Fausto Weimar Acerbi Júnior

Continuidade espacial para características dendrométricas (numero de fustes e volume) em plantios de eucalyptus grandis

O objetivo deste estudo foi verificar a continuidade espacial do numero de fustes e do volume nas diferentes formas e intensidades amostrais de Eucalyptus grandis com idade entre 3 e 4 anos. A area de estudo abrangeu quatro talhoes, totalizando 104,71 ha, pertencentes a Ripasa S/A Celulose e Papel. Os dados para a realizacao do estudo de variabilidade espacial foram coletados em parcelas circulares e em parcelas em linhas distribuidas sistematicamente na area, nas intensidades de 1:4 (1 parcela a cada 4 ha), 1:7 e 1:10. Foi possivel verificar que, tanto em numero de fustes quanto em volume, os dados apresentaram distribuicao aproximadamente normal. Pela analise variografica, foi verificado que as caracteristicas numero de fustes e volume de madeira apresentaram-se estruturadas espacialmente. O modelo exponencial foi o que se ajustou melhor aos semivariogramas experimentais das caracteristicas nas diferentes formas de parcela e intensidade amostral. A continuidade espacial foi detectada em todas as intensidades amostrais e formas de parcelas avaliadas, quanto a numero de fustes. Portanto, o uso da estatistica espacial no processamento dessa variavel aumentara a precisao das estimativas. No caso de volume, na intensidade amostral 1:10 nao foi possivel detectar continuidade espacial. Em tal condicao, deve-se utilizar a estatistica classica para processamento do inventario florestal.

Application of LIDAR to forest inventory for tree count in stands of Eucalyptus sp

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.

CHANGE DETECTION IN BRAZILIAN SAVANNAS USING SEMIVARIOGRAMS DERIVED FROM NDVI IMAGES

The Normalized Difference Vegetation Index (NDVI) is often used to extract information from vegetated areas since it is directly related to vegetation parameters such as percent of ground cover, photosynthetic activity of the plant and leaf area index. The aim of this paper was to analyze the potencial of semivariograms generated from NDVI values to detect changes in vegetated areas, analyzing their behavior (shape) and derived metrics (range, sill and nugget). Semivariograms were generated from NDVI values derived from Landsat TM images of May 2010, June 2010 and July 2011. The study area is located in the northern state of Minas Gerais, Brazil, and is covered by Brazilian savannas vegetation, totalizing 1,596 ha. Semivariograms were generated after the exploratory data analysis. Models were fitted, validated and their metrics analyzed. The results showed a very clear trend where the shape of semivariograms, sill and range were different when deforestation occurred and were similar when the area had not been changed. The model that generated best fit was the Gaussian, however, the three models tested showed behavior that makes it possible to detect changes in vegetation. It suggests that further researches should explore the degree to which the semivariogram can be used to quantify this spatial variability as well as to analyze the influence of sazonality for changing detection in vegetated areas.

Assessment of geostatistical features for object-based image classification of contrasted landscape vegetation cover

Accurate mapping and monitoring of savanna and semiarid woodland biomes are needed to support the selection of areas of conservation, to provide sustainable land use, and to improve the understanding of vegetation. The potential of geostatistical features, derived from medium spatial resolution satellite imagery, to characterize contrasted landscape vegetation cover and improve object-based image classification is studied. The study site in Brazil includes cerrado sensu stricto, deciduous forest, and palm swamp vegetation cover. Sentinel 2 and Landsat 8 images were acquired and divided into objects, for each of which a semivariogram was calculated using near-infrared (NIR) and normalized difference vegetation index (NDVI) to extract the set of geostatistical features. The features selected by principal component analysis were used as input data to train a random forest algorithm. Tests were conducted, combining spectral and geostatistical features. Change detection evaluation was performed using a confusion matrix and its accuracies. The semivariogram curves were efficient to characterize spatial heterogeneity, with similar results using NIR and NDVI from Sentinel 2 and Landsat 8. Accuracy was significantly greater when combining geostatistical features with spectral data, suggesting that this method can improve image classification results.Accurate mapping and monitoring of savanna and semiarid woodland biomes are needed to support the selection of areas of conservation, to provide sustainable land use, and to improve the understanding of vegetation. The potential of geostatistical features, derived from medium spatial resolution satellite imagery, to characterize contrasted landscape vegetation cover and improve object-based image classification is studied. The study site in Brazil includes cerrado sensu stricto, deciduous forest, and palm swamp vegetation cover. Sentinel 2 and Landsat 8 images were acquired and divided into objects, for each of which a semivariogram was calculated using near-infrared (NIR) and normalized difference vegetation index (NDVI) to extract the set of geostatistical features. The features selected by principal component analysis were used as input data to train a random forest algorithm. Tests were conducted, combining spectral and geostatistical features. Change detection evaluation was performed using a confusion matrix and its accuracies. The semivariogram curves were efficient to characterize spatial heterogeneity, with similar results using NIR and NDVI from Sentinel 2 and Landsat 8. Accuracy was significantly greater when combining geostatistical features with spectral data, suggesting that this method can improve image classification results.

Sustainable Forest Management of Native Vegetation Remnants in Brazil

A region’s species diversity is an important factor, resulting as a component of social and economical development when used wisely. The correct commercialization of a region’s natural resources guaranties the preservation of local culture and habitat maintenance by means of the obtained income. Hence, the idea of sustainability arises, a widespread theoretical theme which is beginning to gain force in Brazil’s consumer market. The principal conceptual shift was the erroneous notion that timber resources from forests are inexhaustible, since the processes of recomposition/restoration naturally occur after exploration. Indeed a system is capable of regeneration, but this is tied to a series of factors that are usually not respected in areas illegally explored. According to a conference realized in Melbourne by Raison et al. (2001), the concept of sustainability must encompass social and economic conditions such as: respect the forest growth rate; legislation based control; productive capacity; ecosystem’s health and vitality; soil and water resource protection; carbon balance and preservation of biological diversity. Under this scenario, Brazil presents great potential for the use of its natural resources. This is due to the country’s vast territorial extension (8.5 million km2) and high diversity of recurrent vegetation physiognomies. The country possesses about 5.2 million km2 of forest land (60% of its territory), of this total, 98.7% consists of natural forest formation and 1.3% of planted forests. The forest types found in Brazil can be classified as Cerrado (Brazilian savanna), Amazonia (tropical rainforest), Mata Atlântica (Atlantic rainforest), Pantanal (wetlands) and Caatinga (semi-arid forest) as well of transition areas which promotes a mixture of habitats. In many cases, the deforestation of these environments is associated with illegal logging practices coupled with agriculture and cattle-raising. The damage caused by this include modifications of the carbon cycle and consequential rise of CO2 emissions; forest fragmentation; alteration of the hydraulic cycle; species extinction; rural exodus and loss of local fauna and flora diversity. Possibly the most logical use of these forests is the application of sustainable forest management for wood production destined for fire wood, charcoal and logs for industrial purposes. The motives for this strategy are evident, involving aspects attached to the reduction

Object-based land-cover change detection applied to Brazilian seasonal savannahs using geostatistical features

ABSTRACT A new method for remote-sensing land-use/land-cover (LULC) change detection is proposed to eliminate the effects of forest phenology on classification results. This method is insensitive to spectral changes caused by vegetation seasonality and uses an object-based approach to extract geostatistical features from bitemporal Landsat TM (Thematic Mapper) images. We first create image objects by multiresolution segmentation to extract geostatistical features (semivariogram parameters and indices) and spectral information (average values) from NDVI (normalized difference vegetation index), acquired in the wet and dry seasons, as input data to train a Support Vector Machine algorithm. We also used the image difference traditional change-detection method to validate the effectiveness of the proposed method. We used two classes: (1) LULC change class and (2) seasonal change class. Using the most geostatistical features, the change detection results are considerably improved compared with the spectral features and image differencing technique. The highest accuracy was achieved by the sill (σ2 overall variability) semivariogram parameter (95%) and the AFM (area first lag–first maximum) semivariogram index (88.33%), which were not affected by vegetation seasonality. The results indicate that the geostatistical context makes possible the use of bitemporal NDVI images to address the challenge of accurately detecting LULC changes in Brazilian seasonal savannahs, disregarding changes caused by phenological differences, without using a dense time series of remote-sensing images. The challenge of extracting accurate semivariogram curves from objects of long and narrow shapes requires further study, along with the relationship between the scale of segmentation and image spatial resolution, including the type of change and the initial land-cover class.

Determinação do volume de madeira em povoamento de eucalipto por escâner a laser aerotransportado

The objective of this work was to evaluate the possibility of estimating the diameter at breast height (DBH) with tree height and number data derived from airborne laser scanning (LiDAR, light detection and ranging) dataset, and to determine the timber volume of an Eucalyptus sp. stand from these variables. The total number of detected trees was obtained using a local maxima filtering. Plant height estimated by LiDAR showed a nonsignificant tendency to underestimation. The estimate for DBH was coherent with the results found in the forest inventory; however, it also showed a tendency towards underestimation due to the observed behavior for height. The variable number of stems showed values close to the ones observed in the inventory plots. LiDAR underestimated the total timber volume in the stand in 11.4%, compared to the total volume delivered to the industry. The underestimation tendency of tree height (5% mean value) impacted the individual tree volume estimate and, consequently, the stand volume estimate. However, it is possible to obtain regression equations that estimate DBH with good precision, from the LiDAR plant height derived data. The parabolic model is the one that provides the best estimates for timber volumetric yield of eucalyptus stands.

Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks

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 to signal to noise-related issues. This study investigates which methods generate the most appropriate smoothed curves of vegetation signatures on MODIS NDVI time series. The filtering techniques compared were the HANTS algorithm which is based on Fourier analyses and Wavelet temporal algorithm which uses the wavelet analysis to generate the smoothed curves. The study was conducted in four different regions of the Minas Gerais State. The smoothed data were used as input data vectors for vegetation classification by means of artificial neural networks for comparison purpose. A comparison of the results was ultimately discussed in this work showing encouraging results and similarity between the two filtering techniques used.

Use of artificial neural networks and geographic objects for classifying remote sensing imagery

The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1) to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2) to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3) to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes. RESUMO: Conduziu-se este trabalho, com o objetivo de se alcancar o desenvolvimento de uma metodologia para a criacao de um mapa de uso e cobertura do solo na regiao norte do estado de MG, onde, alem de atividades agropecuarias, predominam vegetacoes nativas de cerrado, florestas estacionais deciduais e extensas areas de vereda. Utilizando parcelas inventariadas e imagens dos sensores Rapideye, Landsat TM e MODIS, foram tracados tres objetivos especificos: testar o uso de tecnicas de segmentacao de imagens para uma classificacao baseada em objetos contemplando informacoes espectrais, espaciais e temporais; Testar o uso de imagens de alta resolucao espacial (Rapideye) combinadas a series temporais Landsat-TM, visando a captar os efeitos da sazonalidade, e a classificacao dos dados por meio de Redes Neurais Artificiais. Por meio da serie temporal de imagens MODIS e parcelas inventariadas, foram extraidas as assinaturas temporais das principais fisionomias presentes na regiao, observando-se, assim, os melhores periodos do ano a serem representados no processo de classificacao. Os objetos criados na segmentacao das imagens Rapideye, juntamente com a serie temporal Landsat TM, foram classificados por dez diferentes arquiteturas de redes MultiLayerParceptron. Os resultados mostraram que metodologia atende aos propositos do estudo e as caracteristicas das fisionomias presentes na regiao. Com excelentes valores de acuracia para as classes nativas, o estudo mostra a importância da adequacao da base de dados em trabalhos de classificacao e da importância de uma segmentacao que atenda aos propositos do trabalho.

DETECÇÃO DA EXPANSÃO DA ÁREA MINERADA NO QUADRILÁTERO FERRÍFERO, MINAS GERAIS, NO PERÍODO DE 1985 A 2011 ATRAVÉS DE TÉCNICAS DE SENSORIAMENTO REMOTO

O objetivo deste estudo foi analisar a evolucao da area minerada no Quadrilatero Ferrifero (QF), Minas Gerais, e quantificar a area coberta com vegetacao florestal nativa que foi suprimida por esta atividade durante os ultimos 26 anos. Foram utilizadas imagens TM/Landsat 5 correspondente a area de estudo nos anos de 1985, 1989, 2000 e 2011. Para cada ano de analise, utilizando tecnicas de interpretacao visual de imagens, foram criados poligonos identificando e delimitando as areas mineradas. Para a analise da supressao da vegetacao florestal nativa pela mineracao, as imagens de 1985 foram classificadas gerando um mapa tematico de uso e cobertura do solo do QF. Em seguida, os poligonos de mineracao identificados em cada ano de analise foram sobrepostos ao mapa da vegetacao nativa possibilitando o calculo da area de vegetacao florestal nativa suprimida pela expansao da mineracao. Entre os anos de 1985 a 2011, observou-se aumento de 213% na area minerada no QF. As areas suprimidas de vegetacao florestal nativa entre os anos de 1985 a 1989, 1989 a 2000 e 2000 a 2011 corresponderam respectivamente a 324,42 ha, 948,98 ha e 1989,68 ha, com uma perda total de vegetacao nativa de 3.263,07 ha