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No. de sistema: 000060424

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040 _ _ a| ECO
c| ECO
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245 0 0 a| Combining LiDAR data and airborne imagery of very high resolution to improve aboveground biomass estimates in tropical dry forests
506 _ _ a| Acceso en línea sin restricciones
520 1 _ a| Knowledge of the spatial distribution of aboveground biomass (AGB) is crucial to guide forest conservation and management to maintain carbon stocks. LiDAR has been highly successful for this purpose, but has limited availability. Very-high resolution (<1 m) orthophotos can also be used to estimate AGB because they allow a fine distinction of forest canopy grain. We evaluated the separate and joint performance of orthophotos and LiDAR data to estimate AGB in two types of tropical dry forests in the Yucatan Peninsula. Woody plants were surveyed in twenty 0.1 ha plots in a semideciduous forest at Kaxil Kiuic Biocultural Reserve (RBKK) and 28 plots in a semievergreen forest at Felipe Carrillo Puerto (FCP). We fitted three regression models: one based on LiDAR data, another based on orthophoto variables calculated for forest canopy and canopy opening fractions, and a third model that combined both sets of variables. Variation in AGB was decomposed into LiDAR, orthophotos and joint components using variation-partitioning analyses. In FCP, regression models using LiDAR data only showed higher fit (R2 = 0.82) than orthophoto variables only (R² = 0.70). In contrast, orthophotos had a slightly higher fit (R² = 0.91) than LiDAR (R2 = 0.88) in RBKK, because orthophoto variables characterize very well the horizontal structure of canopies on this site. The model that combined both data sets showed a better fit (R2 = 0.85) only in FCP, which has a more complex forest structure. The largest percentage of AGB variation (88 per cent in RBKK and 67 per cent in FCP) was explained by the joint contribution of LiDAR and orthophotos. We conclude that both LiDAR and orthophotos provide accurate estimation of AGB, but their relative performance varies with forest type and structural complexity. Combining the two sets of variables can further improve the accuracy of AGB estimation, particularly in forests with complex vegeta
530 _ _ a| Disponible en línea
533 _ _ a| Reproducción electrónica en formato PDF
538 _ _ a| Adobe Acrobat profesional 6.0 o superior
650 _ 4 a| Biomasa aérea
650 _ 4 a| Bosques tropicales secos
650 _ 4 a| Lidar
650 _ 4 a| Aerofotografía
650 _ 4 a| Sensores remotos
651 _ 4 a| Reserva Biocultural Kaxil Kiuic, Oxkutzcab (Yucatán, México)
651 _ 4 a| Felipe Carrillo Puerto (Quintana Roo, México)
700 1 _ a| Reyes Palomeque, Gabriela
e| autora
700 1 _ a| Manuel Dupuy, Juan
e| autor
700 1 _ a| Johnson, Kristofer D.
e| autor
700 1 _ a| Castillo Santiago, Miguel Ángel
e| autor
700 1 _ a| Hernández Stefanoni, José Luis
e| autor
n| 8521370100
773 0 _
t| Forestry An International Journal of Forest Research
g| Volume 92, número 5 (October 2019), p. 599–615
x| 1464-3626
856 4 1 u| https://academic.oup.com/forestry/article-abstract/92/5/599/5518561
z| Artículo electrónico
902 _ _ a| BG / MM
904 _ _ a| Mayo 2020
905 _ _ a| Artecosur
905 _ _ a| Biblioelectrónica
LNG eng
Cerrar
Combining LiDAR data and airborne imagery of very high resolution to improve aboveground biomass estimates in tropical dry forests
Reyes Palomeque, Gabriela (autora)
Manuel Dupuy, Juan (autor)
Johnson, Kristofer D. (autor)
Castillo Santiago, Miguel Ángel (autor)
Hernández Stefanoni, José Luis (autor)
Nota: Disponible en línea
Acceso en línea sin restricciones
Contenido en: Forestry An International Journal of Forest Research. Volume 92, número 5 (October 2019), p. 599–615. ISSN: 1464-3626
No. de sistema: 60424
Tipo: Artículo
PDF


Inglés

"Knowledge of the spatial distribution of aboveground biomass (AGB) is crucial to guide forest conservation and management to maintain carbon stocks. LiDAR has been highly successful for this purpose, but has limited availability. Very-high resolution (<1 m) orthophotos can also be used to estimate AGB because they allow a fine distinction of forest canopy grain. We evaluated the separate and joint performance of orthophotos and LiDAR data to estimate AGB in two types of tropical dry forests in the Yucatan Peninsula. Woody plants were surveyed in twenty 0.1 ha plots in a semideciduous forest at Kaxil Kiuic Biocultural Reserve (RBKK) and 28 plots in a semievergreen forest at Felipe Carrillo Puerto (FCP). We fitted three regression models: one based on LiDAR data, another based on orthophoto variables calculated for forest canopy and canopy opening fractions, and a third model that combined both sets of variables. Variation in AGB was decomposed into LiDAR, orthophotos and joint components using variation-partitioning analyses. In FCP, regression models using LiDAR data only showed higher fit (R2 = 0.82) than orthophoto variables only (R² = 0.70). In contrast, orthophotos had a slightly higher fit (R² = 0.91) than LiDAR (R2 = 0.88) in RBKK, because orthophoto variables characterize very well the horizontal structure of canopies on this site. The model that combined both data sets showed a better fit (R2 = 0.85) only in FCP, which has a more complex forest structure. The largest percentage of AGB variation (88 per cent in RBKK and 67 per cent in FCP) was explained by the joint contribution of LiDAR and orthophotos. We conclude that both LiDAR and orthophotos provide accurate estimation of AGB, but their relative performance varies with forest type and structural complexity. Combining the two sets of variables can further improve the accuracy of AGB estimation, particularly in forests with complex vegeta"


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