ON THE GENERATION OF A FOREST BIOMASS MAP FOR NORTHEAST CHINA: SAR INTERFEROMETRIC PROCESSING AND DEVELOPMENT OF CLASSIFICATION

ALGORITHM

Maurizio Santoro (1), Oliver Cartus (1), Christiane Schmullius (1), Urs Wegmüller (2), Charles Werner (2), Andreas Wiesmann (2), Yong Pang (3), Zengyuan Li (3)

(1) Department of Geoinformatics and Earth Observation,

Friedrich-Schiller University, Loebdergraben 32, D-07743 Jena, Germany

(2) Gamma Remote Sensing Research and Consulting AG, Worbstrasse 225, 3073 Gümligen, Switzerland

(3) Research Institute of Forest Resource and Information Techniques,

Chinese Academy of Forestry, Beijing, 100091, China

ABSTRACT The experience gained in more than a decade of investigations based on ERS-1/2 coherence images for forest applications is currently exploited to map forest biomass of a 1.5 Million km2 large region in Northeast China. For the generation of the biomass map more than 250 ERS-1/2 tandem image pairs are used. The dataset consists of multi-temporal and multi-baseline image frames acquired between 1995 and 1998. For 223 image pairs the geocoded coherence, backscatter and terrain information images with 50 x 50 m pixel size have been obtained. Data quality and geolocational accuracies are very high and coherence contrast is in most cases at an optimal level for forestry applications. For biomass classification, the SIBERIA coherence model and the Interferometric Water Cloud Model (IWCM) were evaluated at test sites in China and, for reference, in Siberia. The original SIBERIA coherence model did not always follow the trend in the measurements. For the IWCM, the use of the MODIS Vegetation Continuous Fields (VCF) tree cover product as training input resulted in model parameters estimates in line with results obtained by using forest inventory data. These results suggest that (i) the SIBERIA coherence model must be improved to represent multi-temporal coherence and different forest conditions, and (ii) the IWCM with VCF as input information should be further tested. 1. INTRODUCTION ERS-1/2 tandem coherence has been acknowledged to provide reliable estimates of forest stem volume in boreal forests. Several experiments conducted at test sites showed that multi-temporal coherence acquired under winter-stable conditions can provide stem volumes comparable to in situ measurements [1, 2]. For large area mapping the SIBERIA (SAR Imaging for Boreal Ecology and Radar Interferometry Applications) Project [3] has demonstrated that accurate forest biomass maps can be obtained by combining ERS-1/2 coherence and JERS backscatter. Forests in China have been undergoing dramatic changes during the last several decades and thus the need to monitor them is dramatically increasing. For this reason the Forest DRAGON project (Forest Related Development of Radar Applications for Geomatic Operational Networks) has been initiated in the framework of the DRAGON co-operation Project between the European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST). ERS-1/2 and ENVISAT ASAR data are used to generate forest biomass maps respectively for the mid 1990s and for 2004-2005. In this paper we report on the interferometric processing of the ERS-1/2 dataset and the development of a classification algorithm for forest biomass in Northeast China. 2. STUDY AREA AND GROUND DATA China can be divided into four forest regions: Northeast China (NE), Southeast China (SE), Central China (between Southeast and Northeast) and West China (see Fig. 1). The Northeast and the Southeast are the two most important forest regions in China. Northeast China covers approximately 1.5 Million km2, including eastern Inner Mongolia and the Heilongjiang, Jilin and Liaoning provinces. Forests are mainly distributed over the mountains. In this region three test sites have been selected: the Tuqiang forest compartment in Daxinganling, the Dailing forest compartment in

Xiaoxinganling and the Lushuihe forest compartment on the Changbai Mountains. The northern part of the region is characterised by cold-temperate needle-leaf forests dominated by larch. The rest is mainly covered with deciduous broad-leaf forests of the temperate zone. Forests at the northernmost test site of Daxingaling are rather young because of a large fire occurred approximately 20 years ago; stem volume in this area reaches 120 m3/ha. At the other test sites stem volume is larger and reaches 400 m3/ha (mean value: 200 m3/ha). For all three test sites forest maps have been selected and digitised. In the forest map the smallest unit is the forest stand sub-compartment, which is also the smallest operational unit in China forest management planning. In a sub-compartment soil, relief and tree conditions are uniform; furthermore, each forest stand sub-compartment has uniform management plan and method. The attributes of each sub-compartment are described by field measurements. For each forest stand sub-compartment the information on soil, tree, understory vegetation, disturbance history, management methods etc. are contained in an inventory database. In the digital forest map, each polygon corresponds to a forest stand sub-compartment.

(a) (b) Fig. 1. (a) Forest DRAGON project areas with respect to study phase, (b) and zoom on Northeast China with the three test sites located by square boxes (up: Daxingaling, middle: Xiaoxinganling, low: Changbaishan). 3. SATELLITE DATA Full coverage ERS-1 and ERS-2 tandem data was obtained over Northeast China during two acquisition periods (1995-1996 and 1997-1998). The 1995-1996 acquisition was part of the first ERS-1/2 global coverage campaign in tandem mode (i.e. one-day between acquisitions). The 1997-1998 dataset stems from the SIBERIA Project acquisition period during September-October 1997 and June-August 1998 [3]. For forest applications image pairs with perpendicular component of the baseline longer than 400 m were discarded. This resulted in 502 frames available, corresponding to 251 one-day interferometric image pairs. Almost 2/3 of the data were acquired during the winter-spring seasons of 1995-1996. The additional dataset was acquired between September and October 1997; 1% of data was acquired in June 1998. Because of the restriction on the baseline, the dataset considered in Forest DRAGON is characterised by gaps, which mainly occur in non-forested areas. Most of Northeast China is covered with one acquisition; multi-temporal datasets consisting of 2-3 frames (completely or partially overlapping) can be found locally. For the generation of the forest biomass maps, SAR backscatter and interferometric SAR (InSAR) coherence are used. InSAR processing consisted of co-registration of Single Look Complex (SLC) master and slave images at sub-pixel level, common range and azimuth band filtering, slope estimation and computation of coherence using an adaptive window size [4]. For each processing step, indicators were used to assess the quality of the results (standard deviation of co-registration offsets, coherence bias and dynamic range). Standard deviation of the co-registration offsets was always below 0.2 pixels, which ensured that decorrelation due to processing is negligible. Since the coherence of water bodies was always < 0.05, the coherence bias could be considered negligible. The forest/non-forest coherence contrast was mostly of the order of 0.3-0.4, thus representing the optimal condition for forestry applications using repeat-pass C-band InSAR.

The coherence and the corresponding calibrated intensity images have been geocoded to 50x50 m pixel size using the Shuttle Radar Topography Mission 90m DEM as height reference. Geocoding was performed using an automatic procedure based on the offset estimation between image chips in the SAR and in the map geometry. To assess the geolocational accuracy of a single frame the percentage of image chips with co-registration offsets having a SNR above a given threshold was used. Additionally the overlap between consecutive frames was considered. Areas with topography were well geocoded since the percentage of chips with SNR above the threshold was typically > 50%. In case of flat areas (e.g. agricultural fields, lakes or desert) we could observe relative shifts, mostly of 1-2 pixels (i.e. <= 100 m). Besides the coherence and backscatter images, maps of local incidence angle, pixel area normalisation factor, local topography and layover/shadow have been produced. In particular the layover/shadow masks were used to mask out areas of strong topography, which would have negatively affected the coherence modelling and the retrieval of forest biomass. Since the quality of 10% of the frames was too low for interferometric processing, the final ERS-1/2 tandem dataset produced in Forest DRAGON consisted of 446 frames, corresponding to 223 coherence images. Fig. 2 illustrates a false colour composite of the ERS-1/2 dataset for Northeast China.

Fig. 2. False colour composites of ERS-1/2 coherence (Red), ERS-1 backscatter (Green) and ERS-1/2 backscatter difference (Blue) for Northeast China.

4. METHODOLOGY The development of a forest biomass classification method is based on the SIBERIA Project algorithm [5]. This fully automated classification procedure allowed the retrieval of forest biomass in a 1 Million km2 large area in Siberia, discriminating four forest stem volume classes (0-20, 20-50, 50-80, >80 m3/ha) and two non-forest classes (water, smooth open areas). The algorithm makes use of ERS-1/2 tandem coherence and JERS intensity images and classifies biomass on a satellite frame basis. Both the coherence γ and radar backscatter σ are modelled as having a simple parametric exponential relationship with respect to stem volume v.

( ) 3752175)( a

v

eaav−

⋅⋅++= γγγ (1)

2175

0 )( bv

ebv−

⋅−= σσ (2)

In Eqs. 1 and 2 γ75 and σ75 are unknown and need to be determined using training data. To avoid test-site specific dependencies that limit the applicability of the algorithm to large areas the parameters were estimated using a satellite frame-based approach. For Siberia it was found that γ75 in Eq. 1 and σ75 in Eq. 2 could be derived from histogram analysis since they were highly correlated to the coherence of the dense forest class (>80 m3/ha). The remaining coefficients in Eqs. 1 and 2 were determined by means of regression using a large number of test sites. Since the estimates were found to be rather similar, it was concluded that averages could well represent the general trend in the coherence and backscatter measurements throughout the 1 Million km2 large area (a1=0.330, a2=0.581, a3=122.1; b1=2.46, b2=107.34). For the classification, ERS-1/2 coherence, γ; and JERS intensity, σ0, class centres were obtained for each stem volume class centre. These values were then used as input to a Maximum Likelihood classifier. A more complex model describing the relationship between stem volume, V, and coherence, γfor, is the Interferometric Water Cloud Model [6]:

( ) ( )( )( )

−−

−−+= −

−−−−

h

hhjV

for

vegveg

V

for

grgrfor e

eej

eeV α

αωββ

ωαα

σσ

γσσ

γγ1

1)( 0

0

0

0

(3)

The IWCM describes the coherence of a forest γfor in terms of a ground and a vegetation contribution weighted by the forest transmissivity. In Eq. 3 γgr and γveg represent the ground and vegetation temporal coherence respectively. σ0

gr and σ0

veg describe the ground and vegetation backscatter contribution to the total forest backscatter σ0for. The coefficient β is

related to the two-way forest transmissivity, which is empirically expressed as Ve β− . The last term in Eq. 3 represents the volume decorrelation contribution, with α being the two-way tree attenuation per meter, h the thickness of the forest canopy and ω the InSAR topographic coefficient. In Eq. 3 the five parameters γgr, γveg, σ0

gr, σ0veg and β are unknown a

priori. These parameters are commonly estimated using least squares regression based on a training set of coherence and stem volume measurements. This procedure has been shown to be robust for different environmental conditions [1]. Nonetheless, it depends on the availability of a training set, which makes the approach strongly test site dependent and therefore of limited use for mapping large areas. 5. RESULTS The fit of Eq. 1 for winter coherence data at several test sites in China and, for reference, in Siberia was considered with two different training procedures. In Fig. 3 we report three examples for the two model fits. The green curves represent the fit obtained with the original model, i.e. with γ75 determined from the coherence histogram of the frame covering the test site and the remaining coefficients as obtained in Siberia. The blue curve describes the case with γ75 as for the previous case but with the remaining coefficients being determined at the test site. Figure 3 shows that the original SIBERIA coherence model was generally not able to predict the trend in the measurements, the deviation at the Chinese test sites being quite significant (e.g. see Fig. 3a and b). For the Siberian test sites the original model was able to fit the data in a few cases (e.g. see Fig. 3c). When the SIBERIA coherence model was trained to the particular test site, the general trend was instead well described, although the parameters obtained were strongly dependent on the test site. As a consequence the possibility of extending the SIBERIA algorithm to include a large range of forest types, environmental conditions and baselines seems to be limited.

(a) (b) (c)

Fig. 3. Stem volume versus ERS-1/2 tandem coherence for two test sites in Northeast China (a and b) and one in Siberia/Russia (c). All coherence images were acquired during winter under frozen conditions. Since the SIBERIA coherence model parameters appeared to depend on test site and image acquisition properties (i.e. weather and baseline), we considered the use of the IWCM, which has physically-based model parameters. To avoid the dependency of the model training on test site data, we followed a new approach based on the MODIS Vegetation Continuous Field tree cover product (VCF) [7]. The VCF product provides global sub-pixel estimates of landscape components (tree cover, herbaceous cover, and bare cover) at 500 m pixel size. The basic idea is that since γgr and σ0

gr represent the ground coherence and backscatter, they can be determined by masking the corresponding images for low tree cover percentage. Similarly γveg and σ0

veg can be determined by masking the corresponding images for high tree cover percentage and then compensating for residual ground coherence. The coefficient β can be considered at a first approximation to be constant. To make the VCF product and the coherence images comparable, at first the coherence images were downsampled to 500 x 500 m pixel size by means of pixel aggregation. The standard deviation of the coherence within a VCF pixel was also computed, serving as indicator of the degree of homogeneity of the forest cover within the VCF pixel. Theoretically, the smaller the standard deviation of the coherence, the more homogenous the forest cover within the VCF pixel. Fig. 4 shows an example of the relationship between the ERS-1/2 tandem winter coherence and VCF values for the test site of Chunsky in Siberia. The vertical bars represent the span of coherence values for a given tree cover percentage. To assess the impact of pixels with mixed forest cover on the relationship between coherence and tree cover percentage, we set several upper thresholds for the standard deviation of coherence and masked out each time pixels that had standard deviation of coherence higher than the given threshold. Coherence was found to decrease for increasing tree cover percentage. However, both the trend between coherence and tree cover percentage and the length of the vertical bars depended on the upper threshold used, i.e. on the degree of homogeneity of the forest cover within a VCF pixel.

Fig. 4. ERS-1/2 tandem coherence as a function of VCF tree cover percentage. As threshold for the standard deviation of coherence 0.07 was used.

Table 1. Comparison of forest coherence values for two VCF classes and IWCM parameters estimated using forest inventory. Test site: Chunsky, Siberia.

Max σγ Mean γfor - VCF < 10% Mean γfor - VCF > 75% 0.20 0.75 0.49 0.15 0.75 0.49 0.10 0.76 0.49 0.07 0.82 0.49

γgr=0.83 γveg= 0.45 Table 1 reports the mean values of coherence for low and high tree cover percentage as function of an upper threshold used for the standard deviation of coherence at the Chunsky test site. For reference the IWCM γgr and γveg parameters obtained with the traditional training method (see [1] for details) have been included. The mean coherence of areas with less than 10% tree cover was found to increase for decreasing threshold, being almost equal to the estimate for γgr, when the threshold reached 0.07. The mean coherence for areas with more than 75% tree cover did not depend on the threshold and was approximately 0.05 above the estimate of γveg. The reason for this difference should be related to the fact that the measured coherence has a non-zero ground contribution whereas γveg represents the coherence for the ideal case of a completely opaque forest canopy. This assumption was verified by computing in Eq. 3 the remaining ground contribution for the maximum stem volume found in the area (V= 350 m3/ha) and the volume decorrelation term. The corresponding value for γveg computed from Eq. 3 coincided with the value obtained by training the model with forest inventory data, this showing that the model training approach based on VCF seems to be promising. 6. CONCLUSIONS In this paper we reported on the activities of the Forest DRAGON Project related to the use of ERS-1/2 tandem data for forest biomass mapping. For a 1.5 Million km2 large area corresponding to Northeast China almost full coverage coherence and backscatter dataset has been obtained. The forest biomass retrieval algorithm developed in the SIBERIA Project has been tested for different environmental conditions, baselines and forest types in China and Siberia. The SIBERIA coherence model performs poorly under different conditions. Improvement of the algorithm does not seem to be feasible unless changes are made to the coherence model itself to take separately into account the effect of environmental conditions and forest structure. Given that only a small coherence dataset is available for the test areas, this seems to be hard to be accomplished. As alternative, we considered the Interferometric Water Cloud model and trained it with information obtained from an external data source, the MODIS Vegetation Continuous field tree cover percentage. First results show that the model parameters estimates obtained from the VCF product are in agreement with the parameters estimates obtained from regressions using forest inventory data. 7. ACKNOWLEDGMENTS The authors would like to thank Y-L Desnos, A. Zmuda, B. Rosich, R. Malosti, V. Amans and ESA’s EO-Helpdesk for their help for SAR data acquisition and processing. 8. REFERENCES 1. Santoro M., et al., Stem Volume Retrieval in Boreal Forests with ERS-1/2 Interferometry, Remote Sensing of Environment, Vol. 81, 19-35, 2002. 2. Askne J. and Santoro M., Multitemporal repeat pass SAR interferometry of boreal forests II, IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, 1219-1228, 2005. 3. Schmullius C., et al., SIBERIA - SAR Imaging for Boreal Ecology and Radar Interferometry Applications, EC- Center for Earth Observation, Project Reports, Contract No. ENV4-CT97-0743-SIBERIA, Final Report 2001. 4. Werner C., et al., GAMMA SAR and interferometric processing software, Proc. ERS-Envisat Symposium, Gothenburg, 16-20 October, ESA Publications Division, Nordwijk, The Netherlands, SP-461, CD-ROM, 2000. 5. Wagner W., et al., Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data, Remote Sensing of Environment, Vol. 85, 125-144, 2003. 6. Askne J., et al., C-band repeat-pass interferometric SAR observations of the forest, IEEE Transactions on Geoscience and Remote Sensing, Vol. 35, 25-35, 1997. 7. Hansen M. C., et al., Towards an operational MODIS continuous field of percent tree cover algorithm: Examples using AVHRR and MODIS data, Remote Sensing of Environment, Vol. 83, 303-319, 2002.