Bridging theory and implementation – Testing an abstract classification system for practical mapping by field survey and 3D aerial photographic interpretation

Ullerud, Heidrun Asgeirsdatter; Bryn, Anders; Skånes, Helle

doi:10.1080/00291951.2020.1717595

Cited by 4 publications

(4 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dinerstein et al, 2017;Keith et al, 2020). In particular, the number of high-latitude specific PFTs is insufficient to realistically represent the biodiversity of these ecoregions, as pointed out by Bjordal (2018) and Vowles and Björk (2017). Comparisons between PFT profiles obtained by DGVM and profiles obtained by DM suggest specific vegetation types that need to be better represented in DGVMs, either by improving an existing PFT or by adding a new PFT (e.g.…”

Section: Missing Pftsmentioning

confidence: 99%

Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

et al. 2021

Self Cite

View full text Add to dashboard Cite

Abstract. Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterized strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVMs) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products and less often by other vegetation products or by in situ field observations. In this study, we evaluate the performance of three methods for spatial representation of present-day vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DMs), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV; Community Land Model 4.5 Bio-Geo-Chemical cycles and Dynamical Vegetation). While DGVMs predict PFT profiles based on physiological and ecological processes, a DM relies on statistical correlations between a set of predictors and the modelled target, and the RS dataset is based on classification of spectral reflectance patterns of satellite images. PFT profiles obtained from an independently collected field-based vegetation dataset from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVMs often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, three new environmental variables (e.g. minimum temperature in May, snow water equivalent in October and precipitation seasonality) were selected as the threshold for the establishment of these high-latitude PFTs. We performed a series of sensitivity experiments to investigate if these thresholds improve the performance of the DGVM method. Based on our results, we suggest implementation of one of these novel PFT-specific thresholds (i.e. precipitation seasonality) in the DGVM method. The results highlight the potential of using PFT-specific thresholds obtained by DM in development of DGVMs in broader regions. Also, we emphasize the potential of establishing DMs as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.

show abstract

Section: Missing Pftsmentioning

confidence: 99%

Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Field‐based land cover mapping is time‐consuming and expensive. To map efficiently, the field workers use aerial photos for spatial delineation (Cherrill & McClean, 1999b; Ihse, 2007; Ullerud et al, 2020). Interpreting aerial photos requires experience and knowledge and relies on highly trained field workers (Morgan & Gergel, 2010).…”

Section: Discussionmentioning

confidence: 99%

Quantification of accuracy in field‐based land cover maps: A new method to separate different components

Haga¹,

Nilsen

Ullerud

et al. 2021

Applied Vegetation Science

Self Cite

View full text Add to dashboard Cite

Aim Many thematic land cover maps, such as maps of vegetation types, are based on field inventories. Studies show inconsistencies among field workers in such maps, explained by inter‐observer variation in classification and/or spatial delineation of polygons. In this study, we have tested a new method to assess the accuracy of these two components independently. Location Four study sites dominated by different ecosystems in southeast Norway. Methods We have used a vegetation‐based land cover classification system adapted to a map scale of 1:5,000. First, a consensus map, a map that can be considered an approximation of a flawless map, was established. Secondly, the consensus map was adapted to test the accuracy of classification and polygon delineation independently. We used 10 field workers to generate a consensus map, and 14 new field workers (in pairs) to test the accuracy (n = 7). Results The results show that the accuracy of polygon delineation is lower than that of land cover classification. This is in contrast with previous studies, but previous research designs have not enabled a separation of the two accuracy components. Conclusion We recommend strengthening the training and harmonization of field workers in general, and increasing the emphasis on polygon delineation.

show abstract

“…areas such as countries (Ullerud et al, 2020). Area-frame surveys based upon stratified statistical sampling may, however, provide accurate, area-representative, homogeneous and unbiased land-cover and land-use data for large areas.…”

Section: The Ar Reference Datasetmentioning

confidence: 99%

Improving the representation of high-latitude vegetation in Dynamic Global Vegetation Models

Horvath

Tang

Halvorsen

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterised strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVM) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products, but rarely by other vegetation products or by in-situ field observations. In this study, we evaluate the performance of three methods for spatial representation of vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DM), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV). PFT profiles obtained from an independently collected vegetation data set from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVM often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, we suggest implementation of three novel PFT-specific thresholds for establishment in the DGVM. We performed a series of sensitivity experiments to demonstrate that these thresholds improve the performance of the DGVM. The results highlight the potential of using PFT-specific thresholds obtained by DM in development and benchmarking of DGVMs for broader regions. Also, we emphasize the potential of establishing DM as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.

show abstract

Bridging theory and implementation – Testing an abstract classification system for practical mapping by field survey and 3D aerial photographic interpretation

Cited by 4 publications

References 40 publications

Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

Quantification of accuracy in field‐based land cover maps: A new method to separate different components

Improving the representation of high-latitude vegetation in Dynamic Global Vegetation Models

Contact Info

Product

Resources

About