Rodnei Rizzo
University of São Paulo
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Publication
Featured researches published by Rodnei Rizzo.
Revista Brasileira De Ciencia Do Solo | 2013
Osmar Bazaglia Filho; Rodnei Rizzo; Igo F. Lepsch; Hélio do Prado; Felipe Haenel Gomes; Jairo Antônio Mazza; José Alexandre Melo Demattê
Since different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000) of a 182-ha sugarcane farm in the county of Rafard, Sao Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs). For the Digital Soil Map (DSM), spectral data were extracted from Landsat 5 Thematic Mapper (TM) imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS). To compare the conventional and digital (DSM) soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps decreased towards the lower levels of classification and the great influence of the surveyor on both the mapping and definition of MUs in the soil map. The average correspondence between the conventional and DSM maps was similar. Therefore, the method used to obtain the DSM yielded similar results to those obtained by the conventional technique, while providing additional information about the landscape of each soil, useful for applications in future surveys of similar areas.
Revista Brasileira De Ciencia Do Solo | 2014
Rodnei Rizzo; José Alexandre Melo Demattê; Fabrício da Silva Terra
Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of Sao Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearsons linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearsons correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho ferricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.
Bragantia | 2011
José Alexandre Melo Demattê; Marco Antonio Melo Bortoletto; Gustavo M. Vasques; Rodnei Rizzo
This study aimed to derive mathematical models to predict the soil organic matter content based on soil color obtained by a colorimeter in the Munsell color system. A total of 907 soil samples were collected in the region of Porto Grande (Amapa, Brazil) and analyzed in the laboratory for chemical properties, particle size distribution and color of dry and wet samples. The Munsell color components value and croma obtained using a colorimeter were used to predict soil organic matter content based on stepwise multiple linear regression. Models derived using all samples had R2 of 0.66 for wet samples and 0.56 for dry samples, respectively, when validated using independent samples. It was possible to improve the models by separating the samples by soil class or texture. The models derived using colors obtained from wet samples were systematically better than those based on dry samples. Among soil classes, best results were obtained for Argissolos (Ultisols) and Latossolos (Oxisols), both having an R2 of independent validation of 0.73 (wet sample). For texture, best results were obtained for very clayey soils, with an R2 of validation of 0.81 (wet sample). The soil organic matter prediction models based on soil color have simplicity and potential to be used in the laboratory and in the field with quick and unnecessary chemical products, especially for Ultisols and Oxisols of clayey texture.
Remote Sensing | 2016
José Alexandre Melo Demattê; Leonardo Ramirez-Lopez; Rodnei Rizzo; Marcos Rafael Nanni; Peterson Ricardo Fiorio; Caio Troula Fongaro; Luiz G. Medeiros Neto; José Lucas Safanelli; Pedro Paulo da Silva Barros
There is a consensus about the necessity to achieve a quick soil spatial information with few human resources. Remote/proximal sensing and pedotransference are methods that can be integrated into this approach. On the other hand, there is still a lack of strategies indicating on how to put this in practice, especially in the tropics. Thus, the objective of this work was to suggest a strategy for the spatial prediction of soil classes by using soil spectroscopy from ground laboratory spectra to space images platform, as associated with terrain attributes and spectral libraries. The study area is located in Sao Paulo State, Brazil, which was covered by a regular grid (one per ha), with 473 boreholes collected at top and undersurface. All soil samples were analyzed in laboratory (granulometry and chemical), and scanned with a VIS-NIR-SWIR (400–2500 nm) spectroradiometer. We developed two traditional pedological maps with different detail levels for comparison: TFS-1 regarding orders and subgroups; and TFS-2 with additional information such as color, iron and fertility. Afterwards, we performed a digital soil map, generated by models, which used the following information: (i) predicted soil attributes from undersurface layer (diagnostic horizon), obtained by using a local spectral library; (ii) spectral reflectance of a bare soil surface obtained by Landsat image; and (iii) derivative of terrain attributes. Thus, the digital map was generated by a combination of three variables: remote sensing (Landsat data), proximal sensing (laboratory spectroscopy) and relief. Landsat image with bare soil was used as a first observation of surface. This strategy assisted on the location of topossequences to achieve soil variation in the area. Afterwards, spectral undersurface information from these locations was used to modelize soil attributes quantification (156 samples). The model was used to quantify samples in the entire area by spectra (other 317 samples). Since the surface was bare soil, it was sampled by image spectroscopy. Indeed, topsoil spectral laboratory information presented great similarity with satellite spectra. We observed angle variation on spectra from clayey to sandy soils as differentiated by intensity. Soil lines between bands 3/4 and 5/7 were helpful on the link between laboratory and satellite data. The spectral models of soil attributes (i.e., clay, sand, and iron) presented a high predictive performance (R2 0.71 to 0.90) with low error. The spatial prediction of these attributes also presented a high performance (validations with R2 > 0.78). The models increased spatial resolution of soil weathering information using a known spectral library. Elevation (altitude) improved mapping due to correlation with soil attributes (i.e., clay, iron and chemistry). We observed a close relationship between soil weathering index map and laboratory spectra + image spectra + relief parameters. The color composite of the 5R, 4G and 3B had great performance on the detection of soils along topossequences, since colors went from dark blue to light purple, and were related with soil texture and mineralogy of the region. The comparison between the traditional and digital soil maps showed a global accuracy of 69% for the TFS-1 map and 62% in the TFS-2, with kappa indices of 0.52 and 0.45, respectively. We randomly validated both digital and traditional maps with individual plots at field. We achieve a 75% and 80% agreement for digital and traditional maps, respectively, which allows us to conclude that traditional map is not necessarily the truth and digital is very close. The key of the strategy was to use bare soil image as a first step on the indication of soil variation in the area, indicating in-situ location for sample collection in all depths. The current strategy is innovative since we linked sensors from ground to space in addition with relief parameters and spectral libraries. The strategy indicates a more accurate map with less soil samples and lower cost.
Revista Ciencia Agronomica | 2015
José Alexandre Melo Demattê; Rodnei Rizzo; Victor Wilson Botteon
New tools for soil mapping are needed to increase speed and accuracy of pedological mapping processes. This study integrated various technologies to map soils of the Piracicaba region in Sao Paulo State, Brazil. Each technology was expected to provide different information to design a detailed map. We carried out field survey and soil sampling for laboratory analysis. Initially, we conducted field visits to obtain soil patterns of a reference site. We applied the acquired patterns to an validation site, based solely on information obtained from remote sensing and cartographic databases, namely LANDSAT 7/ETM, digital elevation models (DEM) and aerial photographs. We integrated the information from each product to generate the map of the validation site, which was validated by field inspection. Textural classification using satellite imaging ranged from 21-51% of accuracy. Band 5 in the sensor showed the best performance to discriminate between clayey and sandy soils. Aerial photographs provided the best information because, besides their own inherent characteristics, they operate on a larger scale and result in a map with up to 50 polygons, while DEM reached a maximum of 30 polygons. The digital mapping technology generated 45 mapping units. Finally, the mapping efficiently separated the Latosols from the other classes; however, in some cases there was confusion in the identification of Cambisols and litholic Neosols.
Remote Sensing | 2018
Caio Troula Fongaro; José Alexandre Melo Demattê; Rodnei Rizzo; José Lucas Safanelli; Wanderson Mendes; André Carnieletto Dotto; Luiz Eduardo Vicente; Marston Héracles Domingues Franceschini; Susan L. Ustin
Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0-20 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83; RMSE = 65.0 g kg-1) and sand (R2 = 0.86; RMSE = 79.9 g kg-1). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.
Bragantia | 2011
José Alexandre Melo Demattê; Rodnei Rizzo; Marco Antonio Melo Bortoletto
O conhecimento do solo e um aspecto essencial para a aplicacao de manejo adequado da cultura. Conciliar a pedologia ao desenvolvimento tecnologico e essencial para o progresso desta ciencia. O presente trabalho tem por objetivo avaliar o uso de produtos do sensoriamento remoto conciliados a um sistema de informacoes geograficas, na caracterizacao de solos da regiao de Piracicaba. Para tanto, incursoes a campo e coleta de dados em laboratorio foram realizadas. Posteriormente, compilaram-se informacoes referentes a caracteristicas qualitativas de rede drenagem e relevo utilizando-se estereoscopia em fotografias aereas. Caracteristicas quantitativas de altimetria e declividade atraves de modelo numerico de terreno, alem de caracteristicas espectrais para os diferentes solos e texturas obtidas a partir de imagens do Landsat 7. Tal metodologia resultou em um banco de dados, no qual se notam diferencas entre todos os solos em pelo menos um dos aspectos avaliados. As caracteristicas qualitativas da rede de drenagem diferenciaram os solos, nao ocorrendo o mesmo com os parâmetros quantitativos de relevo. No caso, os Latossolos e Neossolo Quartzarenicos tiveram grande similaridade. Por outro lado, houve diferenca entre as caracteristicas espectrais destes solos. As caracteristicas de altitude tiveram maior contribuicao na diferenciacao dos solos em relacao ao parâmetro declividade. A avaliacao integrada de informacoes geotecnologicas permite obter um panorama proximo a real classe do solo.
Geoderma | 2016
Rodnei Rizzo; José Alexandre Melo Demattê; Igo F. Lepsch; Bruna Cristina Gallo; Caio Troula Fongaro
European Journal of Soil Science | 2015
G. M. Vasques; José Alexandre Melo Demattê; R. A. Viscarra Rossel; L. Ramírez López; Fabrício da Silva Terra; Rodnei Rizzo; C. R. De Souza Filho
Geoderma | 2017
José Alexandre Melo Demattê; Veridiana Maria Sayão; Rodnei Rizzo; Caio Troula Fongaro