Yrjö Rauste

The Global Rain Forest Mapping project— a review

The Global Rain Forest Mapping (GRFM) project is an international endeavour led by the National Space Development Agency of Japan (NASDA), with the aim of producing spatially and temporally contiguous Synthetic Aperture Radar (SAR) data sets over the tropical belt on the Earth by use of the JERS-1 L-band SAR, through the generation of semi-continental, 100 m resolution, image mosaics. The GRFM project relies on extensive collaboration with the National Aeronautics and Space Administration (NASA), the Joint Research Centre of the European Commission (JRC) and the Japanese Ministry of International Trade and Industry (MITI) for data acquisition, processing, validation and product generation. A science programme is underway in parallel with product generation. This involves the agencies mentioned above, as well as a large number of international organizations, universities and individuals to perform field activities and data analysis at different levels. The GRFM project was initiated in 1995 and, through a dedicated data acquisition policy by NASDA, data acquisitions could be completed within a 1.5-year period, resulting in a spatially and temporally homogeneous coverage to encompass the entire Amazon Basin from the Atlantic to the Pacific; Central America up to the Yucatan Peninsular in Mexico; equatorial Africa from Madagascar and Kenya in the east to Sierra Leone in the west; and south-east Asia, including Papua New Guinea and northern Australia. Over the Amazon and Congo river basins, the project aimed to provide complete cover at two different seasons, featuring the basins at high and low water. In total, the GRFM acquisitions comprise some 13000 SAR scenes, which are currently in the course of being processed and compiled into image mosaics. In March 1999, SAR mosaics over the Amazon Basin (one out of two seasonal coverages) and equatorial Africa (both seasonal coverages) were completed; the data are available on CD-ROM and, at a coarser resolution, via the Internet. Coverage of the second-season Amazon and Central America will be completed during 1999, with the south-east Asian data sets following thereafter. All data are being provided free of charge to the international science community for research and educational purposes.

Radar-based forest biomass estimation

Abstract The potential of radar-based tree biomass estimation has been studied using polarimetric SAR data from the Freiburg test site of the MAESTRO I Campaign and scatterometer data from a test site in Finland. Using the Freiburg SAR data and polarizaiton synthesis, the most suitable polarization combination was obtained. In P band, the maximum correIalions, which were found ncar the linear H V polarization, were up to 0·75. In the Finnish test site, a strong negative correlation “correlation coefficient -0·65” existed between the pine biomass and X-VV backscatter. When the combination of X and C bands “measured by the HUTSCAT seatterometer” was used, a correlation coefficient of 0·81 was obtained.

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%.

The Global Rain Forest Mapping Project JERS-1 radar mosaic of tropical Africa: development and product characterization aspects

The Global Rain Forest Mapping Project (GRFM) is an international collaborative effort initiated and managed by the National Space Development Agency of Japan (NASDA). The main goal of the project is to produce a high resolution wall-to-wall map of the entire tropical rain forest domain in four continents using the L-band SAR onboard the JERS-1 spacecraft. The processing phase, which entails the generation of wide area radar mosaics from the raw SAR data, was split according to the geographic area. In this paper, the focus is on the part related to Africa. The GRFM projects goal calls for the coverage of a continental scale area of several million km 2 using a sensor with the resolution of tens of meters. In the case of the African continent, this entails the assemblage of some 3900 high resolution SAR scenes into a bitemporal mosaic at 100 m pixel spacing and with known geometric accuracy. While this fact opens up an entire new perspective for vegetation mapping in the tropics, it presents a number of technical challenges. In this paper, we report on the solutions adopted in the GRFM Africa mosaic development and discuss some quantitative and qualitative aspects related to the characterization and validation of the GRFM products. In particular, the mosaic geolocation and its validation are discussed in detail. Indeed, the internal geometric consistency (subpixel accuracy in the coregistration of the two dates), and the absolute geolocation (residual mean squared error of 240 m with respect to ground control points) are key features of the GRFM Africa mosaic. Other important aspects that are discussed are the multiresolution decomposition approach, which allows for tracking the evolution of natural phenomena with scale; the internal semi-automatic radiometric calibration, which minimizes artifacts in the mosaic; and the thematic information content for vegetation mapping, which is illustrated by a few examples elaborated by visual interpretation. Experience gained so far indicates that the GRFM products constitute an important source of information for global environmental studies.

Volume Scattering Modeling in PolSAR Decompositions: Study of ALOS PALSAR Data Over Boreal Forest

Model-based approaches for decomposing polarimetric backscatter data from boreal forest are discussed in this paper. Several model-based decompositions are analyzed with respect for the most accurate estimation of the volume scattering component. A novel generalized model for description of the volume contribution is proposed when observed backscatter from forest indicates that media does not follow azimuthal symmetry case. The model can be adjusted to the polarimetric synthetic aperture radar (PolSAR) data itself, taking into consideration higher sensitivity of HH against VV backscattering term to the presence of canopy at L-band. The model is general enough to allow a broad range of canopies to be modeled and is shown to comply with several earlier proposed volume scattering mechanism models. It is afterward incorporated in the Freeman-Durden three-component decomposition, yielding an improved modification. The performance of the proposed modification is evaluated using multitemporal ALOS PALSAR data acquired over Kuortane area in central Finland, representing typical mixed boreal forestland. Several decompositions are also benchmarked in order to see how they satisfy physical requirements when decomposing covariance matrix into a weighted sum of individual scattering mechanism contributions. When using experimental data, the proposed decomposition is shown to better satisfy non-negativity constraints for the covariance matrix eigenvalues at each decomposition step with less additional PolSAR data averaging needed. Discussed decompositions are also evaluated for the accuracy of initial stratification based on dominating scattering mechanism using ground reference data.

An overview of the JERS-1 SAR Global Boreal Forest Mapping (GBFM) project

Boreal ecosystems play an essential role in global climate regulation. Forests constitute pools of terrestrial carbon and are generally considered as global sinks of atmospheric CO/sub 2/, contributing to attenuating the greenhouse effect. Large amounts of carbon are also stored in boreal lakes, bogs and wetlands, partially released as CH/sub 4/ and other trace gases to the atmosphere during the spring and summer months. Human activities in the forest zone are however reducing the size of the carbon pool and climate change is triggering shorter winters and earlier thaw onset, changing the natural equilibrium. Given its global importance, there is a need to map and monitor the boreal zone, and as the changes occur on all from local, regional to global scales, fine resolution information over vast areas is required. The Global Boreal Forest Mapping (GBFM) project is an international collaborative undertaking initiated by NASDA in 1996, as a follow-on to the tropical-focused Global Rain Forest Mapping (GRFM) project [A. Rosenqvist et al., (2000)]. Utilising the L-band Synthetic Aperture Radar (SAR) on the Japanese Earth Resources Satellite (JERS-1). one of the main objectives of the GBFM project is the generation of extensive, pan-boreaL SAR image mosaics, to provide snap-shots of the forest wetland and open water status in the mid-1990s. Mosaics over Canada, Alaska. Siberia and Europe have been generated, available on the Internet and on DVD free of charge for research and educational purposes. The GBFM project also entails research activities in North America, Siberia and northern Europe, aimed at advancing scientific applications of L-band SAR data in the boreal zone.

Large-scale vegetation maps derived from the combined L-band GRFM and C-band CAMP wide area radar mosaics of Central Africa

Abstract A new dataset has been compiled by combining the wide area Synthetic Aperture Radar (SAR) mosaics over Central Africa generated in the context of the NASDA Global Rain Forest Mapping (GRFM) and the ESA/EC Central Africa Mosaic Projects (CAMP). The CAMP mosaic consists of more than 700 SAR scenes acquired over the Central Africa region (6° S-8° N and 5° E-26° E) by the ESA ERS satellites; the acquisitions were performed in 1994 (July, August) and in 1996 (January, February) in two different seasonal conditions. The GRFM Africa mosaic consists of some 3900 JERS-1 images acquired over the region (10° S-10° N, 14° W and 42° E) at two dates (January-March 1996 and October-November 1996). In this paper the methods used for combining the two wide area radar mosaics are at first presented. The GRFM Africa mosaic was processed using a block adjustment algorithm with the inclusion of external observations derived from high precision maps along the coastline, which assures an absolute geolocation residual mean squared error of 240 m with respect to ground control points. On the other hand, the CAMP mosaic was compiled taking into account only the internal relative geometric accuracy. Therefore the GRFM dataset was taken as the reference system and the C-band ERS layer composed by rectifying each ERS frame, after down-sampling at 100 m pixel spacing, to the reference mosaic. The rectification procedure uses a set of tie-points measured automatically between each ERS frame and the homologous subset in the JERS mosaic. Due to the different characteristics of the two sensors (microwave centre frequency, viewing geometry, polarization) and the different acquisition dates, each mosaic presents a different window over the same ecosystem. This fact suggests that a new dimension in terms of thematic information content can be added by the fusion of the two datasets. In support of this statement, the complementary characteristics of the two sensors are first discussed with respect to observations related to the vegetation cover in the Congo River floodplain. The potential of the combined dataset for vegetation mapping at the regional scale is further demonstrated by a classification pursuit of the main vegetation types in the central part of the Congo Basin. The main land-cover classes are: lowland rain forest, permanently flooded forest, periodically flooded forest, swamp grassland, and savannah. The classification map is validated using a compilation of national vegetation maps derived from other high resolution remote sensing data or by ground surveys. This first thematic result already confirms that the combined contributions from the L-band and the C-band sensors improve the information extraction capability. Indeed, the radar-derived vegetation map contains better spatial detail than any existing map, especially with respect to the extent of flooded formations.

Improved Mapping of Tropical Forests With Optical and SAR Imagery, Part II: Above Ground Biomass Estimation

Performance of the above ground (dry) biomass estimation with the medium resolution optical (ALOS AVNIR) data and radar (ALOS PALSAR) data was evaluated on a tropical forest site in Lao PDR (Laos). The average biomass of ground reference plots was relatively low, 78 t/ha, due to strong anthropogenic influence in most of the study area. The biomass estimates were computed using linear regression analysis and the Probability method that combines unsupervised clustering and fuzzy estimation. The predictions were validated with independent field plot data. With all the methods and data types, the root mean square error (RMSE) ranged from 33.6 t/ha to 40.1 t/ha (44.2% and 52.8% of mean biomass, respectively). The Probability method produced a larger dynamic range to the predictions than the regression models, which saturated at approximately 100 t/ha. Large errors for higher biomass plots increased the RMSE of Probability over the RMSE of the regression models. The bias ranged from -0.8 to 3.9% except with the Probability model for PALSAR data where the bias was 12.5%. Our study showed that PALSAR data were nearly as good for the biomass estimation as the AVNIR data. A combination of mono-temporal ALOS PALSAR and ALOS AVNIR data did not improve biomass estimation over the performance obtained with AVNIR data alone. For the Probability method, ground reference data should be more representative than that available in this study.

Combining AVHRR and ATSR satellite sensor data for operational boreal forest fire detection

The potential to combine data from two different satellite systems was studied to increase fire detection sensitivity and image acquisition frequency in real-time fire detection and fire control. A fully automatic fire detection algorithm was applied to all scenes that were acquired using both satellite systems. Local fire authorities were notified about each detected fire in their territory using real-time fire reports that were sent by telefax. The average time from the start of National Oceanic and Atmospheric Administration (NOAA), Advanced Very High Resolution Radiometer (AVHRR) image acquisition until the sending of a telefax fire report was 25 min. During the straw-burning season in April 2000, the Along Track Scanning Radiometer (ATSR) instrument detected twice as many fires as the AVHRR per unit image area. The main reason for this may be the average resolution cell of the ATSR, which is half the size of that of the AVHRR in terms of area. The response from fire authorities was used to estimate the number of correct alerts and false alarms. A false alarm rate of 12% and 7% was obtained in the fire seasons of 1999 and 2000, respectively.

Land Cover and Soil Type Mapping From Spaceborne PolSAR Data at L-Band With Probabilistic Neural Network

This paper evaluates performance of fully polarimetric SAR (PolSAR) data in several land cover mapping studies in the boreal forest environment, taking advantage of the high canopy penetration capability at L-band. The studies included multiclass land cover mapping, forest-nonforest delineation, and classification of soil type under vegetation. PolSAR data used in the study were collected by the ALOS PALSAR sensor in 2006-2007 over a managed boreal forest site in Finland. A supervised classification approach using selected polarimetric features in the framework of probabilistic neural network (PNN) was adopted in the study. It has no assumptions about statistics of the polarimetric features, using nonparametric estimation of probability distribution functions instead. The PNN-based method improved classification accuracy compared with standard maximum-likelihood approach. The improvement was considerably strong for soil type mapping under vegetation, indicating notable non-Gaussian effects in the PolSAR data even at L-band. The classification performance was strongly dependent on seasonal conditions. The PolSAR feature data set was further modified to include a number of recently proposed polarimetric parameters (surface scattering fraction and scattering diversity), reducing the computational complexity at practically no loss in the classification accuracy. The best obtained accuracies of up to 82.6% in five-class land cover mapping and more than 90% in forest-nonforest mapping in wall-to-wall validation indicate suitability of PolSAR data for wide-area land cover and forest mapping.