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| Registros recuperados : 21 | |
| 9. |  | RAINS, D. W.; VALENTINE, R. C.; HOLLANDER, A. Genetic engineering of osmoregulation impact on plant productivity for food chemicals, and energy. New York: Plenum Press, c1981. 381p. (Basic Life Science, 14). Symposium on genetic Engineering of osmoregulation impact on plant productivity for Food, Chemicals, and Energy, Brook Naven National Laboratories, 1979.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 14. | | LYONS, J. M.; VALENTINE, R. C.; PHILLIPS, D. A.; RAINS, D. W.; HUFFAKER, R. C. (ed.). Genetic engineering of symbiotic nitrogen fixation and conservation of fixed nitrogen. New York: Plenum, 1981. 698 p. Proceedings of a Symposium on Enhancing Biological Production of Ammonia from Atmospheric Nitrogen and Soil Nitrate, 1980, Tahoe City, California, held June 29 - July 3, 1980.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 18. | | BISPO, P. da C.; PARDINI, M.; PAPATHANASSIOU, K. P.; KUGLER, F.; BALZTER, H.; RAINS, D.; SANTOS, J. R. dos; RIZAEV, I. G.; TANSEY, K.; SILVA, M. F. da; ARAUJO, L. S. de. Mapping forest successional stages in the Brazilian Amazon using forest heights derived from TanDEM-X SAR interferometry. Remote Sensing of Environment, v. 232, 2019. Article 111194.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 19. | | BISPO, P. da C.; RODRÍGUEZ-VEIGA, P.; ZIMBRES, B.; MIRANDA, S. do C. de; CEZARE, C. H. G.; FLEMING, S.; BALDACCHINO, F.; LOUIS, V.; RAINS, D.; GARCIA, M.; ESPIRITO-SANTO, F. D. B.; ROITMAN, I.; PACHECO-PASCAGAZA, A. M.; GOU, Y.; ROBERTS, J.; BARRETT, K.; FERREIRA, L. G.; SHIMBO, J. Z.; ALENCAR, A.; BUSTAMANTE, M.; WOODHOUSE, I. H.; SANO, E. E.; OMETTO, J. P.; TANSEY, K.; BALZTER, H. Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach. Remote Sensing, v. 12, n. 17, 2020.| Biblioteca(s): Embrapa Cerrados. |
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| 20. | | MELO, D. de C. D.; ANACHE, J. A. A.; BORGES, V. P.; GONZALEZ MIRALLES, D.; MARTENS, B.; FISHER, J. B.; NÓBREGA, R. L. B.; MORENO, Á.; CABRAL, O. M. R.; RODRIGUES, T. R.; BEZERRA, B.; SILVA, C. M. S. e; MEIRA NETO, A. A.; MOURA, M. S. B. de; MARQUES, T. V.; CAMPOS, S.; NOGUEIRA, J. de S.; ROSOLEM, R.; SOUZA, R. M. S.; ANTONINO, A. C. D.; HOLL, D.; GALLEGUILLOS, M.; PEREZ-QUEZADA, J.; VERHOEF, A.; KUTZBACH, L.; LIMA, J. R. de S.; SOUZA, E. S. de; GASSMANN, M. I.; PÉREZ, C. F.; TONTI, N. E.; POSSE, G.; RAINS, D.; OLIVEIRA, P. T.; WENDLAND, E. Are remote sensing evapotranspiration models reliable across South American ecoregions? Water Resources Research, v. 57, n. 11, e2020WR028752, 2021.| Biblioteca(s): Embrapa Meio Ambiente; Embrapa Semiárido. |
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| Registros recuperados : 21 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
27/11/2019 |
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Data da última atualização: |
27/11/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
BISPO, P. da C.; PARDINI, M.; PAPATHANASSIOU, K. P.; KUGLER, F.; BALZTER, H.; RAINS, D.; SANTOS, J. R. dos; RIZAEV, I. G.; TANSEY, K.; SILVA, M. F. da; ARAUJO, L. S. de. |
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Afiliação: |
POLYANNA DA CONCEICAO BISPO, University of Leicester; MATTEO PARDINI, German Aerospace Center; KONSTANTINUS P PAPATHANASSIOU, German Aerospace Center; FLORIAN KUGLER, German Aerospace Center; HEIKO BALTZER, Ghent University; DOMINIK RAINS, University of Leicester; JOAO ROBERTO DOS SANTOS, INPE; IGOR G RIZAEV, University of Bristol; KEVIN TANSEY, University of Leicester; MAIZA FERREIRA DA SILVA, SGE; LUCIANA SPINELLI DE ARAUJO, CNPMA. |
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Título: |
Mapping forest successional stages in the Brazilian Amazon using forest heights derived from TanDEM-X SAR interferometry. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Remote Sensing of Environment, v. 232, 2019. Article 111194. |
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DOI: |
https://doi.org/10.1016/j.rse.2019.05.013 |
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Idioma: |
Inglês |
|
Conteúdo: |
Abstract: Knowledge of the spatial patterns of successional stages (i.e., primary and secondary forest) in tropical forests allows to monitor forest preservation, mortality and regeneration in relation to natural and anthropogenic disturbances. Different successional stages have also different capabilities of re-establishing carbon stocks. Therefore, a successful discrimination of successional stages over wide areas can lead to an improved quantification of above ground biomass and carbon stocks. The reduction of the mapping uncertainties is especially a challenge due to high heterogeneity of the tropical vegetation. In this framework, the development of innovative remote sensing approaches is required. Forests (top) height (and its spatial distribution) are an important structural parameter that can be used to differentiate between different successional stages, and can be provided by Interferometric Synthetic Aperture Radar (InSAR) acquisitions. In this context, this paper investigates the potential of forest heights estimated from TanDEM-X InSAR data and a LiDAR digital terrain model (DTM) for separating successional stages (primary or old growth and secondary forest at different stages of succession) by means of a maximum likelihood classification. The study was carried out in the region of the Tapajós National Forest (Pará, Brazil) in the Amazon biome. The forest heights for three years (2012, 2013 and 2016) were estimated from a single-polarization in bistatic mode using InSAR model-based inversion techniques aided by the LiDAR digital terrain model. The validation of the TanDEM-X forest heights with independent LiDAR H100 datasets was carried out in the location of seven field inventory plots (measuring 50?×?50?m, equivalent to 0.25?ha), also allowing for the validation of the LiDAR datasets against the field data. The validation of the estimated heights showed a high correlation (r?=?0.93) and a low uncertainty (RMSE?=?3?m). The information about the successional stages and forest heights from field datasets was used to select training samples in the LiDAR and TanDEM-X forest heights to classify successional stages with a maximum likelihood classifier. The identification of different stages of forest succession based on TanDEM-X forest heights was possible with an overall accuracy of about 80%. MenosAbstract: Knowledge of the spatial patterns of successional stages (i.e., primary and secondary forest) in tropical forests allows to monitor forest preservation, mortality and regeneration in relation to natural and anthropogenic disturbances. Different successional stages have also different capabilities of re-establishing carbon stocks. Therefore, a successful discrimination of successional stages over wide areas can lead to an improved quantification of above ground biomass and carbon stocks. The reduction of the mapping uncertainties is especially a challenge due to high heterogeneity of the tropical vegetation. In this framework, the development of innovative remote sensing approaches is required. Forests (top) height (and its spatial distribution) are an important structural parameter that can be used to differentiate between different successional stages, and can be provided by Interferometric Synthetic Aperture Radar (InSAR) acquisitions. In this context, this paper investigates the potential of forest heights estimated from TanDEM-X InSAR data and a LiDAR digital terrain model (DTM) for separating successional stages (primary or old growth and secondary forest at different stages of succession) by means of a maximum likelihood classification. The study was carried out in the region of the Tapajós National Forest (Pará, Brazil) in the Amazon biome. The forest heights for three years (2012, 2013 and 2016) were estimated from a single-polarization in bistatic mode usi... Mostrar Tudo |
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Palavras-Chave: |
Forest height; TanDEM-X. |
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Thesagro: |
Floresta Tropical; Mapa; Sensoriamento Remoto. |
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Thesaurus NAL: |
Forest succession; Height; Interferometry; synthetic aperture radar; Tropical forests. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/205662/1/ARAUJO-SPINELLI-Mapping-Forest-2019.pdf
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|
Marc: |
LEADER 03451naa a2200373 a 4500
001 2115303
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008 2019 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.rse.2019.05.013$2DOI
100 1 $aBISPO, P. da C.
245 $aMapping forest successional stages in the Brazilian Amazon using forest heights derived from TanDEM-X SAR interferometry.$h[electronic resource]
260 $c2019
520 $aAbstract: Knowledge of the spatial patterns of successional stages (i.e., primary and secondary forest) in tropical forests allows to monitor forest preservation, mortality and regeneration in relation to natural and anthropogenic disturbances. Different successional stages have also different capabilities of re-establishing carbon stocks. Therefore, a successful discrimination of successional stages over wide areas can lead to an improved quantification of above ground biomass and carbon stocks. The reduction of the mapping uncertainties is especially a challenge due to high heterogeneity of the tropical vegetation. In this framework, the development of innovative remote sensing approaches is required. Forests (top) height (and its spatial distribution) are an important structural parameter that can be used to differentiate between different successional stages, and can be provided by Interferometric Synthetic Aperture Radar (InSAR) acquisitions. In this context, this paper investigates the potential of forest heights estimated from TanDEM-X InSAR data and a LiDAR digital terrain model (DTM) for separating successional stages (primary or old growth and secondary forest at different stages of succession) by means of a maximum likelihood classification. The study was carried out in the region of the Tapajós National Forest (Pará, Brazil) in the Amazon biome. The forest heights for three years (2012, 2013 and 2016) were estimated from a single-polarization in bistatic mode using InSAR model-based inversion techniques aided by the LiDAR digital terrain model. The validation of the TanDEM-X forest heights with independent LiDAR H100 datasets was carried out in the location of seven field inventory plots (measuring 50?×?50?m, equivalent to 0.25?ha), also allowing for the validation of the LiDAR datasets against the field data. The validation of the estimated heights showed a high correlation (r?=?0.93) and a low uncertainty (RMSE?=?3?m). The information about the successional stages and forest heights from field datasets was used to select training samples in the LiDAR and TanDEM-X forest heights to classify successional stages with a maximum likelihood classifier. The identification of different stages of forest succession based on TanDEM-X forest heights was possible with an overall accuracy of about 80%.
650 $aForest succession
650 $aHeight
650 $aInterferometry
650 $asynthetic aperture radar
650 $aTropical forests
650 $aFloresta Tropical
650 $aMapa
650 $aSensoriamento Remoto
653 $aForest height
653 $aTanDEM-X
700 1 $aPARDINI, M.
700 1 $aPAPATHANASSIOU, K. P.
700 1 $aKUGLER, F.
700 1 $aBALZTER, H.
700 1 $aRAINS, D.
700 1 $aSANTOS, J. R. dos
700 1 $aRIZAEV, I. G.
700 1 $aTANSEY, K.
700 1 $aSILVA, M. F. da
700 1 $aARAUJO, L. S. de
773 $tRemote Sensing of Environment$gv. 232, 2019. Article 111194.
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Embrapa Meio Ambiente (CNPMA) |
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