Aníbal Gusso

Algorithm for Soybean Classification Using Medium Resolution Satellite Images

An accurate estimation of soybean crop areas while the plants are still in the field is highly necessary for reliable calculation of real crop parameters as to yield, production and other data important to decision-making policies related to government planning. An algorithm for soybean classification over the Rio Grande do Sul State, Brazil, was developed as an objective, automated tool. It is based on reflectance from medium spatial resolution images. The classification method was called the RCDA (Reflectance-based Crop Detection Algorithm), which operates through a mathematical combination of multi-temporal optical reflectance data obtained from Landsat-5 TM images. A set of 39 municipalities was analyzed for eight crop years between 1996/1997 and 2009/2010. RCDA estimates were compared to the official estimates of the Brazilian Institute of Geography and Statistics (IBGE) for soybean area at a municipal level. Coefficients R2 were between 0.81 and 0.98, indicating good agreement of the estimates. The RCDA was also compared to a soybean crop map derived from Landsat images for the 2000/2001 crop year, the overall map accuracy was 91.91% and the Kappa Index of Agreement was 0.76. Due to the calculation chain and pre-defined parameters, RCDA is a timesaving procedure and is less subjected to analyst skills for image interpretation. Thus, the RCDA was considered advantageous to provide thematic soybean maps at local and regional scales.

Mapeamento da temperatura da superfície terrestre com uso do sensor AVHRR/NOAA

The objective of this work was to evaluate the suitable use of AVHRR/NOAA (Advanced Very High Resolution Radiometer/National Oceanic and Atmospheric Administration) on land surface temperature (LST) mapping in Rio Grande do Sul State by means of the comparison of three classic algorithms. The methods of Becker & Li, Sobrino et al. and Kerr et al. were compared for the minimum LST estimation, using nocturnal and predawn images. Both emissivity and LST data were obtained by means of mathematical combinations of the visible, near-infrared and thermal detected radiation of the AVHRR/NOAA sensor. The AVHRR sensor is suitable for LST mapping for the overall conditions of soil coverage in Rio Grande do Sul, once the estimated LST, by the three tested methods, was close to the measured air temperature at the selected locations. Sobrino et al. was the most adequate method.

Analysis of soybean cropland expansion in the southern Brazilian Amazon and its relation to economic drivers

The agricultural dynamics of soybean expansion have long been recognized as a major driver of excessive land cover change on the southwestern border of the Brazilian Amazon. The hypothesis that the soybean market exerts an influence on land use was investigated by the association between economic indicators and soybean crop dynamics in the state of Mato Grosso (western Brazil). We integrated a historical series of satellite data of soybean cropland expansion and the two main economic variables (selling prices and production costs) for soybean in Mato Grosso. We focused on the relation between profit (the difference between the average soybean price and production costs) and land-use transition to soybean from 2001 to 2013. The spatially explicit analysis showed that the overall accuracy between the resulting first-time use and the most recent soybean crop area in 2013 was 96.75%, with a Kappa index of 0.63. However, dissimilar values found between Omission and Commission indicators suggest that most of the expanded areas prior to 2013 (5.57 million ha) were under a high dynamical range of land uses. Although there is no direct relation between either the deforestation rate or expansion trends (first-time-use rate) and profit, the results strongly suggest (R 2 =0.81) that profit exerts a direct and non-negligible influence on the evolution of consolidated land use for soybean in Mato Grosso State

Canopy temperatures distribution over soybean crop fields using satellite data in the Amazon biome frontier

ABSTRACT During the studied time window, between 2003 and 2010, there was an important increase of land use conversion into new soybean areas (first-time-use) in Mato Grosso state (MT) in Brazil. Uncertainties of future scenario of Brazilian agriculture and increase in the frequency of extreme events, such as the occurrence of high temperatures, is highly likely to produce yield loss on summer crops. The MT is the largest producer of soybeans and accounted for 28.2% of the national production in 2013. The objective of this study was to investigated specific characterization of land surface temperature distribution over the soybean crop fields canopies (canopy-LST) due to massive land use conversion into new soybean areas and its impacts on yield. Satellite imagery data from Aqua and Terra/MODIS sensors (Moderate Resolution Imaging Spectroradiometer) were compared to official agricultural statistics covering eight densely cultivated regions in the studied period. Results show that within the period from flowering to grain filling canopy-LST exhibits a non-negligible relation to yield. It is expected an additional loss of 4.9% on soybean yield for each 1oC of canopy-LST above the obtained optimal level of canopy-LST with 28.4oC, associated to the higher yield averages. The difference between overall average of canopy-LST and air temperature was found 4.2 oC.

Relation between temperature and relief through NOAA images

This work analyze the temperature and relief relationship duringthe 2006 frosts happened in the main producer winter cereals region,through the characteristics of regional relief and minimum surfacetemperatures obtained of satellite NOAA-12 images. The results showedthat the well-known relationship between surface temperature and altitudecan be represented throughout NOAA-12 images.

A SEARCH FOR CORRELATIONS BETWEEN SEA SURFACE TEMPERATURES AND OPTICAL REFLECTANCES IN CHILE AND BRAZIL DERIVED FROM AVHRR/NOAA IMAGES

A search is made for indicators of the presence of phytoplankton, using satellite images of the Pacific (Central Chile) and Atlantic (South Brazil). AVHRR/NOAA-16 and -17 visible (1 and 2) and thermal (4 and 5) channels were used to perform a detection test, respectively, the Suspended Particulate Matter (SPM) and the Sea Surface Temperature (SST). In Brazilian waters, a positive correlation is found between SST and SPMs reflectance. This is interpreted as due to phytoplankton being more abundant in colder waters, where nutrients availability are higher because CO2 dissolution rates, thus being a favorable environment for phytoplankton contents, which when mixed with SPM, tends to reduce the total water reflectance, since organic matter causes absorption at red wavelengths. A comparison is made with results for colder Pacific, where an opposite trend is found. It is noted that the Pacific shelf, off Chile, is narrower than the Atlantics off Brazil, leading to circulation processes, which have a different influence on particulate matter contents. Its also concluded that NOAA data is suitable for these studies, despite the fact its spectral resolution is poorer comparing to specialized ocean studies satellites, a disadvantage compensed by its wider spectral and radiometric range and higher imaging frequency