Real time vegetation analysis through data provided by glam website
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1. The document describes a web-based application developed to capture NDVI images from the GLAM project website and analyze vegetation seasonal dynamics. 2. Sixty NDVI images from July 2007 to June 2012 of Roorkee, India were captured monthly from the GLAM website and classified. Pattern analysis graphs were plotted to predict vegetation growth periods. 3. The graphs showed rice growth from July to October with harvesting in late October. Wheat growth was from December to March with harvesting in late March. The region remained largely green with stable water bodies over the five years.
![INTERNATIONAL JOURNAL OF CIVIL ENGINEERING AND
International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308
(Print), ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME
TECHNOLOGY (IJCIET)
ISSN 0976 – 6308 (Print)
ISSN 0976 – 6316(Online)
Volume 4, Issue 1, January- February (2013), pp. 132-137
IJCIET
© IAEME: www.iaeme.com/ijciet.asp
Journal Impact Factor (2012): 3.1861 (Calculated by GISI)
www.jifactor.com
© IAEME
REAL TIME VEGETATION ANALYSIS THROUGH DATA PROVIDED
BY GLAM WEBSITE
1 2 3
Umesh Chandra , Kamal Jain , S.K Jain
1&2
(Geomatics Section, Civil Engineering, IIT Roorkee, India)
3
(Applied Mathematics, Bits Mesra, Ranchi, Jharkhand, India)
ABSTRACT
NDVI data are suitable to examine the longer term event like the growth of vegetation
through a season. GLAM Project published time series database of NDVI images of 8 and 16
day composting periods which is regularly updated. In the present study a web based
application is developed for capturing temporal NDVI images from the GLAM project
website, these images are further used for monitoring vegetation seasonal dynamics of the
selected region. This system is more suitable for automatic forecast of the vegetation pattern
change; the data needed for this type of analysis is not only very costly but also not easily
available. To depict vegetation growth analysis of the selected region, pattern analysis graphs
are plotted at the end by using images provided by a third party website in the client
application.
Keywords: Global Agricultural Monitoring (GLAM) Project, Moderate-resolution Imaging
Spectroradiometer (MODIS) , Normalized Difference Vegetative Index (NDVI), Seasonal
Dynamics, Web Based Application, Foreign Agricultural Service (FAS)
1. INTRODUCTION
GLAM is a concurrently funded project of the U.S. Department of Agriculture
(USDA) and the National Aeronautics and Space Administration (NASA) to assimilate
NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) data and products into
an existing decision support system (DSS) operated by the International Production
Assessment Division (IPAD) of FAS [1]. A global NDVI time-series database, with a spatial
resolution of 250 meters has been assembled using a 16-day composting period, allowing for
inter-annual comparisons of growing season dynamics. This MODIS NDVI dataset is
automatically re-projected and mosaicked to suit the FAS regions of interest [1].
132](https://image.slidesharecdn.com/realtimevegetationanalysisthroughdataprovidedbyglamwebsite-130306010451-phpapp01/85/Real-time-vegetation-analysis-through-data-provided-by-glam-website-1-320.jpg)
![International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308
(Print), ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME
GLAM launches a website (http://pekko.geog.umd.edu/usda/test/index.php)
containing temporal NDVI images. These images are suitable to examine the longer term
event like the growth of vegetation through a season [2]. It gives a measure of the
vegetative cover on the land surface over wide areas because vegetation tends to absorb
strongly the red wavelengths of sunlight and reflect on the near-infrared wavelengths. The
Normalized Difference Vegetation Index (NDVI) is a measure of the difference in
reflectance between these wavelength ranges. NDVI takes values between -1 and 1, with
values 0.5 indicating dense vegetation and values <0 indicating no vegetation [2]. Dense
vegetation shows up very strongly in the imagery, and areas with little or no vegetation
are also clearly identified. It is also suitable to recognize water and ice [2]. NDVI data is
too costly and also not easily available so in the current study a web based application is
designed and developed for capturing NDVI images provided by GLAM project website
(Modis data) for monitoring vegetation seasonal dynamics of the selected region .This
study is most suitable for automatic forecast of the vegetation pattern changes.
2. DATA USED
In the present study GLAM Project website 16 day composting period NDVI data
with a spatial resolution of 250 meters is used as shown in Fig.1 and Fig.2. For analysing
seasonal behaviour sixty images from July 2007 to June 2012 are captured from the
GLAM website on a monthly basis of Roorkee, Haridwar District, India.
Fig.1. GLAM Web page showing provided information according to the temporal,
regional etc. attributes value passed by user.
133](https://image.slidesharecdn.com/realtimevegetationanalysisthroughdataprovidedbyglamwebsite-130306010451-phpapp01/85/Real-time-vegetation-analysis-through-data-provided-by-glam-website-2-320.jpg)
![International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308
(Print), ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME
Fig.7 Client Application shows a pattern graph of Water Bodies of Roorkee area for 5
years (approx. No change).
5. ACKNOWLEDGEMENT
We deeply thank, the Global Agricultural Monitoring (GLAM) Project for publishing
vital NDVI data that play very important role in the present study.
REFERENCES
[1] http://www.pecad.fas.usda.gov/glam.htm
[2] http://www.met.rdg.ac.uk/~swsgrime/artemis/ch3/ndvi/ndvi.html
137](https://image.slidesharecdn.com/realtimevegetationanalysisthroughdataprovidedbyglamwebsite-130306010451-phpapp01/85/Real-time-vegetation-analysis-through-data-provided-by-glam-website-6-320.jpg)
Real time vegetation analysis through data provided by glam website
- 1. INTERNATIONAL JOURNAL OF CIVIL ENGINEERING AND International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME TECHNOLOGY (IJCIET) ISSN 0976 – 6308 (Print) ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), pp. 132-137 IJCIET © IAEME: www.iaeme.com/ijciet.asp Journal Impact Factor (2012): 3.1861 (Calculated by GISI) www.jifactor.com © IAEME REAL TIME VEGETATION ANALYSIS THROUGH DATA PROVIDED BY GLAM WEBSITE 1 2 3 Umesh Chandra , Kamal Jain , S.K Jain 1&2 (Geomatics Section, Civil Engineering, IIT Roorkee, India) 3 (Applied Mathematics, Bits Mesra, Ranchi, Jharkhand, India) ABSTRACT NDVI data are suitable to examine the longer term event like the growth of vegetation through a season. GLAM Project published time series database of NDVI images of 8 and 16 day composting periods which is regularly updated. In the present study a web based application is developed for capturing temporal NDVI images from the GLAM project website, these images are further used for monitoring vegetation seasonal dynamics of the selected region. This system is more suitable for automatic forecast of the vegetation pattern change; the data needed for this type of analysis is not only very costly but also not easily available. To depict vegetation growth analysis of the selected region, pattern analysis graphs are plotted at the end by using images provided by a third party website in the client application. Keywords: Global Agricultural Monitoring (GLAM) Project, Moderate-resolution Imaging Spectroradiometer (MODIS) , Normalized Difference Vegetative Index (NDVI), Seasonal Dynamics, Web Based Application, Foreign Agricultural Service (FAS) 1. INTRODUCTION GLAM is a concurrently funded project of the U.S. Department of Agriculture (USDA) and the National Aeronautics and Space Administration (NASA) to assimilate NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) data and products into an existing decision support system (DSS) operated by the International Production Assessment Division (IPAD) of FAS [1]. A global NDVI time-series database, with a spatial resolution of 250 meters has been assembled using a 16-day composting period, allowing for inter-annual comparisons of growing season dynamics. This MODIS NDVI dataset is automatically re-projected and mosaicked to suit the FAS regions of interest [1]. 132
- 2. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME GLAM launches a website (http://pekko.geog.umd.edu/usda/test/index.php) containing temporal NDVI images. These images are suitable to examine the longer term event like the growth of vegetation through a season [2]. It gives a measure of the vegetative cover on the land surface over wide areas because vegetation tends to absorb strongly the red wavelengths of sunlight and reflect on the near-infrared wavelengths. The Normalized Difference Vegetation Index (NDVI) is a measure of the difference in reflectance between these wavelength ranges. NDVI takes values between -1 and 1, with values 0.5 indicating dense vegetation and values <0 indicating no vegetation [2]. Dense vegetation shows up very strongly in the imagery, and areas with little or no vegetation are also clearly identified. It is also suitable to recognize water and ice [2]. NDVI data is too costly and also not easily available so in the current study a web based application is designed and developed for capturing NDVI images provided by GLAM project website (Modis data) for monitoring vegetation seasonal dynamics of the selected region .This study is most suitable for automatic forecast of the vegetation pattern changes. 2. DATA USED In the present study GLAM Project website 16 day composting period NDVI data with a spatial resolution of 250 meters is used as shown in Fig.1 and Fig.2. For analysing seasonal behaviour sixty images from July 2007 to June 2012 are captured from the GLAM website on a monthly basis of Roorkee, Haridwar District, India. Fig.1. GLAM Web page showing provided information according to the temporal, regional etc. attributes value passed by user. 133
- 3. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME Fig.2. GLAM Web page showing detail image (250 Meter/pixel) resolution. 3. METHODOLOGY To fulfill its defined objectives a browser based application is developed and designed in visual studio 2008, which captures the temporal images for the time period of 5 years (July 2007 to June 2012) of monthly basis provided by MODIS website, further classification is done at run time to give true colors to the image and at the end a pattern analysis graph is plotted to predict the vegetation seasonal activities. GLAM Project Web Data General Interface Development Adapter Class Temporal Images Classification Pattern Analysis Graphs Users Fig.3 General Methodology The complete methodology is divided into multiple phases shown in Fig.3 134
- 4. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME In the first phase a general web based interface is created for interacting with the GLAM Project website, this interface contains two parts one for opening third party website (GLAM) and another for client application which contains buttons for getting an image, the user can select the region, data type and temporal attribute through the interface provided for third party website according to its requirement. In the second phase a specific adapter class is developed for capturing the spatial images, this class is specifically built to save the front end images provided by GLAM project website, if there is any change occurs in the GLAM website data format, style etc. then only this adapter class need to be rebuilt. In the third phase the spatial temporal images captured from GLAM website is classified into eleven classes, in order to, it can be easily identified by the end user. Fig.4 shows the classified images captured for the month of September of five years from 2007 to 2011 of Roorkee, Haridwar District, India. Besides it a database is also created which contains the percentage amount of the particular vegetation type present on the classified image at run time for further query processing. September 2007 September 2008 September 2009 September 2010 September 2011 Fig.4 Classified images of the month of Septemeber from 2007 to 2011. In the next step the pattern analysis graph is plotted to predict the vegetation seasonal activities with the help of the database created by capturing sixty images from July 2005 to June 2012. These Graphs are plotted between months and the percentage amount of the particular vegetation for five years. 135
- 5. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME 4. RESULTS AND CONCLUSIONS As depicted in the Pattern Analysis Graph of Roorkee area for the time period of 5 years (July 2007 to June 2012) growth period for Rice varies from July to October and harvesting time is in the end of October and for the wheat growth period varies December to March and harvesting time is in the end of March as shown in Fig.5 where picks present in the graph shows the shift of growing periods of the vegetation and the sudden falls shows the harvesting time accordingly. Fig.6 shows the pattern graph of sparse vegetation, it is clearly visible from it that the selected region is very greeny. It is also clearly visible in Fig.7 that water bodies of Roorkee region remain the same i.e. approximately there is no change appears. Fig.5 Client Application shows a pattern graph of Sparse Vegetation of Roorkee area for 5 years. Fig.6 Client Application shows a pattern graph of Dense Vegetation of Roorkee area for 5 years. 136
- 6. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME Fig.7 Client Application shows a pattern graph of Water Bodies of Roorkee area for 5 years (approx. No change). 5. ACKNOWLEDGEMENT We deeply thank, the Global Agricultural Monitoring (GLAM) Project for publishing vital NDVI data that play very important role in the present study. REFERENCES [1] http://www.pecad.fas.usda.gov/glam.htm [2] http://www.met.rdg.ac.uk/~swsgrime/artemis/ch3/ndvi/ndvi.html 137