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

Stoddart, Jaz and Alves de Almeida, Danilo Roberti and Silva, Carlos Alberto and Gorgens, Eric Bastos and Keller, Michael and Valbuena, Ruben (2022). A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits. Remote Sensing. 14 :4 , 933
[Research article]

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Abstract

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.

Authors/Creators:Stoddart, Jaz and Alves de Almeida, Danilo Roberti and Silva, Carlos Alberto and Gorgens, Eric Bastos and Keller, Michael and Valbuena, Ruben
Title:A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits
Series Name/Journal:Remote Sensing
Year of publishing :2022
Volume:14
Number:4
Article number:933
Number of Pages:20
Publisher:MDPI
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
(A) Swedish standard research categories 2011 > 1 Natural sciences > 105 Earth and Related Environmental Sciences > Environmental Sciences (social aspects to be 507)
Keywords:vegetation structure, carbon stock, LiDAR, modelling
URN:NBN:urn:nbn:se:slu:epsilon-p-118342
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-118342
Additional ID:
Type of IDID
DOI10.3390/rs14040933
Web of Science (WoS)000821668400001
ID Code:28699
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:01 Sep 2022 07:45
Metadata Last Modified:01 Sep 2022 07:51

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