Multitemporal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery was used to assess coastline morphological changes in southeastern Brazil. A spectral linear mixing approach (SLMA) was used to estimate fraction imagery representing amounts of vegetation, clean water (a proxy for shade) and soil. Fraction abundances were related to erosive and depositional features. Shoreline, sandy banks (including emerged and submerged banks) and sand spits were highlighted mainly by clean water and soil fraction imagery. To evaluate changes in the coastline geomorphic features, the fraction imagery generated for each data set was classified in a contextual approach using a segmentation technique and ISOSEG, an unsupervised classification. Evaluation of the classifications was performed visually and by an error matrix relating ground-truth data to classification results. Comparison of the classification results revealed an intense transformation in the coastline, and that erosive and depositional features are extremely dynamic and subject to change in short periods of time.

The growing intensity of impervious surface area (ISA) is one of the most striking effects of urban growth. The expansion of ISA gives rise to a set of changes on the physical environment, impacting the quality of life of the human population as well as the dynamics of fauna and flora. Hence, due to its importance, the present study aimed to examine the ISA distribution in the Metropolitan Region of São Paulo (MRSP), Brazil, using satellite imagery from the Landsat-8 Operational Land Imager (OLI) instrument. In contrast to other investigations that primarily focus on the accuracy of the estimate, the proposal of this study is—besides generating a robust estimate—to perform an integrated analysis of the impervious-surface distribution at pixel scale with the variability present in different territorial units, namely municipalities, sub-prefecture and districts. The importance of this study is that it strengthens the use of information related to impervious cover in the territorial planning, providing elements for a better understanding and connection with other spatial attributes. Reducing the dimensionality of the dataset (visible, near-infrared and short-wave infrared bands) by Karhune–Loeve analysis, the first three principal components (PCs) contained more than 99% of the information present in the original bands. Projecting PC1, PC2 and PC3 onto a series of two-dimensional (2D) scatterplots, four endmembers—Low Albedo (Dark), High Albedo (Substrate), Green Vegetation (GV) and Non-Photosynthetic Vegetation (NPV)—were visually selected to produce the unmixing estimates. The selected endmembers fitted the model well, as the propagated error was consistently low (root-mean-square error = 0.005) and the fraction estimates at pixel scale were found to be in accordance with the physical structures of the landscape. The impervious surface fraction (ISF) was calculated by adding the Dark and Substrate fraction imagery. Reconciling the ISF with reference samples revealed the estimates to be reliable (R² = 0.97), regardless of an underestimation error (~8% on average) having been found, mostly over areas with higher imperviousness rates. Intra-pixel variability was combined with the territorial units of analysis through a modification of the Lorenz curve, which permitted a straightforward comparison of ISF values at different reference scales. Good adherence was observed when the original 30-m ISF was compared to a resampled 300-m ISF, but with some differences, suggesting a systematic behavior with the degradation of pixel resolution tending to underestimate lower fractions and overestimate higher ones; furthermore, discrepancies were bridged with the increase of scale analysis. The analysis of the IFS model also revealed that, in the context of the MRSP, gross domestic product (GDP) has little potential for explaining the distribution of impervious areas on the municipality scale. Finally, the ISF model was found to be more sensitive in describing impervious surface response than other well-known indices, such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI).

A spectral linear-mixing model using Landsat ETM+ imagery was undertaken to estimate fraction images of green vegetation, soil and shade in an indigenous land area in the state of Mato Grosso in the central-western region of Brazil. The fraction images were used to classify different types of land use and vegetation cover. The fraction images were classified by the following two methods: (a) application of a segmentation based on the region-growing technique; and (b) grouping of the regions segmented using the per-region unsupervised classifier named ISOSEG. Adopting a 75% threshold, ISOSEG generated 44 clusters that were grouped into eight land-use and vegetation-cover classes. The mapping achieved an average accuracy of 83%, showing that the methodology is efficient in mapping areas of great land-use and vegetation-cover diversity, such as that found in the Brazilian cerrado (savanna).

Objetivo: Contextualizar o uso de ferramentas de gestão da Floresta Urbana para promoção da qualidade de vida maximizando os serviços ambientais que as árvores prestam para os citadinos.Metodologia: Apresentar o estado da arte do desenvolvimento de tecnologias de monitoramento fundamentadas em plataformas espaciais e terrestres para a gestão da floresta urbana, por meio da aplicação de métodos, técnicas e tecnologias de monitoramento, controle e automação de processos de levantamento e mapeamento, com vistas à tomada de decisão.Originalidade/Relevância: A relevância deste trabalho está na apresentação das diferentes ferramentas para gestão da floresta urbana de maneira atualizada, aliando os recursos tecnológicos às necessidades dos gestores públicos e, com isso, contribuindo para a qualidade de vida da população urbana.Resultados: Algumas ferramentas vêm ganhando notoriedade em decorrência das suas vantagens em captarem informações, como os Veículos Aéreos Não Tripulados (VANTs). Tecnologias de sensoriamento remoto tem se tornado ferramentas obrigatórias na gestão de cidades, no planejamento urbano e ambiental. A análise da vegetação na área urbana, distribuição e classificação efetuada por meio da extração do NDVI (Normalized Difference Vegetation Index) também é utilizada para caracterizar grandes áreas e localizar exemplares arbóreos com suspeitas de deficiência na saúde vegetal. Os sensores LiDAR executam varredura a laser propiciando análises topográficas e da cobertura florestal que possibilitam mapear, simultaneamente, o terreno sob as árvores e a sua altura estimada. As técnicas de sensoriamento remoto e Sistema de Informação Geográfica (SIG) são combinadas para mapear o uso e mudanças na cobertura da terra e medir a taxa de expansão urbana e perda de vegetação. Outras ferramentas computacionais promissoras são a Inteligência Artificial (IA) e o aprendizado de máquinas que se propõe a elaborar dispositivos que simulem a capacidade humana de raciocinar, perceber, tomar decisões e resolver problemas.Contribuições sociais / para a gestão: A utilização de ferramentas de gestão para a floresta urbana são recursos necessários e mais ágeis que podem auxiliar os gestores na tomada de decisão para a construção de uma cidade mais sustentável e inclusiva.

Abordagem GEOBIA para a análise da dinâmica do uso da terra e cobertura vegetal no município de Brotas-SP, Brasil