Drone‐based photogrammetry‐derived crown metrics for predicting tree and oil palm water use

Ahongshangbam, Joyson; Khokthong, Watit; Ellsäßer, Florian; Hendrayanto, Hendrayanto; Hölscher, Dirk; Röll, Alexander

doi:10.1002/eco.2115

Cited by 16 publications

(20 citation statements)

References 59 publications

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“…Compared to the DBH approach (51% uncertainty), our crown-metric-based approach reduced scaling uncertainties (17%). This finding is in line with a previous study, where drone-derived crown volume substantially reduced E t uncertainties in oil palm agroforests and monocultures [5].…”

Section: Discussionsupporting

confidence: 93%

“…Among the 42 identified sap-flux trees, we found close correlations between tree water use and crown metrics (best: crown surface area, R 2 = 0.76). Such a relationship has already been indicated for trees and palms in an agroforest [5]. Pooling these data suggests that a universal scaling may apply for trees, but palms are different, and seem to follow another scaling factor ( Figure A3).…”

Section: Discussionsupporting

confidence: 59%

“…Due to often relatively high unexplained variability in the DBH to water use relationship, resulting uncertainties of E t at the stand-level are also relatively high. In a tropical agroforest, Ahongshangbam et al [5] found that drone-derived crown metrics correlated much better with tree water use than DBH. In consequence, uncertainties associated with the scaling to stand-level E t were reduced considerably.…”

Section: Introductionmentioning

confidence: 99%

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Airborne Tree Crown Detection for Predicting Spatial Heterogeneity of Canopy Transpiration in a Tropical Rainforest

et al. 2020

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Tropical rainforests comprise complex 3D structures and encompass heterogeneous site conditions; their transpiration contributes to climate regulation. The objectives of our study were to test the relationship between tree water use and crown metrics and to predict spatial variability of canopy transpiration across sites. In a lowland rainforest of Sumatra, we measured tree water use with sap flux techniques and simultaneously assessed crown metrics with drone-based photogrammetry. We observed a close linear relationship between individual tree water use and crown surface area (R2 = 0.76, n = 42 trees). Uncertainties in predicting stand-level canopy transpiration were much lower using tree crown metrics than the more conventionally used stem diameter. 3D canopy segmentation analyses in combination with the tree crown–water use relationship predict substantial spatial heterogeneity in canopy transpiration. Among our eight study plots, there was a more than two-fold difference, with lower transpiration at riparian than at upland sites. In conclusion, we regard drone-based canopy segmentation and crown metrics to be very useful tools for the scaling of transpiration from tree- to stand-level. Our results indicate substantial spatial variation in crown packing and thus canopy transpiration of tropical rainforests.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

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Airborne Tree Crown Detection for Predicting Spatial Heterogeneity of Canopy Transpiration in a Tropical Rainforest

et al. 2020

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“…Many field-based studies underpinned the specific phenology and growth of oil palm and its key physiological processes (Noor and Harun, 2004;Lamade and Bouillet, 2005;Sunaryathy et al, 2015;Ahongshangbam et al, 2019). Models developed based on these field observations provide a useful tool for large-scale simulation of oil palm growth and yields and their impacts on the regional carbon, water and energy budgets.…”

Section: Introductionmentioning

confidence: 99%

Oil palm modelling in the global land surface model ORCHIDEE-MICT

Ciais

et al. 2021

Geosci. Model Dev.

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Abstract. Oil palm is the most productive oil crop that provides ∼ 40 % of the global vegetable oil supply, with 7 % of the cultivated land devoted to oil plants. The rapid expansion of oil palm cultivation is seen as one of the major causes for deforestation emissions and threatens the conservation of rain forest and swamp areas and their associated ecosystem services in tropical areas. Given the importance of oil palm in oil production and its adverse environmental consequences, it is important to understand the physiological and phenological processes of oil palm and its impacts on the carbon, water and energy cycles. In most global vegetation models, oil palm is represented by generic plant functional types (PFTs) without specific representation of its morphological, physical and physiological traits. This would cause biases in the subsequent simulations. In this study, we introduced a new specific PFT for oil palm in the global land surface model ORCHIDEE-MICT (v8.4.2, Organising Carbon and Hydrology in Dynamic Ecosystems–aMeliorated Interactions between Carbon and Temperature). The specific morphology, phenology and harvest process of oil palm were implemented, and the plant carbon allocation scheme was modified to support the growth of the branch and fruit component of each phytomer. A new age-specific parameterization scheme for photosynthesis, autotrophic respiration and carbon allocation was also developed for the oil palm PFT, based on observed physiology, and was calibrated by observations. The improved model generally reproduces the leaf area index, biomass density and fruit yield during the life cycle at 14 observation sites. Photosynthesis, carbon allocation and biomass components for oil palm also agree well with observations. This explicit representation of oil palm in a global land surface model offers a useful tool for understanding the ecological processes of oil palm growth and assessing the environmental impacts of oil palm plantations.

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“…Many field-based studies underpinned the specific phenology and growth of oil palm and its key physiological processes (Ahongshangbam et al, 2019;Lamade and Bouillet, 2005;Noor and Harun, 2004;Sunaryathy et al, 2015). Models developed based on these field observations provide a useful tool for large-scale simulation of oil palm growth and yields and their impacts on the regional carbon, water and energy budgets.…”

Section: Introductionmentioning

confidence: 99%

Oil palm modelling in the global land-surface model ORCHIDEE-MICT

Xu¹,

Ciais²,

Yu³

et al. 2020

Preprint

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Abstract. Oil palm is the most productive oil crop that provides ~40 % of the global vegetable oil supply, with 7 % of the cultivated land devoted to oil plants. The rapid expansion of oil palm cultivation is seen as one of the major cause for deforestation emissions and threatens the conservation of rain forest and swamp areas and their associated ecosystem services in tropical areas. Given the importance of oil palm in oil production and its adverse environmental consequences, it is important to understand the physiological and phenological processes of oil palm and its impacts on the carbon, water and energy cycles. In most global vegetation models, oil palm is represented by generic plant functional types (PFT) without specific representation of its morphological, physical and physiological traits. This would cause biases in the subsequent simulations. In this study, we introduced a new specific PFT for oil palm in the global land surface model ORCHIDEE-MICT (v8.4.2). The specific morphology, phenology and harvest process of oil palm were implemented, and the plant carbon allocation scheme was modified to support the growth of branch, leaf and fruit component of each phytomer. A new age-specific parameterization scheme for photosynthesis, autotrophic respiration, and carbon allocation was also developed for the oil palm PFT, based on observed physiology, and was calibrated by observations. The improved model generally reproduces the leaf area index, biomass density and fruit yield during the life cycle at 14 observation sites. Photosynthesis, carbon allocation and biomass components for oil palm also agree well with observations. This explicit representation of oil palm in global land surface model offers a useful tool for understanding the ecological processes of oil palm growth and assessing the environmental impacts of oil palm plantations.

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Drone‐based photogrammetry‐derived crown metrics for predicting tree and oil palm water use

Cited by 16 publications

References 59 publications

Airborne Tree Crown Detection for Predicting Spatial Heterogeneity of Canopy Transpiration in a Tropical Rainforest

Airborne Tree Crown Detection for Predicting Spatial Heterogeneity of Canopy Transpiration in a Tropical Rainforest

Oil palm modelling in the global land surface model ORCHIDEE-MICT

Oil palm modelling in the global land-surface model ORCHIDEE-MICT

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