Processing and Use of Satellite Images in Order to Extract Useful Information in Precision Agriculture

Image analysis methods were developed and diversified greatly in recent years due to increasing speed and accuracy in providing information regarding land cover and vegetation in urban areas. The aim of this paper is to process satellite images for monitoring agricultural areas. Satellite images used in this study are medium and high resolution images taken from QuickBird and SPOT systems. Based on these images, a supervised classification was performed of a very large area, having as result the land use classes. Supervised classification can be defined as the ability to group the pixels that compose the satellite image, digitally, in accordance with their real significance. Gaussian algorithm of maximum similarity (Maximum likelihood) was used, referred to in the specialty literature as maximum likelihood method or probabilistic classification, and based on the use of probability theory (function Gaussian) to compare the spectral values of each pixel in hand with statistical " fingerprint "of each area of interest. Practically, conditional probabilities were calculated of belonging to one class or another. The points in the middle of the group have a higher probability of belonging to the certain class, probability intervals (concentric isolines or contours of equal probability) being delimited graphically by izocontours expressing spectral variations within each set of training

The Potential of Pig Sludge Fertilizer for Some Pasture Agricultural Lands’ Improvement: Case Study in Timiș County, Romania

In the context of the current energy crisis, pig sludge may be a more accessible fertilizer resource for different categories of farmers and agro-ecosystems, in order to support soil fertility and agricultural production. The present study presents results regarding the influence of pig sludge on soil quality and the spatial and temporal variability of a pasture agro-ecosystem, in the area of Ciacova locality, Timiș County, Romania. The pig sludge was fermented for a period of 6 months in fermentation tanks and was applied at a rate of 80 m3 ha−1 y−1 between 2013 and 2019, on two pasture plots (P808, P816). In the study period (2013–2019), the agrochemical indices studied presented the values: pH = 5.90 ± 0.09 (P816-6-13) and pH = 6.90 ± 0.06 (P808-7-18); P = 10.20 ± 2.26 ppm (P808-4-13) and P = 69.10 ± 3.04 ppm (P808-5-19); K = 176.00 ± 7.44 ppm (P816-4-13) and K = 429.00 ± 7.33 ppm (P816-3-19); NI = 2.45% ± 0.08% (P816-6-13) and NI = 3.87% ± 0.06% (P816-6-19). The variability of the land, i.e., the pasture category, evaluated based on the NDVI index (seven NDVI classes were generated, C1 to C7) decreased under the influence of pig sludge, the values of the variation coefficients being CVNDVI = 17.5098 in 2019 compared to CVNDVI = 41.5402 in 2013 for P808 and CVNDVI = 32.0685 in 2019, compared to CVNDVI = 52.2031 in 2013 for P816. It was found that the land area decreased (2019 compared to 2013) from classes C1 to C4 NDVI (low NDVI values, NDVI < 0.5) and the area increased within classes C6 and C7 NDVI (high NDVI values, NDVI > 0.5)