Assessing the Usefulness of LiDAR for Monitoring the Structure of a Montane Forest on a Subtropical Oceanic Island

Remote Sensing

Ángel B. Fernández López¹  Jesús Parada-Díaz²  Juana María González-Mancebo²  Marcelino J. del Arco Aguilar²  Luis A. Gómez González³ 

[1] Parque Nacional de Garajonay, Edificio las Creces, Local 1, Portal 3, C/Ruiz de Padrón y Avenida del 5° Centenario, 38800 San Sebastián de la Gomera, Santa Cruz de Tenerife, Spain;Plant Conservation and Biogeography Research Group, Departamento de Botánica Ecología y Fisiología Vegetal, Universidad de La Laguna, C/Astrofísico Francisco Sánchez, s/n, 38200 La Laguna, Santa Cruz de Tenerife, Spain;TRAGSATEC Grupo TRAGSA, Gerencia de Tragsatec UT 4. Dpto. La Gomera-El Hierro/Proyectos Agsa-Mapi. Avda/Quinto Centenario, s/n, Edif. San José, Local 4, 38800 San Sebastián de la Gomera, Santa Cruz de Tenerife, Spain;

关键词: laurel forest;    young-growth forest;    old-growth forest;    above-ground biomass;    allometric equations;    plant conservation;   

DOI  :  10.3390/rs14040994

来源: DOAJ

【 摘 要 】

The monitoring of ecosystems and forests is an urgent requirement in the current framework of global change. It is particularly necessary on oceanic islands where their rich biodiversity is highly vulnerable, with many narrow-ranged endemic species. Quantifying and mapping forest health through key ecological variables are essential steps for management, but it will also be challenging and may require a lot of resources. Remote sensing has the potential to be a very useful tool to assess the development and conservation status of forests. We assessed the applicability of the light detection and ranging (LiDAR) on the laurel forests of La Gomera, making allometric equations for various measurements of the forest structure, linking field inventory from 2019 and 2017 LiDAR data through standard linear regressions. Decision trees and logistic regressions were also used to assess the performance of LiDAR in the recognition of young-growth and old-growth laurel forests. The obtained allometric models were a good fit in general and their predictions were in line with already known data. Likewise, decision tree and logistic regression to distinguish young-growth and old-growth forests had a similar performance in both cases, with a high to medium-high degree of accuracy. Therefore, LiDAR was revealed to be a useful tool for the monitoring of the laurel forest by the managers.

【 授权许可】

Unknown