| 1. |
- Miura, Y., et al.
(författare)
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SOIL MOISTURE MONITORING OF AGRICULTURAL FIELDS IN BURKINA FASO USING DUAL POLARIZED SENTINEL-1A DATA
- 2019
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Ingår i: International Geoscience and Remote Sensing Symposium (IGARSS). - : IEEE. - 9781538691540 ; , s. 7045-7048
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Konferensbidrag (refereegranskat)abstract
- We investigated the correlation between backscatter and soil moisture considering precipitation and crop effects using dual polarized Sentinel-1A data. The analyzed data consist of a time-series of 38 Sentinel-1A GRD images acquired on a 12-days repeat cycle from July 2017 to October 2018 over Sapone in Burkina Faso. We show that the temporal change of backscatter corresponds to the soil moisture content rather than crops.
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| 2. |
- Allard, Anna, et al.
(författare)
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Fångst av vegetationsdata och Natura 2000-habitat i fjällen genom flygbildstolkning i IRF med punktgittermetodik
- 2007
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- I denna rapport visas att flygbildstolkning med punktgittermetodik har stor potential för att ta fram tillförlitliga och kostnadseffektiva data på tillstånd och förändringar i fjällens vegetation. Projektet är finansierat av Naturvårdsverkets svenska miljöövervakning.Inom denna studie har följande delmål nåtts1) Utprovning av försöksdesign har gjorts med antal och fördelning av punkter, samt design och val av tolkningsvariabler.2) Ett operativt tolkningssystem för punktgittertolkning har tagits fram.3) Tolkningsprecisionen för olika variabler och Natura 2000-habitat är utvärderade mot fältdata.4) Metodens kvaliteter är utvärderade.5) Beräkning av tidsåtgång och kostnadsberäkningar är gjorda och ställda mot en polygonbaserad metod för översiktlig tolkning av NILS 5x5 km ruta.Förutom dessa delmål har även säkerheten ytterligare undersökts via ett personvariationstest av tolkade data.Resultaten från de tolkade fältpunkterna visar att överensstämmelsen med fältinventeringen är mycket god. Ett gott resultat fanns från testet med personvariation, med några få problemområden. Metoden har några begränsningar. För flygbildstolkning i punktgitter med den metodik som föreslås här förutsätts att det finns välutbildade tolkare. För en van vegetationstolkare krävs det i storleksordning en tilläggsutbildning på minst 1 månad i tolkning av fjällvegetation, inklusive fältbesök. En exempelsamling av bilder och ett antal nycklar för tolkning bör tas fram.Punktgittermetoden är operativt användbar, vissa kompletterande fältstudier behövs. Metoden har följande generella kvaliteter.Metoden är enkel att implementera.Punktgittermetoden är en kostnadseffektiv metod för att fånga landskapsdata. Den är snabbare än polygontolkning.Datafångst från en 5 x 5 km ruta kan göras på 1-3 dagar, beroende på antal punkter i gittret.Det är enkelt att bearbeta och analysera punktgitterdata.Precisionen i arealskattningarna kommer att kunna enkelt beräknas.Tolkningen i punktgitter kan verifieras genom jämförelse med NILS fältdata.Metoden är lämplig för förändringsstudier. Exempelvis kan den utgöra ett snabbt och effektivt sätt att statistiskt uppdatera information om area av vegetationstyper från vegetationskartorna över de svenska fjällen.Metoden kan fånga upp ovanligare naturtyper i 5 x 5 km ytan förutsatt att många punkter tolkas.Metoden kan användas för urval av objekt för riktade fältinventeringar till ovanligare habitat.Resultaten från metoden kan utgöra viktiga data för annan forskning, exempelvis som träningsdata till satellitbildsklassificeringar.
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| 3. |
- Axelsson, Arvid, et al.
(författare)
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Tree species classification using Sentinel-2 imagery and Bayesian inference
- 2021
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 0303-2434. ; 100
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Tidskriftsartikel (refereegranskat)abstract
- The increased temporal frequency of optical satellite data acquisitions provides a data stream that has the potential to improve land cover mapping, including mapping of tree species. However, for large area operational mapping, partial cloud cover and different image extents can pose challenges. Therefore, methods are needed to assimilate new images in a straightforward way without requiring a total spatial coverage for each new image. This study shows that Bayesian inference applied sequentially has the potential to solve this problem. To test Bayesian inference for tree species classification in the boreo-nemoral zone of southern Sweden, field data from the study area of Remningstorp (58°27′18.35″ N, 13°39′8.03″ E) were used. By updating class likelihood with an increasing number of combined Sentinel-2 images, a higher and more stable cross-validated overall accuracy was achieved. Based on a Mahalanobis distance, 23 images were automatically chosen from the period of 2016 to 2018 (from 142 images total). An overall accuracy of 87% (a Cohen’s kappa of 78.5%) was obtained for four tree species classes: Betula spp., Picea abies, Pinus sylvestris, and Quercus robur. This application of Bayesian inference in a boreo-nemoral forest suggests that it is a practical way to provide a high and stable classification accuracy. The method could be applied where data are not always complete for all areas. Furthermore, the method requires less reference data than if all images were used for classification simultaneously.
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| 4. |
- Bargues Tobella, Aida, et al.
(författare)
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The effect of trees on preferential flow and soil infiltrability in an agroforestry parkland in semiarid Burkina Faso
- 2014
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Ingår i: Water Resources Research. - 0043-1397 .- 1944-7973. ; 50, s. 3342-3354
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Tidskriftsartikel (refereegranskat)abstract
- Water scarcity constrains the livelihoods of millions of people in tropical drylands. Tree planting in these environments is generally discouraged due to the large water consumption by trees, but this view may neglect their potential positive impacts on water availability. The effect of trees on soil hydraulic properties linked to groundwater recharge is poorly understood. In this study, we performed 18 rainfall simulations and tracer experiments in an agroforestry parkland in Burkina Faso to investigate the effect of trees and associated termite mounds on soil infiltrability and preferential flow. The sampling points were distributed in transects each consisting of three positions: (i) under a single tree, (ii) in the middle of an open area, and (iii) under a tree associated with a termite mound. The degree of preferential flow was quantified through parameters based on the dye infiltration patterns, which were analyzed using image analysis of photographs. Our results show that the degree of preferential flow was highest under trees associated with termite mounds, intermediate under single trees, and minimal in the open areas. Tree density also had an influence on the degree of preferential flow, with small open areas having more preferential flow than large ones. Soil infiltrability was higher under single trees than in the open areas or under trees associated with a termite mound. The findings from this study demonstrate that trees have a positive impact on soil hydraulic properties influencing groundwater recharge, and thus such effects must be considered when evaluating the impact of trees on water resources in drylands.
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| 5. |
- Brownstein, Catherine A., et al.
(författare)
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An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge
- 2014
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Ingår i: Genome Biology. - : Springer Science and Business Media LLC. - 1465-6906 .- 1474-760X. ; 15:3, s. R53-
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Tidskriftsartikel (refereegranskat)abstract
- Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.
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| 6. |
- Elsik, Christine G., et al.
(författare)
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The Genome Sequence of Taurine Cattle : A Window to Ruminant Biology and Evolution
- 2009
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 324:5926, s. 522-528
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Tidskriftsartikel (refereegranskat)abstract
- To understand the biology and evolution of ruminants, the cattle genome was sequenced to about sevenfold coverage. The cattle genome contains a minimum of 22,000 genes, with a core set of 14,345 orthologs shared among seven mammalian species of which 1217 are absent or undetected in noneutherian (marsupial or monotreme) genomes. Cattle-specific evolutionary breakpoint regions in chromosomes have a higher density of segmental duplications, enrichment of repetitive elements, and species-specific variations in genes associated with lactation and immune responsiveness. Genes involved in metabolism are generally highly conserved, although five metabolic genes are deleted or extensively diverged from their human orthologs. The cattle genome sequence thus provides a resource for understanding mammalian evolution and accelerating livestock genetic improvement for milk and meat production.
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| 7. |
- Forsman, Mona, et al.
(författare)
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Bias of cylinder diameter estimation from ground-based laser scanners with different beam widths : a simulation study
- 2018
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Ingår i: ISPRS journal of photogrammetry and remote sensing (Print). - Amsterdam : Elsevier. - 0924-2716 .- 1872-8235. ; 135, s. 84-92
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Tidskriftsartikel (refereegranskat)abstract
- In this study we have investigated why diameters of tree stems, which are approximately cylindrical, are often overestimated by mobile laser scanning. This paper analyzes the physical processes when using ground-based laser scanning that may contribute to a bias when estimating cylinder diameters using circle-fit methods. A laser scanner simulator was implemented and used to evaluate various properties, such as distance, cylinder diameter, and beam width of a laser scanner-cylinder system to find critical conditions. The simulation results suggest that a positive bias of the diameter estimation is expected. Furthermore, the bias follows a quadratic function of one parameter - the relative footprint, i.e., the fraction of the cylinder width illuminated by the laser beam. The quadratic signature opens up a possibility to construct a compensation model for the bias.
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| 8. |
- Gilichinsky, Michael, et al.
(författare)
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Application of national forest inventory for remote sensing classification of ground lichen in nothern Sweden
- 2010
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Ingår i: ISPRS Archives. - : International Society for Photogrammetry and Remote Sensing. ; 38-4-8, s. 146-152
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Konferensbidrag (refereegranskat)abstract
- Lichen is a major forage resource for reindeer and may constitute up to 80% of a reindeer's winter diet. The reindeer grazing area in Sweden covers almost half of the country, with reindeer using mountainous areas in the summer and forested areas in the winter. Knowledge about the spatial distribution of ground lichens is important for both practical and sustainable decisionmaking purposes. Since the early 1980s, remote sensing research of lichen cover in northern environments has focused on reindeer grazing issues. The objective of the present study was to use lichen information from the Swedish Forest Inventory (NFI) for classification of satellite data into ground lichen classes. The classification procedure was focused on using of NFI plots as training sets for supervised classification of the ground lichen cover in purpose to classify areas with different lichen coverage. The present research has shown the advantage of use forest inventory plot data by assessment of three methods: mahalanobis distance (MD) classification, maximum likelihood (ML) classification and spectral mixture analysis (SMA). The results of this study demonstrate high classification accuracy of SPOT imagery in distinction between lichenabundant and lichen-poor areas by mahalanobis distance classifier (overall accuracy 84.3%, kappa=0.68). The highest classification accuracy for Landsat scene was achieved by maximumlikelihood classification (overall accuracy 76.8%, kappa=0.53). The continuation research on more detailed fragmentation of lichen cover into fractions is proposed.
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| 9. |
- Gilichinsky, Michael, et al.
(författare)
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Mapping ground lichens using forest inventory and optical satellite data
- 2011
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Ingår i: International Journal of Remote Sensing. - : Informa UK Limited. - 0143-1161 .- 1366-5901. ; 32:2, s. 455-472
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Tidskriftsartikel (refereegranskat)abstract
- Lichen is a major forage resource for reindeer and may constitute up to 80% of areindeer’s winter diet. The reindeer grazing area in Sweden covers almost half of thecountry, with reindeer using mountainous areas in the summer and forested areas inthe winter. Knowledge about the spatial distribution of ground lichens is importantfor both practical and decision-making purposes. Since the early 1980s, remotesensing research of lichen cover in northern environments has focused on reindeergrazing issues. The objective of this study was to use lichen information collected inthe Swedish National Forest Inventory (NFI) as training data to classify opticalsatellite images into ground lichen cover classes. The study site was located within thereindeer husbandry area in northern Sweden and consisted of the common areabetween two contiguous Satellite Pour l’Observation de la Terre (SPOT)-5 scenesand one Landsat-7 Enhanced Thematic Mapper Plus (ETMþ) scene. Three classificationmethods were tested: Mahalanobis distance, maximum likelihood andspectral mixture analysis. Post-classification calibration was applied using a membershipprobability threshold in order to match the NFI-measured proportions oflichen coverage classes. The classification results were assessed using an independentlycollected field dataset (229 validation areas). The results demonstrated highclassification accuracy of SPOT imagery for the classification of lichen-abundantand lichen-poor areas when using theMahalanobis distance classifier (overall accuracy84.3%, kappa ¼ 0.68). The highest classification accuracy for Landsat wasachieved using a maximum likelihood classification (overall accuracy 76.8%, kappa¼ 0.53). These results provided an initial indication of the utility of NFI data astraining data in the process of mapping lichen classes over large areas.
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| 10. |
- Husson, Eva, et al.
(författare)
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Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation
- 2017
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Monitoring of aquatic vegetation is an important component in the assessment of freshwater ecosystems. Remote sensing with unmanned aircraft systems (UASs) can provide sub-decimetre-resolution aerial images and is a useful tool for detailed vegetation mapping. In a previous study, non-submerged aquatic vegetation was successfully mapped using automated classification of spectral and textural features from a true-colour UAS-orthoimage with 5-cm pixels. In the present study, height data from a digital surface model (DSM) created from overlapping UAS-images has been incorporated together with the spectral and textural features from the UAS-orthoimage to test if classification accuracy can be improved further. We studied two levels of thematic detail: (a) Growth forms including the classes of water, nymphaeid, and helophyte; and (b) dominant taxa including seven vegetation classes. We hypothesized that the incorporation of height data together with spectral and textural features would increase classification accuracy as compared to using spectral and textural features alone, at both levels of thematic detail. We tested our hypothesis at five test sites (100 m × 100 m each) with varying vegetation complexity and image quality using automated object-based image analysis in combination with Random Forest classification. Overall accuracy at each of the five test sites ranged from 78% to 87% at the growth-form level and from 66% to 85% at the dominant-taxon level. In comparison to using spectral and textural features alone, the inclusion of height data increased the overall accuracy significantly by 4%-21% for growth-forms and 3%-30% for dominant taxa. The biggest improvement gained by adding height data was observed at the test site with the most complex vegetation. Height data derived from UAS-images has a large potential to efficiently increase the accuracy of automated classification of non-submerged aquatic vegetation, indicating good possibilities for operative mapping,
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| 11. |
- Husson, Eva, et al.
(författare)
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Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images
- 2016
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Aquatic vegetation has important ecological and regulatory functions and should be monitored in order to detect ecosystem changes. Field data collection is often costly and time-consuming; remote sensing with unmanned aircraft systems (UASs) provides aerial images with sub-decimetre resolution and offers a potential data source for vegetation mapping. In a manual mapping approach, UAS true-colour images with 5-cm-resolution pixels allowed for the identification of non-submerged aquatic vegetation at the species level. However, manual mapping is labour-intensive, and while automated classification methods are available, they have rarely been evaluated for aquatic vegetation, particularly at the scale of individual vegetation stands. We evaluated classification accuracy and time-efficiency for mapping non-submerged aquatic vegetation at three levels of detail at five test sites (100 m 100 m) differing in vegetation complexity. We used object-based image analysis and tested two classification methods (threshold classification and Random Forest) using eCognition®. The automated classification results were compared to results from manual mapping. Using threshold classification, overall accuracy at the five test sites ranged from 93% to 99% for the water-versus-vegetation level and from 62% to 90% for the growth-form level. Using Random Forest classification, overall accuracy ranged from 56% to 94% for the growth-form level and from 52% to 75% for the dominant-taxon level. Overall classification accuracy decreased with increasing vegetation complexity. In test sites with more complex vegetation, automated classification was more time-efficient than manual mapping. This study demonstrated that automated classification of non-submerged aquatic vegetation from true-colour UAS images was feasible, indicating good potential for operative mapping of aquatic vegetation. When choosing the preferred mapping method (manual versus automated) the desired level of thematic detail and the required accuracy for the mapping task needs to be considered.
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| 12. |
- Karlson, Martin, et al.
(författare)
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Assessing the potential of multi-seasonal WorldView-2 imagery for mapping West African agroforestry tree species
- 2016
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 1569-8432 .- 0303-2434 .- 1872-826X. ; 50:August, s. 80-88
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Tidskriftsartikel (refereegranskat)abstract
- High resolution satellite systems enable efficient and detailed mapping of tree cover, with high potential to support both natural resource monitoring and ecological research. This study investigates the capability of multi-seasonal WorldView-2 imagery to map five dominant tree species at the individual tree crown level in a parkland landscape in central Burkina Faso. The Random Forest algorithm is used for object based tree species classification and for assessing the relative importance of WorldView-2 predictors. The classification accuracies from using wet season, dry season and multi-seasonal datasets are compared to gain insights about the optimal timing for image acquisition. The multi-seasonal dataset produced the most accurate classifications, with an overall accuracy (OA) of 83.4%. For classifications based on single date imagery, the dry season (OA = 78.4%) proved to be more suitable than the wet season (OA = 68.1%). The predictors that contributed most to the classification success were based on the red edge band and visible wavelengths, in particular green and yellow. It was therefore concluded that WorldView- 2, with its unique band configuration, represents a suitable data source for tree species mapping in West African parklands. These results are particularly promising when considering the recently launched WorldView-3, which provides data both at higher spatial and spectral resolution, including shortwave infrared bands.
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| 13. |
- Karlson, Martin, et al.
(författare)
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Error Characteristics of Pan-Arctic Digital Elevation Models and Elevation Derivatives in Northern Sweden
- 2021
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 13:22
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Tidskriftsartikel (refereegranskat)abstract
- Many biochemical processes and dynamics are strongly controlled by terrain topography, making digital elevation models (DEM) a fundamental dataset for a range of applications. This study investigates the quality of four pan-Arctic DEMs (Arctic DEM, ASTER DEM, ALOS DEM and Copernicus DEM) within the Kalix River watershed in northern Sweden, with the aim of informing users about the quality when comparing these DEMs. The quality assessment focuses on both the vertical accuracy of the DEMs and their abilities to model two fundamental elevation derivatives, including topographic wetness index (TWI) and landform classification. Our results show that the vertical accuracy is relatively high for Arctic DEM, ALOS and Copernicus and in our study area was slightly better than those reported in official validation results. Vertical errors are mainly caused by tree cover characteristics and terrain slope. On the other hand, the high vertical accuracy does not translate directly into high quality elevation derivatives, such as TWI and landform classes, as shown by the large errors in TWI and landform classification for all four candidate DEMs. Copernicus produced elevation derivatives with results most similar to those from the reference DEM, but the errors are still relatively high, with large underestimation of TWI in land cover classes with a high likelihood of being wet. Overall, the Copernicus DEM produced the most accurate elevation derivatives, followed by slightly lower accuracies from Arctic DEM and ALOS, and the least accurate being ASTER.
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| 14. |
- Karlson, Martin, 1980-, et al.
(författare)
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Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest
- 2015
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 7:8, s. 10017-10041
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Tidskriftsartikel (refereegranskat)abstract
- Accurate and timely maps of tree cover attributes are important tools for environmental research and natural resource management. We evaluate the utility of Landsat 8 for mapping tree canopy cover (TCC) and aboveground biomass (AGB) in a woodland landscape in Burkina Faso. Field data and WorldView-2 imagery were used to assemble the reference dataset. Spectral, texture, and phenology predictor variables were extracted from Landsat 8 imagery and used as input to Random Forest (RF) models. RF models based on multi-temporal and single date imagery were compared to determine the influence of phenology predictor variables. The effect of reducing the number of predictor variables on the RF predictions was also investigated. The model error was assessed using 10-fold cross validation. The most accurate models were created using multi-temporal imagery and variable selection, for both TCC (five predictor variables) and AGB (four predictor variables). The coefficient of determination of predicted versus observed values was 0.77 for TCC (RMSE = 8.9%) and 0.57 for AGB (RMSE = 17.6 tons∙ha−1). This mapping approach is based on freely available Landsat 8 data and relatively simple analytical methods, and is therefore applicable in woodland areas where sufficient reference data are available.
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| 15. |
- Karlson, Martin
(författare)
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Remote Sensing of Woodland Structure and Composition in the Sudano-Sahelian zone : Application of WorldView-2 and Landsat 8
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Woodlands constitute the subsistence base of the majority of people in the Sudano-Sahelian zone (SSZ), but low availability of in situ data on vegetation structure and composition hampers research and monitoring. This thesis explores the utility of remote sensing for mapping and analysing vegetation, primarily trees, in the SSZ. A comprehensive literature review was first conducted to describe how the application of remote sensing has developed in the SSZ between 1975 and 2014, and to identify important research gaps. Based on the gaps identified in the literature review, the capabilities of two new satellite systems (WorldView-2 and Landsat 8) for mapping woodland structure and composition were tested in an area in central Burkina Faso.The results shows that WorldView-2 represents a useful data source for mapping individual trees: 85.4% of the reference trees were detected in the WorldView-2 data and tree crown area was estimate with an average error of 45.6%. In addition, WorldView-2 data produced high classification accuracies for five locally important tree species. The highest overall classification accuracy (82.4%) was produced using multi-temporal WorldView-2 data. Landsat 8 data proved more suitable for mapping tree canopy cover as compared to aboveground biomass in the woodland landscape. Tree canopy cover and aboveground biomass was predicted with 41% and 66% root mean square error, respectively, at pixel level.This thesis demonstrates the potential of easily accessible data from two satellite systems for mapping important tree attributes in woodland areas, and discusses how the usefulness of remote sensing for analyzing vegetation can be further enhanced in the SSZ.
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| 16. |
- Karlson, Martin, et al.
(författare)
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The Potential of Sentinel-2 for Crop Production Estimation in a Smallholder Agroforestry Landscape, Burkina Faso
- 2020
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Ingår i: Frontiers in Environmental Science. - : Frontiers Media SA. - 2296-665X. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Crop production statistics at the field scale are scarce in African countries, limiting potential research on yield gaps as well as monitoring related to food security. This paper examines the potential of using Sentinel-2 time series data to derive spatially explicit estimates of crop production in an agroforestry parkland in central Burkina Faso. This type of landscape is characterized by agricultural fields where cereals (millet and sorghum) and legumes (cowpea) are intercropped under a relatively dense tree canopy. We measured total above ground biomass (AGB) and grain yield in 22 field plots at the end of two growing seasons (2017 and 2018) that differed in rainfall timing and amount. Linear regression models were developed using the in situ crop production estimates and temporal metrics derived from Sentinel-2 time series. We studied several important aspects of satellite-based crop production estimation, including (i) choice of vegetation indices, (ii) effectiveness of different time periods for image acquisition and temporal metrics, (iii) consistency of the method between years, and (iv) influence of intercropping and trees on accuracy of the estimates. Our results show that Sentinel-2 data were able to explain between 41 and 80% of the variation in the in situ crop production measurements, with relative root mean square error for AGB estimates ranging between 31 and 63% in 2017 and 2018, respectively, depending on temporal metric used as estimator. Neither intercropping of cereals and legumes nor tree canopy cover appeared to influence the relationship between the satellite-derived estimators and crop production. However, inter-annual rainfall variations in 2017 and 2018 resulted in different ratios of AGB to grain yield, and additionally, the most effective temporal metric for estimating crop production differed between years. Overall, this study demonstrates that Sentinel-2 data can be an important resource for upscaling field measurements of crop production in this agroforestry system in Burkina Faso. The results may be applicable in other areas with similar agricultural systems and increase the availability of crop production statistics.
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| 17. |
- Karlson, Martin, et al.
(författare)
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Tree Crown Mapping in Managed Woodlands (Parklands) of Semi-Arid West Africa Using WorldView-2 Imagery and Geographic Object Based Image Analysis
- 2014
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Ingår i: Sensors. - Basel : MDPI AG. - 1424-8220 .- 1424-3210. ; 14:12, s. 22643-22669
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Tidskriftsartikel (refereegranskat)abstract
- Detailed information on tree cover structure is critical for research and monitoring programs targeting African woodlands, including agroforestry parklands. High spatial resolution satellite imagery represents a potentially effective alternative to field-based surveys, but requires the development of accurate methods to automate information extraction. This study presents a method for tree crown mapping based on Geographic Object Based Image Analysis (GEOBIA) that use spectral and geometric information to detect and delineate individual tree crowns and crown clusters. The method was implemented on a WorldView-2 image acquired over the parklands of Saponé, Burkina Faso, and rigorously evaluated against field reference data. The overall detection rate was 85.4% for individual tree crowns and crown clusters, with lower accuracies in areas with high tree density and dense understory vegetation. The overall delineation error (expressed as the difference between area of delineated object and crown area measured in the field) was 45.6% for individual tree crowns and 61.5% for crown clusters. Delineation accuracies were higher for medium (35–100 m2) and large (?100 m2) trees compared to small (
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| 18. |
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| 19. |
- Lindgren, Nils, et al.
(författare)
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Using Optical Satellite Data and Airborne Lidar Data for a Nationwide Sampling Survey
- 2015
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 7, s. 4253-4267
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Tidskriftsartikel (refereegranskat)abstract
- A workflow for combining airborne lidar, optical satellite data and National Forest Inventory (NFI) plots for cost efficient operational mapping of a nationwide sample of 5x 5 km squares in the National Inventory of Landscapes in Sweden (NILS) landscape inventory in Sweden is presented. Since the areas where both satellite data and lidar data have a common data quality are limited, and impose a constraint on the number of available NFI plots, it is not feasible to perform classifications in a single step. Instead a stratified approach where canopy cover and canopy height are first predicted from lidar data trained with NFI plots is proposed. From the lidar predictions a forest stratum is defined as grid cells with more than 3m mean tree height and more than 10% vertical canopy cover, the remaining grid cells are defined as open land. Both forest and open land are then classified into broad vegetation classes using optical satellite data. The classification of open land is trained with aerial photo interpretation and the classification of the forest stratum is trained with a new set of NFI plots. The result is a rational procedure for nationwide sample based vegetation characterization.
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| 20. |
- Minola, Lorenzo, et al.
(författare)
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Wind stilling-reversal across Sweden: The impact of land-use and large-scale atmospheric circulation changes
- 2022
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Ingår i: International Journal of Climatology. - : Wiley. - 0899-8418 .- 1097-0088. ; 42:2, s. 1049-1071
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Tidskriftsartikel (refereegranskat)abstract
- This study analyses for the first time the break in the stilling detected by previous research around 2010, with focus in Sweden using homogenized near-surface mean and gust wind speed observations for 1997–2019. During the recent past two decades, both mean and gust wind magnitude and frequency (exceeding the 90th percentile) underwent nonlinear changes, driven by the dominant winter variability. In particular, consistent with previous studies, the significant (p <.05) stilling ceased in 2003, followed by no clear trend afterwards. The detected stilling-reversal is linked to large-scale atmospheric circulation changes, in particular to the North Atlantic Oscillation for both mean and gust wind changes, and the intensity changes of extratropical cyclones passing across Sweden especially for wind gusts. Furthermore, in different wind change phases, the observed wind distribution did not vary uniformly for the various wind speed ranges; instead, strong winds drove most of the changes. In the same way, increases in gust winds are greater compared to changes in mean wind speed conditions. The stilling-reversal is also identified by the ERA5 reanalysis, where large-scale atmospheric circulation changes are captured. But the background slowdown detected in most stations does not appear in the ERA5 data as the observed increase in forest cover is not considered in the reanalysis. This study reveals that, in addition to the large-scale interannual variability, changes in surface roughness (e.g., changes in forest cover) contribute to the observed wind variability across Sweden.
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| 21. |
- Nilsson, Mats, et al.
(författare)
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Estimating annual cuttings using multi-temporal satellite data and field data from the Swedish NFI
- 2009
-
Ingår i: International Journal of Remote Sensing. - : Informa UK Limited. - 0143-1161 .- 1366-5901. ; 30, s. 5109-5116
-
Tidskriftsartikel (refereegranskat)abstract
- Many countries have ongoing national forest inventories (NFIs) that provide reliable information on current forest conditions and changes in the forest landscape. These inventories are often based on data collected using field inventory procedures and the results are presented in terms of forest statistics for different geographical areas. The Swedish NFI has decided to combine their field data with optical satellite data by using post-stratification to obtain improved and unbiased estimates of forest variables. The method has been shown to reduce the sampling error (standard error) by 10-35% for variables such as stem volume and forest area. The objective of this study is to investigate the effect on sampling error for the estimated annual clear-felled area when the NFI plots are post-stratified by cuttings mapped from multi-temporal satellite images. Clear-felled areas mapped by the Swedish Forest Agency using image pairs (SPOT and Landsat) from the years 2001/2002, 2002/2003, 2003/2004, and 2004/2005 were used to post-stratify the NFI plots. The study area covers approximately a 1.3 Mha forest land area in Coastal Vasterbotten. It was found that the sampling error (standard error) for the annually clear-felled area was reduced by 31% using post-stratification compared to use of field data alone.
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| 22. |
- Nordkvist, Karin, et al.
(författare)
-
Laserskanning och digital fotogrammetri i skogsbruket. (2. uppl.)
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Förord till första upplagan Flygburen laserskanning och automatiserad användning av digital fotogrammetri håller för närvarande på att revolutionera metoderna för skogsinventering. Detta kompendium är en första ansats att utarbeta ett läromedel på svenska inom området. Kompendiet är framtaget för distanskursen Laserskanning och digital fotogrammetri i skogsbruket vid Institutionen för skoglig resurshushållning, SLU, hösten 2012. Vi hoppas att det även kan få en vidare spridning i det svenska skogsbruket, samt att det kan vidareutvecklas inför kommande kurstillfällen allteftersom nya erfarenheter inom detta snabbt expanderande område vinns. Författarna är därför också tacksamma för synpunkter på kompendiets innehåll och förslag till kompletteringar. Dessa kan skickas till huvudförfattaren Karin Nordkvist (e-post karin.nordkvist@slu.se). Kompendiet har finansierats av Erik Johan Ljungbergs Utbildningsfond och SLU. Vid SLU har Jonas Bohlin, Magnus Ekström, Johan Holmgren, Eva Lindberg och Jörgen Wallerman bidragit till kompendiet. Värdefulla bidrag till innehållet har även lämnats av: Helen Rost och Martin Sjödin, Blom Sweden AB; Fredrik Walter, Dianthus AB; Ulf Söderman, FORAN Remote Sensing AB; Thomas Brethvad, COWI; Andreas Rönnberg, Lantmäteriet; Anders Boberg, Tyréns. Där inget annat anges är figurerna gjorda av Karin Nordkvist. Dessa figurer samt kompendiets text är licensierade under Creative Commons Erkännande-DelaLika 2.5. Umeå i september 2012 Karin Nordkvist och Håkan Olsson Förord till andra upplagan I den andra upplagan hösten 2013 så har kompendiet kompletterats med övningsuppgifter för bearbetning av laserskannerdata för skogliga ändamål, vilka återfinns i ett appendix. Övningsuppgifterna och främst anpassade för kurser vid SLU. Uppgifterna är utformade av Karin Nordkvist, Emma Sandström och Heather Reese vid SLU. Även övningsuppgifterna är finansierade av Erik Johan Ljungbergs Utbildningsfond och SLU. Endast mindre ändringar har gjorts av texten i övrigt.
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| 23. |
- Nordkvist, Karin, et al.
(författare)
-
Vegetation classification in the Swedish sub-arctic using a combination of optical satellite images and airborne laser scanner data
- 2011
-
Konferensbidrag (refereegranskat)abstract
- The aim of this pilot study was to investigate to which degree the accuracy of automated vegetation classification in the Swedish sub-arctic could be improved by combining optical satellite data with airborne laser scanner (ALS) data, compared to using satellite data only. This information is of interest in an ongoing discussion about the possible inclusion of the mountains in northern Sweden in the national laser scanning that started in 2009. A SPOT 4 scene and ALS data from an Optech ALTM Gemini scanner, both from 2010, were used in maximum likelihood classification. Data for training and validation was obtained from 279 plots with 20 m radius that were visited in field 2010. These plots were located near Abisko in northern Sweden (lat. 68° 23' N, long. 18° 53' E), on the north and south side of Lake Torne Träsk. A classification scheme with 7 classes based on the Swedish mountain vegetation map was used. Classification using only SPOT data gave an over-all accuracy of 75.6%, and the combination of SPOT data and ALS data increased the accuracy to 81.4%
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| 24. |
- Olvmo, Mats, 1956, et al.
(författare)
-
Sub-arctic palsa degradation and the role of climatic drivers in the largest coherent palsa mire complex in Sweden (Vissatvuopmi), 1955-2016
- 2020
-
Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
-
Tidskriftsartikel (refereegranskat)abstract
- Substantial palsa degradation has occurred in Fennoscandia, which is considered to be driven by global climate change. Deeper understanding of the role of different climatic drivers on palsa decay, however, is lacking. We use meteorological data and aerial photographs from 1955 to 2016 to statistically identify the most important climatic drivers affecting changes in lateral-temporal palsa decay rates in the largest coherent palsa complex in Sweden, Vissatvuopmi. We show that wetter, warmer and shorter winters are the main causes of large and rapid changes in lateral-palsa extent since the mid-1950s. By analyzing meteorological data from the 1880s to present, we show that average annual temperature conditions have been unfavourable for palsas for more than a century and average annual precipitation conditions have been unfavourable since the 1940s. The decay rates have likely been amplified over the past 50-60 years, and in particular over the most recent decades, due to the combined effect of adverse air temperature and precipitation conditions. Palsa loss is expected to continue, most likely at a higher rate than today, with serious ecological impacts as a consequence.
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| 25. |
- Olvmo, Mats, 1956, et al.
(författare)
-
Vissátvuopmi palsmyr – en naturtyp på väg att försvinna
- 2020
-
Ingår i: Geologiskt Forum. - 1104-4721. ; 108, s. 4-11
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Det förändrade klimatet i Norden får konskevenser för naturen på många sätt. En naturtyp som påverkas är palsmyrarna som finns i nordligaste Sverige i områden där permafrost förekommer. En detaljerad studie av dessa palsmyrar visar att de inom en ganska snar framtid kan komma att försvinna.
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| 26. |
- Persson, Magnus, et al.
(författare)
-
Tree Species Classification with Multi-Temporal Sentinel-2 Data
- 2018
-
Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 10:11
-
Tidskriftsartikel (refereegranskat)abstract
- The Sentinel-2 program provides the opportunity to monitor terrestrial ecosystems with a high temporal and spectral resolution. In this study, a multi-temporal Sentinel-2 data set was used to classify common tree species over a mature forest in central Sweden. The tree species to be classified were Norway spruce (Picea abies), Scots pine (Pinus silvestris), Hybrid larch (Larix x marschlinsii), Birch (Betula sp.) and Pedunculate oak (Quercus robur). Four Sentinel-2 images from spring (7 April and 27 May), summer (9 July) and fall (19 October) of 2017 were used along with the Random Forest (RF) classifier. A variable selection approach was implemented to find fewer and uncorrelated bands resulting in the best model for tree species identification. The final model resulting in the highest overall accuracy (88.2%) came from using all bands from the four image dates. The single image that gave the most accurate classification result (80.5%) was the late spring image (27 May); the 27 May image was always included in subsequent image combinations that gave the highest overall accuracy. The five tree species were classified with a user's accuracy ranging from 70.9% to 95.6%. Thirteen of the 40 bands were selected in a variable selection procedure and resulted in a model with only slightly lower accuracy (86.3%) than that using all bands. Among the highest ranked bands were the red edge bands 2 and 3 as well as the narrow NIR (near-infrared) band 8a, all from the 27 May image, and SWIR (short-wave infrared) bands from all four image dates. This study shows that the red-edge bands and SWIR bands from Sentinel-2 are of importance, and confirms that spring and/or fall images capturing phenological differences between the species are most useful to tree species classification.
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| 27. |
- Reese, Heather, et al.
(författare)
-
C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter
- 2011
-
Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 115, s. 1387-1400
-
Tidskriftsartikel (refereegranskat)abstract
- Semi-empirical topographic normalization methods (e.g.. C-correction) have been widely used to correct illumination differences in optical satellite data. The objective of this study was to examine the precision and accuracy of the C-correction's empirical parameter, c, as a function of the sample from which it was derived. Three sampling methods were compared: a random sample, a sample stratified on north and south aspects, and a sample stratified by cosine of the solar incidence angle, i. In the latter, power allocation was used to determine the quantity of observations for each stratum. Four overlapping satellite images were used (two Landsat 5 TM and two SPOT 5 HRG) with different acquisition dates and large solar zenith angles over an alpine region in Sweden. The sample stratified by cosine of i produced c with the highest precision from repeated trials and had coefficients of determination (R-2) twice as high as those from the other sampling methods. Use of power allocation in the cosine of i stratified sample enabled better representation of spectral variability: this was particularly important for the NIR band where the outcome of c differed according to sampling method. Evaluations using t-tests and classification accuracy showed that c derived from the cosine of i stratified sample correctly normalized a larger percentage of the evaluation data. The distribution of cosine of i in the study area, the spectral variability and vegetation types exert influences to consider when sampling to derive c. Although sampling was restricted to alpine vegetation only, some vegetation classes may have benefitted from separate c-parameter calculation. In general, dry alpine heath and alpine grass heath had relatively higher c-parameters, mesic alpine heath was slightly lower, and alpine willow and alpine meadow had lower c-parameters for the near-infrared band. The cosine of i stratified sampling method using power allocation may be useful for calculation of c for vegetation conditions other than those presented here, as well as for other empirical parameters (e.g., the Minnaert constant, k). (C) 2011 Elsevier Inc. All rights reserved.
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| 28. |
- Reese, Heather
(författare)
-
Classification of Sweden’s forest and alpine vegetation using optical satellite and inventory data
- 2011
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Creation of accurate vegetation maps from optical satellite data requires use of reference data to aid in interpretation or to verify map results. Reference data may be taken, for example, from field visits, aerial photo-interpretation, or ground-based inventories. National inventories are a potential source of reference data useful in land cover mapping projects. This thesis addresses aspects of mapping forest and alpine vegetation in Sweden through combined use of optical satellite data and inventory data. Issues such as reference and satellite data pre-processing, spatial scale, quantity and quality of reference data, and classification methods have been examined. Optical satellite data with pixel sizes ranging from 10 to 300 m have been used together with reference data from the Swedish National Forest Inventory (NFI), National Inventory of Landscapes in Sweden (NILS), a point sample based on the Terrestrial Habitat Monitoring program (THUF), and a forest stand database. Results include modifications to common remote sensing methods, such as introducing iterative adjustment of prior probabilities in Maximum Likelihood classification, and improved topographic normalization (C-correction) of satellite data. Probability-based samples such as NFI, NILS and THUF provide data necessary for assignment of prior probabilities, estimation of continuous values, and are useful as training and validation data. For managed boreal forest stands, coarser pixel (60 m) AWiFS data were nearly as effective for stem volume estimation as SPOT 5 data (10 m). On the other hand, the most accurate classification of detailed alpine vegetation types (72.9% overall accuracy) was from SPOT 5 data combined with elevation derivatives, while classifications of Landsat TM (25 m), AWiFS, and MERIS (300 m) were less accurate. Non-parametric methods (e.g., random forests, decision/regression trees) produced higher classification accuracies than traditional parametric methods for alpine vegetation. The quantity of reference data affected classification accuracy, as more reference data produced higher map accuracy, although other factors such as distribution and quality of the reference data should be considered. As seen in this thesis, the characteristics of the landscape exert an influence on satellite and training data requirements, classification methods and resulting map accuracy.
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| 29. |
- Reese, Heather, et al.
(författare)
-
Combining airborne laser scanning data and optical satellite data for classification of alpine vegetation
- 2014
-
Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 27, s. 81-90
-
Tidskriftsartikel (refereegranskat)abstract
- Climate change and outdated vegetation maps are among the reasons for renewed interest in mapping sensitive alpine and subalpine vegetation. Satellite data combined with elevation derivatives have been shown to be useful for mapping alpine vegetation, however, there is room for improvement.The inclusion of airborne laser scanning data metrics has not been widely investigated for alpine vegetation. This study has combined SPOT 5 satellite data, elevation derivatives, and laser data metrics for a 25 km x 31 km study area in Abisko, Sweden. Nine detailed vegetation classes defined by height, density and species composition in addition to snow/ice, water, and bare rock were classified using a supervised Random Forest classifier. Several of the classes consisted of shrub and grass species with a maximum height of 0.4 m or less. Laser data metrics were calculated from the nDSM based on a 10 m x 10 m grid, and after variable selection, the metrics used in the classification were the 95th and 99th height percentiles, a vertical canopy density metric, the mean and standard deviation of height, a vegetation ratio based on the raw laser data point cloud with a variable height threshold (from 0.1 to 1.0 m with 0.1 m intervals), and standard deviation of these vegetation ratios. The satellite data used in classification was all SPOT bands plus NDVI and NDII, while the elevation derivatives consisted of elevation, slope and the Saga Wetness Index. Overall accuracy when using the combination of laser data metrics, elevation derivatives and SPOT 5 data increased by 6% as compared to classification of SPOT and elevation derivatives only, and increased by 14.2% compared to SPOTS data alone. The classes which benefitted most from inclusion of laser data metrics were mountain birch and alpine willow. The producer's accuracy for willow increased from 18% (SPOT alone) to 41% (SPOT + elevation derivatives) and then to 55% (SPOT + elevation derivatives + laser data) when laser data were included, with the 95th height percentile and Saga Wetness Index contributing most to willow's improved classification. Addition of laser data metrics did not increase the classification accuracy of spectrally similar dry heath (< 0.3 m height) and mesic heath (0.3-1.0 m height), which may have been a result of laser data penetration of sparse shrub canopy or laser data processing choices. The final results show that laser data metrics combined with satellite data and elevation derivatives contributed overall to a better classification of alpine and subalpine vegetation. (c) 2013 Elsevier B.V. All rights reserved.
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| 30. |
- Reese, Heather, et al.
(författare)
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Combining point clouds from image matching with SPOT 5 multispectral data for mountain vegetation classification
- 2015
-
Ingår i: International Journal of Remote Sensing. - 0143-1161 .- 1366-5901. ; 36, s. 403-416
-
Tidskriftsartikel (refereegranskat)abstract
- There is a need to replace outdated vegetation maps over Sweden's mountain region; the ability and accuracy of creating such maps with automated methods and remotely sensed data has been a topic of recent research. While spectral information is a key data input for mapping mountain vegetation, the addition of three-dimensional (3D) data has also proven useful in classification. Point clouds from photogrammetric image matching (IM) or from airborne laser scanning (ALS) are potential 3D data sources. In this study, vegetation height and density metrics from IM and ALS data were classified both alone and in combination with SPOT 5 (Systeme Probatoire d'Observation de la Terre) satellite data and elevation data (elevation, slope, and a wetness index). A Random Forest classification was used to map alpine and subalpine vegetation over Abisko, Sweden. The most notable result in this study was higher producer's accuracy of the mountain birch classification when using IM metrics alone (98%) as compared to ALS data alone (89%). Classification of IM, SPOT, and elevation data combined gave the same overall accuracy (83%) as when using ALS, SPOT, and elevation data combined (also 83%). While most of the alpine vegetation classes were poorly classified using either the IM or ALS metrics alone, the IM point cloud appeared to contain more information for lower-growing (<2 m) vegetation than the ALS point cloud.
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| 31. |
- Reese, Heather, et al.
(författare)
-
Comparison of Resourcesat-1 AWiFS and SPOT-5 data over managed boreal forest stands
- 2009
-
Ingår i: International Journal of Remote Sensing. - : Informa UK Limited. - 0143-1161 .- 1366-5901. ; 30, s. 4957-4978
-
Tidskriftsartikel (refereegranskat)abstract
- In this study, the utility of Advanced Wide Field Sensor (AWiFS) data in relation to stem volume estimation for managed boreal forest stands was investigated. Multiple linear regressions were used to predict stem volume (m(3) ha(-1)) with standwise mean spectral values as the independent variables. For comparison, two SPOT-5 images were used, one with nearly simultaneous acquisition. The adjusted coefficient of determination (R(adj)(2)) using AWiFS data to predict stem volume was 0.573 (SE = 56.9%) while SPOT had an R(adj)(2) of 0.598 (SE = 55.2%). All bands were negatively correlated, with the shortwave infrared (SWIR) band having the single strongest correlation with stem volume. When stem volume was predicted based on stand size, AWiFS and SPOT produced R(adj)(2) values of 0.310 and 0.293, respectively, for stands less than 2 ha in size. Predictive ability increased with stand size, with the highest R(adj)(2) at 20 ha (R(adj)(2) = 0.677 AWiFS, R(adj)(2) = 0.692 SPOT). For stands of 20 ha and larger, the correlation between stem volume and near-infrared (NIR) reflectance increased while decreasing for the visible bands. The explanation behind the trends observed may be due to the management practices in the area. The best two-band predictor of stem volume was the NIR and red band combination for AWiFS, and the NIR and SWIR bands for SPOT. Discriminant analysis of basic forest types showed similar results for AWiFS (65.6% correct) and SPOT (66.4% correct).
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| 32. |
- Reese, Heather, et al.
(författare)
-
Fjällen på kartan
- 2011
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Ingår i: Miljötrender från SLU. - 1403-4743. ; , s. 5-5
-
Tidskriftsartikel (populärvet., debatt m.m.)
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| 33. |
|
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| 34. |
- Reese, Heather, 1964, et al.
(författare)
-
Multi-scale remote sensing observations of a palsa in degradation phase
- 2021
-
Ingår i: EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14455, 2021. - : Copernicus GmbH.
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- <p>The Viss&#225;tvuopmi palsa complex (N 68&#176;74&#8242;50&#8242;&#8242;, E 21&#176;11&#8242;30&#8221;) is the largest coherent palsa complex in Sweden (ca 274 ha). Aerial photo-interpretation over an area covered by plateau palsas showed a 30% decline in lateral area -- from ca 70 to 49 ha -- that occurred between 1955 to 2016 (Olvmo et al., 2020). Within Viss&#225;tvuopmi, we have more closely studied two single palsas, one dome-shaped and one ridge-shaped, for changes in extent, height and vegetation composition. Manual interpretation of aerial photography between 1955 and 2016 show lateral degradation of 35% and 54% for the dome and ridge palsas, respectively. Since 2018 we have monitored the palsas using images from drones as well as analysis of Planet Dove and Sentinel-2 satellite imagery. Photogrammetry is used to produce orthophotos as well as digital surface models (DSMs) from the drone images, and compared to earlier LiDAR and aerial photo DSMs, to study lateral and vertical degradation.</p><p>The drone-generated DSMs from 2018, 2019 and 2020 show further lateral degradation of the two large palsas. In 2020 a rapid change in vegetation composition was seen on the dome-shaped palsa, where a 250 m<sup>2</sup> area of <em>Betula nana</em> and <em>Empetrum hermaphroditum</em> transitioned to lichen. This vegetation change could be seen in spectral data from both drone and satellite platforms. The future development of this palsa, monitored annually using both fine and medium spatial resolution data, will give insight into the timing and signs of the individual palsas in stages of degradation.</p>
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| 35. |
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| 36. |
- Roberge, Cornelia, et al.
(författare)
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Improving the precision of sample-based forest damage inventories through two-phase sampling and post-stratification using remotely sensed auxiliary information
- 2016
-
Ingår i: Environmental Monitoring and Assessment. - : Springer Science and Business Media LLC. - 0167-6369 .- 1573-2959. ; 188
-
Tidskriftsartikel (refereegranskat)abstract
- Many countries have a national forest inventory (NFI) designed to produce statistically sound estimates of forest parameters. However, this type of inventory may not provide reliable results for forest damage which usually affects only small parts of the forest in a country. For this reason, specially designed forest damage inventories are performed in many countries, sometimes in coordination with the NFIs. In this study, we evaluated a new approach for damage inventory where existing NFI data form the basis for two-phase sampling for stratification and remotely sensed auxiliary data are applied for further improvement of precision through post-stratification. We applied Monte Carlo sampling simulation to evaluate different sampling strategies linked to different damage scenarios. The use of existing NFI data in a two-phase sampling for stratification design resulted in a relative efficiency of 50 % or lower, i.e., the variance was at least halved compared to a simple random sample of the same size. With post-stratification based on simulated remotely sensed auxiliary data, there was additional improvement, which depended on the accuracy of the auxiliary data and the properties of the forest damage. In many cases, the relative efficiency was further reduced by as much as one-half. In conclusion, the results show that substantial gains in precision can be obtained by utilizing auxiliary information in forest damage surveys, through two-phase sampling, through post-stratification, and through the combination of these two approaches, i.e., post-stratified two-phase sampling for stratification.
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| 37. |
- Scharn, Ruud, et al.
(författare)
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Vegetation responses to 26 years of warming at Latnjajaure Field Station, northern Sweden
- 2022
-
Ingår i: Arctic Science. - 2368-7460. ; 8:3, s. 858-877
-
Tidskriftsartikel (refereegranskat)abstract
- Climate change is rapidly warming high latitude and high elevation regions influencing plant community composition. Changes in vegetation composition have motivated the coordination of ecological monitoring networks across the Arctic, including the International Tundra Experiment (ITEX). We have established a long-term passive warming experiment using open-top chambers, which includes five distinct plant communities (Dry Heath; Tussock Tundra; and Dry, Mesic, and Wet Meadow). We have measured changes in plant community composition based on relative abundance differences over 26 years. In addition, relative abundance changes in response to fertilization and warming treatments were analysed based on a 7-year Community-Level Interaction Program (CLIP) experiment. The communities had distinct soil moisture conditions, leading to community specific responses of the plant growth forms (deciduous shrubs, evergreen shrubs, forbs and graminoids). Warming significantly affected growth forms, but the direction of the response was not consistent across the communities. Evidence of shrub expansion was found in nearly all communities, with soil moisture determining whether it was driven by deciduous or evergreen shrubs. Graminoids increased in relative abundance in the Dry Meadow due to warming. Growth form responses to warming are likely mediated by edaphic characteristics of the communities and their interactions with climate.
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| 38. |
- Scharn, Ruud, et al.
(författare)
-
Vegetation responses to 26 years of warming at Latnjajaure Field Station, northern Sweden
- 2022
-
Ingår i: Arctic Science. - Ottawa, ON : Canadian Science Publishing. - 2368-7460. ; 8:3, s. 858-877
-
Tidskriftsartikel (refereegranskat)abstract
- Climate change is rapidly warming high latitude and high elevation regions influencing plant community composition. Changes in vegetation composition have motivated the coordination of ecological monitoring networks across the Arctic, including the International Tundra Experiment. We have established a long-term passive warming experiment using open-top chambers, which includes five distinct plant communities (Dry Heath; Tussock Tundra; and Dry, Mesic, and Wet Meadow). We measured changes in plant community composition based on relative abundance differences over 26 years. In addition, relative abundance changes in response to fertilization and warming treatments were analyzed based on a seven-year Community-Level Interaction Program experiment. The communities had distinct soil moisture conditions, leading to community-specific responses of the plant growth forms (deciduous shrubs, evergreen shrubs, forbs, and graminoids). Warming significantly affected growth forms, but the direction of the response was not consistent across the communities. Evidence of shrub expansion was found in nearly all communities, with soil moisture determining whether it was driven by deciduous or evergreen shrubs. Graminoids increased in relative abundance in the Dry Meadow due to warming. Growth form responses to warming are likely mediated by edaphic characteristics of the communities and their interactions with climate.
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| 39. |
- Soja, Maciej, 1985, et al.
(författare)
-
Mapping Tree Height in Burkina Faso Parklands with TanDEM-X
- 2021
-
Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 13:14
-
Tidskriftsartikel (refereegranskat)abstract
- Mapping of tree height is of great importance for management, planning, and research related to agroforestry parklands in Africa. In this paper, we investigate the potential of spotlight-mode data from the interferometric synthetic aperture radar (InSAR) satellite system TanDEM-X (TDM) for mapping of tree height in Sapone, Burkina Faso, a test site characterised by a low average canopy cover (similar to 15%) and a mean tree height of 9.0 m. Seven TDM acquisitions from January-April 2018 are used jointly to create high-resolution (similar to 3 m) maps of interferometric phase height and mean canopy elevation, the latter derived using a new, model-based processing approach compensating for some effects of the side-looking geometry of SAR. Compared with phase height, mean canopy elevation provides a more accurate representation of tree height variations, a better tree positioning accuracy, and better tree height estimation performance when assessed using 915 trees inventoried in situ and representing 15 different species/genera. We observe and discuss two bias effects, and we use empirical models to compensate for these effects. The best-performing model using only TDM data provides tree height estimates with a standard error (SE) of 2.8 m (31% of the average height) and a correlation coefficient of 75%. The estimation performance is further improved when TDM height data are combined with in situ measurements; this is a promising result in view of future synergies with other remote sensing techniques or ground measurement-supported monitoring of well-known trees.
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| 40. |
- Strannegård, Claes, 1962, et al.
(författare)
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Ecosystem Models Based on Artificial Intelligence
- 2022
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Ingår i: 34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022. - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- Ecosystem models can be used for understanding general phenomena of evolution, ecology, and ethology. They can also be used for analyzing and predicting the ecological consequences of human activities on specific ecosystems, e.g., the effects of agriculture, forestry, construction, hunting, and fishing. We argue that powerful ecosystem models need to include reasonable models of the physical environment and of animal behavior. We also argue that several well-known ecosystem models are unsatisfactory in this regard. Then we present the open-source ecosystem simulator Ecotwin, which is built on top of the game engine Unity. To model a specific ecosystem in Ecotwin, we first generate a 3D Unity model of the physical environment, based on topographic or bathymetric data. Then we insert digital 3D models of the organisms of interest into the environment model. Each organism is equipped with a genome and capable of sexual or asexual reproduction. An organism dies if it runs out of some vital resource or reaches its maximum age. The animal models are equipped with behavioral models that include sensors, actions, reward signals, and mechanisms of learning and decision-making. Finally, we illustrate how Ecotwin works by building and running one terrestrial and one marine ecosystem model.
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| 41. |
- Wallerman, Jörgen, et al.
(författare)
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Forest mapping using 3D data from SPOT-5 HRS and Z/I DMC
- 2010
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Ingår i: IEEE International Geoscience and Remote Sensing Symposium proceedings. - 2153-6996 .- 2153-7003. ; , s. 64-67
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The nation-wide Airborne Laser Scanning (ALS) currently performed by the Swedish National Land Survey will provide a new and accurate Digital Elevation Model (DEM). These data will enable new and cost-efficient assessments of vegetation height using Canopy Height Models (CHMs) derived as the difference between a Digital Surface Model (DSM) and the DEM. In this context, the High Resolution Stereoscopic (HRS) sensor onboard SPOT-5 and the airborne Z/I Digital Mapping Camera (DMC) used for operational aerial photography by the Swedish National Land Survey are of main interest. Previous research has shown that reliable tree height data are a powerful source of information for forest management planning. This study investigated the possibilities to map forest variables using CHMs derived from either the SPOT-5 HRS or Z/I DMC sensor together with ALS DEM data, in combination with spectral data from the SPOT-5 High Resolution Geometric (HRG) sensor. The results when using the Z/I DMC CHM in combination with SPOT-5 HRG data showed Root Mean Square Errors for standwise prediction of mean tree height, stem diameter, and stem volume of 7.3%, 9.0%, and 19%, respectively. The SPOT-5 HRS CHM in combination with SPOT-5 HRG data improved the SPOT HRG based estimates from 13% to 10%, 15% to 13%, and 31% to 23%, for tree height, stem diameter, and stem volume, respectively. Adding CHM data to a SPOT-5 HRG based prediction model improved the mapping accuracy between 13% to 44%. In conclusion, the obtained accuracies may be sufficient for operational forest management planning.
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| 42. |
- Wolters, S., et al.
(författare)
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Upscaling proximal sensor N-uptake predictions in winter wheat (Triticum aestivum L.) with Sentinel-2 satellite data for use in a decision support system
- 2021
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Ingår i: Precision Agriculture. - : Springer Science and Business Media LLC. - 1385-2256 .- 1573-1618. ; 22, s. 1263-1283
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Tidskriftsartikel (refereegranskat)abstract
- Total nitrogen (N) content in aboveground biomass (N-uptake) in winter wheat (Triticum aestivum L.) as measured in a national monitoring programme was scaled up to full spatial coverage using Sentinel-2 satellite data and implemented in a decision support system (DSS) for precision agriculture. Weekly field measurements of N-uptake had been carried out using a proximal canopy reflectance sensor (handheld Yara N-Sensor) during 2017 and 2018. Sentinel-2 satellite data from two processing levels (top-of-atmosphere reflectance, L1C, and bottom-of-atmosphere reflectance, L2A) were extracted and related to the proximal sensor data (n = 251). The utility of five vegetation indices for estimation of N-uptake was compared. A linear model based on the red-edge chlorophyll index (CI) provided the best N-uptake prediction (L1C data: r2 = 0.74, mean absolute error; MAE = 14kgha−1) when models were applied on independent sites and dates. Use of L2A data, rather than L1C, did not improve the prediction models. The CI-based prediction model was applied on all fields in an area with intensive winter wheat production. Statistics on N-uptake at the end of the stem elongation growth stage were calculated for 4169 winter wheat fields > 5ha. Within-field variation in predicted N-uptake was > 30kgNha−1 in 62% of these fields. Predicted N-uptake was compared against N-uptake maps derived from tractor-borne Yara N-Sensor measurements in 13 fields (1.7–30ha in size). The model based on satellite data generated similar information as the tractor-borne sensing data (r2 = 0.81; MAE = 7kgha−1), and can therefore be valuable in a DSS for variable-rate N application. © 2021, The Author(s).
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