Trend analysis of NDVI for detecting vegetation change of cuddalore, Tamil Nadu

Felix Yovan1, Reddy V Nithya1,


Research Articles | Published:

Print ISSN : 0970-4078.
Online ISSN : 2229-4473.
Website:www.vegetosindia.org
Pub Email: contact@vegetosindia.org
Doi: 10.5958/2229-4473.2018.00079.4
First Page: 95
Last Page: 100
Views: 68


Keywords: Remote Sensing, Landsat, NDVI, Change Detection.


Abstract


Remote sensing is a useful tool for monitoring vegetation around the globe. Landsat satellite data from USGS are much useful for the researchers and students to efficiently download Landsat TM/ETM/OLI is widely used for vegetation analysis. Vegetation analysis makes use NDVI (Normalized Difference Vegetation Index) in estimating the health of vegetation temporally for growth period. This study makes use Landsat TM, ETM and Landsat 8 OLI data for used for classifying vegetation indices to arrive spatial distribution. NDVI model maker in ERDAS Imagine software was implied to select corresponding Red and NIR bands for deriving the indices that indicate the greenness, has been qualitatively used to infer the changes in vegetation. Thus three sets of data corresponding to year 2006, 2009 and 2016 used for change detection to understand the variability of NDVI between the years. A classification system was introduced as Non-suitable, low, medium, high, very high based n index values among the satellite data outputs. The results of analysis has showed that the yearly pattern of NDVI is dependent on the climatic characteristics and differences in number of pixels, area, percent of area for five classes for the years of 2006 (non-suitable: 139597px; 125.64sqkm; 26.73% of area, low: 229840px; 1.81sqkm; 44.00% of area, medium: 123501px; 0.04sqkm; 23.64% of area, high: 29370px; 0.02sqkm; 5.62% of area, veryhigh: 16px, 0.01sqkm; 0.0030% of area), 2009 (nonsuitable: 44805px; 40.32sqkm; 8.54 of area, low: 246266px; 0.25sqkm; 46.96% of area, medium: 179661px; 0.18sqkm; 34.26% of area, high: 53639px; 0.05sqkm; 10.23% of area, very high: 6px; 0sqkm; 0% of area), 2016 (non-suitable: 20526px; 18.47sqkm; 3.93% of area, low: 366009px; 329.41sqkm; 70.07% of area, medium: 135346px, 121.81sqkm; 25.91% of area, high: 444px; 0.40sqkm; 0.09% of area, very high: 0px; 0sqkm; 0% of area). And the result displayed in this research is that vegetation in the year of 2016 is decreased as compared to the years of 2006 and 2009.


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References



Acknowledgements



Author Information


Felix Yovan1 Reddy V Nithya1
1CSE Department, Sathyabama Institute of Science and Technology, Rajiv Gandhi Road, Jeppiaar Nagar, Chennai-600119

2Centre for Remote Sensing and Geoinformatics Sathyabama Institute of Science and Technology, Rajiv Gandhi Road, Jeppiaar Nagar, Chennai-600119

*Corresponding author: Centre for Remote Sensing and Geoinformatics, Sathyabama Institute of Science and Technology, Rajiv Gandhi Road, Jeppiaar Nagar, Chennai, 600119 Email: nethajim@gmail.com