JOURNAL OF APPLIED ENGINEERING SCIENCES VOL. 1(14), issue 4_2011 ISSN 2247-3769 ISSN-L 2247-3769 (Print) / e-issn:2284-7197 USING MULTISPECTRAL SATELLITE IMAGES FOR VAIS Manuel Bucharest University, e-mail: manuel.vais@sipg.ro A B S T R A C T Based on Image Processing and GIS software, the paper describes the possibility of using the Multispectral Satellite Images for up dating vector data in a geodatabase. Received: October 2011 Accepted: October 2011 Revised: November 2011 Available online: November 2011 Keywords: GIS, Remote Sensing, NDVI INTRODUCTION On 23 th of July 1972 was launced the first remote sensing satellite from the comercial mission ERTS Earth Resources Technology Satellite, renamed later LANDSAT Land Satellite. This moment means the start of a new period in spatial activities. After this a number of other remote sensing satellites were launched providing a huge amount of satellite images. Based on the existance of this imagery many procedures and routines for extracting informations were developed, used in many domains of activity that need spatial analysis. MATERIALS AND METHODS 1. Update vector data in a geodatabase The optical sensor mounted on board of LANDSAT 1, named MSS Multi Spectral Scanner, had four spectral channels two of them in the visible part and the other two from near infra red (NIR) part of electromagnetic spectrum. That means each satellite image contains four black and white images for each spectral channel, that allow us to obtain, based on it, colour composit images. For LANDSAT MSS images, in [1] was introduced an empirical transformation for each pixel in order to improve the image for vegetation research goal. This transformation named NDVI Normalized Difference Vegetation Index, has the following form: where: NIR - RED NDVI= (1) NIR+ RED NIR means the spectral value for each pixel in the spectral range of Near Infra Red channel; RED means the spectral value for each pixel in the spectral range of visible red channel. In the tables 1-4, we present summarised wave length of spectral chanells for different remote sensing satellites with such optical sensor.
USING MULTISPECTRAL SATELLITE IMAGES FOR VAIS M., pp. 77-82 Table 1. Wave length of spectral channells for multispectral sensor on board of NASA s remote sensing satellites As we can see the number of spectral channels is similar or bigger in the sensor configurations. For LANDSAT ETM (LANDSAT Enhanced Thematic Mapper) having three channels in Visible and four channels in Infra Red, we can search different forms and significance for NDI Normalized Difference Index defined as follow: where: IR - VIS NDI= (2) IR+ VIS IR means the spectral value for each pixel in the spectral range of one Infra Red channel; VIS means the spectral value for each pixel in the spectral range of one visible channel. On the other hand, being an empirical formula, we need to verify, for each sensor configuration, it s consistency. Such verification can be done using Sobel edge detection filter applied to satellite image after NDI transformation compared with detailed maps and informations obtained after a global survey for interested area. Table 2. Wave length of spectral channells for multispectral sensor on board of other USA s remote sensing satellites
JOURNAL OF APPLIED ENGINEERING SCIENCES VOL. 1(14), issue 4_2011 ISSN 2247-3769 ISSN-L 2247-3769 (Print) / e-issn:2284-7197 Table 3. Wave length of spectral channells for multispectral sensor on board of European s remote sensing satellites So, we verify, for LANDSAT ETM images, the following NDI: where: Channel 5 - Channel 2 NDWI= (3) Channel 5+ Channel 2 NDWI means Normalized Difference Water Index; Channel 5 means the spectral value of spectral channel 5 from Infra Red; Channel 2 means the spectral value of spectral channel 2 from Visible. Table 4. Wave length of spectral channells for multispectral sensor on board of other remote sensing satellites (indian, japanese, russian,...)
USING MULTISPECTRAL SATELLITE IMAGES FOR VAIS M., pp. 77-82 For exemplification we apply this transformation using a LANDSAT ETM image (index 181-29 din 07.06.2000) see Figure 1. Fig. 1. Frame from LANDSAT ETM 181 29, colour composite RGB Applying this transformation (NDWI) we obtained a new image in which the water is selected. We can see in Figure 2 a small part of Danube river inside roumanin teritory. Fig. 2. Frame from LANDSAT ETM 181 29, after NDWI transformation
JOURNAL OF APPLIED ENGINEERING SCIENCES VOL. 1(14), issue 4_2011 ISSN 2247-3769 ISSN-L 2247-3769 (Print) / e-issn:2284-7197 Applying Sobel filter for edge detection, we obtain water zone as a poligon see figure 3. Fig. 3. Frame from LANDSAT ETM 181 29, after NDWI transformation and Sobel filter These contours, after georeferentiation of the satellite image, can be selected and using GIS tools can be transformed into vector data (poligons, in our case) in order to up date a spatial data base. CONCLUSIONS From the all presented above results the usefulness of using multispectral satellite images for up-dating vector data in a geo data base. For using multispectral satellite images in order to up-date vector data in a geo data base we need to take into account the image spatial resolution according with the needed accuracy level. Also, it is necessary to check for each sensor, based on spectral configuration, the consistency of NDI. REFERENCES 1. ROUSE, J.W., JR., R.H. HAAS, J.A. SCHELL, and D.W. DEERING (1973), Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Prog. Rep. RSC 1978-1, Remote Sensing Center, Texas A&M Univ., College Station, 93 p. (NTIS No. E73-106393). 2. MANUEL VAIS (2011), ContribuŃii la problema mişcării satelińilor de teledetecńie şi utilizarea imaginilor de teledetecńie pentru monitorizarea contaminării cu produse petroliere în domeniul marin (Contributions to remote sensing satellite movement and usage of remote sensing images
USING MULTISPECTRAL SATELLITE IMAGES FOR VAIS M., pp. 77-82 for oil pollution in marine environment), Universitatea Bucureşti, Şcoala doctorală în Geologie, Teză de doctorat (Bucharest University Doctoral School for Geology). 3. MANUEL VAIS (2011), Utilizarea imaginilor satelitare de teledetecńie (Using remote sensing satellite images), in Monitorul de Petrol şi Gaze nr. 2, pp. 44-51.