Luis Marcelo Tavares de Carvalho

Universidade Federal de Lavras (UFLA), Lavras, Minas Gerais, Brazil

Are you Luis Marcelo Tavares de Carvalho?

Claim your profile

Publications (33)5.86 Total impact

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Lands (broader concept than soils, including all elements of the environment: soils, geology, topography, climate, water resources, flora and fauna, and the effects of anthropogenic activities) of the state of Minas Gerais are in different soil, climate and socio-economics conditions and suitability for the production of agricultural goods is therefore distinct and mapping of agricultural suitability of the state lands is crucial for planning guided sustainability. Geoprocessing uses geographic information treatment techniques and GIS allows to evaluate geographic phenomena and their interrelationships using digital maps. To evaluate the agricultural suitability of state lands, we used soil maps, field knowledge, forest inventories and databases related to Ecological-Economic Zoning (EEZ) of Minas Gerais, to develop a map of land suitability in GIS. To do this, we have combined the maps of soil fertility, water stress, oxygen deficiency, vulnerability to erosion and impediments to mechanization. In terms of geographical expression, the main limiting factor of lands is soil fertility, followed by lack of water, impediments to mechanization and vulnerability to erosion. Regarding agricultural suitability, the group 2 (regular suitability for crops) is the most comprehensive, representing 45.13% of the state. For management levels A and B, low and moderate technological level, respectively, the most expressive suitability class is the regular, followed by the restricted class and last, the adequate class, while for the management level C (high technological level) the predominant class is the restricted. The predominant most intensive use type is for crops, whose area increases substantially with capital investment and technology (management levels B and C).
    Ciência e Agrotecnologia 12/2013; 37(6):538-549. · 0.40 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Characterizations of land-cover dynamics are among the most important applications of Earth observation data, providing insights into management, policy and science. Recent progress in remote sensing and associated digital image processing offers unprecedented opportunities to detect changes in land cover more accurately over increasingly large areas, with diminishing costs and processing time. The advent of high-spatial-resolution remote-sensing imagery further provides opportunities to apply change detection with object-based image analysis (OBIA), that is, object-based change detection (OBCD). When compared with the traditional pixel-based change paradigm, OBCD has the ability to improve the identification of changes for the geographic entities found over a given landscape. In this article, we present an overview of the main issues in change detection, followed by the motivations for using OBCD as compared to pixel-based approaches. We also discuss the challenges caused by the use of objects in change detection and provide a conceptual overview of solutions, which are followed by a detailed review of current OBCD algorithms. In particular, OBCD offers unique approaches and methods for exploiting high-spatial-resolution imagery, to capture meaningful detailed change information in a systematic and repeatable manner, corresponding to a wide range of information needs.
    International Journal of Remote Sensing 07/2012; 33(14):4434-4457. · 1.14 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Light Detection and Ranging, or LIDAR, has become an effective ancillary tool to extract forest inventory data and for use in other forest studies. This work was aimed at establishing an effective methodology for using LIDAR for tree count in a stand of Eucalyptus sp. located in southern Bahia state. Information provided includes in-flight gross data processing to final tree count. Intermediate processing steps are of critical importance to the quality of results and include the following stages: organizing point clouds, creating a canopy surface model (CSM) through TIN and IDW interpolation and final automated tree count with a local maximum algorithm with 5 x 5 and 3 x 3 windows. Results were checked against manual tree count using Quickbird images, for verification of accuracy. Tree count using IDW interpolation with a 5x5 window for the count algorithm was found to be accurate to 97.36%. This result demonstrates the effectiveness of the methodology and its use potential for future applications.
    Cerne 06/2012; 18(2):175-184. · 0.22 Impact Factor
  • Source
    04/2012; , ISBN: 978-953-51-0511-4
  • [Show abstract] [Hide abstract]
    ABSTRACT: Conservation Units are among the best methods found to secure biodiversity conservation. The physical and biotic characteristics of high altitude areas such as Serra de Carrancas and Luminárias, in Minas Gerais state, make these places a home to endemic species and to rich biodiversity. However, these environments are highly susceptible to fast advancing erosive processes that potentially lead to soil, habitat and species loss. This study aims to evaluate the physical and biotic characteristics of the Serra de Carrancas and Luminárias region using Natural Vulnerability indicators, and also to propose Conservation Unit implementation in areas which, as per this index, are considered environmentally critical and highly sensitive to anthropic actions. Biotic and abiotic indicators, as managed by a geographic information system, identifi ed the most vulnerable areas in the study site and, given the sensitivity and scope of Serra de Carrancas and Luminárias, a State Park was proposed. The natural vulnerability index proved to be an effective tool to pinpoint prospective conservation unit areas, gathering important environmental factors and thus improving the effi ciency of conservation strategies.
    Cerne 01/2011; 17(2):151-159. · 0.22 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Multi-temporal images are now of standard use in remote sensing of vegetation during monitoring and classification.Temporal vegetation signatures (i. e., vegetation indices as functions of time) generated, poses many challenges, primarily due tosignal to noise-related issues. This study investigates which methods generate the most appropriate smoothed curves of vegetationsignatures on MODIS NDVI time series. The filtering techniques compared were the HANTS algorithm which is based on Fourieranalyses and Wavelet temporal algorithm which uses the wavelet analysis to generate the smoothed curves. The study was conductedin four different regions of the Minas Gerais State. The smoothed data were used as input data vectors for vegetation classificationby means of artificial neural networks for comparison purpose. A comparison of the results was ultimately discussed in this workshowing encouraging results and similarity between the two filtering techniques used.
    Cerne. 01/2010;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Temporal vegetation signatures (i. e., vegetation indices as functions of time) generated using the MODIS instrument poses many challenges, primarily due to signal to noise-related issues Bruce et al. (2006). This study investigates which methods best generate smoothed curves of vegetation signatures on MODIS NDVI time series. The filtering techniques compared were the HANTS algorithm, Verhoef (1996), which is based on Fourier analyses and Wavelet temporal algorithm which uses the wavelet analysis to generate the smoothed curves. The smoothed data were used as input data vectors for vegetation classification by means of Artificial neural networks. Statistics of the classifications reveal that the Wavelet filtering algorithm outperforms the original time series and the HANTS fft derived algorithms in all cases in all the classification algorithms.
    01/2009;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes the methodology used for monitoring changes in forest and natural vegetation cover in Minas Gerais, Brazil. The state of Minas Gerais is characterized by a large geographical extent and an intense fragmentation history. Both characteristics limit the application of traditional change detection algorithms demanding a large number of trained inpterpreters to separate noise from real land cover changes. Hence, the objective of this work was to develop and implement a procedure for digital change detection which is less sensitive to erros cause by misregistration, phonological state of the vegetation and atmospheric conditions. Landsat images form circa 2005 and circa 2007 acquired over Minas Gerais, as well as a land cover map produced in 2005 were used for change detection. The methodology is based on multiresolution wavelet analysis and local maxima detection in order to pinpoint multscale change objects. The change objects were recursively segmented from coarse to fine scale levels. The results showed that the method is less sensitive to radiometric and geometric misregistration. A new data base relative to vegetation cover classes for the year 2007 was produced and the change statistics were presented for the state of Minas Gerais Palavras-chave: remote sensing, image processing, forest monitoring, sensoriamento remoto, processamento de imagens, monitoramento florestal.
    01/2009;
  • [Show abstract] [Hide abstract]
    ABSTRACT: O objetivo deste trabalho foi caracterizar a dinâmica sazonal do cerrado, floresta estacional semidecidual e decidual nonorte do estado de Minas Gerais, Brasil. Séries multitemporais dos índices de vegetação NDVI (índice de vegetação da diferençanormalizada) e EVI (índice de vegetação melhorado) derivados do sensor MODIS, foram comparadas analisando o perfil temporale os resultados de classificação das imagens. Os resultados mostraram que: (1) Os índices de vegetação estudados refletiram opadrão sazonal das fisionomias, diferenciando os períodos chuvosos e os períodos de seca; (2) a fisionomia floresta estacionaldecidual apresentou menores valores dos índices e maior variação; (3) as fisionomias cerrado e floresta estacional semidecidualapresentaram alto valores dos índices e baixa variação; (4) de acordo com os resultados das classificações o melhor índice para omapeamento das fisionomias na área de estudo foi o NDVI, porém ambos podem ser usados para avaliar a dinâmica sazonal davegetação; e (5) estudos precisam ser realizados explorando algoritmos de extração de feições para melhorar a acuracidade domapeamento das fisionomias cerrado, floresta decídua e semidecidua na área de estudo.
    Cerne. 01/2008;
  • Source
    07/2007: pages 237-270;
  • Source
    Frederico Pereira Reis, Luciano T. de Oliveira, Luis Marcelo T. de Carvalho
    [Show abstract] [Hide abstract]
    ABSTRACT: The present work aims at performing automatic tree crown counting in planted Eucalypt forests. High resolution remote sensing imagery and digital image processing using Lee Filters and the unsupervised classification algorithm ISODATA were used. The classification result that best extracted tree crowns was exported to a GIS for tree counting. The resulting number of trees was compared to the number obtained by visual interpretation. Resumo. O presente trabalho objetiva, através da utilização de imagens de sensoriamento remoto de alta resolução e processamento digital de imagens, realizar a contagem automática de copas individuais em um povoamento plantado de Eucalyptus spp., através da técnica que associa o filtro de Lee com o classificador não-supervisionado ISODATA. Após as etapas de processamento, o resultado da classificação que mais representou as copas das árvores foi exportado para ambiente SIG (Sistema de Informações Geográficas), onde foi realizada a contagem automática dos polígonos que representaram melhor as copas. Os resultados foram comparados com o parâmetro, que foi obtido através da interpretação visual da imagem.
    IX Brazilian Symposium on Geoinformatics, 25-28 November, Campos do Jordão, São Paulo, Brazil; 01/2007
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes the research carried out within the framework of the Ecological Economical,Zoning of Minas Gerais (ZEE-MG) to model vegetation vulnerability derived by a number,of spatial inference methods. Methods based on weighted overlay, fuzzy logic, and neural networks were compared in terms of visual similarity between maps, the degree of restrictiveness concerning vulnerability, and the easiness of implementation. It was concluded,that weighted,overlay is the best approach,to be used within the ZEE-MG. Resumo. Este artigo descreve os estudos realizados durante os trabalhos do
    IX Brazilian Symposium on Geoinformatics, 25-28 November, Campos do Jordão, São Paulo, Brazil; 01/2007
  • Source
    Gleyce Campos Dutra, Luis Marcelo Tavares de Carvalho, Ary Teixeira de Oliveira Filho
    IX Brazilian Symposium on Geoinformatics, 25-28 November, Campos do Jordão, São Paulo, Brazil; 01/2007
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This present work describes the classification of the Phytophysiognomies present in the Brazilian Cerrado biome through the means Artificial Intelligence; data from remote sensing images and other sources served as input for these algorithms to generate the vegetation maps. The data acquired was of many types so that it fully described the various Phytophysiognomies present in biome and served as training data for the machine learning algorithms. Various statistical and neuro-computation based algorithms were used for pattern recognition in the data so that we could build a good generalization model for the biome. A vegetation map was successfully generated with each algorithm. Finally a comparison among these algorithms was made so that we could find the best algorithm that fitted the problem of mapping this biome.
    IX Brazilian Symposium on Geoinformatics, 25-28 November, Campos do Jordão, São Paulo, Brazil; 01/2007
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This meta paper is the thematic map accuracy assessment of "Mapeamento da Flora Nativa e dos Reflorestamentos de Minas Gerais". It will enclose the Minas Gerais State all and it will use, for attainment of points you show that they portray the truth terrestrial the procedure of random estratified sampling. In this sampling design will be placed 250 points in the thematic class with bigger area of covering in the State and, excessively, divided proportionally in the too much class. One expects to get with this study an assesment of the thematic classification of the "Mapeamento da Flora Nativa e dos Reflorestamentos de Minas Gerais" to assure the trustworthiness of the information for it generated. Moreover, to more establish a strategy of adjusted sampling for this evaluation, guaranteeing efficiency of costs and statistical severity.
    01/2007;
  • Source
    Luis Marcelo Tavares De Carvalho, Julio Neil Louzada
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes the methodological approach used to derive the vegetation component of natural vulnerability within the frame work of the Ecologic-Economic Zoning of Minas Gerais State, Brazil. The compiled datasets consisted of a landcover map containing information on natural vegetation, planted forests, water bodies, urbanized areas and agriculture. Besides, a map of priority areas considered for vegetation conservation was included in the analysis. Both datasets were produced by the Forestry State Institute. From these maps, twelve indicators of vegetation integrity were derived: Regional relevance of vegetation ecosystems (fields, rocky fields, savanna park, savanna, woody savanna, vereda, and deciduous, semi-deciduous and evergreen forests); Degree of conservation; Degree of heterogeneity; and Priority for conservation. These indicators were combined by weighted overlay in order to derive a vegetation vulnerability map to be further combined with other biotic and abiotic components to form the synthetic map of Natural Vulnerability of the State of Minas Gerais. Palavras-chave: Ecologic-Economic Zoning, native flora, ecosystem conservation, ecosystem heterogeneity, ecosystem relevance, Zoneamento Ecológico-Econômico, flora nativa, conservação de ecossistemas, heterogeneidade de ecossistemas, relevância de ecossistemas.
    01/2007;
  • Symone Maria de Melo Figueiredo, Luis Marcelo Tavares de Carvalho
    [Show abstract] [Hide abstract]
    ABSTRACT: This study evaluated the accuracy of mapping land cover in Capixaba, state of Acre, Brazil, using decision trees. Elevenattributes were used to build the decision trees: TM Landsat datafrom bands 1, 2, 3, 4, 5, and 7; fraction images derived from linearspectral unmixing; and the normalized difference vegetation index (NDVI). The Kappa values were greater than 0,83, producingexcellent classification results and demonstrating that the technique is promising for mapping land cover in the study area.
    Cerne. 01/2006;
  • Source
    Symone Maria de Melo Figueiredo, Luis Marcelo Tavares de Carvalho
    [Show abstract] [Hide abstract]
    ABSTRACT: This study evaluated the accuracy of mapping land cover in Capixaba, state of Acre, Brazil, using decision trees. Eleven attributes were used to build the decision trees: TM Landsat datafrom bands 1, 2, 3, 4, 5, and 7; fraction images derived from linear spectral unmixing; and the normalized difference vegetation index (NDVI). The Kappa values were greater than 0,83, producing excellent classification results and demonstrating that the technique is promising for mapping land cover in the study area.
    Cerne 01/2006; 12(1):38-47. · 0.22 Impact Factor
  • Source
    L.T. Oliveira, L.M.T. Carvalho, F. Weimar Acerbi
    [Show abstract] [Hide abstract]
    ABSTRACT: The main objective of this paper was to select procedures to enhance the spatial resolution of images acquired by the ETM+ and ASTER sensors using image fusion. Images with spatial resolution degraded to 60 meters were simulated in order to compare different methods of data fusion and to produce images with spatial resolution enhanced to 30 meters. Qualitative and quantitative comparisons with the original images were achieved using the following statistical measures: bias, variance of the difference, standard deviation, correlation and the RMSE. The non-decimated biorthogonal wavelet transform with Antonini 7/9 filter was selected as the best method because it presented less modification of the spectral information when compared to the original images.
    01/2005;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a case study on the use of features derived from remote sensing data for mapping the highly fragmented semideciduous Atlantic forest in Brazil. Innovative aspects of this research include the evaluation of different feature sets in order to improve land cover mapping. The feature sets were defined based on expert knowledge and on data mining techniques to be input to traditional and machine learning algorithms for pattern recognition, viz. maximum likelihood, univariate decision trees, multivariate decision trees, and neural networks. The results showed that the maximum likelihood classification using temporal texture descriptors as extracted with wavelet transforms was most accurate to classify the semideciduous Atlantic forest. In this study, a special accuracy measure was used: the so-called class mapping accuracy. Maximum likelihood performed relatively well, with forest mapping accuracies ranging from 34.5 to 51.3%. In contrast, accuracies for neural networks ranged from 19.0 to 45.2%. Classification confusion occurred mainly with coffee and eucalyptus plantations. Univariate trees provided the most robust results for different feature sets, with accuracies ranging from 39.6 to 46.7%. Temporal information of vegetation indices was more important than image texture, terrain topography and raw spectral information for discriminating semideciduous Atlantic forest.
    International Journal of Applied Earth Observation and Geoinformation 01/2004; · 2.54 Impact Factor