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

Farooq Ahmad

doi:10.1590/s1982-45132012000300014

Farooq Ahmad

Open Access

https://doi.org/10.1590/s1982-45132012000300014

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Journal: Sociedade & Natureza	Publication Date: Dec 1, 2012
Citations: 54	License type: cc-by

Affiliation: University of the Punjab

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.

Highlights

Assessing and monitoring the state of the earth surface is a key requirement for global change research (NRC, 1999; LAMBIN et al, 2001; JUNG et al, 2006; XIE, 2008)
Image preprocessing commonly comprises a series of operations, including but not limited to bad lines replacement, radiometric correction, geometric correction, image enhancement and masking variations may exist for images acquired by different sensors (SCHOWENGERDT, 1983; CAMPBELL, 1987; XIE, 2008)
transformed normalized difference vegetation index (TNDVI) model was applied upon ETM+ 1999 and 2002 images and further change detection technique was used for extraction of soil potential sites in district Sargodha

Summary

INTRODUCTION

Assessing and monitoring the state of the earth surface is a key requirement for global change research (NRC, 1999; LAMBIN et al, 2001; JUNG et al, 2006; XIE, 2008). A good case in vegetation mapping by using remote sensing technology is the spectral radiances in the red and near-infrared (NIR) regions, in addition to others The radiances in these regions could be incorporated into the spectral vegetation indices (VI) that are directly related to the intercepted fraction of photosynthetically active radiation (ASRAR et al, 1984; GALIO et al, 1985; XIE, 2008). Vegetation indices were applied upon 1999 and 2002 ETM+ images and further change detection technique was used to develop the EVI, SAVI, NDVI and TNDVI maps. Because of the different characteristics of spectral sensors in the Landsat image series, it is necessary to correct the spectral reflectance between images acquired by those sensors This is especially necessary in long-term vegetation cover monitoring research where either Landsat TM or ETM+ images are used (Xie, 2008). Hyperspectral data could provide much more possibilities compared with multi-spectral data in detecting and quantifying sparse vegetation because it provides a continuous spectrum across a range in wavelengths (KUMAR et al, 2001; FRANK; MENZ, 2003)

RESULTS

DISCUSSION AND CONCLUSIONS