| 1. |
- McGinn, Steven, et al.
(författare)
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New Technologies for DNA analysis-A review of the READNA Project.
- 2016
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Ingår i: New Biotechnology. - : Elsevier BV. - 1876-4347 .- 1871-6784.
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Forskningsöversikt (refereegranskat)abstract
- The REvolutionary Approaches and Devices for Nucleic Acid analysis (READNA) project received funding from the European Commission for 4 1/2 years. The objectives of the project revolved around technological developments in nucleic acid analysis. The project partners have discovered, created and developed a huge body of insights into nucleic acid analysis, ranging from improvements and implementation of current technologies to the most promising sequencing technologies that constitute a 3(rd) and 4(th) generation of sequencing methods with nanopores and in situ sequencing, respectively.
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| 2. |
- Andersen-Hoppe, Annemette, et al.
(författare)
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Deltagarkultur : i teori och praktik
- 2011
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Texterna i denna digitala antologi är författade av studenterna och lärarna på kursen ”Deltagarkultur”, som är en del av Interreg-projektet Öresundsregionen som kreativ metapol. Texterna är författade av tjänstemän på kulturförvaltningar i Skåne och Danmark, samt av lärare från Malmö högskola. En gemensam nämnare är att de alla ringar in olika både möjligheter och problem med deltagarkultur i praktiken. Vad händer när idealen möter verkligheten? Hur uppfattar deltagarna själva sin roll i de olika kulturella eller pedagogiska projekt de mer eller mindre frivilligt söker sig till eller blir indragna i? Texterna spänner över ett brett spektrum av praktiker, erfarenheter och problemställningar. De använder olika begrepp och angreppsätt för att närma sig alltifrån deltagarstyrda musikfestivaler och konstprojekt till litteraturläsning och projektarbeten på lärarutbildningen. Nya former för kulturstöd som utmanar traditionella bidragssystem undersöks i en av texterna. Läsaren introduceras för fenomen som crowd funding, crowd sourcing, mikrofinansiering och viral spridning. Filosofins hantering av lek och begär konfronteras med kulturpolitiska visioner och policydokument i en annan text. Sist men inte minst ägnar sig texterna åt intensiv självreflexion – vilket inte skall förväxlas med självupptagenhet.
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| 3. |
- Fransson, Johan E.S., et al.
(författare)
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Estimation of Forest Stem Volume using ALOS-2 PALSAR-2 Satellite Images
- 2016
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Ingår i: Proceedings of 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016; Beijing; China; 10-15 July 2016. - 9781509033324 ; 2016-November, s. Art no 7730388, Pages 5327-5330
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Konferensbidrag (refereegranskat)abstract
- © 2016 IEEE. A first evaluation of ALOS-2 PALSAR-2 data for forest stem volume estimation has been performed at a coniferous dominated test site in southern Sweden. Both the Fine Beam Dual (FBD) polarization and the Quad-polarimetric mode were investigated. Forest plots with stem volume reaching up to a maximum of about 620 m 3 ha -1 (corresponding to 370 tons ha -1 ) were analyzed by relating backscatter intensity to field data using an exponential model derived from the Water Cloud Model. The estimation accuracy of stem volume at plot level (0.5 ha) was calculated in terms of Root Mean Square Error (RMSE). For the best case investigated an RMSE of 39.8% was obtained using one of the FBD HV-polarized images. The corresponding RMSE for the FBD HH-polarized images was 43.9%. In the Quad-polarimetric mode the lowest RMSE at HV- and HH-polarization was found to be 43.1% and 66.1%, respectively.
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| 4. |
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| 5. |
- Lindberg, Eva, et al.
(författare)
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Potential of mapping forest damage from remotely sensed data
- 2022
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Remote sensing is an efficient tool for mapping, monitoring, and assessing forest damage and the risk of damage. This report presents ongoing research on those topics with preliminary results as well as research planned by the Department of Forest Resource Management, SLU in Umeå, in the near future. The damage types include spruce bark beetle attacks, storm damage, and forest fire. The report also outlines proposed continued research in the area and possible collaborations within and outside SLU.
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| 6. |
- Mukhopadhyay, Ritwika, et al.
(författare)
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Comparing TanDEM-X InSAR forest stand volume prediction models trained using field and ALS data
- 2023
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Ingår i: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. - : IEEE. - 9798350320107 - 9798350331745 - 9798350320091 ; , s. 3253-3256
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Konferensbidrag (refereegranskat)abstract
- Remote sensing (RS) techniques have been used for mapping forest variables, such as stem volume (important for forest management activities associated with timber production), over large areas which can be updated more frequently than with field inventory (FI) data. In this study, wall-to-wall TanDEM-X synthetic aperture radar images were used as auxiliary RS data for model-based prediction of stand-level volumes for two models, trained using volumes computed from FI (A) and airborne laser scanning estimations (B), respectively. The models were validated with harvester data available for independent stands. It was observed that the performance of model B was slightly better compared to model A based on adjusted R2 and root mean squared error values. Therefore, it can be concluded that a completely RS based approach for prediction and mapping of stand volumes would be as promising as a method based on FI data along with being cost- and labour-efficient.
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| 7. |
- Mukhopadhyay, Ritwika, et al.
(författare)
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Computation of prediction intervals for forest aboveground biomass predictions using generalized linear models in a large-extent boreal forest region
- 2024
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Ingår i: Forestry (London). - : Oxford University Press. - 0015-752X .- 1464-3626.
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Tidskriftsartikel (refereegranskat)abstract
- Remotely sensed data have an important application for estimation of forest variables, e.g. height, volume, and aboveground biomass (AGB). The increased use of remotely sensed data implemented along with model-based inference has shown improved efficiency in prediction and mapping of such forest variables. In this study, plot-level airborne laser scanning data and Swedish National Forest Inventory field reference data were used to predict AGB using generalized linear models (GLMs) assuming Gamma and Tweedie distributions for the field observed AGB. The GLMs were selected considering the convenience of not correcting transformation bias as it is required in other regression models with transformed response variable. To overcome the challenge in providing reliable uncertainty estimates for the estimated forest variable map products at individual pixel-scale, we focused on computing 95% prediction intervals (PIs) for Gamma and Tweedie GLMs with a square root link function. The relative uncertainties were computed as the ratio between the half-width of the PIs and the predicted AGBs. The AGB-airborne laser scanning models were developed with root mean square error values of 22.6 Mgha-1 (26%) and 21.7 Mgha-1 (25%), respectively, for the Gamma and Tweedie GLMs. Two methods were applied to compute PIs for the Gamma GLM, one using the R package 'ciTools' and another derived through asymptotic theory. It was found that the 95% PIs computed using 'ciTools' had the most accurate coverage probability in comparison to the other method. An extended version of these PIs was also utilized for the Tweedie GLMs. The range of PIs associated with the prediction of AGB were narrower for lower predicted AGB values compared with the length of higher predicted AGB values. Comparing the two fitted models, the Gamma GLM showed lower relative uncertainties for the lower range of predicted AGBs, whereas the Tweedie GLM showed lower relative uncertainties for the higher range of predicted AGBs. Overall, the Tweedie GLM provided a better model fit for AGB predictions.
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| 8. |
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| 9. |
- Persson, Henrik, et al.
(författare)
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Combining TanDEM-X and Sentinel-2 for large-area species-wise prediction of forest biomass and volume
- 2021
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 96
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Tidskriftsartikel (refereegranskat)abstract
- In this study, data from the satellite sensors TanDEM-X and Sentinel-2 were combined with national field inventory data to predict forest above-ground biomass (AGB) and stem volume (VOL) over a large area in Sweden. The data sources were evaluated both separately and in combination. The study area covers approximately 20,000,000 ha and corresponds to about 70% of the Swedish forest area. The study area was divided into tiles of 2.5 x 2.5 km(2), which were processed sequentially. The field plots were inventoried on 7 m and 10 m circular plots by the Swedish National Forest Inventory, and plot AGB and VOL at the year of the satellite data were estimated based on a 10-year period of field data. The AGB and VOL were modelled using the k nearest neighbor (kNN) algorithm, with k = 5 neighbors. The combined use of two data sources with different scene extents enabled the generation of seamless AGB and VOL maps. Moreover, the kNN algorithm provided the VOL divided per tree species, which was used for classification of the dominant tree species at stand-level. The overall accuracy for the dominant tree species classification was 77%. The predicted AGB and VOL rasters were evaluated using 549 field inventoried forest stands distributed over Sweden. The RMSE for the predictions based on both data sources were 31.4 t/ha (29.1%) for AGB, and 59.0 m(3)/ha (30.2%) for VOL. By estimating and removing the variance due to sampling (the stand values were estimated from sample plots), the RMSE was improved to 18.0 t/ ha (16.6%). The evaluated approach of using kNN was suitable for estimating forest variables from a combination of different satellite sensors, provided sufficient field reference data are available. The TanDEM-X data were most important for the AGB and VOL predictions, while Sentinel-2 data were essential to map the tree species.
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| 10. |
- Persson, Henrik, et al.
(författare)
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Combining TanDEM-X, Sentinel-2 and field data for prediction of species-wise stem volumes
- 2020
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Konferensbidrag (refereegranskat)abstract
- In this study, stem volume measured by the Swedish National Forest Inventory were modelled using the k nearest neighbor (kNN) algorithm, with k=1, 3, or 5 neighbors. As independent variables, the combination of two satellite sensors were used: the active radar sensor TanDEM-X and the passive optical sensor Sentinel-2. The results indicate that stem volume per species can be predicted relatively accurately, mainly due to the inclusion of Sentinel-2 data, while the total stem volume is largely predicted well due to inclusion of the TanDEM-X phase height. The prediction of total stem volume was, however, not significantly improved with the additional spectral information from Sentinel-2 about the tree species. The kNN method is somewhat limited in the highest range of volumes, since no extrapolation is supported. Thus, it is important to have a reference dataset representing the entire range of the population for a successful application. The main advantage of combining the two data sources is the convenient procedure of obtaining both the tree species classification and volumes (divided per species) in a single method. It is concluded, that when sufficient reference data are available, the kNN approach with a combination of radar and optical data provides additional information about the stem volumes (in terms of tree species), but without improving the prediction of the total stem volume accuracy.
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