems [31].…”