Rune Solberg

Subpixel mapping of snow cover in forests by optical remote sensing

Forest represents a challenging problem for snow-cover mapping by optical satellite remote sensing. To investigate reflectance variability and to improve the mapping of snow in forested areas, a method for subpixel mapping of snow cover in forests (SnowFrac) has been developed. The SnowFrac method is based on linear spectral mixing modelling of snow, trees and snow-free ground. The focus has been on developing a physically based reflectance model which uses a forest-cover map as prior information. The method was tested in flat terrain covered by spruce, pine and birch forests, close to the Jotunheimen region of South Norway. Experiments were carried out using a completely snow-covered Landsat Thematic Mapper (TM) scene, aerial photos and in situ reflectance measurements. A detailed forest model was photogrammetrically derived from the aerial photos. Modelled and observed TM reflectances were compared. In the given situation, the results demonstrate that snow and individual tree species, in addition to cast shadows on the snow surface from single trees, are the most influencing factors on visible and near-infrared reflectance. Modelling of diffuse radiation reduced by surrounding trees slightly improve the results, indicating that this effect is less important. The best results are obtained for pine forest and mixed pine and birch forest. Future work will focus on deriving a simplified reflectance model suitable for operational snow-cover mapping in forests.

A large-scale evaluation of features for automatic detection of oil spills in ERS SAR images

The authors study the performance of automatic methods for oil spill detection in ERS SAR images. The presented algorithm has three main parts: (i) detection of dark spots; (ii) feature extraction; and (iii) dark spot classification. The dark spot detection locates all spots which can possibly be oil slicks in the image. For each slick, a set of backscatter, textural, and geometrical features are extracted. The dark spots are then classified into possible oil slicks and look-alikes based on the extracted features. Based on the current study, the authors believe that a semi-automatic oil slick identification system which can discriminate between oil slicks and look-alikes can be developed. To achieve this, some new features describing the surroundings of a slick and the slick itself must be defined and tested.

An automatic system for operational snow-cover monitoring in the Norwegian mountain regions

Describes an operational snow-cover monitoring system using satellite imagery. The system has for several years been developed for the largest Norwegian hydroelectric company, the Norwegian Energy Corporation (Statkraft). The snow-cover data is an important information source for the hydrological simulation models run in the melting season from April to August. Accurate predictions of water amounts from the snow-covered areas are crucial for power station production planning and planning of long-term contracts. The system can be operated in semi-automatic and automatic modes and is based on NOAA AVHRR imagery. Geocoded imagery is calibrated against pre-selected stable calibration areas before the classification. The classification algorithm is based on an empirical reflectance-to-snow-cover model. The classified result is then combined with various GIS data sources depending on the desired result. Ongoing work for further development is also presented: Image data aggregation from a time series of partially clouded imagery, terrain normalization, automatic geocoding, and utilization of microwave data.<<ETX>>

Use of hidden Markov models and phenology for multitemporal satellite image classification: applications to mountain vegetation classification

Ground cover classification based on a single satel- lite image can be challenging. The work reported here concerns the use of multitemporal satellite image data in order to alleviate this problem. We consider the problem of vegetation mapping and model the phenological evolution of the vegetation using a Hidden Markov Model (HMM). The different vegetation classes can be in one of a predefined set of states related to their phenological development. The characteristics of a given class are specified by the state transition probabilities as well as the probability of given satellite observations for that class and state. Classification of a specific pixel is thus reduced to selecting the class that has the highest probability of producing a given series of observations for that pixel. Compared to standard classification techniques such as maximum likelihood (ML) classification, the proposed scheme is flexible in that it derives its properties not only from image specific training data, but also from a model of the temporal behavior of the ground cover. It is shown to produce results that compare favorably to those obtained using ML classification on single satellite images, it also generalizes better than this approach. Obtaining good ground cover classifications based on a single satellite image can be challenging. The work reported here concerns the use of multitemporal satellite image data in order to alleviate this problem. We will consider an application of these methods to mapping of high mountain vegetation in Norway. The traditional mapping method based on manual field work is prohibitively expensive and alternatives are therefore sought. Vegetation classification based on satellite images is an interesting alternative, but the complexity of the vegetation ground cover is high and the use of multitemporal satellite image acquisitions is shown to improve the classifi- cation quality. This document is organized as follows: In the next section, we briefly recapitulate previous work related to multitemporal satellite image classification and phenological models. In section IV we discuss the HMM and how it can be used for classification. In section V we show results of the application of our algorithm, conclusions are given in section VI.

Traffic Monitoring Using Very High Resolution Satellite Imagery

Very high resolution satellite images allow automated monitoring of road traffic conditions. Satellite surveillance has several obvious advantages over current methods, which consist of expensive single-point measurements made from pressure sensors, video surveillance, etc., in/or close to the road. The main limitation of using satellite surveillance is the time resolution; the continuously changing traffic situation must be deduced from a snapshot image. In cooperation with the Norwegian Road Authorities, we have developed an approach for detection of vehicles in Quick-Bird images. The algorithm consists of a segmentation step followed by object-based maximum likelihood classification. Additionally, we propose a new approach for prediction of vehicle shadows. The shadow information is used as a contextual feature in order to improve classification. The correct classification rate was 89 percent, excluding noise samples. The proposed method tends to underestimate the number of vehicles when compared to manual counts and in-road equipment counts.

Investigating the feasibility of the globsnow snow water equivalent data for climate research purposes

This paper presents the efforts for creating two global scale snow dataset covering 15 and 30 years of satellite-based observations, one describing the extent of snow cover (SE) the other describing the snow water equivalent (SWE) characteristics. The main emphasis of the paper is describing the validation work carried out for the SWE product that will cover the non-mountainous regions of Northern Hemisphere on a daily basis starting from 1979. The work has been carried out within the ESA Globsnow project.

Monitoring Soil Erosion in Agricultural Fields by Ers-1 Sar

Under the North Sea Agreement, Norway is obliged to reduce the discharge of nutrient salts from agricultural fields with 50% by 1995. In order to determine whether the policy have the desired effect, optical remote sensing will probably be used for monitoring the agricultural areas. However, the weather conditions in Norway in the late autumn makes it difficult to obtain cloud-free images, and is thus a limiting factor. In the paper an experiment is described that was carried out to determine whether S A R imagery can be used to detect late tillage in the autumn. The results of classifying images which have been speckle reduced, show that ploughed and stubble fields can be discriminated with a high degree of confidence when the soil moisture level is high. The best result is obtained using a field by field classification method. The experiments have also shown that radiometric and geometric correction of the image data using a terrain model is necessary to obtain the required accuracy.

Remote Sensing of Snow Cover

Snow was easily identified in the first image obtained from the Television Infrared Operational Satellite-1 (TIROS-1) weather satellite in 1960 because the high albedo of snow presents a good contrast with most other natural surfaces. Subsequently, the National Oceanic and Atmospheric Administration (NOAA) began to map snow using satellite-borne instruments in 1966. Snow plays an important role in the Earth s energy balance, causing more solar radiation to be reflected back into space as compared to most snow-free surfaces. Seasonal snow cover also provides a critical water resource through meltwater emanating from rivers that originate from high-mountain areas such as the Tibetan Plateau. Meltwater from mountain snow packs flows to some of the world s most densely-populated areas such as Southeast Asia, benefiting over 1 billion people (Immerzeel et al., 2010). In this section, we provide a brief overview of the remote sensing of snow cover using visible and near-infrared (VNIR) and passive-microwave (PM) data. Snow can be mapped using the microwave part of the electromagnetic spectrum, even in darkness and through cloud cover, but at a coarser spatial resolution than when using VNIR data. Fusing VNIR and PM algorithms to produce a blended product offers synergistic benefits. Snow-water equivalent (SWE), snow extent, and melt onset are important parameters for climate models and for the initialization of atmospheric forecasts at daily and seasonal time scales. Snowmelt data are also needed as input to hydrological models to improve flood control and irrigation management.

Impact of satellite-based lake surface observations on the initial state of HIRLAM. Part I: evaluation of remotely-sensed lake surface water temperature observations

Lake 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 ESAs 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).

Multi-sensor monitoring of snow parameters in Nordic mountainous areas

Hydropower users require timely updated information about snow coverage and snow melting during the important snow-melting period in Nordic mountains. In this paper we report results from a series of experiments performed to map snow parameters with optical and radar remote sensing. A near real-time pre-operational system has been developed to provide timely snow cover mapping over Nordic mountainous areas for hydropower users. The multi sensor and multi temporal snow cover maps are based on single sensor snow maps from SAR and optical sensors. Each data acquisition over the area are classified into snow maps and projected on a common grid. A confidence raster in also produced where the accuracy of the classification of each pixel in the snow map is represented as a confidence value between 0 and 100% depending on incidence angle, probability of clouds and wet/dry. Each single sensor product is fused to the latest multisensor product with its associated confidence image to produce an updated snow map. The sensors used in the demonstration of the preoperational multi sensor snow mapping system are Envisat ASAR and Terra Modis. Testing has been done in 2003 and 2004 and continues in the melting season of 2005.