Estimation and Mapping of Carbon Stocks in Riparian Forests by using a Machine Learning Approach with Multiple Geodata

Suchenwirth, Leonhard; Förster, Michael; Lang, Friederike; Kleinschmit, Freiburg Birgit

doi:10.1127/1432-8364/2013/0181

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…This study provides a good example of how CART can be used in the multisites synthesis. As a classification method, the CART analysis has been widely applied at the landscape scale studies using spatial data (Fellman, Buma, Hood, Edwards, & D'Amore, 2017;Rothwell, Futter, & Dise, 2008;Suchenwirth, Forster, Lang, & Kleinschmit, 2013)…”

Section: Modeling the Sensitivities Of Nepmentioning

confidence: 99%

Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems

Arain

Black

et al. 2019

Global Change Biology

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Climate extremes such as heat waves and droughts are projected to occur more frequently with increasing temperature and an intensified hydrological cycle. It is important to understand and quantify how forest carbon fluxes respond to heat and drought stress. In this study, we developed a series of daily indices of sensitivity to heat and drought stress as indicated by air temperature (Ta) and evaporative fraction (EF). Using normalized daily carbon fluxes from the FLUXNET Network for 34 forest sites in North America, the seasonal pattern of sensitivities of net ecosystem productivity (NEP), gross ecosystem productivity (GEP) and ecosystem respiration (RE) in response to Ta and EF anomalies were compared for different forest types. The results showed that warm temperatures in spring had a positive effect on NEP in conifer forests but a negative impact in deciduous forests. GEP in conifer forests increased with higher temperature anomalies in spring but decreased in summer. The drought‐induced decrease in NEP, which mostly occurred in the deciduous forests, was mostly driven by the reduction in GEP. In conifer forests, drought had a similar dampening effect on both GEP and RE, therefore leading to a neutral NEP response. The NEP sensitivity to Ta anomalies increased with increasing mean annual temperature. Drier sites were less sensitive to drought stress in summer. Natural forests with older stand age tended to be more resilient to the climate stresses compared to managed younger forests. The results of the Classification and Regression Tree analysis showed that seasons and ecosystem productivity were the most powerful variables in explaining the variation of forest sensitivity to heat and drought stress. Our results implied that the magnitude and direction of carbon flux changes in response to climate extremes are highly dependent on the seasonal dynamics of forests and the timing of the climate extremes.

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Section: Modeling the Sensitivities Of Nepmentioning

confidence: 99%

Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems

Arain

Black

et al. 2019

Global Change Biology

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The interplay of sedimentation and carbon accretion in riparian forests

et al. 2014

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Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

Suchenwirth

Stümer²,

Schmidt

et al. 2014

Forests

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Among the machine learning tools being used in recent years for environmental applications such as forestry, self-organizing maps (SOM) and the k-nearest neighbor (kNN) algorithm have been used successfully. We applied both methods for the mapping of organic carbon (C org ) in riparian forests due to their considerably high carbon storage capacity. Despite the importance of floodplains for carbon sequestration, a sufficient scientific foundation for creating large-scale maps showing the spatial C org distribution is still missing. We estimated organic carbon in a test site in the Danube Floodplain based on RapidEye remote sensing data and additional geodata. Accordingly, carbon distribution maps of vegetation, soil, and total C org stocks were derived. Results were compared and statistically evaluated with terrestrial survey data for outcomes with pure remote sensing data and for the combination with additional geodata using bias and the Root Mean Square Error (RMSE). Results show that SOM and kNN approaches enable us to reproduce spatial patterns of riparian forest C org stocks. While vegetation C org has very high RMSEs, outcomes for soil and total C org stocks are less biased with a lower RMSE, especially when remote sensing and additional geodata are conjointly applied. SOMs show similar percentages of RMSE to kNN estimations. OPEN ACCESSForests 2014, 5 1636

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Estimation and Mapping of Carbon Stocks in Riparian Forests by using a Machine Learning Approach with Multiple Geodata

Cited by 3 publications

References 35 publications

Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems

Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems

The interplay of sedimentation and carbon accretion in riparian forests

Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

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