Peter Horvath

• Horvath, Peter; Halvorsen, Rune; Simensen, Trond & Bryn, Anders (2021). A comparison of three ways to assemble wall-to-wall maps from distribution models of vegetation types. GIScience & Remote Sensing. ISSN 1548-1603. 58(8), s. 1458–1476. doi: 10.1080/15481603.2021.1996313. Fulltekst i vitenarkiv Vis sammendrag

  Distribution modeling methods are used to provide occurrence probability surfaces for modeled targets. While most often used for modeling species, distribution modeling methods can also be applied to vegetation types. However, surfaces provided by distribution modeling need to be transformed into classified wall-to-wall maps of vegetation types to be useful for practical purposes, such as nature management and environmental planning. The paper compares the performance of three methods for assembling predictions for multiple vegetation types, modeled individually, into a wall-to-wall map. The authors used grid-cell based probability surfaces from distribution models of 31 vegetation types to test the three assembly methods. The first, a probability-based method, selected for each grid cell the vegetation type with the highest predicted probability of occurrence in that cell. The second, a performance-based method, assigned the vegetation types, ordered from high to low model performance, to a fraction of the grid cells given by the vegetation type’s prevalence in the study area. The third, a prevalence-based method, differed from the performance-based method by assigning vegetation types in the order from low to high prevalence. Thus the assembly methods worked in two principally different ways: the probability-based method assigned vegetation types to grid cells in a cell-by-cell manner, and both the performance-based method and prevalence-based method assigned them in a type-by-type manner. All methods were evaluated by use of reference data collected in the field, more or less independently of the data used to parameterize the vegetation-type models. Quantity, allocation, and total disagreement, as well as proportional dissimilarity metrics, were used for evaluation of assembly methods. Overlay analysis showed 38.1% agreement between all three assembly methods. The probability-based method had the lowest total disagreement with, and proportional dissimilarity from, the reference datasets, but the differences between the three methods were small. The three assembly methods differed strongly with respect to the distribution of the total disagreement on its quantity and allocation components: the cell-by-cell assignment method strongly favored allocation disagreement and the type-by-type methods strongly favored quantity disagreement. The probability-based method best reproduced the general pattern of variation across the study area, but at the cost of many rare vegetation types, which were left out of the assembled map. By contrast, the prevalence-based and performance-based methods represented vegetation types in accordance with nationwide area statistics. The results show that maps of vegetation types with wall-to-wall coverage can be assembled from individual distribution models with a quality acceptable for indicative purposes, but all the three tested methods currently also have shortcomings. The results also indicate specific points in the methodology for map assembly that may be improved. area frame survey, assembly strategies, distribution modeling, spatial probabilities, vegetation mapping, vegetation types

• Horvath, Peter; Tang, Hui; Halvorsen, Rune; Stordal, Frode; Tallaksen, Lena M. & Berntsen, Terje Koren [Vis alle 7 forfattere av denne artikkelen] (2021). Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models. Biogeosciences. ISSN 1726-4170. 18(1), s. 95–112. doi: 10.5194/bg-18-95-2021. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Simensen, Trond; Horvath, Peter; Vollering, Julien; Erikstad, Lars; Halvorsen, Rune & Bryn, Anders (2020). Composite landscape predictors improve distribution models of ecosystem types. Diversity and Distributions: A journal of biological invasions and biodiversity. ISSN 1366-9516. 26(8), s. 928–943. doi: 10.1111/ddi.13060. Fulltekst i vitenarkiv Vis sammendrag

  Aim: Distribution modelling is a useful approach to obtain knowledge about the spatial distribution of biodiversity, required for, for example, red-list assessments. While distribution modelling methods have been applied mostly to single species, modelling of communities and ecosystems (EDM; ecosystem-level distribution modelling) produces results that are more directly relevant for management and decision-making. Although the choice of predictors is a pivotal part of the modelling process, few studies have compared the suitability of different sets of predictors for EDM. In this study, we compare the performance of 50 single environmental variables with that of 11 composite landscape gradients (CLGs) for prediction of ecosystem types. The CLGs represent gradients in landscape element composition derived from multivariate analyses, for example “inner-outer coast” and “land use intensity.” Location: Norway. Methods: We used data from field-based ecosystem-type mapping of nine ecosystem types, and environmental variables with a resolution of 100 × 100 m. We built nine models for each ecosystem type with variables from different predictor sets. Logistic regression with forward selection of variables was used for EDM. Models were evaluated with independently collected data. Results: Most ecosystem types could be predicted reliably, although model performance differed among ecosystem types. We identified significant differences in predictive power and model parsimony across models built from different predictor sets. Climatic variables alone performed poorly, indicating that the current climate alone is not sufficient to predict the current distribution of ecosystems. Used alone, the CLGs resulted in parsimonious models with relatively high predictive power. Used together with other variables, they consistently improved the models. Main conclusions: Our study highlights the importance of variable selection in EDM. We argue that the use of composite variables as proxies for complex environmental gradients has the potential to improve predictions from EDMs and thus to inform conservation planning as well as improve the precision and credibility of red lists and global change assessments.conservation planning, distribution modelling, ecosystem classification, ecosystem types, IUCN Red List of Ecosystems, landscape gradients, spatial prediction, species response curves

• Horvath, Peter; Halvorsen, Rune; Stordal, Frode; Tallaksen, Lena Merete; Tang, Hui & Bryn, Anders (2019). Distribution modelling of vegetation types based on area frame survey data. Applied Vegetation Science. ISSN 1402-2001. 22(4), s. 547–560. doi: 10.1111/avsc.12451. Vis sammendrag

  Aim: Many countries lack informative, high‐resolution, wall‐to‐wall vegetation or land cover maps. Such maps are useful for land use and nature management, and for input to regional climate and hydrological models. Land cover maps based on remote sensing data typically lack the required ecological information, whereas traditional field‐based mapping is too expensive to be carried out over large areas. In this study, we therefore explore the extent to which distribution modelling (DM) methods are useful for predicting the current distribution of vegetation types (VT) on a national scale. Location: Mainland Norway, covering ca. 324,000 km2. Methods: We used presence/absence data for 31 different VTs, mapped wall‐to‐wall in an area frame survey with 1081 rectangular plots of 0.9 km2. Distribution models for each VT were obtained by logistic generalised linear modelling, using stepwise forward selection with an F‐ratio test. A total of 116 explanatory variables, recorded in 100 m × 100 m grid cells, were used. The 31 models were evaluated by applying the AUC criterion to an independent evaluation dataset. Results: Twenty‐one of the 31 models had AUC values higher than 0.8. The highest AUC value (0.989) was obtained for Poor/rich broadleaf deciduous forest, whereas the lowest AUC (0.671) was obtained for Lichen and heather spruce forest. Overall, we found that rare VTs are predicted better than common ones, and coastal VTs are predicted better than inland ones. Conclusions: Our study establishes DM as a viable tool for spatial prediction of aggregated species‐based entities such as VTs on a regional scale and at a fine (100 m) spatial resolution, provided relevant predictor variables are available. We discuss the potential uses of distribution models in utilizing large‐scale international vegetation surveys. We also argue that predictions from such models may improve parameterisation of vegetation distribution in earth system models.

• Eriksen, Eva Lieungh; Ullerud, Heidrun Asgeirsdatter; Halvorsen, Rune; Aune, Sigrun; Bratli, Harald & Horvath, Peter [Vis alle 9 forfattere av denne artikkelen] (2018). Point of view: error estimation in field assignment of land-cover types. Phytocoenologia. ISSN 0340-269X. 49(2), s. 135–148. doi: 10.1127/phyto/2018/0293. Fulltekst i vitenarkiv

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• Bryn, Anders; Dalen, Thea Grobstok; Finne, Eirik Aasmo; Heiberg, Hanne; Keetz, Lasse Torben & Nilsen, Irene Brox [Vis alle 30 forfattere av denne artikkelen] (2021). Natur i endring - samspillet mellom klima og økosystemene.
• Keetz, Lasse Torben; Bryn, Anders; Horvath, Peter; Skarpaas, Olav; Tallaksen, Lena M. & Zliobaite, Indre (2021). Using machine learning to model the distribution of Vegetation Types across Norway.
• Fouilloux, Anne Claire; Tang, Hui; Lieungh, Eva; Geange, Sonya Rita; Horvath, Peter & Bryn, Anders (2020). FATES on GALAXY facilitates ecologist and climate modeler collaboration.
• Fouilloux, Anne Claire; Tang, Hui; Lieungh, Eva; Geange, Sonya Rita; Horvath, Peter & Bryn, Anders (2020). Climate JupyterLab as an interactive tool in Galaxy.
• Fouilloux, Anne Claire; Tang, Hui; Lieungh, Eva; Geange, Sonya Rita; Horvath, Peter & Bryn, Anders (2020). Functionally Assembled Terrestrial Ecosystem Simulator (FATES) with Community Land Model in Galaxy.
• Bryn, Anders; Volden, Inger Kristine; Horvath, Peter; Torma, Michal & Stordal, Frode (2019). Hvor raskt stiger tre- og skoggrensene i Norge? Folkeforskning i praksis. .
• Skarpaas, Olav; Bryn, Anders; Torma, Michal; Horvath, Peter & Volden, Inger Kristine (2018). Folkeforskning med mobil-app: tregrenser og naturmangfold.
• Bryn, Anders; Volden, Inger Kristine; Horvath, Peter & Torma, Michal (2018). Folkeforskning: Norge gror igjen - hva skjer i naturen og hvordan påvirkes ferdsel og opplevelser? Vis sammendrag

  Skoggrensa i Norge er på vei oppover. I et forsøk på å bevisstgjøre folk på endringene som skjer i naturen, har Naturhistorisk museum i Oslo og Den Norske Turistforening utviklet folkeforskningsprosjektet Natur i endring. Skjelettet i prosjektet er en app, som gir turgåere anledning til å registrere de øverste skogene og trærne. Appen gir folk en plattform som de selv kan utforske det norske landskapet ut ifra. Deltakerne må løfte blikket på jakt etter høytliggende trær og skoger. Samtidig vil de forhåpentligvis reflektere over hvorfor disse trærne befinner seg akkurat der. Allerede da har de tatt inn over seg noe essensielt, at naturen påvirkes av oss mennesker. Da blir det kanskje lettere å forstå hvorfor klimaendringene ikke bare smelter is på Nordpolen, men at de er med på å endre det norske fjellandskapet i dag. Prosjektet har mottatt finansiell støtte fra Sparebankstiftelsen DNB.

• Bryn, Anders; Torma, Michal; Horvath, Peter & Volden, Inger Kristine (2018). Natur i endring (mobil app for iOS og Android, tilgjengelig på norsk og engelsk).
• Bryn, Anders; Torma, Michal; Volden, Inger Kristine & Horvath, Peter (2018). www.naturiendring.no.
• Horvath, Peter; Halvorsen, Rune; Stordal, Frode; Tang, Hui & Bryn, Anders (2018). Distribution models of vegetation types in Norway.
• Haugen, Marianne Nilsen & Horvath, Peter (2018). Nå kan også du bli «klimaforsker». [Avis]. Aftenposten.
• Bryn, Anders; Horvath, Peter & Volden, Inger Kristine (2017). Høyest til fjells i landet. [Avis]. Gudbrandsdølen Dagningen.
• Bryn, Anders; Horvath, Peter & Volden, Inger Kristine (2017). Norsk høyderekord for liten bjørk. [Avis]. Varden.
• Bryn, Anders; Horvath, Peter & Volden, Inger Kristine (2017). Norsk rekord for lita bjørk. [Avis]. Nationen.
• Bryn, Anders; Horvath, Peter & Volden, Inger Kristine (2017). Norsk rekord for liten bjørk. [Avis]. Tønsberg Blad.
• Horvath, Peter; Tang, Hui; Stordal, Frode & Bryn, Anders (2017). Terrestrial vegetation ecological climatology.
• Bryn, Anders; Volden, Inger Kristine; Horvath, Peter & Stordal, Frode (2017). Skogen er på fjelltur. Aftenposten (morgenutg. : trykt utg.). ISSN 0804-3116. s. 12–13.
• Volden, Inger Kristine; Horvath, Peter & Bryn, Anders (2017). Ei historie om Lærdalsskogen. Sogn Avis. s. 12–12.
• Bryn, Anders; Potthoff, Kerstin; Horvath, Peter; Volden, Inger Kristine; Tang, Hui & Berntsen, Terje Koren [Vis alle 7 forfattere av denne artikkelen] (2017). Greening and browning: 100 years of tree- and forest line dynamics.
• Horvath, Peter (2022). Ecological climatology and distribution modelling. Universitetet i Oslo Det matematisk-naturvitenskapelige fakultet. Fulltekst i vitenarkiv
• Halvorsen, Rune; Wollan, Anders Kvalvåg; Bryn, Anders; Bratli, Harald & Horvath, Peter (2021). Naturtypekart etter NiN for området omkring Veia (Nedre Eiker og Øvre Eiker, Buskerud). Naturhistorisk museum. ISSN 978-82-7970-126-2. Fulltekst i vitenarkiv
• Bryn, Anders & Horvath, Peter (2020). Kartlegging av NiN naturtyper i målestokk 1:5000 rundt flux-tårnet og på Hansbunuten, Finse (Vestland). Naturhistorisk museum, Universitetet i Oslo. ISSN 978-82-7970-122-4. Fulltekst i vitenarkiv
• Halvorsen, Rune; Bryn, Anders; Bratli, Harald & Horvath, Peter (2020). Naturtypekart etter NiN for et område omkring Unsetsætra (Biri, Gjøvik, Oppland). Naturhistorisk Museum. ISSN 978–82–7970–120–0. Fulltekst i vitenarkiv
• Ellefsen, Halvor Weider; Barton, David Nicholas; Ardilla, Pedro; Blumentrath, Stefan; Horvath, Peter & Aamlid, Helene [Vis alle 9 forfattere av denne artikkelen] (2019). MOT EN BLÅGRØNN EIENDOMS-UTVIKLING? «Stresstesting» av Blågrønn Faktor på utvalgte case studier i Bærum Kommune. Arkitekthøyskolen i Oslo. ISSN 978-82-547-0324-3.
• Horvath, Peter; Nilsen, Anne-Barbi & Bryn, Anders (2019). Oppsett og tilrettelegging av QGIS for NiN naturtypekartlegging. Naturhistorisk museum. ISSN 1891-8050. Fulltekst i vitenarkiv
• Barton, David Nicholas; Hauglin, Espen Aukrust; Horvath, Peter & Ellefsen, Halvor Weider (2017). Blue-Green Factor (BGF) mapping in QGIS. The Norwegian Institute for Nature Research. ISSN 978-82-426-3176-3.
• Horvath, Peter; Barton, David Nicholas; Hauglin, Espen Aukrust & Ellefsen, Halvor Weider (2017). Blue-Green Factor (BGF) mapping in QGIS. User Guide and Documentation. NINA rapport. ISSN 978-82-426-3176-3. Vis sammendrag

  The blue-green factor (BGF) is a rapid assessment tool to help quantify minimum municipal requirements for surface water management, vegetation qualities and biodiversity in outdoor areas of property developments. BGF-QGIS makes it possible to calculate the blue-green factor for larger areas than in the original Excel-based methodology, by taking advantage of remote sensing data, GIS datasets, and CAD-based design proposals. It also provides a flexible platform for adjusting the scoring of blue-green qualities to suit specific local conditions and priorities. Norway, blue-green infrastructure, urban ecosystem accounting, Norge, blågrønn infrastruktur, urbant økosystemregnskap

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