期刊
XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III
卷 5-3, 期 -, 页码 353-360出版社
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/isprs-annals-V-3-2022-353-2022
关键词
Mediterranean Forest; Aleppo Pine; UAV; Point Cloud; Machine Learning; Allometric Models; AGB Mapping
资金
- Proyecto Retos Junta de Andalucia, Spain [P18-RT-2327]
- Programa Operativo FEDER Andalucia, Spain [UAL2020-SEJ-D1931]
- Ministry of Agriculture, Fisheries and Sustainable Development of Andalusia
Effective monitoring of Mediterranean forest is crucial for studying the role of forest management in climate change mitigation and environmental service maintenance. This study used UAV image-based data to estimate the dendrometric variables of repopulated Aleppo pine and found that multivariate allometric models performed better at predicting tree trunk diameter.
Effective monitoring of Mediterranean forest is essential to determine the role of forest management in mitigating climate change and ensuring the maintenance of its environmental services. Most of the allometric models to estimate dry above-ground biomass (AGB) at tree level are based on knowing the diameter of the trunk at breast height (DBH). However, it is difficult, if not impossible, to estimate DBH from airborne/spaceborne sensors within the context of a remote sensing-oriented approach, being common to draw upon regression models to relate DBH to remotely sensed dendrometric variables such as total tree height (H) and tree crown diameter (CD). This study uses UAV (unmanned aerial vehicle) image-based data to estimate the dendrometric variables H and CD of the repopulated Aleppo pine (Pinus halepensis Mill.) located in a semiarid continental Mediterranean forest of Almeria (southeast of Spain). DBH data were gathered through field work. Both bivariate (DBH = Phi(H)) and multivariate (DBH = Psi(H, CD)) allometric models were developed by applying least-squares-based regression and machine learning regression methods. The results showed that multivariate allometric models performed better than bivariate at predicting DBH, both in terms of goodness-of-fit and stability against changes in training or testing samples. In addition, least-squares-based regression (linear and potential) provided statistically similar results to those obtained from complex machine learning ensemble algorithms. In this way, the easy-to-apply multivariate linear allometric model DBH = -4.84 + 1.73149H + 3.08114CD (R-2 = 89.23%) would be recommended to locally estimate DBH in Aleppo pine from remotely sensed H and CD data.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据