Proceedings, XXth congress: Proceedings, XXth congress

Altan, M. Orhan

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Multivolume work

Persistent identifier:: 1663674213

Title:: Proceedings, XXth congress

Sub title:: Istanbul, 12 - 23 July 2004

Year of publication:: 2004

Place of publication:: Istanbul

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 1663674213

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.; Auch bezeichnet als XXth International Congress for Photogrammetry and Remote Sensing

Editor:: Altan, M. Orhan

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Document type:: Multivolume work

Volume

Persistent identifier:: 166368779X

Title:: Proceedings, XXth congress

Scope:: IV, 226 Seiten

Year of publication:: 2004

Place of publication:: Istanbul

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 166368779X

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(35,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Altan, M. Orhan

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: ANALYSIS OF CHANGES IN VEGETATION BIOMASS USING MULTITEMPORAL AND MULTISENSOR SATELLITE DATA A. Akkartal, O. Türüdü, and F. S. Erbek

Document type:: Multivolume work

Structure type:: Chapter

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004 The study area takes 600mm rainfall in average of a year. In this production farm, mostly wheat is the main product and has approximately 3000-ton capacity in a year. In addition, sunflower, corn and canola are the other agricultural products that are aggregated. In this study, three multi temporal Landsat-TM and one SPOT- XS data sets were used to analyse the vegetation biomass changes over time. The characteristics of satellite data used are shown in the Table 1. Spectral Spatial Satellite Date resolution resolution (um) (m) Band 1 | 0,45 - 0,52 30 11.05.1987! Band2 | 0,52- 0,60 30 Band3 | 0,63 - 0,69 30 27.05.1995 | Band4 | 0,76 - 0,90 30 Landsat Band5 | 1,55-0,75 30 TM Band6 | 10,4- 12,5 120 07.06.2000 Band 7 | 2,08 - 2,35 30 Band I | 0.50-0.59 20 Sot 12.05.2003 | Band 2 | 0.61-0.68 20 Band 3 | 0.79-0.89 20 Table 1. The characteristics of satellite data used. 3. METHODOLOGY 3.1 Vegetation Indexes In this study, five different types of vegetation indexes, which quantify the concentrations of green leaf vegetation around the globe, were used for biomass analysis. These indexes depend on the reflectance of vegetation, which is very different in near infrared and red bands. Healthy vegetation should absorb the visible light and reflects most of the near infrared light, on the other hand unhealthy vegetation reflects more visible light and less near infrared light. The reflection on visible band is related with the pigments in the leaves of plants but in the near infrared, it depends on the cell structure. Taking the ratio of near infrared band and red band is the simplest vegetation index. Hence, it is called Simple Ratio (SR) or Ratio Vegetation Index (RVI). SR indicates the amount of vegetation. In the resultant SR image, high values, such as more than 20, show for dense vegetation and low values, which are around the value of 1, show for soil, ice and water. However, it doesn't give information related with topography. It only transmits the spectral information; therefore this also gives an opportunity of having uniform spectral classes after classification. Another simple vegetation index is the Difference Vegetation Index (DVI) which is also sensitive to the amount of the vegetation. Mathematically, it is in the form of (near infrared band) — (red band). DVI has the ability to distinguish the soil and vegetation but not in shady areas. Hence, DVI doesn't give proper information when the reflected wavelengths are being affected due to topography, atmosphere or shadows. The more common and known one is the Normalised Difference Vegetation index (NDVI). The algorithm of NDVI is (near infrared band- red band)/(near infrared band + red band). Resulted values change between -1 and +1 regarding to the vegetated area. Such as, if the result is 0,1 or below, it corresponds to an area of rocks; if it is between 0.2 and 0.3, it indicates an area of shrubs or grasslands; if it is between 0.6 and 0.8 it corresponds to an area of tropical rainforests. Transformed Normalised Difference Vegetation index (TNDVI) is the square root of the NDVI. It has higher coefficient of determination for the same variable and this is the difference between TNDVI and NDVI. The formula of TNDVI has always positive values and the variances of the ratio are proportional to mean values. TNDVI indicates a relation between the amount of green biomass that is found in a pixel. (Senseman et.al. 1996) Perpendicular vegetation index (PVI) is one of the complex indices that also including soil emissivity factor. It is based on the linear relationship of red and near infrared reflectance from bare soils. This is called the soil line (Figure 2). PVI is the perpendicular distance from the soil line and it is linearly related to the vegetation cover (Sunar and Taberner 1995). PVI uses Gram-Schmidt orthogonalization to figure out the greenness line, which is perpendicular to the soil line and passes through the %100 vegetation cover points. PVI is effective in detecting dry and green vegetation. This is caused by the sensation of red and near infrared combination to the iron oxide absorption that is in many soils. Mainly PVI indicates the vegetative cover, independent from the soil effects. MEASURED REFLECTANCES NIR REFLECTANCE 1." SOIL LINE REFLECTANCE RED Figure 2. Perpendicular Vegetation Index. 3.2 Geometric Correction To detect the changes in vegetation biomass all images used must be registered to each other. The 1993 Landsat image was taken as the base image for registering. Ten GCPs for each year, which were well distributed through the images, were chosen in registration. The number of GCPs and rms errors were outlined in Table 2. Spot XS image (2003) were resembled to 30 m to be analysed together with the other Landsat images. All the registered images were taken as a multitemporal dataset having 455 x 547 pixels. Base image | Slave image # of GCPs rms error 1987 10 0.5716 1993 2000 10 0.5474 2003 10 0.4373 182 Table 2. Number of GCPs used and rms errors.

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