Avaliação da evolução do índice de vegetação de teledetecção usando de técnicas de processamento de imagens

Vegetation has a substantial role as an indicator of anthropic effects, specifically in cases where urban planning is required. This is especially the case in the management of coastal cities, where vegetation exerts several effects that heighten the quality of life (alleviation of unpleasant weather conditions, mitigation of erosion, aesthetics, among others). For this reason, there is an increased interest in the development of automated tools for studying the temporal and spatial evolution of the vegetation cover in wide urban areas, with an adequate spatial and temporal resolution. We present an automated image processing workflow for computing the variation of vegetation cover using any publicly available satellite imagery (ASTER, SPOT, LANDSAT, MODIS, among others) and a set of image processing algorithms specifically developed. The automatic processing methodology was developed to evaluate the spatial and temporal evolution of vegetation cover, including the Normalized Difference Vegetation Index (NDVI), the vegetation cover percentage and the vegetation variation. A prior urban area digitalization is required. The methodology was applied in Monte Hermoso city, Argentina. The vegetation cover per city block was computed and three transects over the city were outlined to evaluate the changes in NDVI values. This allows the computation of several information products, like NDVI profiles, vegetation variation assessment, and classification of city areas regarding vegetation. The information is available in GIS-readable formats, making it useful as support for urban planning decisions.A vegetação tem um papel importante como indicador de efeitos antrópicos, especificamente nos casos em que o planejamento urbano é necessário. Este é especialmente o caso na gestão de cidades costeiras, onde a vegetação exerce diversos efeitos que elevam a qualidade de vida (alívio de condições climáticas desagradáveis, mitigação da erosão, estética, entre outras). Por essa razão, há um interesse crescente no desenvolvimento de ferramentas automatizadas para o estudo da evolução temporal e espacial da cobertura vegetal em grandes áreas urbanas, com adequada resolução espacial e temporal. Apresentamos um fluxo de trabalho automatizado de processamento de imagens para calcular a variação da cobertura vegetal usando qualquer imagem de satélite publicamente disponível (ASTER, SPOT, LANDSAT, MODIS, entre outros) e um conjunto de algoritmos de processamento de imagem desenvolvidos especificamente. A metodologia de processamento automático foi desenvolvida para avaliar a evolução espacial e temporal da cobertura vegetal, incluindo o Índice de Vegetação da Diferença Normalizada (NDVI), o percentual de cobertura vegetal e a variação da vegetação. Uma digitalização prévia da área urbana foi necessária. A metodologia foi aplicada na cidade de Monte Hermoso, na Argentina. A cobertura vegetal por quarteirão foi computada e três transectos sobre a cidade foram delineados para avaliar as mudanças nos valores de NDVI. Isso permite o cálculo de vários produtos de informação, como perfis de NDVI, avaliação da variação da vegetação e classificação das áreas da cidade em relação à vegetação. A informação está disponível em formatos legíveis pelo GIS, tornando-a útil como suporte para decisões de planejamento urbano.Fil: Revollo Sarmiento, Natalia Veronica. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Revollo Sarmiento, Gisela Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Huamantinco Cisneros, María Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; ArgentinaFil: Delrieux, Claudio Augusto. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Piccolo, Maria Cintia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; Argentin

Investigation of techniques for inventorying forested regions. Volume 2: Forestry information system requirements and joint use of remotely sensed and ancillary data

The author has identified the following significant results. Effects of terrain topography in mountainous forested regions on LANDSAT signals and classifier training were found to be significant. The aspect of sloping terrain relative to the sun's azimuth was the major cause of variability. A relative insolation factor could be defined which, in a single variable, represents the joint effects of slope and aspect and solar geometry on irradiance. Forest canopy reflectances were bound, both through simulation, and empirically, to have nondiffuse reflectance characteristics. Training procedures could be improved by stratifying in the space of ancillary variables and training in each stratum. Application of the Tasselled-Cap transformation for LANDSAT data acquired over forested terrain could provide a viable technique for data compression and convenient physical interpretations

Legal Challenges and Market Rewards to the Use and Acceptance of Remote Sensing and Digital Information as Evidence

Bakgrund I den nutida forskningen är det essentiellt att företag tar hänsyn till medarbetarnas motivation så att de gynnas av det arbetssätt som tillämpas. En arbetsmetod som blivit allt vanligare är konceptet Lean som ursprungligen kommer från den japanska bilindustrin. Lean har idag utvecklats till ett allmängiltigt koncept som tillämpas i flertalet branscher världen över. Trots att konceptet innebär flertalet positiva aspekter har det fått utstå stark kritik när det kommer till de mänskliga aspekterna och forskare har ställt sig frågan om Lean är "Mean". Kritiken härleds främst till medarbetares arbetsmiljö i form av stress och brist på variation, självbestämmande, hälsa och välmående. Få empiriska studier har däremot genomförts som undersöker konsekvenserna som Lean får på medarbetares upplevda motivation. Syfte Vårt syfte är att undersöka och öka förståelsen för medarbetares upplevelser av motivationen i företag som tillämpar Lean. Vidare har studien för avsikt att utreda om det föreligger en paradox mellan Lean och vad som motiverar medarbetare på en arbetsplats. Metod Studien har utgått från en kvalitativ metod via intervjuer. För att göra en djupare undersökning och analysera hur vårt fenomen, motivation, upplevs i en kontext med Lean tillämpade vi Små-N-studier. Vi har även haft en iterativ forskningsansats som förenat den deduktiva och induktiva ansatsen där studien pendlat mellan teorier och empiriska observationer fram tills det slutgiltiga resultatet. Slutsatser Utefter medarbetarnas upplevelser har vi identifierat att det inte föreligger någon paradox mellan Lean och motivation eftersom övervägande antal medarbetare upplevde att de är motiverade även om företaget tillämpar Lean. Dock har studien kunnat urskilja både stödjande och motverkande faktorer när det kommer till medarbetarnas upplevda arbetsförhållanden som i sin tur inverkar på motivationen. De motverkande faktorerna menar vi främst beror på att arbetsförhållandena i somliga fall innehåller höga prestationskrav, målstyrning samt standardiseringar. Vidare upplevs motivationen överlag som mer positiv när företagen använder en mjukare form av Lean där samtliga medlemmars intressen beaktas.Background In modern research, it is essential that companies consider employees’ motivation so that they benefit from the applied practices. A working method that has become increasingly common is the concept Lean, which has its origin in the Japanese automotive industry. Today, Lean has evolved into a universal concept that is applied in many industries worldwide. Although the concept involves numerous positive aspects it has endured strong criticism when it comes to the human aspects and researchers have raised the question if Lean is "Mean". Criticism is derived primarily to employees’ working conditions in terms of stress and lack, variation, autonomy, health and wellbeing. However, few empirical studies have been carried out that examines the impact that Lean has on employees’ experienced motivation. Aim The aim is to increase the understanding of employees’ experienced motivation in companies that practice Lean. Further on the study has the intention to investigate if there is a paradox between Lean and what motivates employees on work. Methodology The study has been conducted through a qualitative method by interviews and to be able to do a deeper examination and analyze how our phenomenon, motivation, is experienced in a Lean context we applied small-N-studies. Our strategy has been iterative, combining both a deductive and inductive approach, where the study has varied between theories and empirical observations until the final result. Conclusions We have identified that there is no paradox between Lean and motivation since the majority of employees’ experienced that they are motivated even though the company practice Lean. Nevertheless the study shows that there are both supportive and counteractive factors when it comes to the employees’ experienced working conditions. The counteractive factors consists foremost of high performance standards, goal steering and standardizations, and have in some cases a negative influence on the working conditions. Furthermore the experienced motivation is more positive overall when the companies use a softer form of Lean where all the members’ interests are taken into account

Accurate and automatic NOAA-AVHRR image navigation using a global contour matching approach

The problem of precise and automatic AVHRR image navigation is tractable in theory, but has proved to be somewhat difficult in practice. The authors' work has been motivated by the need for a fully automatic and operational navigation system capable of geo-referencing NOAA-AVHRR images with high accuracy and without operator supervision. The proposed method is based on the simultaneous use of an orbital model and a contour matching approach. This last process, relying on an affine transformation model, is used to correct the errors caused by inaccuracies in orbit modeling, nonzero value for the spacecraft's roll, pitch and yaw, errors due to inaccuracies in the satellite positioning and failures in the satellite internal clock. The automatic global contour matching process is summarized as follows: i) Estimation of the gradient energy map (edges) in the sensed image and detection of the cloudless (reliable) areas in this map. ii) Initialization of the affine model parameters by minimizing the Euclidean distance between the reference and sensed images objects. iii) Simultaneous optimization of all reference image contours on the sensed image by energy minimization in the domain of the global transformation parameters. The process is iterated in a hierarchical way, reducing the parameter searching space at each iteration. The proposed image navigation algorithm has proved to be capable of geo-referencing a satellite image within 1 pixel.Peer ReviewedPostprint (published version

Improved image classification with neural networks by fusing multispectral signatures with topological data

Automated schemes are needed to classify multispectral remotely sensed data. Human intelligence is often required to correctly interpret images from satellites and aircraft. Humans suceed because they use various types of cues about a scene to accurately define the contents of the image. Consequently, it follows that computer techniques that integrate and use different types of information would perform better than single source approaches. This research illustrated that multispectral signatures and topographical information could be used in concert. Significantly, this dual source tactic classified a remotely sensed image better than the multispectral classification alone. These classifications were accomplished by fusing spectral signatures with topographical information using neural network technology. A neural network was trained to classify Landsat mulitspectral signatures. A file of georeferenced ground truth classifications were used as the training criterion. The network was trained to classify urban, agriculture, range, and forest with an accuracy of 65.7 percent. Another neural network was programmed and trained to fuse these multispectral signature results with a file of georeferenced altitude data. This topological file contained 10 levels of elevations. When this nonspectral elevation information was fused with the spectral signatures, the classifications were improved to 73.7 and 75.7 percent

Remote sensing information sciences research group

Research conducted under this grant was used to extend and expand existing remote sensing activities at the University of California, Santa Barbara in the areas of georeferenced information systems, matching assisted information extraction from image data and large spatial data bases, artificial intelligence, and vegetation analysis and modeling. The research thrusts during the past year are summarized. The projects are discussed in some detail

Multispectral Resource Sampler (MPS): Proof of Concept. Literature survey of atmospheric corrections

Work done in combining spectral bands to reduce atmospheric effects on spectral signatures is described. The development of atmospheric models and their use with ground and aerial measurements in correcting spectral signatures is reviewed. An overview of studies of atmospheric effects on the accuracy of scene classification is provided