Estimation of height and aerial biomass in Eucalyptus globulus plantations using UAV-LiDAR DOI Creative Commons

Lucia Enriquez Pinedo,

Kevin Ortega Quispe,

Dennis Ccopi Trucios

et al.

Trees Forests and People, Journal Year: 2024, Volume and Issue: unknown, P. 100763 - 100763

Published: Dec. 1, 2024

Language: Английский

Changes in GEDI-based measures of forest structure after large California wildfires relative to pre-fire conditions DOI Creative Commons
Matthew L. Clark, Christopher R. Hakkenberg, Theodore N. Bailey

et al.

Remote Sensing of Environment, Journal Year: 2025, Volume and Issue: 323, P. 114718 - 114718

Published: April 4, 2025

Language: Английский

Citations

0
Cross-scale patterns of structure and maximum biomass in late-seral Douglas-fir-dominated rainforests DOI Creative Commons
Russell D. Kramer, Stephen C. Sillett, Sean M.A. Jeronimo

et al.

Forest Ecology and Management, Journal Year: 2025, Volume and Issue: 585, P. 122594 - 122594

Published: April 4, 2025

Language: Английский

Citations

0
Dendroseismological investigation of redwood trees along the North Coast section of the San Andreas Fault DOI Creative Commons
Allyson L. Carroll, Belle Philibosian, Stephen C. Sillett

et al.

Quaternary Science Advances, Journal Year: 2025, Volume and Issue: unknown, P. 100283 - 100283

Published: May 1, 2025

Language: Английский

Citations

0
Satellite Derived Trait Data Slightly Improves Tropical Forest Biomass, NPP and GPP Estimates DOI
Christopher E. Doughty, Camille Gaillard, Patrick Burns

et al.

Journal of Geophysical Research Biogeosciences, Journal Year: 2024, Volume and Issue: 129(7)

Published: July 1, 2024

Abstract Improving tropical forest current biomass estimates can help more accurately evaluate ecosystem services in forests. The Global Ecosystem Dynamics Investigation (GEDI) lidar provides detailed 3D structure and height data, which be used to improve above‐ground estimates. However, there is still debate on how best predict using GEDI data. Here we compare stand predicted by data with the observed of 2,102 inventory plots forests find that adding a remotely sensed (RS) trait map leaf mass area (LMA) significantly ( P < 0.001) improves field predictions, but only small amount r 2 = 0.01). it may also reduce bias residuals because was negative relationship between both LMA 0.34) percentage phosphorus (%P, 0.31) residuals. Leaf spectral (400–1,075 nm) from 523 individual trees along Peruvian elevation gradient Diameter at Breast (DBH) (the critical measurement underlying plot biomass) an 0.01 predicts DBH 0.04. Other sets offer further improvements max temperature T ) Amazonian 0.76 N 66). Finally, for network net primary production (NPP) gross (GPP) 21), traits remote sensing are better predicting fluxes than variables. Overall, maps, especially future improved ones produced Surface Biology Geology, carbon flux predictions significant amount.

Language: Английский

Citations

1
Estimation of height and aerial biomass in Eucalyptus globulus plantations using UAV-LiDAR DOI Creative Commons

Lucia Enriquez Pinedo,

Kevin Ortega Quispe,

Dennis Ccopi Trucios

et al.

Trees Forests and People, Journal Year: 2024, Volume and Issue: unknown, P. 100763 - 100763

Published: Dec. 1, 2024

Language: Английский

Citations

0