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Automated indicator of local variation in vegetal cover Estevan Barbará Teixeira

Automated indicator of local variation in vegetal cover Estevan Barbará Teixeira José Augusto Sapienza Ramos Laboratório de Geotecnologias 2011, August 19 th. Objectives. To study a method of statistical analysis of vegetation cover variation through time.

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Automated indicator of local variation in vegetal cover Estevan Barbará Teixeira

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  1. Automated indicator of local variation in vegetal cover Estevan Barbará Teixeira José Augusto Sapienza Ramos Laboratório de Geotecnologias 2011, August 19th

  2. Objectives • To study a method of statistical analysis of vegetation cover variation through time. • To implement the method using a user-friendly free software

  3. Motivation • More people have access to remote sensing imagery. • Some of these people make decisions based on the data. • The data by itself frequently isn’t enough, demanding analysis. • The methods of evaluation require technical knowledge. • This work proposes an automated analysis of the vegetation index variation to monitor vegetal cover and other changings.

  4. Concepts • Vegetation Index • A calculation of the vegetation rate in a sample of eletromagnetical radiation, such as a raster image generated by a sensor. • Many vegetation indices uses the radiation of the red and near-infrared bands of eletromagnetical spectre. • The red radiation reflects poorly on the clorophyll of green leaves, and the nirradiation is highly reflected by the leaf cell’s shapes.

  5. NDVI • Index choosed to our work; • Acronym of Normalized Differences Vegetation Index; • One of the most used vegetation indices; • Its calculation is based on the formula: • The results varies between -1 and 1.

  6. NDVI • The low results indicates lack of clorophyll and aditionally, likely presence of exposed soil, or water bodies. • The high results indicates high population of features rich in clorophyll, such as green leaves.

  7. Image preparations • The remote sensor’s images are not immediatelly suitable to the NDVI calculation, because of the following factors: • There is some distortion between the eletromagnetic radiation rate that hits the ground and the rate caught by the sensor. • The sensor converts the levels of radiation measured into a digital number, with a particular formula for each sensor calibration.

  8. Imagepreparations – Step1 (SpectralRadiance) • Firstly, the digital number of the pixel (an integer number, for instance, between 0 and 255 in the TM/Landsat sensors.) must be converted on the actual radiance value.

  9. Image preparations – Step 2 (TOA Reflectance) • After, from the radiance reflected at the surface must be calculated the radiance reflected at the top of atmosphere. • The formula uses as variables • the spectral radiance (Ll), • the distance between the Earth and Sun (d), • the mean solar irradiance of the sensor band (ESUNl), • and the solar zenithal angle (qs)

  10. Indicator of NDVI changes • The NDVI variation between different dates can be used to asess the gain or loss of vegetation. • This variation may be caused by fires or deforestation, but also by season changes, plantings and harvests, or atmospherical conditions.

  11. Indicator of NDVI changes • So, this indicator purposes the use of statistical analysis to evaluate the index variation. • The variation is analyzed using its standard deviation.

  12. The software • The algorithm was modelled in a software, which only demands: • The satellite images (specifically, the red and nir bands). • The date of the images. • The solar zenith angle. • A b parameter to adjust the number of standard deviations of the statistic calculation.

  13. Example: Legal Amazon limits and neighborhood, Tocantins state (b = 1.5) 2009, June 14th 2010, June 17th Image result of the method

  14. Example: Neighborhood of Uberaba, Minas Gerais state (b = 0.5) 2010, June 27th 2010, September 7th Image result of the method

  15. Example: Flooding area of Salto Caxias hydroeletric plant, Paraná state (b = 2) 1996, June 10th 1999, May 2nd Image result of the method

  16. Example: Neighborhood of Floresta, Pernambuco state (b = 1) 1994, May 19th 2001, May 6th Image result of the method

  17. Conclusions • From 10 cases tested, we found that: • More tests are clearly needed to the method evaluation. • The method responds well to agriculture and fires. • In some cases, however, the method doesn’t show good results with rivers and/or clouds. • The automated method shows to be suitable to a quick evaluation of some factors. • For more detailed analysis are needed other methods.

  18. Next works • Research ways to solve problems arised from many sources, specially image overlap and georeferencing. • Studies of the application of image filters are under way. • The software is being adapted to plugin for QuantumGIS and ArcGIS.

  19. Thank You Estevan Barbará Teixeira - ebarbara@labgis.uerj.br José Augusto Sapienza Ramos - sapienza@labgis.uerj.br

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