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The title and author names are listed on this sheet as they will be published, both on your paper and on the Table of Contents. Please review and ensure the information is correct and advise us if any changes need to be made. In addition, please review your paper as a whole for typographical and essential corrections.Your PDF proof has been enabled so that you can comment on the proof directly using Adobe Acrobat. For further information on marking corrections using Acrobat, please visit http://journalauthors.tandf.co.uk/production/acrobat.asp; https://authorservices.taylorandfrancis.com/how-to-correct-proofs-with-adobe/ The CrossRef database (www.crossref.org/) has been used to validate the references. AUTHOR QUERIES Q1'This procedure decomposed the annual variation of the EVI and are represented by the mean (H0), by the phase that indicates the cycle of the culture in intervals of 0 -360 , and by the Amplitude (H1) that indicates the maximum vegetation variation for the entire period.' The part of this sentence in bold is unclear. Could author please clarify and reword. Q2There is no mention of (Bouckaert et al. 2010, IBGE -Instituto Brasileiro de Geografia e Estat ıstica 2016) in the text. Please insert a citation in the text or delete the reference as appropriate. Q3Please provide the publisher location "Coutinho et al. 2012". Q4Please provide the publisher location for "K€ oppen and Geiger 1928".

Resumo:Técnicas de Sensoriamento Remoto tem ganhado especial interesse, uma vez que podem ser utilizadas para o monitoramento de sistemas e fenômenos em escala local ou global, de maneira contínua temporal e espacialmente. Redes Neurais Artificias estão entre os métodos que são capazes de trabalhar com grande quantidade de dados, com diversas características e sofrer pouca influência de ruídos. Desta forma, fez-se o uso de Redes Neurais Artificiais com o propósito de classificar dados de sensoriamento remoto. Utilizou-se de dados de alta resolução espacial, como imagens espectrais de aerolevantamento e dados altimétricos Laser Scanner, para a classificação do alvo "árvores". Com isso, gerou-se RNA especialistas na detecção destes alvos. Os dados utilizados são de uma área densamente urbanizada, onde existe grande variabilidade de cotas e características espectrais. Os resultados mostraram que a classificação utilizando dados espectrais e altimétricos resultaram em melhores classificações, do que a utilização apenas de informações espectrais. Testou-se também a influência do tamanho das amostras de treinamento das Redes Neurais Artificiais, gerando assim uma "curva de aprendizado" das RNA. Percebeu-se que conforme se aumenta o tamanho das amostras de treinamento, existe uma tendência em aumentar a acurácia na classificação dos dados. Os acertos globais foram superiores a 87,5% quando utilizando apenas informação espectral e 97,5% quando utilizando dados espectrais e altimétricos.Palavra Chave: Sensoriamento Remoto, Classificação Digital de Imagens, Redes Neurais Artificiais, Imagens de Alta Resolução Espacial, Laser Scanner. Abstract:Remote Sensing techniques has gained special interest, since it can be used for monitoring systems and phenomena in local or global scale, in a temporally and spatially continuous way. Artificial Neural Networks are able to work with large amounts of data, with different

Recent technological advancements in many areas have changed the way that individuals interact with the world. Some daily tasks require visualization skills, especially when in a map-reading context. Augmented Reality systems could provide substantial improvement to geovisualization once it enhances a real scene with virtual information. However, relatively little research has worked on assessing the effective contribution of such systems during map reading. So, this research aims to provide a first look into the usability of an Augmented Reality system prototype for interaction with geoinformation. For this purpose, we have designed an activity with volunteers in order to assess the system prototype usability. We have interviewed 14 users (three experts and 11 non-experts), where experts were subjects with the following characteristics: a professor; with a PhD degree in Cartography, GIS, Geography, or Environmental Sciences/Water Resources; and with experience treating spatial information related to water resources. The activity aimed to detect where the system really helps the user to interpret a hydrographic map and how the users were helped by the Augmented Reality system prototype. We may conclude that the Augmented Reality system was helpful to the users during the map reading, as well as allowing the construction of spatial knowledge within the proposed scenario.