| 1. |
- 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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| 2. |
- 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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| 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. |
- 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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| 5. |
- 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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| 6. |
- 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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| 7. |
- 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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| 8. |
- Olvmo, Mats, 1956, et al.
(författare)
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Sub-arctic palsa degradation and the role of climatic drivers in the largest coherent palsa mire complex in Sweden (Vissatvuopmi), 1955-2016
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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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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| 9. |
- Olvmo, Mats, 1956, et al.
(författare)
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Vissátvuopmi palsmyr – en naturtyp på väg att försvinna
- 2020
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Ingår i: Geologiskt Forum. - 1104-4721. ; 108, s. 4-11
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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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| 10. |
- Persson, Magnus, et al.
(författare)
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Tree Species Classification with Multi-Temporal Sentinel-2 Data
- 2018
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 10:11
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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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