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Research article - Peer-reviewed, 2022

A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits

Stoddart, Jaz; Alves de Almeida, Danilo Roberti; Silva, Carlos Alberto; Gorgens, Eric Bastos; Keller, Michael; Valbuena, Ruben


Current LiDAR-based methods for detecting forest change use a host of statistically selected variables which typically lack a biological link with the characteristics of the ecosystem. Consensus of the literature indicates that many authors use LiDAR to derive ecosystem morphological traits (EMTs)-namely, vegetation height, vegetation cover, and vertical structural complexity-to identify small-scale changes in forest ecosystems. Here, we provide a conceptual, biological model for predicting forest aboveground biomass (AGB) change based on EMTs. We show that through use of a multitemporal dataset it is possible to not only identify losses caused by logging in the period between data collection but also identify regions of regrowth from prior logging using EMTs. This sensitivity to the change in forest dynamics was the criterion by which LiDAR metrics were selected as proxies for each EMT. For vegetation height, results showed that the top-of-canopy height derived from a canopy height model was more sensitive to logging than the average or high percentile of raw LiDAR height distributions. For vegetation cover metrics, lower height thresholds for fractional cover calculations were more sensitive to selective logging and the regeneration of understory. For describing the structural complexity in the vertical profile, the Gini coefficient was found to be superior to foliage height diversity for detecting the dynamics occurring over the years after logging. The subsequent conceptual model for AGB estimation obtained a level of accuracy which was comparable to a model that was statistically optimised for that same area. We argue that a widespread adoption of an EMT-based conceptual approach would improve the transferability and comparability of LiDAR models for AGB worldwide.


vegetation structure; carbon stock; LiDAR; modelling

Published in

Remote Sensing
2022, volume: 14, number: 4, article number: 933
Publisher: MDPI

Authors' information

Stoddart, Jaz
Bangor University
Alves de Almeida, Danilo Roberti
Bangor University
Silva, Carlos Alberto
University of Florida
Gorgens, Eric Bastos
Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM)
Keller, Michael
United States Forest Service
Bangor University
Swedish University of Agricultural Sciences, Department of Forest Resource Management

Sustainable Development Goals

SDG15 Life on land

UKÄ Subject classification

Environmental Sciences
Remote Sensing

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