In:
Journal of Remote Sensing, American Association for the Advancement of Science (AAAS), Vol. 3 ( 2023-01)
Abstract:
Estimating potential height of forests is one of key tasks in forest restoration planning. Since regional maximum height statistics is difficult to account for local heterogeneity, biotic and abiotic mechanism-based methods are required. Different from the mainstream models that possesses either hydraulic constraint or mechanical constraint, we used a more lightweight model based on balance of water availability and consumption, named the Allometric Scaling and Resource Limitations model. Several enhancements were added, making up the third version of the model, and we deployed it using Google Earth Engine (GEE). A map of potential tree height at 90-m resolution is created for beech–maple–birch forests in northeastern United States. Within the oldest forests among the study area, the model reproduces the tree height level of ~25 m with root mean square deviation (RMSD) of 3.71 m from a high-resolution product of canopy height estimates. Under a threshold of 20% deviation, 82.9% of pixels agree with the existing tree heights. Outside of the oldest forests, RMSD raises to 5.01 m, and agreement drops to 75.3%. Over the entire study area, 6.6% total pixels of interest have a predicted height below the current level. A total of 16.7% pixels have larger predictions relative to existing forest heights, with a half of them classified as mistakes of overestimation. Errors may come from uncertainty in climate reanalysis data and inadequate shading effects modeling. Our work confirms the applicability of this lightweight model for this static prediction task and explores the deployment of ecological mechanism-based models on the GEE platform.
Type of Medium:
Online Resource
ISSN:
2694-1589
DOI:
10.34133/remotesensing.0084
Language:
English
Publisher:
American Association for the Advancement of Science (AAAS)
Publication Date:
2023
detail.hit.zdb_id:
3060865-X
