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FRINGE '96 Workshop: ERS SAR Interferometry, 30 September - 2 October 1996
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Feasibility of ERS-1/2 Interferometry for Forest Inventory

Juha HyyppäHelsinki University of Technology, Laboratory of Space Technology, Otakaari 5 A, FIN-02150 Espoo, Finland,
Marcus o
Hannu Hyyppä


The feasibility and usefulness of ERS-1/2 SAR repeat-pass interferometry for estimating forest resources is studied by applying the following strategy: a) interferometric SAR data (coherence and interferograms) are compared with field inventory data and HUTSCAT-derived stand profiles (tree height profiles) to evaluate what forest parameters can be estimated using SAR interferometry alone; b) interferometric SAR data (including interferometric correlation, backscatter intensity and backscatter intensity change) are combined with other potential satellite (Landsat, SPOT, RADARSAT and JERS-1) and airborne data (aerial photographs, image produced by a imaging spectrometer AISA and a 94-GHz imaging radiometer and forest stand profiles produced by a ranging scatterometer HUTSCAT) to test whether interferometric SAR data include additional explanatory power in developing remote sensing-based forest inventory methods.
Keywords: interferometry, ERS-1/2, forest inventory, multi-source.


Forest inventory

Traditional forest inventory is both expensive and time-consuming. In theory, remote sensing methods offer a good alternative and/or a supporting method for traditional forest inventory and, therefore, the utilization of remote sensing techniques has been subject of intensive investigations during the past few years. Most of the developed satellite methods are based on the use of data obtained at visible, near-infrared or infrared bands. Poor availability of such data - due to the limited penetration capability (e.g. through clouds) at these wavelengths - hinders utilization of these methods. This has stimulated research towards the use of radar-based methods, especially since the launch of remote sensing satellites equipped with a synthetic aperture radar (ERS-1, ERS-2, JERS-1, RADARSAT). However, the reported accuracy of satellite-based methods for standwise stem volume estimates has been worse than 40 % irrespective of the spectral band used, Table 1. Hence, airborne measurements have been recognized as a potential tool for small-area inventories, while the aim of satellite-aided studies is mainly concentrated on large-area monitoring. For operational standwise inventory, even single-source airborne data may not be accurate enough. In order to meet the accuracy requirements (typically 15 %) of standwise forest inventory, data fusion, combining several remote sensing data sources, is suggested.

Table 1. Comparison of various methods

for standwise forest inventory.

Method/InstrumentRMSE Coefficient of variation
Ocular field inventory

(Päivinen et al., 1993)

30 m3/ha16 %
Radar-derived stand profiles combined with aerial photography (Inkinen and Hyyppä, 1995) 29.4 m3/ha21.7 %
Radar-derived stand

profile (Hyyppä, 1993)

31 m3/ha26.5 %
Imaging Spectrometer AISA (Mäkisara and Tomppo, 1996) 42.4 m3/ha29.8 %
Aerial photography

(Päivinen et al., 1993)

55.6 m3/ha29.4 %
Landsat TM

(Päivinen et al.,1993)

84.2 m3/ha44.5 %
ERS-1 SAR (Tomppo et al., 1995)90 m3/ha 58.4 %

SAR interferometry and forests

Recent advances in SAR interferometry include detecting subtle changes in the Earth's land and ice surfaces over periods of days to years with a global scale, millimeters accuracy and all-weather capability that are unprecedented. Recent examples illustrate how SAR interferometry can be applied to study glaciers (Goldstein et al. 1993), earthquakes (Massonnet et al. 1993), and volcanoes (Massonnet et al. 1995). SAR interferometry can be used to generate very-high-resolution topographic maps.

Gray and Farris-Manning (1993) reported a loss of coherence at both C- and X-band for forested areas under light to moderate winds (using airborne interferometric SAR) implying a degradation of performance with ERS-1 3-day orbit. Hagberg et al. (1995) found out that coherence was found to be sensitive to temperature changes around 0oC but surprisingly insensitive to wind speed. Hagberg et al. (1995) suggested also that tree height and density of forests can be estimated with interferometric phase information. There are three major phenomena that determine the effective volume scattering distribution of the forests (Hagberg et al., 1995): 1) attenuation of the canopy, which is assumed to be high for boreal forest at C-band, 2) the movements of the scatterers, which are assumed to be largest in the upper part of the trees, and 3) proportion of the area that are filled with trees.

The ranges of backscatter intensities over forests and agricultural fields overlap strongly. Therefore, it is difficult to distinguish between and within these two classes based exclusively on the backscattering intensity at C-band. The interferometric correlation, together with the backscatter intensity and the backscatter intensity change, has proved to a useful tool for the classification of the land-surface classes (Wegmüller et al. 1995).

A laser altimeter is a possible complement to any space-based SAR system. Combined laser or radar altimetry and SAR interferometry can show areas of clear-cutting as well as estimates of the rates of regrowth where extensive logging has occurred. This was the recommendation of 39 scientists gathered in Boulder, Colorado, on February 3-4, 1994, for the SAR Interferometry and Surface Change Detection (RSMAS Technical Report).


The main objective of the on-going project is to evaluate the feasibility and usefulness of ERS-1/2 SAR repeat-pass interferometry for estimating forest resources. The strategy is as follows:

a) interferometric SAR data (backscatter amplitude and its change, coherence and interferograms) are compared with field inventory data and HUTSCAT-derived stand profiles (tree height profiles) to evaluate what forest parameters can be estimated using SAR interferometry alone. The combination of ranging radar together with interferometric SAR images, as suggested in Boulder workshop, are studied for the first time. With this manner the idea proposed by Hagberg et al. (1995) can be verified.

b) interferometric SAR data (including interferometric correlation, backscatter intensity and backscatter intensity change) is combined with other potential multi-temporal satellite data (Landsat, SPOT, RADARSAT and JERS-1) and airborne data (aerial photographs, image produced by a imaging spectrometer AISA and a 94-GHz imaging radiometer and forest stand profiles produced by a ranging scatterometer HUTSCAT) to test whether interferometric SAR data includes additional explanatory power in developing remote sensing-based forest inventory methods.

Seasonal effects, optimum data combinations and interferometric SAR parameters are studied.

Special emphasis is in the evaluation of accuracy and cost-benefit analysis of interferometric SAR techniques compared to the present methods.


Three test sites locate in southern Finland, Teijo (130 km west of Helsinki), Porvoo (30 km east of Helsinki) and Kalkkinen (130 km north of Helsinki) representing a variety of different forest types and covering about 10 000 hectares of forest land. Kalkkinen is the main area of activities with a large multi-source, multi-temporal remote sensing data set.

From the 5000-ha test site Kalkkinen the following information is collected:

  • field inventory data (ground truth)
  • remote sensing data
  • GIS information

Field inventory data

Field inventory data is collected by Uudenmaa-Häme Forestry Center in summer 1996. About 100 parameters describing stand characteristics such as stem volume per hectare, basal area per hectare, mean tree height and tree species are measured for each stand (homogeneous forest areas of about one hectare in size). In order to evaluate the accuracy of field inventory, 40 stands were extremely carefully checked by sample plot measurements. The average value of the stem volume per hectare of the Kalkkinen test site is 141 m3/ha.

Remote sensing data

The remote sensing data set includes satellite data from SPOT, Landsat, ERS-1/2 (Table 1), JERS-1 and Radarsat (SAR) and airborne data from imaging spectrometer AISA, airborne ranging radar (HUTSCAT), 94 GHz airborne imaging radiometer and digitized aerial photographs. The area was successfully measured by SPOT and Landsat satellites in the late August. Color-infrared photographs in a scale of 1:5000, 1:10000, and 1:20000 and imaging spectrometer measurements were conducted from Kalkkinen at the beginning of June. The remote sensing data will be collected by the end of October, with an exception of 94 GHz radiometer measurement, which is scheduled for early spring 1997 under wet snow conditions. The ranging radar HUTSCAT is capable to probe the canopy from the top to the bottom with range resolution of 65 cm. The HUTSCAT profiles are available in all three test sites. The tree-height-determining capability of HUTSCAT is used as a ground truth information for interferograms.

Table 1. ERS-1/2 product specifications
Sat Quadr Track Orbit Frame Date SAR product type


ERS-1 3 408 20937 2367 17.7.1995 both SLC and PRI

ERS-2 3 408 01264 2367 18.7.1995 SLC

ERS-1 3 408 21438 2367 21.8.1995 both SLC and PRI

ERS-2 3 408 01765 2367 22.8.1995 SLC

ERS-1 3 408 21939 2367 25.9.1995 both SLC and PRI

ERS-2 3 408 02266 2367 26.9.1995 SLC

ERS-1 3 408 22440 2367 30.10.1995 both SLC and PRI

ERS-2 3 408 02767 2367 31.10.1995 SLC

ERS-1 3 408 23442 2367 8.1.1996 both SLC and PRI

ERS-2 3 408 03769 2367 9.1.1996 SLC

ERS-1 3 179 24215 2367 2.3.1996 both SLC and PRI

ERS-2 3 179 04542 2367 3.3.1996 SLC

GIS information

Additional information includes digital elevation model (DEM), digital land-use map and base map 1:20000. Air/soil/vegetation temperature and precipitation monitoring statistics in selected areas is also gathered.


After preprocessing (e.g. radiometric correction, geometric correction, geocorrection and orthorectification), the multi-source, multi-temporal remote sensing data is combined with GIS and ground truth data in ARC/INFO system. Standwise predictor variables are calculated using intensity, texture, band ratio transformations, and image processing techniques. Multivariate data analysis techniques are applied to develop models to estimate stand characteristics and biodiversity information.


The project schedule is the following:

Preparatory work (January-May 1996)

  • detailed experimental plan
  • request for satellite images
  • coordination of activities with Finnish Forest Research Institute and Forestry Centre Tapio (Uudenmaa-Häme Forestry Centre)

Data acquisition (June - October 1996)

  • airborne measurements (HUTSCAT),
  • airborne spectrometer measurement (AISA),
  • aerial photographs
  • acquisition of needed satellite images,
  • collection of ground truth information

Preprocessing (September- December 1996)

  • preprocessing of remote sensing data
  • preprocessing of ground truth data
  • production of interferograms and coherence images
  • integration of data into database management GIS

Data analysis (January -October 1997)

  • analysis based on interferometric data only
  • combining interferometric fringes with radar profiles
  • combination of optical and microwave satellite methods
  • other development of methods and inversion algorithms
  • cost-benefit analysis
  • preliminary results reported

Reporting (September - December 1997)

  • final report (ESA, Technology Development Centre, Academy of Finland)
    • papers submitted for several international journals

Anticipated results

The following results are anticipated from this project:

a) The HUTSCAT feature to produce tree height maps with an accuracy of 1.5 metres is used to evaluate the capability and accuracy of ERS-1/2 Tandem interferometric fringes to estimate tree height and density of forests, the idea proposed by Hagberg et al. (1995). As ground truth information, over 300 km of high-accuracy tree height maps are available.

b) Feasibility of a combined set of radar profilometry (HUTSCAT) and SAR interferometry to monitor areas of clear-cuttings, deforestation and defoliation as well as estimate rates of regrowth.

c) Feasibility of combined set of SAR interferometry, optical satellite images (SPOT and Landsat), radar satellite images (JERS-1 and Radarsat) and airborne images to estimates forest stand characteristics?

d) Estimation corcerning the smallest area for which the estimates can be computed reliably; examples are 1) the whole country or a part of the country (order of magnitude 10 million hectares), b) a forestry board district (0.5-1 million hectares), c) a municipality (50 000 hectares), d) forest holding.

e) Optimum interferometric parameters for forest inventory.

f) Suggestion of instruments needed for a forest inventory satellite mission.

g) Costs and benefits of the proposed methods.


Goldstein, R., Engelhardt, H., Kamb, B. and Frolich, R., 1993:
Satellite radar interferometry for monitoring ice sheet motion: application to an Antarctic ice stream. Science, 262, pp. 1525-1530.
Gray, A.L. and Farris-Manning, P., 1993:
Repeat-Pass interferometry with airborne synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 31, pp. 180-191.
Hagberg, J., Ulander, L. and Askne, J., 1995:
Repeat-pass SAR interferometry over forested terrain. IEEE Transactions on Geoscience and Remote Sensing, 33, pp. 331-340.
Hyyppä, J., 1993:
Development and feasibility of airborne ranging radar for forest assessment. Doctor of Technology thesis, Laboratory of Space Technology, Helsinki University of Technology, Espoo, Finland, 112 p.
Inkinen, M., and Hyyppä, J., 1995:
A forest inventory method by combining radar-derived stand profiles and aerial photography. IEEE Transactions on Geoscience and Remote Sensing, 10-14 July 1995, Firenze, Italy, pp. 1909-1911.
Massonet, D., Rossi, M., Carmona, C., Adragna, F., Peltzer, G., Feigl, K. and Rabaute, T., 1993:
The displacement field of the Landers earthquake mapped by radar interferometry. Nature, 364, pp. 138-142.
Massonet, D., Briole, P. and Arnaud, A., 1995:
New insights on Mount Etna from 18 months of radar interferometric monitoring. Nature (in press).

Mäkisara, K., and Tomppo, E., 1996:

Airborne imaging spectrometry in national forest inventory. Proceedings of IGARSS'96 Conference, 27-31 May, 1996, Lincoln, Nebraska.

Päivinen, R., Pussinen, A., and Tomppo, E., 1993:
Assessment of boreal forest stands using field assessment and remote sensing. Proceedings of Earsel 1993 Conference, 19-23 April, 1993, ITC Enshedene, The Netherlands, 8p.
RSMAS Technical Report, 1995:
SAR interferometry and Surface change detection. Report of a Workshop held in Boulder, Colorado, February 3-4, 1994.
Tomppo, E., Mikkelä, P., Veijanen, A., Mäkisara, K., Henttonen, H., Katila, M., Pulliainen, J., Hallikainen, M., and Hyyppä, J., 1995:
Application of ERS-1 SAR data in large area forest inventory. Proceedings of the Second ERS Applications Workshop, 6-8 December, 1995, London, UK.
Wegmüller, U., Werner, C., Nüesch, D. and Borgeaud, 1995:
Land-surface analysis using ERS-1 SAR interferometry. ESA Bulletin, 81, pp. 30-37.

Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry