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2 resultados encontrados para: AUTOR: Johnson, Kristofer
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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) ;
Disponible en línea
Contenido en: Forestry An International Journal of Forest Research Volume 92, número 5 (October 2019), p. 599–615 ISSN: 1464-3626
Resumen en: Inglés |
Resumen en 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

Resumen en: Inglés |
Resumen en inglés

There is an increasing need for approaches to determine reference emission levels and implement policies to address the objectives of Reducing Emissions from Deforestation and Forest Degradation, plus improving forest management, carbon stock enhancement and conservation (REDD+). Important aspects of approaching emissions reductions include coordination and sharing of technology, data, protocols and experiences within and among countries to maximize resources and apply knowledge to build robust monitoring, reporting and veri fi cation (MRV) systems. We propose that enhancing the multiple facets of interoperability could facilitate implementation of REDD+ programs and actions. For this case, interoperability is a collective effort with the ultimate goal of sharing and using information to produce knowledge and apply knowledge gained, by removing conceptual, technological, organizational and cultural barriers. These efforts must come from various actors and institutions, including government ministries/agencies, scientific community, landowners, civil society groups and businesses. Here, we review the case of Mexico as an example of evolving interoperability in developing countries, and highlight challenges and opportunities for implementation of REDD+. Country-specific actions toward a higher degree of interoperability can be complex, expensive and even risky. These efforts provide leadership opportunities and will facilitate science–policy integration for implementation of REDD+, particularly in developing counties.