Kaj Andersson

Multiscale analysis and validation of the MODIS LAI product. I. Uncertainty assessment

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.

Multiscale analysis and validation of the MODIS LAI product II. Sampling strategy

The development of appropriate ground-based validation techniques is critical to assessing uncertainties associated with satellite data-based products. In this paper, the second of a two-part series, we present a method for validation of the Moderate Resolution Imaging Spectroradiometer Leaf Area Index (MODIS LAI) product with emphasis on the sampling strategy for field data collection. Using a hierarchical scene model, we divided 30-m resolution LAI and NDVI images from Maun (Botswana), Harvard Forest (USA) and Ruokulahti Forest (Finland) into individual scale images of classes, region and pixel. Isolating the effects associated with different landscape scales through decomposition of semivariograms not only shows the relative contribution of different characteristic scales to the overall variation, but also displays the spatial structure of the different scales within a scene. We find that (1) patterns of variance at the class, region and pixel scale at these sites are different with respect to the dominance in order of the three levels of landscape organization within a scene; (2) the spatial structure of LAI shows similarity across the three sites, that is, ranges of semivariograms from scale of pixel, region and class are less than 1000 m. Knowledge gained from these analyses aids in formulation of sampling strategies for validation of biophysical products derived from moderate resolution sensors such as MODIS. For a homogeneous (within class) site, where the scales of class and region account for most of the spatial variation, a sampling strategy should focus more on using accurate land cover maps and selection of regions. However, for a heterogeneous (within class) site, accurate point measurements and GPS readings are needed.

AVHRR-Based Forest Proportion Map of the Pan-European Area.

Abstract 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 Commissions 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 EUROSTATs 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%.

Nationwide automatic satellite image registration system

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.

Automatic tree stem delineation supporting forest inventory

The aim of this study was to develop an automatic procedure to detect tree stems in cell phone images. If the stems can be detected, the central projection geometry of the images makes it possible to apply the so called relascope principle to predict the basal area of stems per hectare. The basal area is closely correlated with tree biomass. Images were acquired in an area where detailed tree-wise information is available. The tree stem delineation methods were based on color and edge information to tackle the challenges of occlusions and varying illumination conditions. Geolocalised images can be used as in-situ reference data for remote sensing data analysis to provide wall-to-wall estimates.

Estimation of biomass and other characteristics of boreal forest over extensive areas using NOAA-AVHRR data

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.

Automatic Cloud and Shadow Detection in Optical Satellite Imagery Without Using Thermal Bands—Application to Suomi NPP VIIRS Images over Fennoscandia

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.

Relasphone — Mobile phone and interactive applications to collect ground reference biomass data for satellite image analysis

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.

Finding Common Points From Digitized Video Images

This paper reports a method which was developed for finding common points from digitized video images. The process of finding common points is semiautomatic, guided by the user and is based on the least squares image matching method. To find a pair of control points from two images, the location of the first point has to be pointed out manually from the first image and then the corresponding point is automatically located from the second image. As an application a large image mosaic and a smaller stereo image mosaic were produced using consecutive images.