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

Evaluating Vascular Plant Composition and Species Richness on Horn Island, Mississippi, Using Passive and Active Remote Sensing in Conjunction with Ground Based Measurements

Barrier island vegetation is subjected to chronic abiotic stressors combined with periodic storm events that favor species adapted to harsh environments. These islands are the first landforms to be affected by changes in coastal subsidence and sea-level rise. Evaluating changes in vegetation is important for understanding the impact of global climate change on coastal environments. This study assesses vegetation composition and plant species richness on Horn Island, Mississippi using ground data in conjunction with remotely sensed spectral and LIDAR data. The goals of this research are to: 1) classify and map vegetation composition on Horn Island using hyperspectral and LIDAR data, 2) evaluate changes in vegetation composition through comparison with a vegetation study and classification map from 1979, 3) determine the extent to which vascular plant species richness might be estimated using remotely sensed spectral reflectance indices and spatial variability within these indices, and 4) utilize the vertical distribution of airborne multiple-return LIDAR data to evaluate vascular plant species richness. The vegetation composition of habitat-types on Horn Island can be identified by indicator species that are consistent both over time and among other barrier islands. Additionally, combining airborne hyperspectral and LIDAR data improved the overall classification accuracy of habitats. Although only broad comparisons in vegetation changes could be made between this study and previous maps, these changes were linked with geomorphological changes. In simple linear regressions, various reflectance- and LIDAR-indices correlated significantly (p \u3c 0.05) with richness when habitat-types were considered individually. Regressions of richness with indices derived from within-transect means or spatial variability in reflectance, reflectance band ratios, as well as vertical distribution descriptors and height percentiles from LIDAR data produced estimation errors of 0.4-2.5 species per transect. Best-fit indices from hyperspectral data indicate spectral bands in the near- and mid-infrared spectra are most important in the estimation of plant species richness while LIDAR indices indicate the importance of vegetation height and structural complexity in estimating plant species richness. The capability of utilizing remotely sensed data to classify vegetation composition and estimate species richness provides a promising means of assessing and monitoring vegetation on barrier islands

Spatio-temporal variability in dune plant communities using UAV and multispectral data

O mapeamento da vegetação, através da identificação do tipo e distribuição das comunidades e espécies vegetais, é crucial para analisar a cobertura vegetal e os padrões espaciais. A compreensão das variabilidades espaciais e temporais das plantas dunares em ligação com a morfodinâmica permite uma maior compreensão do dinamismo e evolução dos ambientes costeiros. Tal análise pode contribuir para o desenvolvimento de planos de gestão costeira que ajudam a implementar a biodiversidade costeira e estratégias de protecção. Esta dissertação apresenta uma abordagem para avaliar a utilização de imagens multiespectrais e explorar a variabilidade da vegetação dunar costeira com dados recolhidos à distância por um Veículo Aéreo Não Tripulado (UAV). Foram escolhidas quatro zonas de estudo diferentes na parte oriental da Península de Ancao, distribuídas alongshore, e cobrindo a backhore e a crista das dunas até à base do lee das dunas. Foram utilizados dados de campo e de UAV, em diferentes épocas, nomeadamente ao longo de um período de dois anos. Foi utilizada uma abordagem de classificação em duas etapas, baseada num índice de vegetação de diferença normalizada e num classificador de Floresta Aleatória. Os resultados mostram desempenhos de classificação de alta precisão ao condensar a cobertura do solo em menos classes e também em áreas menos densamente vegetativas. As classificações resultantes foram posteriormente processadas em termos de alterações transfronteiriças e alterações sazonais. Estas técnicas mostram um elevado potencial futuro para avaliar a vegetação das áreas de dunas costeiras e para apoiar a gestão costeira.The mapping of vegetation, by identifying the type and distribution of plant communities and species, is crucial for analysing vegetation coverage and spatial patterns. Understanding dune plant spatial and temporal variabilities in connection with morphodynamics gives further insight in dynamism and evolution of coastal environments. Such analysis can contribute to the development of coastal management plans that helps to implement coastal biodiversity and protection strategies. This dissertation presents an approach to assess the use of multispectral imagery and explore the variability of coastal dune vegetation with remotely sensed data collected by an Unmanned Aerial Vehicle (UAV). Four different study zones were chosen at the eastern part of the Ancao Peninsula, distributed alongshore, and covering the backshore and the dune crest until the base of the dune lee. Field and UAV data were used, in different seasons namely over an extend of two years. A two-step classification approach, based on a normalized difference vegetation index and Random Forest classifier, was used. The Results show high accuracy classification performances when condensing the groundcover into fewer classes and also in less densely vegetated areas. Resulting classifications were further processed in terms of cross-shore changes and seasonal changes. These technics show a high future potential to assess the vegetation of coastal dune areas and to support coastal management

Remote sensing of coastal vegetation in the Netherlands and Belgium

Vegetation maps are frequently used in conservation planning and evaluation. Monitoring commitments, a.o. in relation to the European Habitat Directive, increase the need for efficient mapping tools. This paper explores methods of vegetation mapping with particular attention to automated classification of remotely sensed images. Characteristics of two main types of imagery are discussed, very high spatial resolution false colour images on the one hand and hyperspectral images on the other. The first type has proved its qualities for mapping of - mainly - vegetation structure in dunes and salt marshes. Hyperspectral imagery enables thematic detail but encounters more technical problems

Remote Sensing of the Ecosystem Impact of Invasive Alien Plant Species

Invasive Pflanzenarten können Ökosysteme durch Beeinflussung von einheimischen Pflanzengesellschaften und Ökosystemprozessen verändern. Solche Ökosystemauswirkungen wurden mit Hilfe von Experimenten oder Feldaufnahmen umfassend untersucht. Großflächige Auswirkungen, zum Beispiel auf Habitat- oder Landschaftsebene wurden bisher jedoch kaum untersucht. Mit Hilfe von Fernerkundung können räumlich explizite Informationen über die Verteilung von Arten und Ökosystemeigenschaften erfasst werden und somit die Lücke in der Erforschung der großflächigen Auswirkungen invasiver Arten geschlossen werden. Bisher wurde Fernerkundung vor allem zur Kartierung von Vorkommen invasiver Pflanzenarten eingesetzt, jedoch nur selten zur Abschätzung ihrer Auswirkungen. Diese Arbeit zielt darauf ab, das Potential der Fernerkundung für die Bewertung von Ökosystemauswirkungen invasiver Pflanzenarten zu analysieren. Zu diesem Zweck wurden drei Forschungsarbeiten angefertigt, die verschiedene Aspekte dieses Potenzials beleuchten: (1) Die Ermittlung von Vegetationseigenschaften in von Invasionen betroffenen Ökosystemen, (2) die Analyse von Auswirkungen invasiver Arten auf unterschiedlichen räumlichen Skalen und (3) eine räumlich explizite Darstellung von Ökosystemauswirkungen invasiver Pflanzenarten. Die erste Studie beschäftigt sich mit der Kartierung von Blattstickstoff (N) und -phosphorgehalten (P) in einem Laubmischwald mit Vorkommen der frühblühenden Traubenkirsche (Prunus serotina Ehrh.). Für die Kartierung wurden hyperspektrale und Laserscanning (LiDAR) Daten kombiniert. Die Studie ergab, dass die Bestimmung von N und P aus hyperspektalen Fernerkundungdaten in Baumkronen mit hoher struktureller Heterogenität erschwert wird. Allerdings konnte auch ein Zusammenhang zwischen chemischer Zusammensetzung und der Struktur des Kronendaches festgestellt werden. So konnten die von LiDAR-Daten abgeleiteten Strukturinformationen genutzt werden, um die Vorhersagen von N und P zu verbessern. In der zweiten Studie wurden aus Fernerkundungsdaten erstellte Karten von Ökosystemeigenschaften genutzt, um Gebiete mit und ohne P. serotina zu vergleichen. Die Karten umfassten N und P, sowie das N:P-Verhältnis von Blättern, das Holzvolumen und den Blattflächenindex (LAI). Es wurden sowohl Unterschiede in den Werten von Blattinhaltsstoffen als auch in der Waldstruktur für Standorte mit und ohne P. serotina festgestellt. Diese Unterschiede waren auch auf Bestandsebene erkennbar, wenn auch in geringem Maße. In der dritten Studie wurden hyperspektrale Luftbilder verwendet um die prozentuale Deckung des Kaktusmooses (Campylopus introflexus (Hedw.) Brid.) in einem Dünenökosystem großflächig zu kartieren. Darüber hinaus wurde der Zusammenhang zwischen dem Deckungsgrad von C. introflexus und der Artenvielfalt von Pflanzen untersucht. In Kombination wurden diese Ergebnisse verwendet, um potenzielle Bereiche mit hohen Auswirkungen zu kennzeichnen. Basierend auf diesen drei Studien wurden in dieser Arbeit zwei grundlegende methodische Ansätze zur Analyse von Ökosystemauswirkungen invasiver Pflanzenarten per Fernerkundung identifiziert und angewandt. Der erste Ansatz besteht darin, mit Hilfe von Fernerkundung erstellte Karten von Ökosystemeigenschaften zu verwenden, um diese Eigenschaften in Abhängigkeit des Vorkommens invasiver Arten auszuwerten. Wie gezeigt werden konnte, ist dies auch für große Flächen, beispielsweise auf der Habitat- oder Landschaftsebene, möglich. Somit kann Fernerkundung zu einem besseren Verständnis der Auswirkungen von invasiven Arten beitragen. Der zweite Ansatz basiert auf der Kartierung von Abundanzen invasiver Pflanzenarten. Diese können als Indikator für die Stärke der Auswirkungen genutzt werden. Die resultierenden Karten können verwendet werden, um Bereiche mit hohen Auswirkungen zu identifizieren. Darüber hinaus ermöglicht dieser zweite Ansatz den Vergleich der Auswirkungen zwischen verschiedenen Arten oder Lebensraumtypen und kann somit wertvolle Informationen für Managemententscheidungen liefern. Da die Ableitung vieler Ökosystemeigenschaften aus Fernerkundungsdaten nach wie vor eine Herausforderung darstellt, sollte die zukünftige Forschung darauf abzielen, die Zusammenhänge zwischen den Eigenschaften und der Reflektanz der Vegetation besser zu verstehen. Dies ist eine wesentliche Voraussetzung für eine zuverlässige Vorhersage über verschiedene Lebensräume hinweg. Zukünftige Fernerkundungsstudien, mit dem Ziel invasive Arten zu kartieren, sollten sich auf die Vorhersage von Deckungsgraden konzentrieren. Darüber hinaus sind generalisierte Verfahren wünschenswert, die eine erfolgreiche Identifizierung von Arten unter verschiedenen ökologischen Gegebenheiten gewährleisten. Nicht zuletzt sollte diese Arbeit Invasionsökologen ermutigen, existierende Fernerkundungsprodukte häufiger zu verwenden, um großflächige Auswirkungen von invasiven Pflanzenarten auf Ökosysteme zu analysieren

FINE SCALE MAPPING OF LAURENTIAN MIXED FOREST NATURAL HABITAT COMMUNITIES USING MULTISPECTRAL NAIP AND UAV DATASETS COMBINED WITH MACHINE LEARNING METHODS

Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment where the acquisition of ground control points (GCPs) is extremely difficult. Statistical feature selection methods such as Joint Mutual Information Maximization (JMIM) which is not that widely used in the natural resource field and variable importance (varImp) were used to discriminate spectrally similar habitat communities. A comprehensive approach to training set delineation was implemented including the use of Principal Components Analysis (PCA), Independent Components Analysis (ICA), soils data, and expert image interpretation. The developed approach resulted in robust training sets to delineate and accurately map natural community habitats. Three ML algorithms were implemented Random Forest (RF), Support Vector Machine (SVM), and Averaged Neural Network (avNNet). RF outperformed SVM and avNNet. Overall RF accuracies across the three study sites ranged from 79.45-87.74% for NAIP and 87.31-93.74% for the UAV datasets. Different ancillary datasets including spectral enhancement and image transformation techniques (PCA and ICA), GLCM-Texture, spectral indices, and topography features (elevation, slope, and aspect) were evaluated using the JMIM and varImp feature selection methods, overall accuracy assessment, and kappa calculations. The robustness of the workflow was evaluated with three study sites which are geomorphologically unique and contain different natural habitat communities. This integrated approach is recommended for accurate natural habitat community classification in ecologically complex landscapes

Exploring the utility of the additional WorldView-2 bands and support vector machines in mapping land use/land cover in a fragmented ecosystem, South Africa

Land use/land cover (LULC) classification is a key research field in environmental applications of remote  sensing on the earthfs surface. The advent of new high resolution multispectral sensors with unique bands has  provided an opportunity to map the spatial distribution of detailed LULC classes over a large fragmented area. The objectives of the present study were: (1) to map LULC classes using multispectral WorldView-2 (WV-2) data and SVM in a fragmented ecosystem; and (2) to compare the accuracy of three WV-2 spectral data sets in distinguishing amongst various LULC classes in a fragmented ecosystem. WV-2 image was spectrally  resized to its four standard bands (SB: blue, green, red and near infrared-1) and four strategically located  bands (AB: coastal blue, yellow, red edge and near infrared-2). WV-2 image (8bands: 8B) together with SB and AB subsets were used to classify LULC using support vector machines. Overall classification accuracies of 78.0% (total disagreement = 22.0%) for 8B, 51.0% (total disagreement = 49.0%) for SB, and 64.0% (total disagreement = 36.0%) for AB were achieved. There were significant differences between the performance of all WV-2 subset pair comparisons (8B versus SB, 8B versus AB and SB versus AB) as demonstrated by the results of McNemarfs test (Z score .1.96). This study concludes that WV-2 multispectral data and the SVM classifier have the potential to map LULC classes in a fragmented ecosystem. The study also offers relatively accurate information that is important for the indigenous forest managers in KwaZulu-Natal, South Africa for making informed decisions regarding conservation and management of LULC patterns.Keywords: land use/cover classification, fragmented ecosystem, WorldView-2, support vector  machines