OLIVEIRA, W. S. N.; http://lattes.cnpq.br/0754264082365950; OLIVEIRA, Woslley Sidney Nogueira de.
Résumé:
The irrigated perimeters implemented in the State of Paraiba are considered a costeffective
alternative quite profitable, promotes the generation of jobs and increases
the availability of food. Due to inadequate management of soil and water, that have
caused losses in soil quality of these perimeters, degrading them mainly by
salinization. Remote sensing is an alternative low-cost technology, good temporal
and frequency has the ability to map areas in process of desertification. This
research aim to identify potential areas affected by salts in the irrigated perimeter of
São Gonçalo (PISG), Sousa-PB, through remote sensing techniques. For this study
we used LANDSAT satellite images 8/OLI (average spatial resolution), 216/orbit point
65 of 07/11/2016 date; image of the Google Earth Pro software® from date of
29/02/2016 to serve as auxiliary image and photographic records of the areas on the
spot. The supervised classification technique, using the SCP (semi-automatic plugin)
in software QGIS (Quantum Gis). The measurement of the quality of the classification
took place by means of cross-validation, using statistical parameters such as the
accuracy of the producer (EP), accuracy of the user (EU), global (EG) accuracy and
Kappa index. The area class supposedly salinated (.ASS) presented EP and I of
89.15% and 88.88%, respectively. The Kappa index resulted in a value of .ASS class
0.8684 was classified as being of excellent quality. The overall quality of the
classification is assessed both by EG who presented a 0.9350 value as the Kappa
index 0.9252 valued General, being values that represent a rating of excellent
quality. The class ASS presented the largest minimum and maximum values of
reflectance factor in all the bands in the image, highlighting the band 6 0.47 values
and 0.67, respectively. The value of the area classified as being of .ASS class was
1736.75 acres, 31% of the total area of the PISG. The images reviewed discriminate
salinated areas and not allowed saline through the variations of shade and
reflectance. The images analyzed with the SCP plugin enabled the creation of a map
of supervised classification, indicating the spatial variability of the areas prone to
salinization process. However, it is recommended that the analysis of the physical
and chemical soil parameters of these areas for increased reliability in the quality of
this type of mapping.
