Monitoring boreal forest biomass and carbon storage change by integrating airborne laser scanning, biometry and eddy covariance data

Hopkinson, C.; Chasmer, L.; Barr, Alan G.; Kljun, Natascha; Black, T. Andrew; McCaughey, J. H.

doi:10.1016/j.rse.2016.04.010

Cited by 39 publications

(28 citation statements)

References 78 publications

(102 reference statements)

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“…Ho estimation usually required the inclusion of high height percentiles as concluded by Naesset and Gobakken [17]. V and W estimations normally included either lower or higher height variables, variability metrics, and/or CDM ones as proposed by Silva et al [86] and Hopkinson et al [87].…”

Section: Discussionmentioning

confidence: 99%

Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

et al. 2019

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This study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an area-based approach in Mediterranean Aleppo pine forests. Low-density ALS data were acquired in 2011 and 2016 while 147 forest inventory plots were measured in 2013, 2014, and 2016. Single-tree growth models were used to generate concomitant field data for 2011 and 2016. A comparison of five selection techniques and five regression methods were performed to regress field observations against ALS metrics. The selection of the best regression models fitted for each stand attribute, and separately for both 2011 and 2016, was performed following an indirect approach. Model performance and temporal transferability were analyzed by extrapolating the best fitted models from 2011 to 2016 and inversely from 2016 to 2011 using the direct approach. Non-parametric support vector machine with radial kernel was the best regression method with average relative % root mean square error differences of 2.13% for 2011 models and 1.58% for 2016 ones.

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Section: Discussionmentioning

confidence: 99%

Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

et al. 2019

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“…The insuperable problems of existing estimation-based protocols for net forest carbon sequestration are achievable with existing technology and scientific methods. The eddy covariance method employed at the Howland Forest and NEE1 sites has been applied worldwide as standalone field installations for research purposes [128][129][130] in combination with remote sensing 131,132 and as research networks [133][134][135][136] not only for bulk CO , CH 4 and N 2 O but, in select cases, for corresponding isotopologues. Carbon isotopologues offer additional criteria for ecosystem function and net forest greenhouse gas sequestration that cannot be addressed by CARB-CAR and related estimation protocols.…”

Section: Co 2 Forestmentioning

confidence: 99%

California air resources board forest carbon protocol invalidates offsets

Marino

Mincheva

Doucett

2019

Preprint

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The commercial asset value of sequestered forest carbon is based on protocols employed globally, however, their scientific basis has not been validated. We review and analyze commercial forest carbon protocols and offsets, claimed to have reduced net greenhouse gas emissions, issued by the California Air Resources Board and validated by the Climate Action Reserve (CARB-CAR). CARB-CAR protocol annual offsets, resulting from forest mensuration and growth simulation models, are compared with a population of forest field sites for which annual net ecosystem exchange (NEE) of carbon was measured directly as flux by CO2 eddy covariance, a meteorologically based method integrating forest carbon pools. We characterize differences between the protocols by testing the null hypothesis that the CARB-CAR commercial annual offset data fall within the boundaries of directly measured forest carbon NEE; gC m-2yr-1 are compared for both datasets. Irrespective of geographic location and project type, the CARB-CAR population annual mean value is significantly different from the NEE population mean at the 95% confidence interval, rejecting the null hypothesis. The CARB-CAR population exhibits standard deviation ~5x that of the NEE natural ranges; the variance exceeds the 5% compliance limit for invalidation of CARB-CAR offsets. Exclusion of the soil carbon pool typical for CARB-CAR net carbon budgets pose insuperable carbon accounting uncertainty for offsets that extend to vendor platforms and policies including the United Nations Program on Reducing Emissions from Deforestation and Forest Degradation and the Paris Agreement. NEE methodology for commercial forest carbon offsets ensures in situ molecular specificity, verification of claims for net carbon balance, performance-based pricing and harmonization of carbon protocols for voluntary and compliance markets worldwide, in contrast to continuing uncertainty posed by traditional estimation-based forest carbon protocols.

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“…Remote sensing data can provide a means for accurate and spatially explicit mapping of canopy gaps and characterization of temporal forest canopy gap dynamics as well as the possibility to assess the effects of forest disturbances on mortality, wood volume, biomass and carbon storage over time (Hopkinson et al, ; Stark, ; Valbuena, Maltamo, Mehtätalo, & Packalen, ). In the past years, remote sensing methods have been found to be highly efficient and useful for assessing forest structural properties at a variety of spatial scales.…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing data can provide a means for accurate and spatially explicit mapping of canopy gaps and characterization of temporal forest canopy gap dynamics as well as the possibility to assess the effects of forest disturbances on mortality, wood volume, biomass and carbon storage over time (Hopkinson et al, 2016;Stark, 2012;Valbuena, Maltamo, Mehtätalo, & Packalen, 2017).…”

Section: Introductionmentioning

confidence: 99%

ForestGapR: AnrPackage for forest gap analysis from canopy height models

et al. 2019

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In forest ecosystems, many functional processes are governed by local canopy gap dynamics, caused by either natural or anthropogenic factors. Quantifying the size and spatial distribution of canopy gaps enables an improved understanding and predictive modelling of multiple environmental phenomena. For instance knowledge of canopy gap dynamics can help us elucidate time‐integrated effects of tree mortality, regrowth and succession rates, carbon flux patterns, species heterogeneity and three‐dimensional spacing within structurally complex forest ecosystems. Airborne Laser Scanning (ALS) has emerged as a technology that is well‐suited for mapping forest canopy gaps in a wide variety of forest ecosystems and across spatial scales. New technological and algorithmic advances, including ALS remote‐sensing, coupled with optimized frameworks for data processing and detection of forest canopy gaps, are allowing an enhanced understanding of forest structure and functional processes. This paper introduces ForestGapR, a cutting‐edge open source r package for forest gap analysis from canopy height models derived from ALS and other remote sensing sources. The ForestGapR package offers tools to (a) automate forest canopy gap detection, (b) compute a series of gap statistics, including gap‐size frequency distributions and spatial distribution, (c) map gap dynamics (when multitemporal ALS data are available) and (d) convert forest canopy gaps detected into raster or vector layers as per user requirements. As case studies, we run ForestGapR on ALS data collected over four different tropical forest regions worldwide. We hope this new package will enable further research towards understanding the distribution, dynamics and role of canopy gaps not only in tropical forests, but in other forest types elsewhere.

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Monitoring boreal forest biomass and carbon storage change by integrating airborne laser scanning, biometry and eddy covariance data

Cited by 39 publications

References 78 publications

Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

California air resources board forest carbon protocol invalidates offsets

ForestGapR: AnrPackage for forest gap analysis from canopy height models

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