A mixed pixel- and region-based approach for using airborne laser scanning data for individual tree crown delineation in Pinus radiata D. Don plantations

2013 ◽  
Vol 34 (21) ◽  
pp. 7671-7690 ◽  
Author(s):  
Eduardo González-Ferreiro ◽  
Ulises Diéguez-Aranda ◽  
Laura Barreiro-Fernández ◽  
Sandra Buján ◽  
Miguel Barbosa ◽  
...  
2017 ◽  
Vol 9 (3) ◽  
pp. 231 ◽  
Author(s):  
Chloe Barnes ◽  
Heiko Balzter ◽  
Kirsten Barrett ◽  
James Eddy ◽  
Sam Milner ◽  
...  

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Juan Guerra-Hernández ◽  
Adrián Pascual

Abstract Background The NASA’s Global Ecosystem Dynamics Investigation (GEDI) satellite mission aims at scanning forest ecosystems on a multi-temporal short-rotation basis. The GEDI data can validate and update statistics from nationwide airborne laser scanning (ALS). We present a case in the Northwest of Spain using GEDI statistics and nationwide ALS surveys to estimate forest dynamics in three fast-growing forest ecosystems comprising 211,346 ha. The objectives were: i) to analyze the potential of GEDI to detect disturbances, ii) to investigate uncertainty source regarding non-positive height increments from the 2015–2017 ALS data to the 2019 GEDI laser shots and iii) to estimate height growth using polygons from the Forest Map of Spain (FMS). A set of 258 National Forest Inventory plots were used to validate the observed height dynamics. Results The spatio-temporal assessment from ALS surveying to GEDI scanning allowed the large-scale detection of harvests. The mean annual height growths were 0.79 (SD = 0.63), 0.60 (SD = 0.42) and 0.94 (SD = 0.75) m for Pinus pinaster, Pinus radiata and Eucalyptus spp., respectively. The median annual values from the ALS-GEDI positive increments were close to NFI-based growth values computed for Pinus pinaster and Pinus radiata, respectively. The effect of edge border, spatial co-registration of GEDI shots and the influence of forest cover in the observed dynamics were important factors to considering when processing ALS data and GEDI shots. Discussion The use of GEDI laser data provides valuable insights for forest industry operations especially when accounting for fast changes. However, errors derived from positioning, ground finder and canopy structure can introduce uncertainty to understand the detected growth patterns as documented in this study. The analysis of forest growth using ALS and GEDI would benefit from the generalization of common rules and data processing schemes as the GEDI mission is increasingly being utilized in the forest remote sensing community.


2021 ◽  
Vol 11 ◽  
Author(s):  
David Pont ◽  
Heidi S. Dungey ◽  
Mari Suontama ◽  
Grahame T. Stovold

Phenotyping individual trees to quantify interactions among genotype, environment, and management practices is critical to the development of precision forestry and to maximize the opportunity of improved tree breeds. In this study we utilized airborne laser scanning (ALS) data to detect and characterize individual trees in order to generate tree-level phenotypes and tree-to-tree competition metrics. To examine our ability to account for environmental variation and its relative importance on individual-tree traits, we investigated the use of spatial models using ALS-derived competition metrics and conventional autoregressive spatial techniques. Models utilizing competition covariate terms were found to quantify previously unexplained phenotypic variation compared with standard models, substantially reducing residual variance and improving estimates of heritabilities for a set of operationally relevant traits. Models including terms for spatial autocorrelation and competition performed the best and were labelled ACE (autocorrelation-competition-error) models. The best ACE models provided statistically significant reductions in residuals ranging from −65.48% for tree height (H) to −21.03% for wood stiffness (A), and improvements in narrow sense heritabilities from 38.64% for H to 14.01% for A. Individual tree phenotyping using an ACE approach is therefore recommended for analyses of research trials where traits are susceptible to spatial effects.


2021 ◽  
Vol 15 (03) ◽  
Author(s):  
Honglu Xin ◽  
Yadvinder Malhi ◽  
David A. Coomes ◽  
Yi Lin ◽  
Baoli Liu ◽  
...  

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