Selection of imagery data and classifiers for mapping Brazilian semideciduous Atlantic forests

Carvalho, Luís Marcelo Tavares de; Clevers, J.G.P.W.; Skidmore, Andrew K.; Jong, S.M. de

doi:10.1016/j.jag.2004.02.002

Cited by 31 publications

(18 citation statements)

References 50 publications

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“…These characteristics led to a search for alternative features to enable the discrimination of land cover classes with similar reflectance behaviour (Carvalho et al 2004). Phenological timing is an example of such features, which have the potential to reduce mapping errors.…”

Section: Ndvi Time Series For Mapping Phenological Variability Of Formentioning

confidence: 99%

NDVI Time Series for Mapping Phenological Variability of Forests Across the Cerrado Biome in Minas Gerais, Brazil

Oliveira¹,

Carvalho²,

Oliveira³

et al. 2012

Phenology and Climate Change

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Section: Ndvi Time Series For Mapping Phenological Variability Of Formentioning

confidence: 99%

NDVI Time Series for Mapping Phenological Variability of Forests Across the Cerrado Biome in Minas Gerais, Brazil

Oliveira¹,

Carvalho²,

Oliveira³

et al. 2012

Phenology and Climate Change

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“…To determine the location of patches meeting minimum area requirements, we conducted a supervised classification using the maximum likelihood algorithm [37] in ENVI ver. The range boundary to the west is based on a minimum convex polygon created from all historical past published registries of the species [40] while the Atlantic Ocean serves as the principal range boundary to the east.…”

Section: Analysis Of the Landscapementioning

confidence: 99%

Identifying Important Forest Patches for the Long-Term Persistence of the Endangered Golden-Headed Lion Tamarin (Leontopithecus Chrysomelas)

Zeigler

Fagan

DeFries

et al. 2010

Tropical Conservation Science

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As habitat for the golden-headed lion tamarin (GHLT; Leontopithecus chrysomelas) in Brazil's Atlantic forest becomes smaller and more fragmented, remaining large forest patches may be critical to the persistence of the species. The objectives of our study were to identify the forest patch size that could support a viable population of GHLTs under a range of risk scenarios and to locate patches meeting these size requirements. We found the self-sustaining minimum viable population (MVP) size of GHLTs using the simulation program Vortex under a baseline model and under several anthropogenic disturbance models. We multiplied the MVP size determined in each model scenario by low, medium, and high GHLT population densities to estimate a minimum area requirement. We then used a forest cover map derived through a supervised classification of 2004-2008 Landsat 5TM imagery to locate forest patches meeting the range of minimum area requirements. We found that the MVP size of GHLTs is 70-960 individuals, requiring a forest patch size of 700-18,113 ha depending on the risk level or scenario considered. We found one forest patch that could support a genetically viable, selfsustaining population of GHLTs under the highest level of risk. However, only one federally protected reserve known to currently support GHLTs exists within the range of the species while continuing deforestation, land conversion, and construction projects are real and major threats to the remaining GHLT habitat. Research into the quality and occupancy of the largest patches highlighted here as well as additional protection of habitat needs to be a priority for GHLT conservation.

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“…These drawbacks led to a search for alternative attributes to enable the discrimination of land cover classes with similar reflectance behavior (CARVALHO et al, 2004).…”

Section: Oliveira T C De a Et Almentioning

confidence: 99%

Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks

Oliveira

Carvalho

Oliveira

et al. 2010

CERNE

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Multi-temporal images are now of standard use in remote sensing of vegetation during monitoring and classification. Temporal vegetation signatures (i. e., vegetation indices as functions of time) generated, poses many challenges, primarily due to signal to noise-related issues. This study investigates which methods generate the most appropriate smoothed curves of vegetation signatures on MODIS NDVI time series. The filtering techniques compared were the HANTS algorithm which is based on Fourier analyses and Wavelet temporal algorithm which uses the wavelet analysis to generate the smoothed curves. The study was conducted in four different regions of the Minas Gerais State. The smoothed data were used as input data vectors for vegetation classification by means of artificial neural networks for comparison purpose. A comparison of the results was ultimately discussed in this work showing encouraging results and similarity between the two filtering techniques used.Key words: Remote sensing, signal processing, time series, wavelets analysis, Fourier. MAPAMENTO DE FLORESTAS DECÍCUAS ATRAVÉS DE REDES NEURAIS ARTIFICIAIS E SÉRIES TEMPORAIS DE NDVI MODIS RESUMO: Imagens multitemporais são de pronominal uso no Sensoriamento Remoto, para o monitoramento e classificação da vegetação. As decorrentes assinaturas temporais da vegetação possuem muitos desafios na sua utilização em razão da elevada relação sinal/ruído. Este estudo investigou dois métodos para gerar assinaturas temporais suavizadas de vegetação do índice de vegetação de diferença normalizada (NDVI), sendo estas originadas do sensor MODIS. As técnicas de filtragem utilizadas foram o algoritmo baseado em Fourier HANTS e algoritmo Wavelet

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Selection of imagery data and classifiers for mapping Brazilian semideciduous Atlantic forests

Cited by 31 publications

References 50 publications

NDVI Time Series for Mapping Phenological Variability of Forests Across the Cerrado Biome in Minas Gerais, Brazil

NDVI Time Series for Mapping Phenological Variability of Forests Across the Cerrado Biome in Minas Gerais, Brazil

Identifying Important Forest Patches for the Long-Term Persistence of the Endangered Golden-Headed Lion Tamarin (Leontopithecus Chrysomelas)

Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks

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