Zonage du Bresil a partir d'une serie temporelle d'images modis.

Zoneamento do Brasil a partir de uma série temporal de imagens MODIS . A cartografia das paisagens envolvem geralmente a combinação de informações ambientais e informações sobre as atividades humanas. A qualidade da carta de paisagem resultante depende fortemente da expertise e do método utilizado, assim como da qualidade dos dados que foram usados na sua elaboração. As séries temporais das imagens de satélite aportam uma visão objetiva do território a diferentes datas. Estas imagens podem ser segmentadas para estratificar o espaço em zonas radiometricamente homogêneas. O objetivo deste trabalho é testar este método de estratificação a diferentes escalas espaciais, no Brasil e na região do estado do Maranhão e avaliar as estratificações de forma não supervisionada. Para tanto, uma segmentação orientada à objeto foi realizada utilizando-se o software eCognition a partir de valores dos índices de vegetação EVI (Enhaced Vegetation Index) e de índices de textura advindos de uma série temporal de imagens MODIS com resolução espacial de 250m. Diferentes variáveis radiométricas e diferentes escalas de segmentação foram testadas e avaliadas através de dois indicadores estatísticos. A segmentação obtida foi, então, comparada visualmente aos zoneamentos existentes (GAEZ da FAO e zoneamento agroecológico da Embrapa)

A remote sensing based delineation of the areal extent of smallholder sugarcane fields of South Africa.

Master of Science in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg 2016.The areal extent delineation of smallholder sugarcane fields in fragmented landscapes is a challenge due to their complex spatial configuration (i.e. patchy field sizes) and timeless planting and harvesting dates. Nevertheless, delineating and estimating areas of such farming systems is essential in crop yield estimation as well as food supply inventorying to enhance food security planning for the country. Moreover, estimating the areal extent of fragmented smallholder fields can provide insights into their natural resource uses as well as their contribution to carbon pool. However, the challenge is the lack of robust, applicable methods and platforms that could be used to accurately map these farming systems in a quick, efficient and cost-effective manner. Based on that premise, this study sought to evaluate the utility of remotely sensed data coupled with advanced machine-learning classification algorithms for estimating the areal extent of smallholder sugarcane fields. The scope of this study was limited to (1) evaluating the performance of support vector machine (SVM) at pixel-based image analysis (PBIA) and object-based image analysis (OBIA) platforms in delineating areas of fragmented smallholder sugarcane fields using Landsat 8 Operational Land Imager (OLI) imagery (2) Comparing support vector machine and random forest (RF) in delineating the areal extent of smallholder sugarcane fields based on Landsat 8 OLI imagery. The performance of the two algorithms was determined based on accuracies derived using confusion matrices. Based on objective 1, the findings show no statistical significant difference (p ≥ 0.05) between PBIA and OBIA when using support vector machine (SVM). Furthermore, when comparing SVM with RF an increase of 6% was observed in overall accuracy. Nevertheless, results from the McNemar’s showed that the 6% difference was not significant. From the findings on this study, it was concluded that (1) Support vector machine can reduce the accuracy gap between PBIA and OBIA in delineating areas of smallholder sugarcane fields based on Landsat 8 OLI imagery, (2) Despite observing no statistical significance difference in accuracy, SVM outperformed RF by a margin of 7%. Meanwhile, both RF and SVM have great potential in delineating areas of the fragmented smallholder sugarcane fields

Uso de co-clustering para análise de imagens de altíssima resolução espacial

Dissertação (mestrado) — Universidade de Brasília, Instituto de Geociências, Programa de Pós-Graduação em Geociências Aplicadas, 2022.No contexto de mineração de dados, ao se realizar a classificação em imagens de sensoriamento remoto, a extração de padrões é um importante passo. O uso de coclustering para análise de imagens agrega novas possibilidades de identificação de padrões, no ramo do conhecimento do sensoriamento remoto. Comumente se realiza a busca de padrões em imagens considerando-se cada dimensão por vez, portanto uma única banda. A proposta do uso de técnicas de co-clustering é justamente considerar-se, de forma iterativa, na dimensão espectral, todas as bandas da imagem original, além de camadas criadas, por exemplo de textura e morfologia matemática, simulando novas bandas de imagem. Ao final do processo, resulta-se em clusters de pixels efetivamente classificados. A partir de medidas de similaridade dadas pelos Índice de Jaccard, Índice de Rand e Índice de Rand Ajustado avaliaram-se os agrupamentos resultantes da técnica de co-clustering quando aplicada a um cubo de imagem gerado a partir de uma imagem RGB de altíssima resolução, concatenada a resultados de morfologia matemática e de análise de textura. Utilizou-se o método tradicional de classificação não supervisionada K-médias como base de comparação para avaliação dos resultados encontrados. Concluiu-se que o método é eficiente, desenvolvido a partir de imagens e classificação prévia, disponibilizadas pela ISPRS, classificação essa tratada como verdade para o contexto deste trabalho.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).In the context of data mining, when performing classification on remote sensing images, pattern extraction is an important step. The use of co-clustering for image analysis adds new possibilities for pattern identification in the field of remote sensing knowledge. Commonly, the search for patterns in images is performed considering each dimension individually at a time, therefore, a single band. The proposal for the use of co-clustering techniques is precisely to consider, in an iterative way, in the spectral dimension, all bands of the original image, in addition to created layers of texture and mathematical morphology, simulating new image bands. At the end of the process, effectively classified clusters of pixels are obtained. Based on similarity measures given by the Jaccard Index, Rand Index and Adjusted Rand Index, the resulting clusters of the co-clustering technique were evaluated when applied to an image cube generated from a very high resolution RGB image, concatenated to mathematical morphology and texture analysis results. The traditional method of unsupervised classification Kmeans was used as a basis for comparison to evaluate the results. It was concluded that the method is efficient, developed from images and previous classification, made available by the ISPRS, which was treated as true for the context of this work

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Aplicación de imágenes de satélites y datos LiDAR en la modelización e inventario de Eucalyptus spp en Uruguay

La integración de información de inventarios de campo, con datos procedentes de sensores remotos y su alta correlación con la estructura de la vegetación, permite ajustar modelos precisos para la estimación de la producción forestal. Esto impacta reduciendo costos, tiempos y sesgos, generando productos que son insumos para procesos como la segmentación y la optimización de la cosecha. En este trabajo se presenta una alternativa a los inventarios forestales y al procesamiento de datos, mediante el uso de sensores LiDAR e imágenes multiespectrales. El objetivo general fue evaluar el uso de LiDAR y datos multiespectrales, en plantaciones de Eucalyptus grandis y Eucalyptus dunnii en Uruguay; para mejorar la calidad y la cantidad de información brindada para optimizar los procesos de gestión forestal con respecto a los sistemas de inventario tradicionales. Los resultados obtenidos demuestran la mejora en la precisión y en la calidad de los datos frente a los inventarios tradicionales. Se proporcionan herramientas que permiten mejorar la precisión en cuatro aspectos para la cuantificación y el manejo de la producción forestal: i) el uso de modelos compatibles y aditivos; ii) el modelado de las variables del rodal a gran escala empleando datos de teledetección; iii) la delimitación de zonas homogéneas dentro del rodal basada en una evaluación no supervisada; y iv) un método de programación lineal que optimiza los planes de corta basado en la disponibilidad de madera, el secuestro de carbono y el Valor Actual Neto. Se concluye que la aplicación de herramientas de geomática en el sector forestal supone un cambio fundamental en las prácticas de inventarios, desde su planificación, ejecución y resolución, así como de la capacidad para generar modelos predictivos y de algoritmos de segmentación con mayor precisión. Se comprobó que el uso de datos procedentes de sensores activos y pasivos incrementa las posibilidades de automatización de inventarios forestales, aumentando la resolución espacial y la temporal de la cartografía forestal. Esto, junto con el uso de técnicas estadísticas paramétricas y no paramétricas, constituyen un avance en el campo del manejo forestal en Uruguay.The integration of information from field inventories, with data from remote sensors, and its high correlation with the structure of the vegetation, allows to adjust precise models for the estimation of forest production. This allows for a reduction in costs, time and bias, producing valuable inputs for processes such as segmentation and optimizing the harvest. Here we present an alternative to forest inventories and data processing through the use of LiDAR and multispectral images. The main objective was to evaluate the use of LiDAR information and high-resolution multispectral data in Eucalyptus plantations in Uruguay, to improve the quality and quantity of information provided to optimize forest management processes with respect to traditional inventory systems. The results obtained demonstrate the improvement in precision and quality of the data compared to traditional inventories. Tools that improve precision in four fundamental aspects for the quantification and management of forest production are provided: i) the use of compatible and additives models; ii) modeling of stand variables on a large scale using remote sensing data; iii) delimitation of homogeneous areas within the stand based on an unsupervised assessment; and iv) a method for optimizing felling plans based on timber availability, carbon prices, and harvest age. The main conclusion is that the application of geomatic tools in the forestry sector represent a fundamental change in inventory practices, from planning, execution and resolution, as well as the ability to generate predictive models and segmentation algorithms with greater precision than those obtained with field inventories. Throughout the thesis, it is shown that the use of data from different active and passive sensors increases the possibilities for automating forest inventories, increasing the spatial and temporal resolution of forest cartography. This, together with the use of parametric and non-parametric statistical techniques, constitutes an advance in the field of forest management in Uruguay