Assessing the structural differences between tropical forest types using Terrestrial Laser Scanning

Mathieu Decuyper, Kalkidan Ayele Mulatu, Benjamin Brede, Kim Calders, John Armston, Danaë M.A. Rozendaal, Brice Mora, Jan G.P.W. Clevers, Lammert Kooistra, Martin Herold, Frans Bongers

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

Increasing anthropogenic pressure leads to loss of habitat through deforestation and degradation in tropical forests. While deforestation can be monitored relatively easily, forest management practices are often subtle processes, that are difficult to capture with for example satellite monitoring. Conventional measurements are well established and can be useful for management decisions, but it is believed that Terrestrial Laser Scanning (TLS) has a role in quantitative monitoring and continuous improvement of methods. In this study we used a combination of TLS and conventional forest inventory measures to estimate forest structural parameters in four different forest types in a tropical montane cloud forest in Kafa, Ethiopia. Here, the four forest types (intact forest, coffee forest, silvopasture, and plantations) are a result of specific management practices (e.g. clearance of understory in coffee forest), and not different forest communities or tree types. Both conventional and TLS derived parameters confirmed our assumptions that intact forest had the highest biomass, silvopasture had the largest canopy gaps, and plantations had the lowest canopy openness. Contrary to our expectations, coffee forest had higher canopy openness and similar biomass as silvopasture, indicating a significant loss of forest structure. The 3D vegetation structure (PAVD – Plant area vegetation density) was different between the forest types with the highest PAVD in intact forest and plantation canopy. Silvopasture was characterised by a low canopy but high understorey PAVD, indicating regeneration of the vegetation and infrequent fuelwood collection and/or non-intensive grazing. Coffee forest canopy had low PAVD, indicating that many trees had been removed, despite coffee needing canopy shade. These findings may advocate for more tangible criteria such as canopy openness thresholds in sustainable coffee certification schemes. TLS as tool for monitoring forest structure in plots with different forest types shows potential as it can capture the 3D position of the vegetation volume and open spaces at all heights in the forest. To quantify changes in different forest types, consistent monitoring of 3D structure is needed and here TLS is an add-on or an alternative to conventional forest structure monitoring. However, for the tropics, TLS-based automated segmentation of trees to derive DBH and biomass is not widely operational yet, nor is species richness determination in forest monitoring. Integration of data sources is needed to fully understand forest structural diversity and implications of forest management practices on different forest types.
Original languageEnglish
Pages (from-to)327-335
JournalForest Ecology and Management
Volume429
DOIs
Publication statusPublished - 2018

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forest types
tropical forests
tropical forest
laser
canopy
coffee
monitoring
plantations
deforestation
vegetation
forest management
understory
biomass
management practice
plantation
canopy gaps
tropical montane cloud forests
fuelwood
forest communities
forest inventory

Cite this

@article{5f0bc8a753d748c3975792e15e5d8005,
title = "Assessing the structural differences between tropical forest types using Terrestrial Laser Scanning",
abstract = "Increasing anthropogenic pressure leads to loss of habitat through deforestation and degradation in tropical forests. While deforestation can be monitored relatively easily, forest management practices are often subtle processes, that are difficult to capture with for example satellite monitoring. Conventional measurements are well established and can be useful for management decisions, but it is believed that Terrestrial Laser Scanning (TLS) has a role in quantitative monitoring and continuous improvement of methods. In this study we used a combination of TLS and conventional forest inventory measures to estimate forest structural parameters in four different forest types in a tropical montane cloud forest in Kafa, Ethiopia. Here, the four forest types (intact forest, coffee forest, silvopasture, and plantations) are a result of specific management practices (e.g. clearance of understory in coffee forest), and not different forest communities or tree types. Both conventional and TLS derived parameters confirmed our assumptions that intact forest had the highest biomass, silvopasture had the largest canopy gaps, and plantations had the lowest canopy openness. Contrary to our expectations, coffee forest had higher canopy openness and similar biomass as silvopasture, indicating a significant loss of forest structure. The 3D vegetation structure (PAVD – Plant area vegetation density) was different between the forest types with the highest PAVD in intact forest and plantation canopy. Silvopasture was characterised by a low canopy but high understorey PAVD, indicating regeneration of the vegetation and infrequent fuelwood collection and/or non-intensive grazing. Coffee forest canopy had low PAVD, indicating that many trees had been removed, despite coffee needing canopy shade. These findings may advocate for more tangible criteria such as canopy openness thresholds in sustainable coffee certification schemes. TLS as tool for monitoring forest structure in plots with different forest types shows potential as it can capture the 3D position of the vegetation volume and open spaces at all heights in the forest. To quantify changes in different forest types, consistent monitoring of 3D structure is needed and here TLS is an add-on or an alternative to conventional forest structure monitoring. However, for the tropics, TLS-based automated segmentation of trees to derive DBH and biomass is not widely operational yet, nor is species richness determination in forest monitoring. Integration of data sources is needed to fully understand forest structural diversity and implications of forest management practices on different forest types.",
author = "Mathieu Decuyper and Mulatu, {Kalkidan Ayele} and Benjamin Brede and Kim Calders and John Armston and Rozendaal, {Dana{\"e} M.A.} and Brice Mora and Clevers, {Jan G.P.W.} and Lammert Kooistra and Martin Herold and Frans Bongers",
year = "2018",
doi = "10.1016/j.foreco.2018.07.032",
language = "English",
volume = "429",
pages = "327--335",
journal = "Forest Ecology and Management",
issn = "0378-1127",
publisher = "Elsevier",

}

Assessing the structural differences between tropical forest types using Terrestrial Laser Scanning. / Decuyper, Mathieu; Mulatu, Kalkidan Ayele; Brede, Benjamin; Calders, Kim; Armston, John; Rozendaal, Danaë M.A.; Mora, Brice; Clevers, Jan G.P.W.; Kooistra, Lammert; Herold, Martin; Bongers, Frans.

In: Forest Ecology and Management, Vol. 429, 2018, p. 327-335.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Assessing the structural differences between tropical forest types using Terrestrial Laser Scanning

AU - Decuyper, Mathieu

AU - Mulatu, Kalkidan Ayele

AU - Brede, Benjamin

AU - Calders, Kim

AU - Armston, John

AU - Rozendaal, Danaë M.A.

AU - Mora, Brice

AU - Clevers, Jan G.P.W.

AU - Kooistra, Lammert

AU - Herold, Martin

AU - Bongers, Frans

PY - 2018

Y1 - 2018

N2 - Increasing anthropogenic pressure leads to loss of habitat through deforestation and degradation in tropical forests. While deforestation can be monitored relatively easily, forest management practices are often subtle processes, that are difficult to capture with for example satellite monitoring. Conventional measurements are well established and can be useful for management decisions, but it is believed that Terrestrial Laser Scanning (TLS) has a role in quantitative monitoring and continuous improvement of methods. In this study we used a combination of TLS and conventional forest inventory measures to estimate forest structural parameters in four different forest types in a tropical montane cloud forest in Kafa, Ethiopia. Here, the four forest types (intact forest, coffee forest, silvopasture, and plantations) are a result of specific management practices (e.g. clearance of understory in coffee forest), and not different forest communities or tree types. Both conventional and TLS derived parameters confirmed our assumptions that intact forest had the highest biomass, silvopasture had the largest canopy gaps, and plantations had the lowest canopy openness. Contrary to our expectations, coffee forest had higher canopy openness and similar biomass as silvopasture, indicating a significant loss of forest structure. The 3D vegetation structure (PAVD – Plant area vegetation density) was different between the forest types with the highest PAVD in intact forest and plantation canopy. Silvopasture was characterised by a low canopy but high understorey PAVD, indicating regeneration of the vegetation and infrequent fuelwood collection and/or non-intensive grazing. Coffee forest canopy had low PAVD, indicating that many trees had been removed, despite coffee needing canopy shade. These findings may advocate for more tangible criteria such as canopy openness thresholds in sustainable coffee certification schemes. TLS as tool for monitoring forest structure in plots with different forest types shows potential as it can capture the 3D position of the vegetation volume and open spaces at all heights in the forest. To quantify changes in different forest types, consistent monitoring of 3D structure is needed and here TLS is an add-on or an alternative to conventional forest structure monitoring. However, for the tropics, TLS-based automated segmentation of trees to derive DBH and biomass is not widely operational yet, nor is species richness determination in forest monitoring. Integration of data sources is needed to fully understand forest structural diversity and implications of forest management practices on different forest types.

AB - Increasing anthropogenic pressure leads to loss of habitat through deforestation and degradation in tropical forests. While deforestation can be monitored relatively easily, forest management practices are often subtle processes, that are difficult to capture with for example satellite monitoring. Conventional measurements are well established and can be useful for management decisions, but it is believed that Terrestrial Laser Scanning (TLS) has a role in quantitative monitoring and continuous improvement of methods. In this study we used a combination of TLS and conventional forest inventory measures to estimate forest structural parameters in four different forest types in a tropical montane cloud forest in Kafa, Ethiopia. Here, the four forest types (intact forest, coffee forest, silvopasture, and plantations) are a result of specific management practices (e.g. clearance of understory in coffee forest), and not different forest communities or tree types. Both conventional and TLS derived parameters confirmed our assumptions that intact forest had the highest biomass, silvopasture had the largest canopy gaps, and plantations had the lowest canopy openness. Contrary to our expectations, coffee forest had higher canopy openness and similar biomass as silvopasture, indicating a significant loss of forest structure. The 3D vegetation structure (PAVD – Plant area vegetation density) was different between the forest types with the highest PAVD in intact forest and plantation canopy. Silvopasture was characterised by a low canopy but high understorey PAVD, indicating regeneration of the vegetation and infrequent fuelwood collection and/or non-intensive grazing. Coffee forest canopy had low PAVD, indicating that many trees had been removed, despite coffee needing canopy shade. These findings may advocate for more tangible criteria such as canopy openness thresholds in sustainable coffee certification schemes. TLS as tool for monitoring forest structure in plots with different forest types shows potential as it can capture the 3D position of the vegetation volume and open spaces at all heights in the forest. To quantify changes in different forest types, consistent monitoring of 3D structure is needed and here TLS is an add-on or an alternative to conventional forest structure monitoring. However, for the tropics, TLS-based automated segmentation of trees to derive DBH and biomass is not widely operational yet, nor is species richness determination in forest monitoring. Integration of data sources is needed to fully understand forest structural diversity and implications of forest management practices on different forest types.

U2 - 10.1016/j.foreco.2018.07.032

DO - 10.1016/j.foreco.2018.07.032

M3 - Article

VL - 429

SP - 327

EP - 335

JO - Forest Ecology and Management

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SN - 0378-1127

ER -