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  • 1. Andrei, Constantin-Octavian
    et al.
    Lahtinen, Sonja
    Nordman, Maaria
    Stockholm University, Faculty of Science, Department of Physical Geography. Finnish Geospatial Research Institute FGI, Finland.
    Naranen, Jyri
    Koivula, Hannu
    Poutanen, Markku
    Hyyppa, Juha
    GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station2018In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 10, no 12, article id 1937Article in journal (Refereed)
    Abstract [en]

    Continuous Global Positioning System (GPS) observations have been logged at the Finnish Antarctic research station (Aboa) since February 2003. The station is located in Dronning Maud Land, East Antarctica. Almost 5000 daily observation files have been archived based on yearly scientific expeditions. These files have not been fully analysed until now. This study reports for the first time on the consistent and homogeneous data processing and analysis of the 15-year long time series. Daily coordinates are obtained using Precise Point Positioning (PPP) processing based on two approaches. The first approach is based on the Kalman filter and uses the RTKLIB open source library to produce daily solutions by unconventionally running the filter in the forward and backward direction. The second approach uses APPS web service and is based on GIPSY scientific processing engine. The two approaches show an excellent agreement with less than 3 mm rms error horizontally and 6 mm rms error vertically. The derived position time series is analysed in terms of trend, periodicity and noise characteristics. The noise of the time series was found to be power-law noise model with spectral index closer to flicker noise. In addition, several periodic signals were found at 5, 14, 183 and 362 days. Furthermore, most of the horizontal movement was found to be in the North direction at a rate of 11.23 +/- 0.09 mm/y, whereas the rate in the East direction was estimated to be 1.46 +/- 0.05 mm/y. Lastly, the 15-year long time series revealed a movement upwards at a rate of 0.79 +/- 0.35 mm/y. Despite being an unattended station, Aboa provides one of the most continuous and longest GPS time series in Antarctica. Therefore, we believe that this research increases the awareness of local geophysical phenomena in a less reported area of the Antarctic continent.

  • 2.
    Brown, Ian
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Mwansasu, Simon
    Stockholm University, Faculty of Science, Department of Physical Geography. University of Dar es Salaam, Tanzania.
    Westerberg, Lars-Ove
    Stockholm University, Faculty of Science, Department of Physical Geography.
    L-Band Polarimetric Target Decomposition of Mangroves of the Rufiji Delta, Tanzania2016In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 8, no 2, article id UNSP 140Article in journal (Refereed)
    Abstract [en]

    The mangroves of the Rufiji Delta are an important habitat and resource. The mangrove forest reserve is home to an indigenous population and has been under pressure from an influx of migrants from the landward side of the delta. Timely and effective forest management is needed to preserve the delta and mangrove forest. Here, we investigate the potential of polarimetric target decomposition for mangrove forest monitoring and analysis. Using three ALOS PALSAR images, we show that L-band polarimetry is capable of mapping mangrove dynamics and is sensitive to stand structure and the hydro-geomorphology of stands. Entropy-alpha-anisotropy and incoherent target decompositions provided valuable measures of scattering behavior related to forest structure. Little difference was found between Yamaguchi and Arii decompositions, despite the conceptual differences between these models. Using these models, we were able to differentiate the scattering behavior of the four main species found in the delta, though classification was impractical due to the lack of pure stands. Scattering differences related to season were attributed primarily to differences in ground moisture or inundation. This is the first time mangrove species have been identified by their scattering behavior in L-band polarimetric data. These results suggest higher resolution L-band quad-polarized imagery, such as from PALSAR-2, may be a powerful tool for mangrove species mapping.

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  • 3. Dawson, Samantha K.
    et al.
    Fisher, Adrian
    Lucas, Richard
    Hutchinson, David K.
    Stockholm University, Faculty of Science, Department of Geological Sciences.
    Berney, Peter
    Keith, David
    Catford, Jane A.
    Kingsford, Richard T.
    Remote Sensing Measures Restoration Successes, but Canopy Heights Lag in Restoring Floodplain Vegetation2016In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 8, no 7, article id 542Article in journal (Refereed)
    Abstract [en]

    Wetlands worldwide are becoming increasingly degraded, and this has motivated many attempts to manage and restore wetland ecosystems. Restoration actions require a large resource investment, so it is critical to measure the outcomes of these management actions. We evaluated the restoration of floodplain wetland vegetation across a chronosequence of land uses, using remote sensing analyses. We compared the Landsat-based fractional cover of restoration areas with river red gum and lignum reference communities, which functioned as a fixed target for restoration, over three time periods: (i) before agricultural land use (1987-1997); (ii) during the peak of agricultural development (2004-2007); and (iii) post-restoration of flooding (2010-2015). We also developed LiDAR-derived canopy height models (CHMs) for comparison over the second and third time periods. Inundation was crucial for restoration, with many fields showing little sign of similarity to target vegetation until after inundation, even if agricultural land uses had ceased. Fields cleared or cultivated for only one year had greater restoration success compared to areas cultivated for three or more years. Canopy height increased most in the fields that were cleared and cultivated for a short duration, in contrast to those cultivated for >12 years, which showed few signs of recovery. Restoration was most successful in fields with a short development duration after the intervention, but resulting dense monotypic stands of river cooba require future monitoring and possibly intervention to prevent sustained dominance. Fields with intensive land use histories may need to be managed as alternative, drier flood-dependent vegetation communities, such as black box (Eucalyptus largiflorens) grasslands. Remotely-sensed data provided a powerful measurement technique for tracking restoration success over a large floodplain.

  • 4. Doktor, Daniel
    et al.
    Lausch, Angela
    Spengler, Daniel
    Thurner, Martin
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM). Max Planck Institute for Biogeochemistry, Germany.
    Extraction of Plant Physiological Status from Hyperspectral Signatures Using Machine Learning Methods2014In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 6, no 12, p. 12247-12274Article in journal (Refereed)
    Abstract [en]

    The machine learning method, random forest (RF), is applied in order to derive biophysical and structural vegetation parameters from hyperspectral signatures. Hyperspectral data are, among other things, characterized by their high dimensionality and autocorrelation. Common multivariate regression approaches, which usually include only a limited number of spectral indices as predictors, do not make full use of the available information. In contrast, machine learning methods, such as RF, are supposed to be better suited to extract information on vegetation status. First, vegetation parameters are extracted from hyperspectral signatures simulated with the radiative transfer model, PROSAIL. Second, the transferability of these results with respect to laboratory and field measurements is investigated. In situ observations of plant physiological parameters and corresponding spectra are gathered in the laboratory for summer barley (Hordeum vulgare). Field in situ measurements focus on winter crops over several growing seasons. Chlorophyll content, Leaf Area Index and phenological growth stages are derived from simulated and measured spectra. RF performs very robustly and with a very high accuracy on PROSAIL simulated data. Furthermore, it is almost unaffected by introduced noise and bias in the data. When applied to laboratory data, the prediction accuracy is still good (C-ab: R-2 = 0.94/ LAI: R-2 = 0.80/BBCH (Growth stages of mono-and dicotyledonous plants) : R-2 = 0.91), but not as high as for simulated spectra. Transferability to field measurements is given with prediction levels as high as for laboratory data (C-ab: R-2 = 0.89/LAI: R-2 = 0.89/BBCH: R-2 = similar to 0.8). Wavelengths for deriving plant physiological status based on simulated and measured hyperspectral signatures are mostly selected from appropriate spectral regions (both field and laboratory): 700-800 nm regressing on C-ab and 800-1300 nm regressing on LAI. Results suggest that the prediction accuracy of vegetation parameters using RF is not hampered by the high dimensionality of hyperspectral signatures (given preceding feature reduction). Wavelengths selected as important for prediction might, however, vary between underlying datasets. The introduction of changing environmental factors (soil, illumination conditions) has some detrimental effect, but more important factors seem to stem from measurement uncertainties and plant geometries.

  • 5.
    Karlsson, Johanna Mård
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Lyon, Steve W.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Destouni, Georgia
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
    Temporal Behavior of Lake Size-Distribution in a Thawing Permafrost Landscape in Northwestern Siberia2014In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 6, no 1, p. 621-636Article in journal (Refereed)
    Abstract [en]

    Arctic warming alters regional hydrological systems, as permafrost thaw increases active layer thickness and in turn alters the pathways of water flow through the landscape. Further, permafrost thaw may change the connectivity between deeper and shallower groundwater and surface water altering the terrestrial water balance and distribution. Thermokarst lakes and wetlands in the Arctic offer a window into such changes as these landscape elements depend on permafrost and are some of the most dynamic and widespread features in Arctic lowland regions. In this study we used Landsat remotely sensed imagery to investigate potential shifts in thermokarst lake size-distributions, which may be brought about by permafrost thaw, over three distinct time periods (1973, 1987-1988, and 2007-2009) in three hydrological basins in northwestern Siberia. Results revealed fluctuations in total area and number of lakes over time, with both appearing and disappearing lakes alongside stable lakes. On the whole basin scales, there is no indication of any sustained long-term change in thermokarst lake area or lake size abundance over time. This statistical temporal consistency indicates that spatially variable change effects on local permafrost conditions have driven the individual lake changes that have indeed occurred over time. The results highlight the importance of using multi-temporal remote sensing data that can reveal complex spatiotemporal variations distinguishing fluctuations from sustained change trends, for accurate interpretation of thermokarst lake changes and their possible drivers in periods of climate and permafrost change.

  • 6.
    Kratzer, Susanne
    et al.
    Stockholm University, Faculty of Science, Department of Ecology, Environment and Plant Sciences.
    Kyryliuk, Dmytro
    Stockholm University, Faculty of Science, Department of Ecology, Environment and Plant Sciences.
    Edman, Moa
    Philipson, Petra
    Lyon, Steve W.
    Stockholm University, Faculty of Science, Department of Physical Geography. Ohio State University, USA.
    Synergy of Satellite, In Situ and Modelled Data for Addressing the Scarcity of Water Quality Information for Eutrophication Assessment and Monitoring of Swedish Coastal Waters2019In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 11, no 17Article in journal (Refereed)
    Abstract [en]

    Monthly CHL-a and Secchi Depth (SD) data derived from the full mission data of the Medium Resolution Imaging Spectrometer (MERIS; 2002-2012) were analysed along a horizontal transect from the inner Braviken bay and out into the open sea. The CHL-a values were calibrated using an algorithm derived from Swedish lakes. Then, calibrated Chl-a and Secchi Depth (SD) estimates were extracted from MERIS data along the transect and compared to conventional monitoring data as well as to data from the Swedish Coastal zone Model (SCM), providing physico-biogeochemical parameters such as temperature, nutrients, Chlorophyll-a (CHL-a) and Secchi depth (SD). A high negative correlation was observed between satellite-derived CHL-a and SD (rho = -0.91), similar to the in situ relationship established for several coastal gradients in the Baltic proper. We also demonstrate that the validated MERIS-based estimates and data from the SCM showed strong correlations for the variables CHL-a, SD and total nitrogen (TOTN), which improved significantly when analysed on a monthly basis across basins. The relationship between satellite-derived CHL-a and modelled TOTN was also evaluated on a monthly basis using least-square linear regression models. The predictive power of the models was strong for the period May-November (R-2: 0.58-0.87), and the regression algorithm for summer was almost identical to the algorithm generated from in situ data in Himmerfjarden bay. The strong correlation between SD and modelled TOTN confirms that SD is a robust and reliable indicator to evaluate changes in eutrophication in the Baltic proper which can be assessed using remote sensing data. Amongst all three assessed methods, only MERIS CHL-a was able to correctly depict the pattern of phytoplankton phenology that is typical for the Baltic proper. The approach of combining satellite data and physio-biogeochemical models could serve as a powerful tool and value-adding complement to the scarcely available in situ data from national monitoring programs. In particular, satellite data will help to reduce uncertainties in long-term monitoring data due to its improved measurement frequency.

  • 7.
    Kratzer, Susanne
    et al.
    Stockholm University, Faculty of Science, Department of Ecology, Environment and Plant Sciences.
    Moore, Gerald
    Inherent Optical Properties of the Baltic Sea in Comparison to Other Seas and Oceans2018In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 10, no 3, article id 418Article in journal (Refereed)
    Abstract [en]

    In order to retrieve geophysical satellite products in coastal waters with high coloured dissolved organic matter (CDOM), models and processors require parameterization with regional specific inherent optical properties (sIOPs). The sIOPs of the Baltic Sea were evaluated and compared to a global NOMAD/COLORS Reference Data Set (RDS), covering a wide range of optical provinces. Ternary plots of relative absorption at 442 nm showed CDOM dominance over phytoplankton and non-algal particle absorption (NAP). At 670 nm, the distribution of Baltic measurements was not different from case 1 waters and the retrieval of Chl a was shown to be improved by red-ratio algorithms. For correct retrieval of CDOM from Medium Resolution Imaging Spectrometer (MERIS) data, a different CDOM slope over the Baltic region is required. The CDOM absorption slope, SCDOM, was significantly higher in the northwestern Baltic Sea: 0.018 (+/- 0.002) compared to 0.016 (+/- 0.005) for the RDS. Chl a-specific absorption and a(d) [SPM]*(442) and its spectral slope did not differ significantly. The comparison to the MERIS Reference Model Document (RMD) showed that the SNAP slope was generally much higher (0.011 +/- 0.003) than in the RMD (0.0072 +/- 0.00108), and that the SPM scattering slope was also higher (0.547 +/- 0.188) vs. 0.4. The SPM-specific scattering was much higher (1.016 +/- 0.326 m(2) g(-1)) vs. 0.578 m(2) g(-1) in RMD. SPM retrieval could be improved by applying the local specific scattering. A novel method was implemented to derive the phase function (PF) from AC9 and VSF-3 data. (b) over tilde was calculated fitting a Fournier-Forand PF to the normalized VSF data. (b) over tilde was similar to Petzold, but the PF differed in the backwards direction. Some of the sIOPs showed a bimodal distribution, indicating different water types-e.g., coastal vs. open sea. This seems to be partially caused by the distribution of inorganic particles that fall out relatively close to the coast. In order to improve remote sensing retrieval from Baltic Sea data, one should apply different parameterization to these distinct water types, i.e., inner coastal waters that are more influenced by scattering of inorganic particles vs. open sea waters that are optically dominated by CDOM absorption.

  • 8. Rahmati, Omid
    et al.
    Moghaddam, Davoud Davoudi
    Moosavi, Vahid
    Kalantari, Zahra
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Samadi, Mahmood
    Lee, Saro
    Dieu, Tien
    An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping2019In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 11, no 11, article id 1375Article in journal (Refereed)
    Abstract [en]

    Although sampling strategy plays an important role in groundwater potential mapping and significantly influences model accuracy, researchers often apply a simple random sampling method to determine absence (non-occurrence) samples. In this study, an automated, user-friendly geographic information system (GIS)-based tool, selection of absence samples (SAS), was developed using the Python programming language. The SAS tool takes into account different geospatial concepts, including nearest neighbor (NN) and hotspot analyses. In a case study, it was successfully applied to the Bojnourd watershed, Iran, together with two machine learning models (random forest (RF) and multivariate adaptive regression splines (MARS)) with GIS and remotely sensed data, to model groundwater potential. Different evaluation criteria (area under the receiver operating characteristic curve (AUC-ROC), true skill statistic (TSS), efficiency (E), false positive rate (FPR), true positive rate (TPR), true negative rate (TNR), and false negative rate (FNR)) were used to scrutinize model performance. Two absence sample types were produced, based on a simple random method and the SAS tool, and used in the models. The results demonstrated that both RF (AUC-ROC = 0.913, TSS = 0.72, E = 0.926) and MARS (AUC-ROC = 0.889, TSS = 0.705, E = 0.90) performed better when using absence samples generated by the SAS tool, indicating that this tool is capable of producing trustworthy absence samples to improve groundwater potential models.

  • 9. Rahmati, Omid
    et al.
    Yousefi, Saleh
    Kalantari, Zahra
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Uuemaa, Evelyn
    Teimurian, Teimur
    Keesstra, Saskia
    Tien, Dat
    Dieu, Tien
    Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran2019In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 11, no 16, article id 1943Article in journal (Refereed)
    Abstract [en]

    Mountainous areas are highly prone to a variety of nature-triggered disasters, which often cause disabling harm, death, destruction, and damage. In this work, an attempt was made to develop an accurate multi-hazard exposure map for a mountainous area (Asara watershed, Iran), based on state-of-the art machine learning techniques. Hazard modeling for avalanches, rockfalls, and floods was performed using three state-of-the-art models-support vector machine (SVM), boosted regression tree (BRT), and generalized additive model (GAM). Topo-hydrological and geo-environmental factors were used as predictors in the models. A flood dataset (n = 133 flood events) was applied, which had been prepared using Sentinel-1-based processing and ground-based information. In addition, snow avalanche (n = 58) and rockfall (n = 101) data sets were used. The data set of each hazard type was randomly divided to two groups: Training (70%) and validation (30%). Model performance was evaluated by the true skill score (TSS) and the area under receiver operating characteristic curve (AUC) criteria. Using an exposure map, the multi-hazard map was converted into a multi-hazard exposure map. According to both validation methods, the SVM model showed the highest accuracy for avalanches (AUC = 92.4%, TSS = 0.72) and rockfalls (AUC = 93.7%, TSS = 0.81), while BRT demonstrated the best performance for flood hazards (AUC = 94.2%, TSS = 0.80). Overall, multi-hazard exposure modeling revealed that valleys and areas close to the Chalous Road, one of the most important roads in Iran, were associated with high and very high levels of risk. The proposed multi-hazard exposure framework can be helpful in supporting decision making on mountain social-ecological systems facing multiple hazards.

  • 10. Santoro, Maurizio
    et al.
    Cartus, Oliver
    Fransson, Johan E. S.
    Shvidenko, Anatoly
    McCallum, Ian
    Hall, Ronald J.
    Beaudoin, Andre
    Beer, Christian
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
    Schmullius, Christiane
    Estimates of Forest Growing Stock Volume for Sweden, Central Siberia, and Quebec Using Envisat Advanced Synthetic Aperture Radar Backscatter Data2013In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 5, no 9, p. 4503-4532Article in journal (Refereed)
    Abstract [en]

    A study was undertaken to assess Envisat Advanced Synthetic Aperture Radar (ASAR) ScanSAR data for quantifying forest growing stock volume (GSV) across three boreal regions with varying forest types, composition, and structure (Sweden, Central Siberia, and Quebec). Estimates of GSV were obtained using hyper-temporal observations of the radar backscatter acquired by Envisat ASAR with the BIOMASAR algorithm. In total, 5.310(6) km(2) were mapped with a 0.01 degrees pixel size to obtain estimates representative for the year of 2005. Comparing the SAR-based estimates to spatially explicit datasets of GSV, generated from forest field inventory and/or Earth Observation data, revealed similar spatial distributions of GSV. Nonetheless, the weak sensitivity of C-band backscatter to forest structural parameters introduced significant uncertainty to the estimated GSV at full resolution. Further discrepancies were observed in the case of different scales of the ASAR and the reference GSV and in areas of fragmented landscapes. Aggregation to 0.1 degrees and 0.5 degrees was then undertaken to generate coarse scale estimates of GSV. The agreement between ASAR and the reference GSV datasets improved; the relative difference at 0.5 degrees was consistently within a magnitude of 20-30%. The results indicate an improvement of the characterization of forest GSV in the boreal zone with respect to currently available information.

  • 11.
    Senkondo, William
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography. University of Dar es Salaam, Tanzania; Water Institute, Dar es Salaam, Tanzania.
    Munishi, Subira E.
    Tumbo, Madaka
    Nobert, Joel
    Lyon, Steve W.
    Stockholm University, Faculty of Science, Department of Physical Geography. The Nature Conservancy, USA.
    Comparing Remotely-Sensed Surface Energy Balance Evapotranspiration Estimates in Heterogeneous and Data-Limited Regions: A Case Study of Tanzania's Kilombero Valley2019In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 11, no 11, article id 1289Article in journal (Refereed)
    Abstract [en]

    Evapotranspiration (ET) plays a crucial role in integrated water resources planning, development and management, especially in tropical and arid regions. Determining ET is not straightforward due to the heterogeneity and complexity found in real-world hydrological basins. This situation is often compounded in regions with limited hydro-meteorological data that are facing rapid development of irrigated agriculture. Remote sensing (RS) techniques have proven useful in this regard. In this study, we compared the daily actual ET estimates derived from 3 remotely-sensed surface energy balance (SEB) models, namely, the Surface Energy Balance Algorithm for Land (SEBAL) model, the Operational Simplified Surface Energy Balance (SSEBop) model, and the Simplified Surface Balance Index (S-SEBI) model. These products were generated using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery for a total of 44 satellite overpasses in 2005, 2010, and 2015 in the heterogeneous, highly-utilized, rapidly-developing and data-limited Kilombero Valley (KV) river basin in Tanzania, eastern Africa. Our results revealed that the SEBAL model had a relatively high ET compared to other models and the SSEBop model had relatively low ET compared to the other models. In addition, we found that the S-SEBI model had a statistically similar ET as the ensemble mean of all models. Further comparison of SEB models' ET estimates across different land cover classes and different spatial scales revealed that almost all models' ET estimates were statistically comparable (based on the Wilcoxon's test and the Levene's test at a 95% confidence level), which implies fidelity between and reliability of the ET estimates. Moreover, all SEB models managed to capture the two spatially-distinct ET regimes in KV: the stable/permanent ET regime on the mountainous parts of the KV and the seasonally varied ET over the floodplain which contains a Ramsar site (Kilombero Valley Floodplain). Our results have the potential to be used in hydrological modelling to explore and develop integrated water resources management in the valley. We believe that our approach can be applied elsewhere in the world especially where observed meteorological variables are limited.

  • 12. Soja-Woźniak, Monika
    et al.
    Craig, Susanne E.
    Kratzer, Susanne
    Stockholm University, Faculty of Science, Department of Ecology, Environment and Plant Sciences.
    Wojtasiewicz, Bozena
    Darecki, Miroslaw
    Jones, Chris T.
    A Novel Statistical Approach for Ocean Colour Estimation of Inherent Optical Properties and Cyanobacteria Abundance in Optically Complex Waters2017In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 9, no 4, article id 343Article in journal (Refereed)
    Abstract [en]

    Eutrophication is an increasing problem in coastal waters of the Baltic Sea. Moreover, algal blooms, which occur every summer in the Gulf of Gdansk can deleteriously impact human health, the aquatic environment, and economically important fisheries, tourism, and recreation industries. Traditional laboratory-based techniques for water monitoring are expensive and time consuming, which usually results in limited numbers of observations and discontinuity in space and time. The use of hyperspectral radiometers for coastal water observation provides the potential for more detailed remote optical monitoring. A statistical approach to develop local models for the estimation of optically significant components from in situ measured hyperspectral remote sensing reflectance in case 2 waters is presented in this study. The models, which are based on empirical orthogonal function (EOF) analysis and stepwise multilinear regression, allow for the estimation of parameters strongly correlated with phytoplankton (pigment concentration, absorption coefficient) and coloured detrital matter abundance (absorption coefficient) directly from reflectance spectra measured in situ. Chlorophyll a concentration, which is commonly used as a proxy for phytoplankton biomass, was retrieved with low error (median percent difference, MPD = 17%, root mean square error RMSE = 0.14 in log(10) space) and showed a high correlation with chlorophyll a measured in situ (R = 0.84). Furthermore, phycocyanin and phycoerythrin, both characteristic pigments for cyanobacteria species, were also retrieved reliably from reflectance with MPD = 23%, RMSE = 0.23, R-2 = 0.77 and MPD = 24%, RMSE = 0.15, R-2 = 0.74, respectively. The EOF technique proved to be accurate in the derivation of the absorption spectra of phytoplankton and coloured detrital matter (CDM), with R-2 (lambda) above 0.83 and RMSE around 0.10. The approach was also applied to satellite multispectral remote sensing reflectance data, thus allowing for improved temporal and spatial resolution compared with the in situ measurements. The EOF method tested on simulated Medium Resolution Imaging Spectrometer (MERIS) or Ocean and Land Colour Instrument (OLCI) data resulted in RMSE = 0.16 for chl-a and RMSE = 0.29 for phycocyanin. The presented methods, applied to both in situ and satellite data, provide a powerful tool for coastal monitoring and management.

  • 13. Torbick, Nathan
    et al.
    Persson, Andreas
    Olefeldt, David
    Frolking, Steve
    Salas, William
    Hagen, Stephen
    Crill, Patrick M.
    Stockholm University, Faculty of Science, Department of Geological Sciences.
    Li, Changsheng
    High Resolution Mapping of Peatland Hydroperiod at a High-Latitude Swedish Mire2012In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 4, no 7, p. 1974-1994Article in journal (Refereed)
    Abstract [en]

    Monitoring high latitude wetlands is required to understand feedbacks between terrestrial carbon pools and climate change. Hydrological variability is a key factor driving biogeochemical processes in these ecosystems and effective assessment tools are critical for accurate characterization of surface hydrology, soil moisture, and water table fluctuations. Operational satellite platforms provide opportunities to systematically monitor hydrological variability in high latitude wetlands. The objective of this research application was to integrate high temporal frequency Synthetic Aperture Radar (SAR) and high spatial resolution Light Detection and Ranging (LiDAR) observations to assess hydroperiod at a mire in northern Sweden. Geostatistical and polarimetric (PLR) techniques were applied to determine spatial structure of the wetland and imagery at respective scales (0.5 m to 25 m). Variogram, spatial regression, and decomposition approaches characterized the sensitivity of the two platforms (SAR and LiDAR) to wetland hydrogeomorphology, scattering mechanisms, and data interrelationships. A Classification and Regression Tree (CART), based on random forest, fused multi-mode (fine-beam single, dual, quad pol) Phased Array L-band Synthetic Aperture Radar (PALSAR) and LiDAR-derived elevation to effectively map hydroperiod attributes at the Swedish mire across an aggregated warm season (May-September, 2006-2010). Image derived estimates of water and peat moisture were sensitive (R-2 = 0.86) to field measurements of water table depth (cm). Peat areas that are underlain by permafrost were observed as areas with fluctuating soil moisture and water table changes.

  • 14.
    Viberg, Andreas
    et al.
    Stockholm University, Faculty of Humanities, Department of Archaeology and Classical Studies, Archaeological Research Laboratory.
    Gustafsson, Christer
    Andrén, Anders
    Stockholm University, Faculty of Humanities, Department of Archaeology and Classical Studies, Archaeology.
    Multi-Channel Ground-Penetrating Radar Array Surveys of the Iron Age and Medieval Ringfort Bårby on the Island of Öland, Sweden2020In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 12, no 2, article id 227Article in journal (Refereed)
    Abstract [en]

    As a part of the project “The Big Five”, large-scale multi-channel ground-penetrating radar surveys were carried out at Bårby ringfort (Swedish: borg), Öland, Sweden. The surveys were carried out using a MALÅ Imaging Radar Array (MIRA) system and aimed at mapping possible buried Iron Age and Medieval remains through the interior in order to better understand the purpose of the fort during its periods of use. An additional goal was to evaluate the impact of earlier farming on the preservation of the archaeological remains. The data provided clear evidence of well-preserved Iron Age and Medieval buildings inside the fort. The size and the pattern of the Iron Age houses suggest close similarities with, for example, the previously excavated fort at Eketorp on Öland. Given the presence of a substantial cultural layer together with a large number of artefacts recovered during a metal detection survey, it is suggested that Bårby borg’s primary function during the Iron Age was as a fortified village. The Medieval houses partly cover some of the Iron Age buildings. They are placed in a U-shape with an open square in the middle facing the edge of a limestone cliff. As in the case of Eketorp, it is suggested that the activities during Medieval times changed, but the precise purpose of the Medieval Bårby settlement is still a question open for debate. Future targeted archaeological investigations are needed in order to better understand its purpose. Rescue excavations may also be necessary, as the western steep cliff ledge is eroding and the well-preserved archaeological remains are at risk of being destroyed.

  • 15.
    Wang, Tongmei
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography. Stockholm University, Faculty of Science, Department of Meteorology .
    Zhang, Qiong
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Lossow, Stefan
    Chafik, Léon
    Risi, Camille
    Murtagh, Donal
    Hannachi, Abdel
    Stockholm University, Faculty of Science, Department of Meteorology .
    Stable Water Isotopologues in the Stratosphere Retrieved from Odin/SMR Measurements2018In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 10, no 2, article id 166Article in journal (Refereed)
    Abstract [en]

    Stable Water Isotopologues (SWIs) are important diagnostic tracers for understanding processes in the atmosphere and the global hydrological cycle. Using eight years (2002-2009) of retrievals from Odin/SMR (Sub-Millimetre Radiometer), the global climatological features of three SWIs, (H2O)-O-16, HDO and (H2O)-O-18, the isotopic composition D and O-18 in the stratosphere are analysed for the first time. Spatially, SWIs are found to increase with altitude due to stratospheric methane oxidation. In the tropics, highly depleted SWIs in the lower stratosphere indicate the effect of dehydration when the air comes through the cold tropopause, while, at higher latitudes, more enriched SWIs in the upper stratosphere during summer are produced and transported to the other hemisphere via the Brewer-Dobson circulation. Furthermore, we found that more (H2O)-O-16 is produced over summer Northern Hemisphere and more HDO is produced over summer Southern Hemisphere. Temporally, a tape recorder in (H2O)-O-16 is observed in the lower tropical stratosphere, in addition to a pronounced downward propagating seasonal signal in SWIs from the upper to the lower stratosphere over the polar regions. These observed features in SWIs are further compared to SWI-enabled model outputs. This helped to identify possible causes of model deficiencies in reproducing main stratospheric features. For instance, choosing a better advection scheme and including methane oxidation process in a specific model immediately capture the main features of stratospheric water vapor. The representation of other features, such as the observed inter-hemispheric difference of isotopic component, is also discussed.

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