Biomass estimation using LiDAR data
ABSTRACT: Forest ecosystems play a very important role in carbon cycle because they suppose one of thebiggest carbon reservoirs and sinks. Estimating the aboveground forest biomass is critical tounderstand the global carbon storage process. Different models to estimate aboveground biomassin the Pinu...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Recursos: | Universidad de Cantabria (UC) |
| Repositorio: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unican.es:10902/24336 |
| Acesso em linha: | http://hdl.handle.net/10902/24336 |
| Access Level: | acceso abierto |
| Palavra-chave: | LiDAR Biomass Multiple linear regression |
| Resumo: | ABSTRACT: Forest ecosystems play a very important role in carbon cycle because they suppose one of thebiggest carbon reservoirs and sinks. Estimating the aboveground forest biomass is critical tounderstand the global carbon storage process. Different models to estimate aboveground biomassin the Pinus radiata specie in a specific region of Spain have been developed, using, exclusively,public and accessible data with low point density gathered periodically from Light Detection andRanging (LiDAR) flights. The point clouds data were processed to obtain metrics considered aspredictive variables and afterwards, the multiple regression technique has been applied togenerate the biomass estimation models. The best models explain 76% of its variability with astandard error of 0.26 ton/ha in logarithmic units. The methodology can be considered as highlyautomated and extensible to other territories with similar characteristics. Our results support theuse of this approach for more sustainable management of forest areas. |
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