A method to detect abrupt land cover changes using hierarchical clustering of multi-temporal satellite imagery was developed. The Autochange method outputs the pre-change land cover class, the change magnitude, and the change type. Pre-change land cover information is transferred to post-change imagery based on classes derived by unsupervised clustering, enabling using data from different instruments for pre- and post-change. The change magnitude and change types are computed by unsupervised clustering of the post-change image within each cluster, and by comparing the mean intensity values of the lower level clusters with their parent cluster means. A computational approach to determine the change magnitude threshold for the abrupt change was developed. The method was demonstrated with three summer image pairs Sentinel-2/Sentinel-2, Landsat 8/Sentinel-2, and Sentinel-2/ALOS 2 PALSAR in a study area of 12,372 km2 in southern Finland for the detection of forest clear cuts and tested with independent data. The Sentinel-2 classification produced an omission error of 5.6% for the cut class and 0.4% for the uncut class. Commission errors were 4.9% for the cut class and 0.4% for the uncut class. For the Landsat 8/Sentinel-2 classifications the equivalent figures were 20.8%, 0.2%, 3.4%, and 1.6% and for the Sentinel-2/ALOS PALSAR classification 16.7%, 1.4%, 17.8%, and 1.3%, respectively. The Autochange algorithm and its software implementation was considered applicable for the mapping of abrupt land cover changes using multi-temporal satellite data. It allowed mixing of images even from the optical and synthetic aperture radar (SAR) sensors in the same change analysis.

In land monitoring applications, clouds and shadows are considered noise that should be removed as automatically and quickly as possible, before further analysis. This paper presents a method to detect clouds and shadows in Suomi NPP satellite's VIIRS (Visible Infrared Imaging Radiometer Suite) satellite images. The proposed cloud and shadow detection method has two distinct features when compared to many other methods. First, the method does not use the thermal bands and can thus be applied to other sensors which do not contain thermal channels, such as Sentinel-2 data. Secondly, the method uses the ratio between blue and green reflectance to detect shadows. Seven hundred and forty-seven VIIRS images over Fennoscandia from August 2014 to April 2016 were processed to train and develop the method. Twenty four points from every tenth of the images were used in accuracy assessment. These 1752 points were interpreted visually to cloud, cloud shadow and clear classes, then compared to the output of the cloud and shadow detection. The comparison on VIIRS images showed 94.2% correct detection rates and 11.1% false alarms for clouds, and respectively 36.1% and 82.7% for shadows. The results on cloud detection were similar to state-of-the-art methods. Shadows showed correctly on the northern edge of the clouds, but many shadows were wrongly assigned to other classes in some cases (e.g., to water class on lake and forest boundary, or with shadows over cloud). This may be due to the low spatial resolution of VIIRS images, where shadows are only a few pixels wide and contain lots of mixed pixels.

Forests are widely monitored using a variety of remote sensing data and techniques. Remote sensing offers benefits compared to traditional in-situ forest inventories made by experts. One of the main benefits is that the number of ground reference plots can be significantly reduced. Remote sensing of forests can provide reduced costs and time requirement compared to full forest inventories. The availability of ground reference data has been a bottleneck in remote sensing analysis over wide forested areas, as the acquisition of this data is an expensive and slow process.

The availability of ground reference forest data can be a bottleneck in remote sensing studies. Data may be available in only limited areas because of the cost and lengthy process of traditional forest inventory data collection by professionals. In certain cases, forest inventory data may not be easy to obtain, if not impossible.

A new remote sensing method to estimate the optical broadband black-sky surface albedo using passive microwave radiometer data has been developed. In this research, the Advanced Very High Resolution Radiometer (AVHRR) based shortwave broadband black-sky albedos processed using the algorithms of the new surface broadband albedo CM-SAF product SAL, and the Advanced Microwave Scanning Radiometer (AMSR-E) microwave data from the National Snow and Ice Data Center, Boulder, Colorado, have been employed. To analyze the correspondence between AVHRR-based optical albedo and microwave brightness temperature, AMSR-E frequencies of 6.9, 18.7, and 36.5 GHz have been tested by fitting a third-degree polynomial curve to the SAL albedo and the AMSR-E data points. The best correlation occurs in 6.9 GHz vertical polarization brightness temperature (R-2 = 0.92). To illustrate and compare the spatial variabilities of the SAL and AMSR-E albedos of the sea ice during the melting and refreezing period, maps of 13 weeks have been prepared of both albedos from June to August 2007. The albedo time series from AMSR-E and AVHRR data are calculated for the combined sea ice and open water cover for the Northern Hemisphere as a whole, and for six specific sea ice regions: the Arctic Ocean, the Kara and Barents Seas, the Greenland Sea, the Labrador Sea, Hudson Bay, and the Canadian Archipelago. The standard errors between the optical and passive microwave estimates varied from about 0.001 (Greenland Sea) to 0.04 (Canadian Archipelago) being 0.013 on the average in absolute units. The relative standard errors are then smaller than 5% in most of the regions.

This paper presents the concept of a future disaster management system that utilizes the modern telecommunication and navigation technology. The system uses Synthetic Aperture Radar (SAR) data as key information source. Five critical elements are required to establish the system: 1) data acquisition and high-speed data transfer; 2) data processing; 3) data analysis; 4) data management; and 5) data distribution. A pilot version of the system is being built as an international cooperation activity. Floods and landslides have been selected as the case disaster types in the system development. The system concerns disaster warning and disaster relief aspects. The ongoing project will continue until 2006. This paper presents the outcome of the first project year.

In this paper the Finnish ENVISAT CAL/VAL- project as well as some results obtained in the project will be presented. The project has three major objec- tives. One objective is to implement an operational data processing chain, the other is to analyze the ef- fect of dierent ice properties on the SAR signature and its statistics mainly through empirical backscat- ter studies, and the third is to develop an automated image interpretation system for sea ice mapping.

The development of appropriate ground-based validation techniques is critical to assessing uncertainties associated with satellite data-based products. Here we present a method for validation of the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) product with emphasis on the sampling strategy for field data collection. This paper, the first of two-part series, details the procedures used to assess uncertainty of the MODIS LAI product. LAI retrievals from 30 m ETM+ data were first compared to field measurements from the SAFARI 2000 wet season campaign. The ETM+ based LAI map was thus as a reference to specify uncertainties in the LAI fields produced from MODIS data (250-, 500-, and 1000-m resolutions) simulated from ETM+. Because of high variance of LAI measurements over short distances and difficulties of matching measurements and image data, a patch-by-patch comparison method, which is more realistically implemented on a routine basis for validation, is proposed. Consistency between LAI retrievals from 30 m ETM+ data and field measurements indicates satisfactory performance of the algorithm. Values of LAI estimated from a spatially heterogeneous scene depend strongly on the spatial resolution of the image scene. The results indicate that the MODIS algorithm will underestimate LAI values by about 5% over the Maun site if the scale of the algorithm is not matched to the resolution of the data.

A methodology was developed and applied to estimating forest area and producing forest maps. The method utilizes satellite data and ground reference data. It takes into consideration the fact that a pixel rarely represents any single ground cover class. This is particularly true for low-spatial-resolution data. It also takes into consideration that the spectral classes overlap. The image was first classified using an unsupervised clustering method. A (multinormal) spectral density function was estimated for each class based on the spectral vectors (reflectance values) of the cluster members. Values of the target variable — the proportion of forested area — were determined for the spectral classes using sampling from CORINE (Coordination of Information on the Environment) Land Cover database. Each pixel was assigned class membership probabilities, which were proportional to the value of the density function of the respective class evaluated at the spectral value of the pixel. The estimate of forest area for the pixel was finally computed by multiplying the class membership probabilities by the class forest area and summing over all the classes. The method was applied over a mosaic of 49 Advanced Very High Resolution Radiometer (AVHRR) images acquired from the National Oceanic and Atmospheric Administration (NOAA)-14 satellite. The estimated forest areas were compared with those extracted from the full-coverage CORINE data and with official forest statistics reported to the European Commission's Statistical Office (EUROSTAT). The forest percentage (proportion of forest area of the total land area) of 12 countries of the European Union was underestimated by 1.8% compared to the CORINE data. It was underestimated by 4.2% when compared with EUROSTAT's statistics and 6.0% when compared to United Nations Economic Commission for Europe/Food and Agricultural Organization (UN-ECE/FAO) statistics. The largest underestimation of forest percentage within a country (compared to CORINE) was in France (5.9%). The largest overestimation was found in Ireland, 15.6%.

A harmonised methodology was developed and tested to map forest cover at six sites across Europe. Utilisation of high and medium spatial resolution optical data and ERS SAR data was tested. Both the thematic content and the positional accuracy of the forested areas in the classified data were validated. The medium resolution (approximately 200 m) data are appropriate for regional forest mapping, but on areas with a very dispersed forest structure higher resolution data should be used to achieve better results.

Models using NOAA AVHRR data for estimating the areal distribution of biomass over the Boreal coniferous zone are developed. The method uses corresponding models that are estimated using Landsat TM data as input, and includes a mosaicing scheme for AVHRR images to cover very extensive areas. The estimation method may be adjusted to some other forest characteristics too.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A system for automatic ground control point measurement and rectification of satellite images to a nationwide reference database has been developed. As reference for the rectification a database consisting of more than three hundred thousand features covering the whole Finland has been created. These features are islands and lakes extracted from the nationwide Land Cover Classification, produced from Landsat Thematic Mapper (TM) images. Also the use of digital maps and image mosaics has been tested. Lakes and islands are extracted from the satellite image to be rectified. Their attributes are compared to those in the reference database. Using feature based matching and robust estimation a few hundred ground control points of subpixel accuracy are selected to the rectification. Images of different resolution can be measured automatically using this system. It has been tested using SPOT, Landsat TM and NOAA AVHRR imagery. The search for ground control points takes only a few minutes per satellite image. The accuracy of the result has proved to be at least as good as when measuring the control points manually. The method is tested by computing the parallaxes between the reference features and the rectified images.

Production of large mosaics, stereopairs and stereomosaics from digitized video frames has been studied. The central perspective has been assumed and block adjustment has been used in determination of the camera positions and attitudes for each frame. Different alternative strategies have been studied. A simple method for video camera calibration has been developed. The block adjustment results are used in image rectification and mosaicking. The system has been tested with aerial video data.

Repeat-pass SAR scenes were studied aiming at the use of SAR data in connection with landslides and other natural hazards. The objective was to study the methods that are applicable in forested areas. The SAR data included both C- band (ENVISAT ASAR) and L-band (JERS SAR) scenes. As SAR coherence is a key factor affecting the derivation of elevation data and elevation change data, the effect of land cover type and forest characteristics on coherence was studied in two study sites: one in central Finland and one in central Japan. The SAR scenes had been acquired in summer and winter conditions. The Finnish study site was mainly covered by coniferous boreal forest. It was relatively flat. The Japanese study site included elevations from the sea level up to 3500 meters. The major land cover types in low-lying areas were urban and agricultural land. The highest coherences could be observed in the urban and agricultural areas and lowest in the forest, in particular in mature coniferous forests. The coherence data in the Finnish study site shows that baseline length has a strong influence on coherence. In Japan, the highest coherence was in scene pairs acquired in winter. The reason for lower coherence in summer is the vegetation cover in agricultural areas in summer scenes.

The impact of albedo on snow and sea ice mass balance has been studied. A one-dimensional thermodynamic snow/ice model incorporated with a number of albedo schemes was used to simulate the seasonal Baltic Sea ice. The instantaneous broadband albedo products from the AVHRR satellite instrument have been processed and compared with the modeled albedo. Under freezing weather conditions, the modeled surface albedo is generally in good agreement with the AVHRR product. A strong positive feedback of surface albedo creates rapid melting in early spring. The HIGHTSI model reproduces such events reasonably well by applying several advanced albedo schemes. A dramatic decrease of surface albedo is also revealed by the AVHRR data. The HIGHTSI model provides equally good results on snow and ice mass balance by using a re-constructed AVHRR albedo and calculated albedo from one of the most sophisticated albedo schemes.