| 1. |
- Lembrechts, Jonas J., et al.
(författare)
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Global maps of soil temperature
- 2022
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Ingår i: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 28:9, s. 3110-3144
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Tidskriftsartikel (refereegranskat)abstract
- Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.
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| 2. |
- Junttila, Sofia, et al.
(författare)
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Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
- 2021
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 13:4
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Tidskriftsartikel (refereegranskat)abstract
- Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on vegetation indices from 10 m resolution Sentinel-2 MSI and land surface temperature from 1 km resolution MODIS data. To ensure a precise match between the EC data and the Sentinel-2 observations, a footprint model was applied to derive footprint-weighted daily means of the vegetation indices. Average model parameters for all sites were acquired with a leave-one-out-cross-validation procedure. Both the GPP and the ER models gave high agreement with the EC-derived fluxes (R-2 = 0.70 and 0.56, NRMSE = 14% and 15%, respectively). The performance of the NEE model was weaker (average R-2 = 0.36 and NRMSE = 13%). Our findings demonstrate that using optical and thermal satellite sensor data is a feasible method for upscaling the GPP and ER of northern boreal peatlands, although further studies are needed to investigate the sources of the unexplained spatial and temporal variation of the CO2 fluxes.
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| 3. |
- Pilesjö, Petter, et al.
(författare)
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Analys av geografiska data
- 2020. - 7:1
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Ingår i: Geografisk informationsbehandling: : teori, metoder och tillämpningar - teori, metoder och tillämpningar. - 9789144131740 ; , s. 215-272
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- I detta kapitel i boken Geografisk information Behandling beskrivs och behandlas metoder för hur geografisk data analyseras. Kapitlet omfattar såväl geografisk teori som exempel på tillämpningar. Geografisk informationsbehandling baseras på insamling, lagring, analys och visualisering av geografiska data. Denna indelning utgör också grunden för bokens disposition. Boken är i första hand avsedd för introducerande kurser vid universitet och högskolor men vissa delar är lämpliga även på avancerad nivå. Den är också utmärkt för yrkesverksamma inom GIS som vill öka sina teoretiska kunskaper.Boken innehåller både teoretiska och praktiska delar, där de senare beskriver tillämpningar som exempelvis samhällsplanering, miljöövervakning och kommersiella tjänster. I insamlingsdelen beskrivs hur man anger en position genom att koppla ett koordinatsystem till jordytan. Därefter beskrivs de vanligaste metoderna att samla in geografiska data som satellitbaserade positioneringssystem, flygfotografering, satellitbaserad fjärranalys och laserskanning. Lagringsdelen behandlar hur dessa data lagras i databaser och distribueras via webben. Analysdelen innehåller beskrivningar av de vanligaste analysmetoderna samt kvalitetsfrågor. Resultatet av en geografisk analys visualiseras oftast i form av kartor, vilket är temat för den sista delen av boken.Denna sjunde upplaga har uppdaterats med nya aktuella tillämpningsexempel. Dessa är i denna upplaga beskrivna tillsammans med de teoretiska beskrivningarna för att stärka kopplingen mellan teori och tillämpningar. Dessutom har beskrivningen av webbtillämpningar utökats och utgör nu ett eget kapitel, och även texten om webbvisualiseringar har stärkts i kartografikapitlet. Vidare har det tillkommit beskrivningar av nya mättekniker samt texter om lagring, analys och visualisering av 3D geografiska data.
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| 4. |
- Conradt, Tobias, et al.
(författare)
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Cross-sectoral impacts of the 2018–2019 Central European drought and climate resilience in the German part of the Elbe River basin
- 2023
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Ingår i: Regional Environmental Change. - : Springer Science and Business Media LLC. - 1436-378X .- 1436-3798. ; 23, s. 1-18
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Tidskriftsartikel (refereegranskat)abstract
- The 2018–2019 Central European drought was probably the most extreme in Germany since the early sixteenth century. We assess the multiple consequences of the drought for natural systems, the economy and human health in the German part of the Elbe River basin, an area of 97,175 km2 including the cities of Berlin and Hamburg and contributing about 18% to the German GDP. We employ meteorological, hydrological and socio-economic data to build a comprehensive picture of the drought severity, its multiple effects and cross-sectoral consequences in the basin. Time series of different drought indices illustrate the severity of the 2018–2019 drought and how it progressed from meteorological water deficits via soil water depletion towards low groundwater levels and river runoff, and losses in vegetation productivity. The event resulted in severe production losses in agriculture (minus 20–40% for staple crops) and forestry (especially through forced logging of damaged wood: 25.1 million tons in 2018–2020 compared to only 3.4 million tons in 2015–2017), while other economic sectors remained largely unaffected. However, there is no guarantee that this socio-economic stability will be sustained in future drought events; this is discussed in the light of 2022, another dry year holding the potential for a compound crisis. Given the increased probability for more intense and long-lasting droughts in most parts of Europe, this example of actual cross-sectoral drought impacts will be relevant for drought awareness and preparation planning in other regions.
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| 5. |
- Schubert, Per, et al.
(författare)
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Modeling GPP in the Nordic forest landscape with MODIS time series data-Comparison with the MODIS GPP product
- 2012
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 126, s. 136-147
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Tidskriftsartikel (refereegranskat)abstract
- Satellite sensor-derived data are suitable for regional estimations of several important biophysical variables. Data with a finer spatial resolution should improve regional estimations of GPP (gross primary productivity), since they better capture the variation in a heterogeneous landscape. The main objective of this study was to investigate if MODIS 500 m reflectance data can be used to drive empirical models for regional estimations of GPP in Nordic forests. The performance of the proposed models was compared with the MODIS 1 km GPP product. Linear regression analyses were made on 8-day averages of eddy covariance GPP from three deciduous and ten coniferous sites in relation to MODIS 8-day composite data and 8-day averages of modeled incoming PPFD (photosynthetic photon flux density). Time series of EVI2 (two-band enhanced vegetation index) were calculated from MODIS 500 m reflectance data and smoothed by a curve fitting procedure. For most sites, GPP was fairly strongly to strongly related to the product of EVI2 and PPFD (Deciduous: R-2=0.45-0.86, Coniferous: R-2=0.49-0.90). Similar strengths were found between GPP and the product of EVI2 and MODIS 1 km daytime LST (land surface temperature) (R-2=0.55-0.81, 0.57-0.77) and between GPP and EVI2, PPFD and daytime LST in multiple linear regressions (R-2=0.73-0.89,0.65-0.93). One year of data was collected from all coniferous sites to derive a general empirical model for GPP versus (1) the product of EVI2 and PPFD (R-2=0.70), (2) the product of EVI2 and daytime LST (R-2=0.62) and (3) EVI2, PPFD and daytime LST (R-2=0.72). These three models were then validated at six sites for the remaining years by linearly relating eddy covariance GPP to modeled GPP, which resulted in fairly strong to strong relationships for most sites (R-2=0.49-0.91, RMSE=0.63-1.22 gC m(-2) day(-1), R-2=0.53-0.73, RMSE=0.90-1.43 gC m(-2) day(-1) R-2=0.56-0.87, RMSE=0.79-1.11 gC m(-2) day(-1)). In comparison, similar validation strengths were found for the latest collection 5.1 of the MODIS 1 km GPP product (R-2=0.59-0.88, RMSE=0.80-1.16 gC m(-2) day(-1)). The main conclusion is that the suggested empirical models driven by MODIS 500 m reflectance data can be used for regional estimations of Nordic forest GPP, while preserving a finer resolution than the MODIS 1 km GPP product. (C) 2012 Elsevier Inc. All rights reserved.
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| 6. |
- Kelly, Julia, et al.
(författare)
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Modelling and upscaling ecosystem respiration using thermal cameras and UAVs: Application to a peatland during and after a hot drought
- 2021
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Ingår i: Agricultural and Forest Meteorology. - : Elsevier BV. - 0168-1923. ; 300
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Tidskriftsartikel (refereegranskat)abstract
- Field-based thermal infrared cameras provide surface temperature information at very high spatial and temporal resolution and could complement existing phenological camera and spectral sensor networks. Since temperature is one of the main drivers of ecosystem respiration (ER), field-based thermal cameras offer a new opportunity to model and upscale ER in unprecedented detail. We present such an approach based on manual chamber CO2 flux measurements and thermal imagery from a tower-based camera and from Unmanned Aerial Vehicle (UAV) flights. Data were collected over two growing seasons, including the hot drought of 2018, for the two main vegetation microforms (hummock and hollow) of a hemi-boreal peatland in Sweden. Thermal imagery proved suitable for modelling ER in this ecosystem: ER model accuracies were similar when air, soil or surface temperature measurements were used as input. Our findings allowed us to upscale ER using UAV-derived thermal images and we present maps of ER at sub-decimeter resolution (<7 cm). The significantly different ER measured for each microform highlighted the importance of modelling their ER separately. Not accounting for these differences and the microforms' spatial distribution across the peatland led to a bias in upscaled ER of up to 18%. As a result of the severity and timing of the hot drought in 2018, we observed reductions in the ER of both microforms, but more so for hummocks (-48%) than for hollows (-15%), and modelled ER leveled off at high temperatures. These findings indicate that peatland carbon loss during hot droughts may be lower than expected and strongly relates to vegetation composition. The presented upscaling approach offers a new method to analyse how ER varies across a peatland or within a flux-tower footprint, and to interpret biases that occur when using coarse resolution satellite data to upscale chamber or tower-based flux measurements.
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| 7. |
- Beck, P S A, et al.
(författare)
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A ground-validated NDVI dataset for monitoring vegetation dynamics and mapping phenology in Fennoscandia and the Kola peninsula
- 2007
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Ingår i: International Journal of Remote Sensing. - : Informa UK Limited. - 1366-5901 .- 0143-1161. ; 28:19, s. 4311-4330
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Tidskriftsartikel (refereegranskat)abstract
- An NDVI dataset covering Fennoscandia and the Kola peninsula was created for vegetation and climate studies, using Moderate Resolution Imaging Spectroradiometer 16-day maximum value composite data from 2000 to 2005. To create the dataset, ( 1) the influence of the polar night and snow on the NDVI values was removed by replacing NDVI values in winter with a pixel- specific NDVI value representing the NDVI outside the growing season when the pixel is free of snow; and ( 2) yearly NDVI time series were modelled for each pixel using a double logistic function defined by six parameters. Estimates of the onset of spring and the end of autumn were then mapped using the modelled dataset and compared with ground observations of the onset of leafing and the end of leaf fall in birch, respectively. Missing and poor-quality data prevented estimates from being produced for all pixels in the study area. Applying a 5 km x 5 km mean filter increased the number of modelled pixels without decreasing the accuracy of the predictions. The comparison shows good agreement between the modelled and observed dates ( root mean square error = 12 days, n = 108 for spring; root mean square error = 10 days, n = 26, for autumn). Fennoscandia shows a range in the onset of spring of more than 2 months within a single year and locally the onset of spring varies with up to one month between years. The end of autumn varies by one and a half months across the region. While continued validation with ground data is needed, this new dataset facilitates the detailed monitoring of vegetation activity in Fennoscandia and the Kola peninsula.
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| 8. |
- Dubber, Wilhelm, et al.
(författare)
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Comparing field inventory with mechanistic modelling and light-use efficiency modelling based approaches for estimating forest net primary productivity at a regional level
- 2017
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Ingår i: Boreal Environment Research. - 1239-6095. ; 22, s. 337-352
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Tidskriftsartikel (refereegranskat)abstract
- Monitoring of forest carbon fluxes for the purpose of national greenhouse-gas inventorying and reporting are commonly based on repeated large-scale field measurements. Alternate approaches based on modelling of forest growth offers potential benefits such as cost savings and detailed assessments of involved carbon fluxes. We calculated the net primary productivity (NPP) of Swedish forests using two methods based on mechanistic and light use efficiency (LUE) modelling. The results were evaluated using data from traditional field inventories, and showed large variations in calculated NPP for the two methods. The national mean NPP for each method ranged between 0.35 and 0.59 kg C m- 2 year- 1, with an average regional difference of ±50%. Despite the large differences in calculated NPP, mechanistic modelling was promising for estimating the spatial distribution with an r2 value of 0.92 for predicting NPP of mainland Sweden.
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| 9. |
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| 10. |
- Junttila, Sofia, et al.
(författare)
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Comparison of Light Use Efficiency, Plant Phenology Index, and Light Response Function-Based GPP Models in the Northern Forest Landscape
- 2021
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Ingår i: ; , s. 6917-6920
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Konferensbidrag (refereegranskat)abstract
- Remote sensing-based models are an adequate tool to estimate carbon dioxide (CO2) uptake by terrestrial ecosystems, and to upscale the results from ecosystem to regional or global scales. In this study we compare three models driven by Sentinel-2 derived vegetation indices together with eddy covariance (EC) measured CO2 flux and radiation data in order to estimate gross primary production (GPP) in northern Europe forest ecosystems. By integrating high spatial resolution satellite data with cutting-edge footprint analysis and an accurate GPP model, we aim at more precise estimation of GPP at regional level.
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