Erivelto Mercante

Mapping and discrimination of soya bean and corn crops using spectro-temporal profiles of vegetation indices

The use of remote-sensing technology has been studied as a way to make the monitoring of agricultural crops more efficient, dynamic, and reliable. The use of data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has proved to be an interesting tool regarding the mapping of large areas, however, some challenges still need to be addressed. One of these is the identification of specific types of crops, especially when they have similar phenologies. The purpose of this study was to perform discrimination and mapping of soya bean and corn crops in the state of Paraná, Brazil, for the 2010/2011 and 2011/2012 crop years. A methodology using spectro-temporal profile information of the crops derived from vegetation indices (VIs), the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and the wide dynamic range vegetation index (WDRVI) based on MODIS data was appraised. This method generated a series of maps of the respective crops that were later qualitatively or quantitatively appraised. Some of the maps drawn showed a global accuracy rate above 80% and a kappa coefficient (κ) of over 0.7. The data areas showed an average difference of 6% for the cultivation of soya beans, and 11% for corn when compared to official data. The WDRVI and EVI were similar and showed better performance when compared to the NDVI in the assessments made. The results demonstrate that the soya bean crop was better mapped compared to corn, particularly in terms of the size of the crop area. The use of spectro-temporal profiles of the VIs assisted in obtaining important information, enabling better identification of crops from regional scale mapping using the MODIS data.

Mapping soya bean and corn crops in the State of Paraná, Brazil, using EVI images from the MODIS sensor

ABSTRACT This study aimed to map, separate, and estimate soya bean and corn crop areas in Paraná State, Brazil, in the harvest years 2012/13 and 2013/14, using the enhanced vegetation index (EVI) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Thus, two methodologies were integrated, the first considering heterogeneity on the dates of crop cycles, the scenes required to generate images of minimum and maximum vegetation indexes, creating a colour composite red, green, and blue (RGB), and identifying two cultures simultaneously. In the second methodology, soya bean and corn were identified and mapped using the selection of pure pixels and the supervised classification algorithm Spectral Angle Mapper (SAM). In order to avoid overlapping areas, we multiplied the results from the first and second methodologies to obtain the final separation. The final validation of the mapping was compared to official data, identifying high correlation to crops. Based on Medium-Resolution Linear Imaging Self-Scanner (LISS-III) and Land Remote Sensing Satellite (Landsat-8) images, the similarity of global accuracy (GA) and kappa accuracy indices was determined, being classified as good and excellent, respectively. It showed that the use of the two consortium methodologies for separation and overlap elimination of these crops in the state of Paraná was efficient.

PRAPRAG: software para planejamento racional de máquinas agrícolas

The software PRAPRAG is a tool used for choosing agricultural machines and implements that present the lowest cost per area or produced amount, as well as, to it makes the machines acquisition planning for the agricultural property, from both technical and economical points of view. It was used the programming language Borland Delphi 3.0. From the machine and implement handouts, it was created a database where the user can register and modify their characteristics of use. The software showed to be a useful and friendly tool. The software provides high speed, safety and reliability for the productive and economical process of the properties, at the selection and acquisition of agricultural systems, as well as for the determination of costs with the used labor.

Modelos de regressão lineares para estimativa de produtividade da soja no oeste do Paraná, utilizando dados espectrais

O trabalho teve o objetivo de avaliar modelos lineares de regressao entre resposta espectral e produtividade em soja, na escala regional. Para isso, foram monitorados 36 municipios do oeste do Parana, utilizando cinco imagens do satelite Landsat 5/TM da safra de 2004/2005. Foram realizados os procedimentos de transformacao radiometrica e correcao atmosferica nas imagens, determinando valores fisicos das refletâncias aparente e de superficie. Posteriormente, foram calculados os indices de vegetacao NDVI e GVI, os quais, por meio de regressoes lineares simples e multiplas, compararam-se com as produtividades oficiais dos municipios, obtidas das estatisticas IBGE. Aplicou-se tambem uma analise de diagnostico, para detectar pontos influentes e de colinearidade. Os resultados mostraram que a media dos valores de NDVI e GVI de todas as imagens foi mais bem relacionada com a produtividade do que para cada data separadamente. O uso de regressoes multiplas com os dois indices, em todas as datas, propiciou melhores resultados de relacao com a produtividade.

Spectral characteristics of soybean during the vegetative cycle with landsat 5/TM images in the western Paraná, Brazil

The objective of this study was to analyze changes in the spectral behavior of the soybean crop through spectral profiles of the vegetation indexes NDVI and GVI, expressed by different physical values such as apparent bi-directional reflectance factor (BRF), surface BRF, and normalized BRF derived from images of the Landsat 5/TM. A soybean area located in Cascavel, Parana, was monitored by using five images of Landsat 5/TM during the 2004/2005 harvesting season. The images were submitted to radiometric transformation, atmospheric correction and normalization, determining physical values of apparent BRF, surface BRF and normalized BRF. NDVI and GVI images were generated in order to distinguish the soybean biomass spectral response. The treatments showed different results for apparent, surface and normalized BRF. Through the profiles of average NDVI and GVI, it was possible to monitor the entire soybean cycle, characterizing its development. It was also observed that the data from normalized BRF negatively affected the spectral curve of soybean crop, mainly, during the phase of vegetative growth, in the 12-9-2004 image.

Detection of soybean planted areas through orbital images based on culture spectral dynamics

The soybean is important to the economy of Brazil, so the estimation of the planted area and the production with higher antecedence and reliability becomes essential. Techniques related to Remote Sensing may help to obtain this information at lower cost and less subjectivity in relation to traditional surveys. The aim of this study is to estimate the planted area with soybean culture in the crop of 2008/2009 in cities in the west of the state of Parana, in Brazil, based on the spectral dynamics of the culture and through the use of the specific system of analysis for images of Landsat 5/TM satellite. The obtained results were satisfactory, because the classification supervised by Maximum Verisimilitude - MaxVer along with the techniques of the specific system of analysis for satellite images has allowed an estimate of soybean planted area (soybean mask), obtaining values ​​of the metrics of Global Accuracy with an average of 79.05% and Kappa Index over 63.50% in all cities. The monitoring of a reference area was of great importance for determining the vegetative phase in which the culture is more different from the other targets, facilitating the choice of training samples (ROIs) and avoiding misclassifications.

Spatial autocorrelation of ndvi and gvi indices derived from landsat/tm images for soybean crops in the western of the state of Paraná in 2004/2005 crop season

This research aims at studying spatial autocorrelation of Landsat/TM based on normalized difference vegetation index (NDVI) and green vegetation index (GVI) of soybean of the western region of the State of Parana. The images were collected during the 2004/2005 crop season. The data were grouped into five vegetation index classes of equal amplitude, to create a temporal map of soybean within the crop cycle. Moran I and Local Indicators of Spatial Autocorrelation (LISA) indices were applied to study the spatial correlation at the global and local levels, respectively. According to these indices, it was possible to understand the municipality-based profiles of tillage as well as to identify different sowing periods, providing important information to producers who use soybean yield data in their planning.

Mapeamento de uso e ocupação do solo e qualidade da água de irrigação no município de Salto do Lontra-Paraná

The objective of this study consisted on mapping the use and soil occupation and evaluation of the quality of irrigation water used in Salto do Lontra, in the state of Parana, Brazil. Images of the satellite SPOT-5 were used to perform the supervised classification of the Maximum Likelihood algorithm - MAXVER, and the water quality parameters analyzed were pH, EC, HCO3-, Cl-, PO43-, NO3-, turbidity, temperature and thermotolerant coliforms in two distinct rainfall periods. The water quality data were subjected to statistical analysis by the techniques of PCA and FA, to identify the most relevant variables in assessing the quality of irrigation water. The characterization of soil use and occupation by the classifier MAXVER allowed the identification of the following classes: crops, bare soil/stubble, forests and urban area. The PCA technique applied to irrigation water quality data explained 53.27% of the variation in water quality among the sampled points. Nitrate, thermotolerant coliforms, temperature, electrical conductivity and bicarbonate were the parameters that best explained the spatial variation of water quality.

Comparison measures of maps generated by geostatistical methods

This study uses several measures derived from the error matrix for comparing two thematic maps generated with the same sample set. The reference map was generated with all the sample elements and the map set as the model was generated without the two points detected as influential by the analysis of local influence diagnostics. The data analyzed refer to the wheat productivity in an agricultural area of 13.55 ha considering a sampling grid of 50 x 50 m comprising 50 georeferenced sample elements. The comparison measures derived from the error matrix indicated that despite some similarity on the maps, they are different. The difference between the estimated production by the reference map and the actual production was of 350 kilograms. The same difference calculated with the mode map was of 50 kilograms, indicating that the study of influential points is of fundamental importance to obtain a more reliable estimative and use of measures obtained from the error matrix is a good option to make comparisons between thematic maps.

Spatial statistics applied to soybean production data from Paraná State for 2003-04 to 2009-10 crop-years

In the current study, we performed a soybean production spatial distribution analysis in Parana State. Seven crop-year data, from 2003-04 to 2009-10, obtained from the Parana Department of Agriculture and Supply (SEAB) were used to develop a Boxmap for each crop-year, show soybean production throughout this time interval. Morans index was used to measure spatial autocorrelation among municipalities at an aggregate level, while LISA index local correlation. For each index, different contiguity matrix and order were used and there was a significance level study. As a result, we have showed spatial relationship among cities regarding the production, which allowed the indication of high and low production clusters. Finally, identifying main soybean-producing cities, what may provide supply chain members with information to strengthen the crop production in Parana.