Øystein Rudjord

Information about the state of the Arctic sea ice is becoming increasingly important. This paper describes an approach for automatic retrieval of daily thin sea ice thickness maps around Svalbard. The algorithm uses thermal satellite imagery from MODIS to estimate the surface temperature of the ice and further uses a thermal model of the ice surface to estimate the thickness of the sea ice. The approach is usable for thin sea ice, up to ca. 50 cm thick, during cold weather (freezing) conditions and without cloud cover present. The algorithm is compared with helicopter-borne electromagnetic ice thickness measurements. The comparison yields increasing root-mean-square deviation (RMSD) for thicker ice. The lowest RMSD found is 8.7 cm for ice thickness in the range 10 cm < hi ≤ 20 cm. The highest RMSD found is 25.2 cm for ice thickness in the range 30 cm < hi ≤ 40 cm. The bias shows no such trend, and the overall bias is found to be −5.5 cm. The results show that this is a promising approach, allowing monitoring of thin sea ice thickness at relatively higher spatial resolution.

This article compares four new automatic methods to discriminate between spruce, pine and birch, which are the dominating tree species in Norwegian forests. Airborne laser scanning and hyperspectral data were used. The laser scanning data was used to mask pixels with low or no vegetation in the hyperspectral data. A green–blue ratio was used to remove shadow areas from tree canopies, and the normalized difference vegetation index to remove dead vegetation and non-vegetation. The best method was hyperspectral pixel classification with 160 spectral channels in the visible and near-infrared spectrum, using a deep neural network. This method achieved 87% correct classification rate. Partial least squares regression for hyperspectral pixel classification achieved 78%. Deep neural network image classification using canopy height blended with three hyperspectral channels achieved 74%. A simple pixel classification method based on two spectral indices resulted in 67% correct classification. A possible future improvement is to find a better way to combine hyperspectral data with canopy height data in a deep neural network.

This paper presents a new method to discriminate between spruce, pine and birch, which are the dominating tree species in Norwegian forests. For this purpose, simultaneously acquired airborne laser scanning (ALS) and hyperspectral data are used. The laser scanning data was used to mask pixels with low or no vegetation in the hyperspectral data. From the species-specific spectra, three wavelengths were identified for species discrimination: 544 nm (green), 674 nm (red) and 710 nm (red edge). A decision tree-based pixel classification method obtained 83-86% correct classification. We plan a field revisit to include misclassified trees in an extended in situ data set, and then to re-calibrate and re-run the classifier. There is also potential for improvement by using individual tree crown delineation. Further, the vegetation height could potentially be used to improve classification.

Snow monitoring is essential for prediction of flooding due to rapid snowmelt, to provide snow avalanche risk forecasts and for water resource management – including hydropower production, agriculture, groundwater and drinking water. Sentinel-1 C-band SAR is sensitive to presence of wet snow and can be used to binary snow-wetness classification. Wet-snow mapping into more categories has been demonstrated in the past by using MODIS data. The combination of surface temperature and the temporal development of the effective snow grain size are used to infer approximately how wet the snow is.

Sea ice thickness (SIT) is an important parameter for monitoring Arctic change, modelling and predicting weather and climate, and for navigation and offshore op- erations. However, SIT is still not very well monitored operationally. In the European Commission (EC) FP7 project “POLAR ICE”, three novel SIT products based on different satellite data as well as SIT from a state-of-the- art ocean and sea ice model are fed into a common data handling and distribution system for end users. Each SIT product has different scopes and limitations as to, e.g., spatial and temporal resolution, ice thickness range and geographical domain. The aim of this study is to com- pare the four different SIT products with each other and with SIT in-situ measurements in order to better under- stand the differences and limitations, and possibly give recommendations on how to best profit from the synergy of the different data.

The POLAR ICE project has developed a system for integrating and delivering satellite derived ice information products to operators working in the economically and environmentally important Arctic and Antarctic regions. POLAR ICE has been supported by the European Commission's FP7 programme and undertaken by European and Canadian companies and institutes, who are all partners in the Polar View Earth Observation Limited (PVEO) company. It is the aim of PVEO to commercialise the service that has been developed and demonstrated as a part of POLAR ICE.Access to sea ice information derived from satellite earth observation data is critical to support the increasing numbers of Arctic and Antarctic shipping and off-shore operations and to protect the rapidly changing polar environment.To-date the development of sea ice information capabilities has addressed separate elements of complete service chains. In contrast POLAR ICE has linked these separate elements together, filled in known gaps and built a robust integrated service chain.

This paper presents a research collaboration to develop more automated methods for forest inventory in Norway and Scandinavia. The current situation in the forest industry in Norway is difficult due to reduced timber prices and high labor cost. Currently forest inventory methods combine airborne laser scanning (ALS) data and manual photointerpretation using multispectral imagery (broad visual and near-infrared channels), but extensive field work is needed in addition. This makes forest inventory very expensive for large areas. In this perspective, more automated methods for forest inventory are needed. Specifically, we will focus on methods which combine data from simultaneously acquired airborne laser scanning and imaging spectrometer. The ALS data already provides information on vegetation height. The hyperspectral data may provide information on biophysical and biochemical parameters, and species composition. Combined, the two types of data have the potential of more accurate forest inventory with less fieldwork. Hyperspectral and ALS data were acquired simultaneously for a forest area in Våler municipality, Østfold County, Norway. Field work was conducted to identify examples of individual trees and small clusters of trees of one single species. Approximately 169 tree polygons were delineated using ALS data. A set of sample spectra based on in situ data was prepared for each of the three species pine, spruce and birch. The mean spectra were found for each tree species, by averaging over all the samples. From these data we identified three regions where the tree species may be differentiated: 544 nm (green), 674 nm (red) and 710 nm (red edge). We used the three bands from these regions to create two indices in order to separate the different tree species. Firstly, ALS data was used to create a mask, removing areas where the vegetation was lower than 1 meter. Secondly, thresholds on the green 544 nm band and on broadband NDVI were applied to remove shadows and areas without live vegetation. Finally, the two proposed indices were used to differentiate the tree species. The resulting tree species classification map has clusters of homogeneously classified pixels, corresponding to individual trees or groups of trees of the same species. Close inspection reveals some occasional misclassified pixels and/or pixels of a different class than the majority within a cluster. The pixel-based accuracies for each species are in the range 83-86%. In photointerpretation of broadband multispectral images, it is a well-known problem that young spruce may be confused spectrally with birch, and old spruce with young pine. This indicates a potential to reduce the confusion between birch and spruce and between spruce and pine, by also considering the tree height in the classification method. The tree height, which is possible to derive from the ALS data, is a good proxy for tree age.

ake Surface Water Temperature (LSWT) observations are used to improve the lake surface state in the High Resolution Limited Area Model (HIRLAM), a three-dimensional numerical weather prediction (NWP) model. In this paper, satellite-derived LSWT observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Along-Track Scanning Radiometer (AATSR) are evaluated against in-situ measurements collected by the Finnish Environment Institute (SYKE) for a selection of large- to medium-size lakes during the open-water season. Data assimilation of these LSWT observations into the HIRLAM is in the paper Part II. Results show a good agreement between MODIS and in-situ measurements from 22 Finnish lakes, with a mean bias of −1.13°C determined over five open-water seasons (2007–2011). Evaluation of MODIS during an overlapping period (2007–2009) with the AATSR-L2 product currently distributed by the European Space Agency (ESA) shows a mean (cold) bias error of −0.93°C for MODIS and a warm mean bias of 1.08°C for AATSR-L2. Two additional LSWT retrieval algorithms were applied to produce more accurate AATSR products. The algorithms use ESA's AATSR-L1B brightness temperature product to generate new L2 products: one based on Key et al. (1997) and the other on Prata (2002) with a finer resolution water mask than used in the creation of the AATSR-L2 product distributed by ESA. The accuracies of LSWT retrievals are improved with the Key and Prata algorithms with biases of 0.78°C and −0.11°C, respectively, compared to the original AATSR-L2 product (3.18°C).

Oil spill detection in SAR images operating in a hybrid-polarimetric mode is examined. We propose and review several strategies for oil spill detection in hybrid-polarimetric SAR data. The retrieved measures are successfully applied to SAR data covering oil spill experiments outside Norway and the Deepwater Horizon incident in the Gulf of Mexico. It is shown that, under the assumption of a two-scale Bragg scattering model, a coherence measure may be recovered equally well from hybrid-polarimetric data, as for full-polarimetric data, and that this measure may be retrieved directly from the measurements without the need for any additional assumptions. The results show that low-wind lookalikes may be suppressed at the same time as the contrast of the oil spills is maintained using hybrid-polarimetric data and that multifeature images may be constructed to further enhance the oil spill detection performance. Due to the potential of wide swath widths, we conclude that hybrid-polarity is an attractive mode for future SAR-based oil spill monitoring.

The FLAASH atmospheric correction module is evaluated. First, the user interface is considered. Then the performance of FLAASH is evaluated by testing it on images from four sensors with different properties: Landsat 7 ETM (medium spectral and spatial resolution), QuickBird (high spatial resolution, low spectral resolution), Worldview-2 (high spatial resolution, low spectral resolution) and MODIS (low spatial resolution, high spectral resolution).

In this paper we propose a model-based oil spill detection approach using polarimetic SAR data. The underlying hypothesis is that the co-polarized phase difference is zero when assuming Bragg scattering mechanisms. From this hypothesis we may construct a (linear) model and derive features that discriminates oil slicks from sea water using dual-polarized (VV and HH) SAR data. We also investigate the model when assuming X-Bragg scattering, and derive a feature based on the Pauli decomposition that are invariant to the tilt angle reflection plane. The oil spill detection methodology is evaluated on a Radarsat-2 quad-pol image that covers various types of oil released in an oil-in-sea exercise in Norway. The results show that the oil spills are clearly visible in all of the derived feature-based images. Furthermore, the feature images have a more homogeneous background than the VV-polarized SAR image, in particular the Pauli-based feature image that suppresses a non-oil slick present in the VV-image.