A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits
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)—n...
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| Format: | Article |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/4/933 |
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| author | Jaz Stoddart Danilo Roberti Alves de Almeida Carlos Alberto Silva Eric Bastos Görgens Michael Keller Ruben Valbuena |
| author_facet | Jaz Stoddart Danilo Roberti Alves de Almeida Carlos Alberto Silva Eric Bastos Görgens Michael Keller Ruben Valbuena |
| author_sort | Jaz Stoddart |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:08:56Z |
| format | Article |
| id | doaj.art-275bdf26e21c4c88abc3be20827c4221 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:08:56Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-275bdf26e21c4c88abc3be20827c42212023-11-23T21:54:22ZengMDPI AGRemote Sensing2072-42922022-02-0114493310.3390/rs14040933A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological TraitsJaz Stoddart0Danilo Roberti Alves de Almeida1Carlos Alberto Silva2Eric Bastos Görgens3Michael Keller4Ruben Valbuena5School of Natural Sciences, Bangor University, Bangor LL57 2DG, UKSchool of Natural Sciences, Bangor University, Bangor LL57 2DG, UKForest Biometrics and Remote Sensing Lab (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USADepartment of Forest Engineering, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Alto da Jucuba, Campus JK, Diamantina 39100-000, MG, BrazilUSDA-Forest Service International Institute of Tropical Forestry, Jardin Botanico Sur, 1201 Calle Ceiba, San Juan, PR 00926, USASchool of Natural Sciences, Bangor University, Bangor LL57 2DG, UKCurrent 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.https://www.mdpi.com/2072-4292/14/4/933vegetation structurecarbon stockLiDARmodelling |
| spellingShingle | Jaz Stoddart Danilo Roberti Alves de Almeida Carlos Alberto Silva Eric Bastos Görgens Michael Keller Ruben Valbuena A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits Remote Sensing vegetation structure carbon stock LiDAR modelling |
| title | A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits |
| title_full | A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits |
| title_fullStr | A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits |
| title_full_unstemmed | A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits |
| title_short | A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits |
| title_sort | conceptual model for detecting small scale forest disturbances based on ecosystem morphological traits |
| topic | vegetation structure carbon stock LiDAR modelling |
| url | https://www.mdpi.com/2072-4292/14/4/933 |
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