Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations

Detalhes bibliográficos
Autor(a) principal: Amaral, Cibele Hummel do
Data de Publicação: 2017
Outros Autores: Moura, Yhasmin Mendes de, Galvão, Lênio Soares, Hilker, Thomas, Wu, Jin, Saleska, Scott, Nelson, Bruce Walker, Lopes, Aline Pontes, Wiedeman, Kenia K., Prohaska, Neill, Oliveira, Raimundo Cosme de, Machado, Carolyne Bueno, Aragão, Luiz E. O. C.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: LOCUS Repositório Institucional da UFV
Texto Completo: https://doi.org/10.1016/j.isprsjprs.2017.07.006
http://www.locus.ufv.br/handle/123456789/22065
Resumo: The association between spectral reflectance and canopy processes remains challenging for quantifying large-scale canopy phenological cycles in tropical forests. In this study, we used a tower-mounted hyperspectral camera in an eastern Amazon forest to assess how canopy spectral signals of three species are linked with phenological processes in the 2012 dry season. We explored different approaches to disentangle the spectral components of canopy phenology processes and analyze their variations over time using 17 images acquired by the camera. The methods included linear spectral mixture analysis (SMA); principal component analysis (PCA); continuum removal (CR); and first-order derivative analysis. In addition, three vegetation indices potentially sensitive to leaf flushing, leaf loss and leaf area index (LAI) were calculated: the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and the entitled Green-Red Normalized Difference (GRND) index. We inspected also the consistency of the camera observations using Moderate Resolution Imaging Spectroradiometer (MODIS) and available phenological data on new leaf production and LAI of young, mature and old leaves simulated by a leaf demography-ontogeny model. The results showed a diversity of phenological responses during the 2012 dry season with related changes in canopy structure and greenness values. Because of the differences in timing and intensity of leaf flushing and leaf shedding, Erisma uncinatum, Manilkara huberi and Chamaecrista xinguensis presented different green vegetation (GV) and non-photosynthetic vegetation (NPV) SMA fractions; distinct PCA scores; changes in depth, width and area of the 681-nm chlorophyll absorption band; and variations over time in the EVI, GRND and NDVI. At the end of dry season, GV increased for Erisma uncinatum, while NPV increased for Chamaecrista xinguensis. For Manilkara huberi, the NPV first increased in the beginning of August and then decreased toward September with new foliage. Variations in red-edge position were not statistically significant between the species and across dates at the 95% confidence level. The camera data were affected by view-illumination effects, which reduced the SMA shade fraction over time. When MODIS data were corrected for these effects using the Multi-Angle Implementation of Atmospheric Correction Algorithm (MAIAC), we observed an EVI increase toward September that closely tracked the modeled LAI of mature leaves (3–5 months). Compared to the EVI, the GRND was a better indicator of leaf flushing because the modeled production of new leaves peaked in August and then declined in September following the GRND closely. While the EVI was more related to changes in mature leaf area, the GRND was more associated with new leaf flushing.
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spelling Amaral, Cibele Hummel doMoura, Yhasmin Mendes deGalvão, Lênio SoaresHilker, ThomasWu, JinSaleska, ScottNelson, Bruce WalkerLopes, Aline PontesWiedeman, Kenia K.Prohaska, NeillOliveira, Raimundo Cosme deMachado, Carolyne BuenoAragão, Luiz E. O. C.2018-09-28T12:02:24Z2018-09-28T12:02:24Z2017-09-010924-2716https://doi.org/10.1016/j.isprsjprs.2017.07.006http://www.locus.ufv.br/handle/123456789/22065The association between spectral reflectance and canopy processes remains challenging for quantifying large-scale canopy phenological cycles in tropical forests. In this study, we used a tower-mounted hyperspectral camera in an eastern Amazon forest to assess how canopy spectral signals of three species are linked with phenological processes in the 2012 dry season. We explored different approaches to disentangle the spectral components of canopy phenology processes and analyze their variations over time using 17 images acquired by the camera. The methods included linear spectral mixture analysis (SMA); principal component analysis (PCA); continuum removal (CR); and first-order derivative analysis. In addition, three vegetation indices potentially sensitive to leaf flushing, leaf loss and leaf area index (LAI) were calculated: the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and the entitled Green-Red Normalized Difference (GRND) index. We inspected also the consistency of the camera observations using Moderate Resolution Imaging Spectroradiometer (MODIS) and available phenological data on new leaf production and LAI of young, mature and old leaves simulated by a leaf demography-ontogeny model. The results showed a diversity of phenological responses during the 2012 dry season with related changes in canopy structure and greenness values. Because of the differences in timing and intensity of leaf flushing and leaf shedding, Erisma uncinatum, Manilkara huberi and Chamaecrista xinguensis presented different green vegetation (GV) and non-photosynthetic vegetation (NPV) SMA fractions; distinct PCA scores; changes in depth, width and area of the 681-nm chlorophyll absorption band; and variations over time in the EVI, GRND and NDVI. At the end of dry season, GV increased for Erisma uncinatum, while NPV increased for Chamaecrista xinguensis. For Manilkara huberi, the NPV first increased in the beginning of August and then decreased toward September with new foliage. Variations in red-edge position were not statistically significant between the species and across dates at the 95% confidence level. The camera data were affected by view-illumination effects, which reduced the SMA shade fraction over time. When MODIS data were corrected for these effects using the Multi-Angle Implementation of Atmospheric Correction Algorithm (MAIAC), we observed an EVI increase toward September that closely tracked the modeled LAI of mature leaves (3–5 months). Compared to the EVI, the GRND was a better indicator of leaf flushing because the modeled production of new leaves peaked in August and then declined in September following the GRND closely. While the EVI was more related to changes in mature leaf area, the GRND was more associated with new leaf flushing.engISPRS Journal of Photogrammetry and Remote SensingVolume 131, Pages 52-64, September 2017Elsevier B. 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dc.title.en.fl_str_mv Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations
title Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations
spellingShingle Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations
Amaral, Cibele Hummel do
Phenology
Hyperspectral remote sensing
Tropical species
Leaf flush
Amazon
Seasonality
Dry season
title_short Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations
title_full Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations
title_fullStr Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations
title_full_unstemmed Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations
title_sort Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations
author Amaral, Cibele Hummel do
author_facet Amaral, Cibele Hummel do
Moura, Yhasmin Mendes de
Galvão, Lênio Soares
Hilker, Thomas
Wu, Jin
Saleska, Scott
Nelson, Bruce Walker
Lopes, Aline Pontes
Wiedeman, Kenia K.
Prohaska, Neill
Oliveira, Raimundo Cosme de
Machado, Carolyne Bueno
Aragão, Luiz E. O. C.
author_role author
author2 Moura, Yhasmin Mendes de
Galvão, Lênio Soares
Hilker, Thomas
Wu, Jin
Saleska, Scott
Nelson, Bruce Walker
Lopes, Aline Pontes
Wiedeman, Kenia K.
Prohaska, Neill
Oliveira, Raimundo Cosme de
Machado, Carolyne Bueno
Aragão, Luiz E. O. C.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Amaral, Cibele Hummel do
Moura, Yhasmin Mendes de
Galvão, Lênio Soares
Hilker, Thomas
Wu, Jin
Saleska, Scott
Nelson, Bruce Walker
Lopes, Aline Pontes
Wiedeman, Kenia K.
Prohaska, Neill
Oliveira, Raimundo Cosme de
Machado, Carolyne Bueno
Aragão, Luiz E. O. C.
dc.subject.pt-BR.fl_str_mv Phenology
Hyperspectral remote sensing
Tropical species
Leaf flush
Amazon
Seasonality
Dry season
topic Phenology
Hyperspectral remote sensing
Tropical species
Leaf flush
Amazon
Seasonality
Dry season
description The association between spectral reflectance and canopy processes remains challenging for quantifying large-scale canopy phenological cycles in tropical forests. In this study, we used a tower-mounted hyperspectral camera in an eastern Amazon forest to assess how canopy spectral signals of three species are linked with phenological processes in the 2012 dry season. We explored different approaches to disentangle the spectral components of canopy phenology processes and analyze their variations over time using 17 images acquired by the camera. The methods included linear spectral mixture analysis (SMA); principal component analysis (PCA); continuum removal (CR); and first-order derivative analysis. In addition, three vegetation indices potentially sensitive to leaf flushing, leaf loss and leaf area index (LAI) were calculated: the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and the entitled Green-Red Normalized Difference (GRND) index. We inspected also the consistency of the camera observations using Moderate Resolution Imaging Spectroradiometer (MODIS) and available phenological data on new leaf production and LAI of young, mature and old leaves simulated by a leaf demography-ontogeny model. The results showed a diversity of phenological responses during the 2012 dry season with related changes in canopy structure and greenness values. Because of the differences in timing and intensity of leaf flushing and leaf shedding, Erisma uncinatum, Manilkara huberi and Chamaecrista xinguensis presented different green vegetation (GV) and non-photosynthetic vegetation (NPV) SMA fractions; distinct PCA scores; changes in depth, width and area of the 681-nm chlorophyll absorption band; and variations over time in the EVI, GRND and NDVI. At the end of dry season, GV increased for Erisma uncinatum, while NPV increased for Chamaecrista xinguensis. For Manilkara huberi, the NPV first increased in the beginning of August and then decreased toward September with new foliage. Variations in red-edge position were not statistically significant between the species and across dates at the 95% confidence level. The camera data were affected by view-illumination effects, which reduced the SMA shade fraction over time. When MODIS data were corrected for these effects using the Multi-Angle Implementation of Atmospheric Correction Algorithm (MAIAC), we observed an EVI increase toward September that closely tracked the modeled LAI of mature leaves (3–5 months). Compared to the EVI, the GRND was a better indicator of leaf flushing because the modeled production of new leaves peaked in August and then declined in September following the GRND closely. While the EVI was more related to changes in mature leaf area, the GRND was more associated with new leaf flushing.
publishDate 2017
dc.date.issued.fl_str_mv 2017-09-01
dc.date.accessioned.fl_str_mv 2018-09-28T12:02:24Z
dc.date.available.fl_str_mv 2018-09-28T12:02:24Z
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dc.identifier.uri.fl_str_mv https://doi.org/10.1016/j.isprsjprs.2017.07.006
http://www.locus.ufv.br/handle/123456789/22065
dc.identifier.issn.none.fl_str_mv 0924-2716
identifier_str_mv 0924-2716
url https://doi.org/10.1016/j.isprsjprs.2017.07.006
http://www.locus.ufv.br/handle/123456789/22065
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dc.relation.ispartofseries.pt-BR.fl_str_mv Volume 131, Pages 52-64, September 2017
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