Sensoriamento remoto aplicado ao estudo de densidade demográfica

Santos, Alex Mota dos; Nunes, Fabrízia Gioppo; Santos, Rodrigo Lima

doi:10.4322/mp.978-65-991393-8-3.c1

Cited by 5 publications

(11 citation statements)

References 15 publications

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“…The Amazon Coastal Zone (ACZ) is marked by high temperatures (above 20°C) with low annual thermal variation, high precipitation rate (above 2,000 mm/year) and cloudiness (Nittrouer, Brunskill and Figueiredo 1995;Santos 2006). It comprises the coastal strip from the Orinoco Delta, in Venezuela, to the São Marcos Bay, in Maranhão, and presents several humid environments, such as beaches, tidal plains, salt and sweet marshes, estuaries, mangroves, lowland and upland forests, lagoons, and islands (Figure 1B;Souza Filho et al 2005;Santos 2006). The study area is located in Brazil, at the southern portion of the Amapá State coast, in the city of Mazagão (Figure 1A).…”

Section: Study Areamentioning

confidence: 99%

“…In Amapá State, the coastal zone extends for about 350 km from the mouth of the Amazon River towards the sea. Besides being located in the largest equatorial forest on the planet, it receives direct influence from the discharge of the Amazon River and its main tributaries, and is conditioned to a regime of strong currents and macro-tides with amplitudes of up to 12 m (Santos 2006;Santos et al 2008;Pereira et al 2009). Its coastal plain comprises a range of Quaternary sediments of fluvial, fluvio-lacustrine and fluvio-marine environments, and an extensive range of plateaus (Silveira 1998;Silveira and Santos 2006).…”

Section: Study Areamentioning

confidence: 99%

“…The dominant vegetation classes are lowland forests, upland forests, herbaceous and shrub lands, and savanna (Rabelo 2008). The lowland forests are located on the banks of the Amazon River estuary and inside of the rivers that drain the coastal plain, being subject to a daily cycle of floods and ebbs related to the tides, with species that reach up to 20 m in height (Santos 2006). The upland forest is characterized by higher structure species (above 25 m), associated with low plateau and submontane environments (Rabelo 2008).…”

Section: Study Areamentioning

confidence: 99%

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Challenges of relief modeling in flat areas: a case study in the Amazon coast floodplains

Reis,

Polidori

2024

Bol. Ciênc. Geod.

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This study aimed to evaluate the performance of eight digital elevation models (DEMs), i.e., one digital terrain model (DTM) obtained from airborne P-band radar (BCDCA DTM), one digital surface model (DSM) obtained from airborne X-band radar (BCDCA DSM) and six DSMs from orbital sensors (AW3D30, ASTER GDEM, Copernicus DEM, NASADEM, SRTM, Topodata), for the morphological characterization of the floodplains of Amapá (Brazil). All DEMs were resampled to the same mesh size and compared by visual and statistical analysis in terms of elevation and slope. The comparison demonstrated that the DTM obtained from P-band radar images was the most consistent one in representing the landforms, as it is less sensitive to vegetation. The behavior of the automated hydrographic network extraction was also analyzed, showing that even the DTM was not able to detect drainage lines across flat landscapes with centimeter elevation variations. As the comparisons were made with a common 30 m grid, the conclusions are limited to this scale and the effect of a change of scale is discussed. In view of the difficulty of automatically extracting the network in a plain, the possibility to reduce the modelling to a 2D approach, based on external hydrographic data, is also discussed.

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Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

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Challenges of relief modeling in flat areas: a case study in the Amazon coast floodplains

Reis,

Polidori

2024

Bol. Ciênc. Geod.

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“…These cells were used as reference units to aggregate data from various sources. The cell space was created from the IBGE statistical grid [71] using a cell size commonly used to identify urban patterns [72][73][74][75]. We decided to use a cellular grid because this spatial structure can be easily generated for any study area and is the most suitable spatial unit to integrate data from different formats and sources.…”

Section: Creating and Integrating Variables Into Cell Gridsmentioning

confidence: 99%

Identifying Urban and Socio-Environmental Patterns of Brazilian Amazonian Cities by Remote Sensing and Machine Learning

et al. 2023

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Identifying urban patterns in the cities in the Brazilian Amazon can help to understand the impact of human actions on the environment, to protect local cultures, and secure the cultural heritage of the region. The objective of this study is to produce a classification of intra-urban patterns in Amazonian cities. Concretely, we produce a set of Urban and Socio-Environmental Patterns (USEPs) in the cities of Santarém and Cametá in Pará, Brazilian Amazon. The contributions of this study are as follows: (1) we use a reproducible research framework based on remote sensing data and machine learning techniques; (2) we integrate spatial data from various sources into a cellular grid, separating the variables into environmental, urban morphological, and socioeconomic dimensions; (3) we generate variables specific to the Amazonian context; and (4) we validate these variables by means of a field visit to Cametá and comparison with patterns described in other works. Machine learning-based clustering is useful to identify seven urban patterns in Santarém and eight urban patterns in Cametá. The urban patterns are semantically explainable and are consistent with the existing scientific literature. The paper provides reproducible and open research that uses only open software and publicly available data sources, making the data product and code available for modification and further contributions to spatial data science analysis.

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“…Os softwares de SIG vem ganhando destaque no cenário das pesquisas cientifica tendo diversos pesquisadores utilizando suas ferramentas em seus estudos. Facilitam a análise de dados ambientais através de: visualização, localização, condição, predição e medição (PARANHOS FILHO, 2010). O SIG tem um bom custo-benefício otimizando os resultados e diminuindo o tempo gasto na produção do trabalho.…”

Section: Introductionunclassified

Aplicação Das Geotecnologias Na Análise Ambiental Do Potencial Erosivo Do Município De Bataguassu/MS

Vieira¹

2021

Ágora

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O homem transforma constantemente a paisagem, em escala e tempo variados, de acordo com suas necessidades e interesses. Estas alterações, ocasionalmente, podem promover degradações ambientais, como os processos erosivos. Neste contexto, este trabalho tem como finalidade analisar o potencial erosivo no município de Bataguassu/MS, através da análise dos aspectos físicos (pluviosidade, solos, declividade e geologia) do meio ambiente. O município localiza-se na microrregião de Nova Andradina/MS, na região leste do estado de Mato Grosso do Sul. Para atingir o objetivo proposto o trabalho apoia-se nos seguintes materiais e procedimentos metodológicos: dados referentes aos aspectos físicos de Bataguassu/MS; uso das geotecnologias, destaque para o software de SIG (Sistema de Informação Geográfica) o ArcGIS modelo 10.3; elaboração de mapas temáticos dos aspectos físicos e sua integração para elaboração do mapa de potencial erosivo, seguindo adaptação metodológica do Ross (1994). O mapa de potencial erosivo foi dividido em classes temáticas (muito fraca; fraca; média; forte; muito forte), as quais evidenciam os diferentes níveis de susceptibilidade a erosão do município de Bataguassu/MS. Assim, acredita-se que a identificação dos locais de potencial erosivo pode auxiliar na definição das diretrizes a serem implementadas e nortear o planejamento ambiental do município.

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Sensoriamento remoto aplicado ao estudo de densidade demográfica

Cited by 5 publications

References 15 publications

Challenges of relief modeling in flat areas: a case study in the Amazon coast floodplains

Challenges of relief modeling in flat areas: a case study in the Amazon coast floodplains

Identifying Urban and Socio-Environmental Patterns of Brazilian Amazonian Cities by Remote Sensing and Machine Learning

Aplicação Das Geotecnologias Na Análise Ambiental Do Potencial Erosivo Do Município De Bataguassu/MS

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