Peter Horvath

• Naas, Adam Eindride; Keetz, Lasse Torben; Halvorsen, Rune; Horvath, Peter; Mienna, Ida Marielle & Simensen, Trond [Show all 7 contributors for this article] (2024). Choice of predictors and complexity for ecosystem distribution models: effects on performance and transferability. Ecography. ISSN 0906-7590. doi: 10.1111/ecog.07269.
• Naas, Adam Eindride; Keetz, Lasse Torben; Halvorsen, Rune; Horvath, Peter; Mienna, Ida Marielle & Simensen, Trond [Show all 7 contributors for this article] (2024). Choice of predictors and complexity for ecosystem distribution models: effects on performance and transferability. Ecography. ISSN 0906-7590. p. 1–14. doi: 10.1111/ecog.07269.
• Pirk, Norbert; Aalstad, Kristoffer; Yilmaz, Yeliz A.; Vatne, Astrid; Popp, Andrea & Horvath, Peter [Show all 12 contributors for this article] (2023). Snow-vegetation-atmosphere interactions in alpine tundra. Biogeosciences. ISSN 1726-4170. 20(11), p. 2031–2047. doi: 10.5194/bg-20-2031-2023. Full text in Research Archive Show summary

  The interannual variability of snow cover in alpine areas is increasing, which may affect the tightly coupled cycles of carbon and water through snow–vegetation–atmosphere interactions across a range of spatio-temporal scales. To explore the role of snow cover for the land–atmosphere exchange of CO2 and water vapor in alpine tundra ecosystems, we combined 3 years (2019–2021) of continuous eddy covariance flux measurements of the net ecosystem exchange of CO2 (NEE) and evapotranspiration (ET) from the Finse site in alpine Norway (1210 m a.s.l.) with a ground-based ecosystem-type classification and satellite imagery from Sentinel-2, Landsat 8, and MODIS. While the snow conditions in 2019 and 2021 can be described as site typical, 2020 features an extreme snow accumulation associated with a strong negative phase of the Scandinavian pattern of the synoptic atmospheric circulation during spring. This extreme snow accumulation caused a 1-month delay in melt-out date, which falls in the 92nd percentile in the distribution of yearly melt-out dates in the period 2001–2021. The melt-out dates follow a consistent fine-scale spatial relationship with ecosystem types across years. Mountain and lichen heathlands melt out more heterogeneously than fens and flood plains, while late snowbeds melt out up to 1 month later than the other ecosystem types. While the summertime average normalized difference vegetation index (NDVI) was reduced considerably during the extreme-snow year 2020, it reached the same maximum as in the other years for all but one of the ecosystem types (late snowbeds), indicating that the delayed onset of vegetation growth is compensated to the same maximum productivity. Eddy covariance estimates of NEE and ET are gap-filled separately for two wind sectors using a random forest regression model to account for complex and nonlinear ecohydrological interactions. While the two wind sectors differ markedly in vegetation composition and flux magnitudes, their flux response is controlled by the same drivers as estimated by the predictor importance of the random forest model, as well as by the high correlation of flux magnitudes (correlation coefficient r=0.92 for NEE and r=0.89 for ET) between both areas. The 1-month delay of the start of the snow-free season in 2020 reduced the total annual ET by 50 % compared to 2019 and 2021 and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink (−31 to −6 gC m−2 yr−1) to a source (34 to 20 gC m−2 yr−1). These results underpin the strong dependence of ecosystem structure and functioning on snow dynamics, whose anomalies can result in important ecological extreme events for alpine ecosystems.

• Keetz, Lasse Torben; Lieungh, Eva; Karimi-Asli, Kaveh; Geange, Sonya Rita; Gelati, Emiliano & Tang, Hui [Show all 24 contributors for this article] (2023). Climate–ecosystem modelling made easy: The Land Sites Platform. Global Change Biology. ISSN 1354-1013. 29(15), p. 4440–4452. doi: 10.1111/gcb.16808. Full text in Research Archive Show summary

  Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.

• Naas, Adam Eindride; Halvorsen, Rune; Horvath, Peter; Wollan, Anders Kvalvåg; Bratli, Harald & Brynildsrud, Katrine Marie [Show all 16 contributors for this article] (2023). What explains inconsistencies in field-based ecosystem mapping? Applied Vegetation Science. ISSN 1402-2001. 26(1). doi: 10.1111/avsc.12715. Full text in Research Archive Show summary

  Questions Field-based ecosystem mapping is prone to observer bias, typically resulting in a mismatch between maps made by different mappers, that is, inconsistency. Experimental studies testing the influence of site, mapping scale, and differences in experience level on inconsistency in field-based ecosystem mapping are lacking. Here, we study how inconsistencies in field-based ecosystem maps depend on these factors. Location Iškoras and Guollemuorsuolu, northeastern Norway, and Landsvik and Lygra, western Norway. Methods In a balanced experiment, four sites were field-mapped wall-to-wall to scales 1:5000 and 1:20,000 by 12 mappers, representing three experience levels. Thematic inconsistency was calculated by overlay analysis of map pairs from the same site, mapped to the same scale. We tested for significant differences between sites, scales, and experience-level groups. Principal components analysis was used in an analysis of additional map inconsistencies and their relationships with site, scale and differences in experience level and time consumption were analysed with redundancy analysis. Results On average, thematic inconsistency was 51%. The most important predictor for thematic inconsistency, and for all map inconsistencies, was site. Scale and its interaction with site predicted map inconsistencies, but only the latter were important for thematic inconsistency. The only experience-level group that differed significantly from the mean thematic inconsistency was that of the most experienced mappers, with nine percentage points. Experience had no significant effect on map inconsistency as a whole. Conclusion Thematic inconsistency was high for all but the dominant thematic units, with potentially adverse consequences for mapping ecosystems that are fragmented or have low coverage. Interactions between site and mapping system properties are considered the main reasons why no relationships between scale and thematic inconsistency were observed. More controlled experiments are needed to quantify the effect of other factors on inconsistency in field-based mapping.

• 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), p. 1458–1476. doi: 10.1080/15481603.2021.1996313. Full text in Research Archive Show summary

  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 [Show all 7 contributors for this article] (2021). Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models. Biogeosciences. ISSN 1726-4170. 18(1), p. 95–112. doi: 10.5194/bg-18-95-2021. Full text in Research Archive Show summary

  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 Conservation Biogeography. ISSN 1366-9516. 26(8), p. 928–943. doi: 10.1111/ddi.13060. Full text in Research Archive Show summary

  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), p. 547–560. doi: 10.1111/avsc.12451. Show summary

  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 [Show all 9 contributors for this article] (2018). Point of view: error estimation in field assignment of land-cover types. Phytocoenologia. ISSN 0340-269X. 49(2), p. 135–148. doi: 10.1127/phyto/2018/0293. Full text in Research Archive Show summary

  Questions: Substantial variation between observers has been found when comparing parallel land-cover maps, but how can we know which map is better? What magnitude of error and inter-observer variation is expected when assigning land-cover types and is this affected by the hierarchical level of the type system, observer characteristics, and ecosystem properties? Study area: Hvaler, south-east Norway. Methods: Eleven observers assigned mapping units to 120 stratified random points. At each observation point, the observers first assigned a mapping unit to the point independently. The group then decided on a ‘true’ reference mapping unit for that point. The reference was used to estimate total error. ‘Ecological distance’ to the reference was calculated to grade the errors. Results: Individual observers frequently assigned different mapping units to the same point. Deviating assignments were often ecologically close to the reference. Total error, as percentage of assignments that deviated from the reference, was 35.0% and 16.4% for low and high hierarchical levels of the land-covertype system, respectively. The corresponding figures for inter-observer variation were 42.8% and 19.4%, respectively. Observer bias was found. Particularly high error rates were found for land-cover types characterised by human disturbance. Conclusions: Access to a ‘true’ mapping unit for each observation point enabled estimation of error in addition to the inter-observer variation typically estimated by the standard pairwise comparisons method for maps and observers. Three major sources of error in the assignment of land-cover types were observed: dependence on system complexity represented by the hierarchical level of the land-cover-type system, dependence on the experience and personal characteristics of the observers, and dependence on properties of the mapped ecosystem. The results support the necessity of focusing on quality in land-cover mapping, among commissioners, practitioners and other end users.

View all works in Cristin

• Edvardsen, Anette; Halvorsen, Rune; Bratli, Harald; Bryn, Anders; Dervo, Børre Kind & Erikstad, Lars [Show all 11 contributors for this article] (2024). Natur i Norge. Variasjon satt i system. Universitetsforlaget. ISBN 978-82-15-06986-9. 434 p.

View all works in Cristin

• Bryn, Anders; Koren Berntsen, Terje; Horvath, Peter; Isaksen, Ketil; Liahjell, Gunnar Thorsen & Naas, Adam Eindride [Show all 10 contributors for this article] (2024). På vei mot nye høyder: tre- og skoggrensene stiger. Arr - Idéhistorisk tidsskrift. ISSN 0802-7005. p. 87–100.
• Bryn, Anders; Horvath, Peter; Naas, Adam Eindride & Tjessem, Ingrid Vesterdal (2023). Kartlegging av naturtyper etter Natur i Norge (NiN). Biolog. ISSN 0801-0722. p. 16–23.
• Tjessem, Ingrid Vesterdal; Bryn, Anders; Horvath, Peter & Naas, Adam Eindride (2023). Changing treeline: A 150-year perspective​.
• Bryn, Anders; Horvath, Peter; Volden, Inger Kristine; Stordal, Frode; Torma, Michal & Tjessem, Ingrid Vesterdal [Show all 7 contributors for this article] (2023). Citizen science for treeline mapping.
• Bryn, Anders; Brynildsrud, Katrine Marie; Horvath, Peter; Keetz, Lasse Torben; Lieungh, Eva & Naas, Adam Eindride [Show all 9 contributors for this article] (2023). “Ground-truth" from field-based ecosystem mapping.
• Bryn, Anders; Horvath, Peter; Torma, Michal; Naas, Adam Eindride; Volden, Inger Kristine & Liahjell, Gunnar Thorsen [Show all 10 contributors for this article] (2022). Treeline research at Natural history museum (UiO) .
• Bryn, Anders; Dalen, Thea Grobstok; Finne, Eirik Aasmo; Heiberg, Hanne; Keetz, Lasse Torben & Nilsen, Irene Brox [Show all 30 contributors for this article] (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? Show summary

  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». [Newspaper]. Aftenposten.
• Bryn, Anders; Horvath, Peter & Volden, Inger Kristine (2017). Høyest til fjells i landet. [Newspaper]. Gudbrandsdølen Dagningen.
• Bryn, Anders; Horvath, Peter & Volden, Inger Kristine (2017). Norsk høyderekord for liten bjørk. [Newspaper]. Varden.
• Bryn, Anders; Horvath, Peter & Volden, Inger Kristine (2017). Norsk rekord for lita bjørk. [Newspaper]. Nationen.
• Bryn, Anders; Horvath, Peter & Volden, Inger Kristine (2017). Norsk rekord for liten bjørk. [Newspaper]. 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. p. 12–13.
• Volden, Inger Kristine; Horvath, Peter & Bryn, Anders (2017). Ei historie om Lærdalsskogen. Sogn Avis. p. 12–12.
• Bryn, Anders; Potthoff, Kerstin; Horvath, Peter; Volden, Inger Kristine; Tang, Hui & Berntsen, Terje Koren [Show all 7 contributors for this article] (2017). Greening and browning: 100 years of tree- and forest line dynamics.
• Bryn, Anders; Naas, Adam Eindride; Horvath, Peter; Stordal, Frode; Torma, Michal & Volden, Inger Kristine [Show all 7 contributors for this article] (2002). Tree- and forest line dynamics in Norway – causes, consequences and monitoring .
• Bryn, Anders; Andersen, Guri Sogn; Bekkby, Trine; Bratli, Harald; Dervo, Børre Kind & Dolan, Margaret [Show all 14 contributors for this article] (2023). Hovedveileder for feltbasert kartlegging. Terrestrisk, limnisk og marin naturvariasjon etter NiN (3.0). Artsdatabanken. ISSN 978-82-92838-61-7. Full text in Research Archive
• Horvath, Peter (2022). Ecological climatology and distribution modelling. Universitetet i Oslo. Full text in Research Archive
• 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. Full text in Research Archive
• 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. Full text in Research Archive
• 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. Full text in Research Archive
• Ellefsen, Halvor Weider; Barton, David Nicholas; Ardilla, Pedro; Blumentrath, Stefan; Horvath, Peter & Aamlid, Helene [Show all 9 contributors for this article] (2019). MOT EN BLÅGRØNN EIENDOMS-UTVIKLING? «Stresstesting» av Blågrønn Faktor på utvalgte case studier i Bærum Kommune. Arkitektur- og designhøgskolen 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. Full text in Research Archive Show summary

  Metodene for feltbasert kartlegging av naturtyper i Norge er i utvikling. Kartlegging baserer seg nå i hovedsak på bruk av digitale plattformer med tilrettelagt programvare. Siden lanseringen av Natur i Norge (NiN 2.0) i 2015 har det vært behov for opplæring i digital kartlegging av naturtyper i felt. Til bruk i undervisningen ved Universitetet i Oslo har vi tilrettelagt QGIS (versjon 3.2) for feltbasert kartlegging av NiN-naturtyper. Denne rapporten viser oppsettet og gir veiledning i bruken slik at andre kan benytte seg av QGIS i sin undervisning eller opplæring av ansatte. Oppsettet kan også brukes til kartlegging for andre formål, f.eks. til forskningsformål eller arealundersøkelser. Det brukertilpassede oppsettet ligger fritt tilgjengelig på GitHub.

• 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. Show summary

  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

View all works in Cristin