real time vegetation analysis through data provided by glam website

International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308

(Print), ISSN 0976 – 6316(Online) Volume 4, Issue 1, January- February (2013), © IAEME

132

REAL TIME VEGETATION ANALYSIS THROUGH DATA PROVIDED

BY GLAM WEBSITE

Umesh Chandra 1, Kamal Jain

2 , S.K Jain

3

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].

INTERNATIONAL JOURNAL OF CIVIL ENGINEERING AND

TECHNOLOGY (IJCIET)

ISSN 0976 – 6308 (Print)

ISSN 0976 – 6316(Online)

Volume 4, Issue 1, January- February (2013), pp. 132-137 © IAEME: www.iaeme.com/ijciet.asp

Journal Impact Factor (2012): 3.1861 (Calculated by GISI) www.jifactor.com

IJCIET

© IAEME



133

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.



134

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.

Fig.3 General Methodology

The complete methodology is divided into multiple phases shown in Fig.3

Temporal

Images

GLAM Project Web Data

General Interface Development

Adapter Class

Users

Classification

Pattern Analysis Graphs



135

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.



136

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.



137

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

real time vegetation analysis through data provided by glam website

Documents

growth of vegetation

temporal ndvi images

vegetation pattern

vegetation growth analysis

dense vegetation

glam project website

print issn

technology ijciet issn