*Manuscript
1
Prediction of Plot-Level Forest Variables Using TerraSAR-X Stereo SAR Data
2 3
Mika Karjalainen1, Ville Kankare 2, Mikko Vastaranta2, Markus Holopainen2, Juha Hyyppä1
4 5
1
Finnish Geodetic Institute, Geodeetinrinne 2, 02430 Masala, Finland
6
2
University of Helsinki, Department of Forest Resource Management, P.O. Box 27
7
(Latokartanonkaari 7), 00014 Helsingin Yliopisto, Finland
8 9
Corresponding author: Mika Karjalainen,
[email protected]
10 11 12
Abstract
13
Promising results have been obtained in recent years in the use of high-resolution X-band stereo SAR
14
satellite images (with the spatial resolution being in order of meters) in the extraction of elevation
15
data. In the case of forested areas, the extracted elevation values appear to be somewhere between
16
the ground surface and the top of the canopy, depending on the forest characteristics. If the ground
17
surface elevations are known by using a Digital Terrain Model derived from Airborne Laser Scanning
18
surveys, it is possible to obtain information related to forest resources. To the best of our
19
knowledge, this paper, presents the first attempt to obtain forest variables at plot level based on
20
TerraSAR-X stereo SAR images (non-interferometric data). The study set consisted of 109 circular
21
test plots for which forest variables were observed by performing tree-specific measurements. The
22
statistical features were calculated for each test plot from the elevation values extracted from stereo
23
SAR data. This was followed by predicting field-observed plot-level forest variables from the features
24
derived from stereo SAR data using the Nearest Neighbors approach which employs the Random
25
Forest technique in selection of the nearest neighbors. The relative errors (RMSE%) for predicting
1
26
the stem volume, basal area, mean forest canopy height, and mean diameter values were 34.0%,
27
29.0%, 14.0%, and 19.7%, respectively. The results indicate that there was no clear saturation level
28
in stem volume estimation. In this case study, stem volumes were predicted up to about 400 m3/ha.
29
In the light of these results, stereo SAR data appears to be an interesting remote-sensing technique
30
for future forest inventories. For example, stereo SAR data could have high potential in forest
31
inventories as the SAR-based features can be adapted to the methods currently used in inventories.
32 33
Keywords: TerraSAR-X, stereo SAR data, radargrammetry, plot-level forest variables
34
2
35
1. Introduction
36
Approximately 86% of the land area in Finland is forested. In order to obtain up-to-date information
37
about the status of the forest resources, national forest inventories (NFIs) are carried out regularly in
38
Finland (Parviainen et al., 2007). The foundation of NFIs is provided by systematic field sampling in
39
which several forest related variables are surveyed. The results of the latest inventory, which was
40
the 10th in order, were published in 2008 (Finnish Forest Research Institute, 2011). In the early years
41
NFIs were carried out approximately once every ten years, but now the aim is to conduct inventories
42
more frequently, once every five years or so. NFIs provide accurate wide-area information about
43
forest resources, but if detailed maps are needed, ancillary information such as remote-sensing data
44
has to be applied.
45 46
Airborne Laser Scanning (ALS) is becoming a standard technique in the mapping of forest resources.
47
In the review by Hyyppä et al. (2008), it was concluded that ALS data can provide accurate
48
information about stem volumes in the boreal forest zone, and that the accuracy of this information
49
can be comparable to the accuracy that can be achieved based on field surveys. If the point density
50
of ALS data is high enough (higher than 5 points/m2), information can be obtained even at the level
51
of individual trees (Yu et al., 2010). A project was launched in 2008 to create a nation-wide Digital
52
Terrain Model (DTM) of Finland based on ALS surveys with a point density of 0.5 points/m2. The
53
purpose is to complete the surveys in about ten years (National Land Survey of Finland, 2011).
54
Similar activity has been carried out and is currently being carried out in many other countries.
55
Besides creating the DTM, the ALS data can also be used in forest inventories, and it is most
56
probable that previously unseen accuracy of national forest information will be obtained. However,
57
if the aim is to carry out NFIs more frequently than once every ten years, there could be a niche for
58
other remote sensing data sources in addition to ALS data. Such remote sensing techniques could be
59
based on digital aerial images, hyper-spectral data, high-resolution optical satellite images, or
3
60
Synthetic Aperture Radar (SAR) images. The capabilities of various remote-sensing data in forest
61
mapping have been reviewed, e.g. by Koch (2010). Considering the increased updating frequency
62
and the requirement for more detailed forest maps, there is a need to develop novel remote-sensing
63
techniques. In this light, SAR satellite images appear promising, as relatively large areas could be
64
covered in a short period of time thanks to the cloud-penetration capability of radar imaging.
65 66
SAR images contain the following information at the pixel level: 1) radar backscattering intensity, 2)
67
phase of the backscattered signal, and 3) range measurement based on the time of flight
68
information of the radar pulse. Radar intensity corresponds to the strength of the backscattered
69
signal compared to the strength of the transmitted signal and it is a function of the SAR system
70
parameters (such as the wavelength and the polarization of the used electro-magnetic radiation)
71
and target parameters (such as the target area roughness compared to the used radar wavelength
72
and dielectric properties). The phase information in the single-channel SAR data is quasi-random and
73
therefore useless for target interpretation. However, phase information is an essential part of multi-
74
polarized data analysis and SAR interferometry. The range information in conjunction with the
75
georeferencing information of the SAR antenna (orbit data) and Digital Elevation Model (DEM) is
76
typically used in the ortho-rectification of the SAR data to the map coordinate system. The range
77
information can also be used to extract elevation information. When a particular point can be
78
observed from two or more SAR images with different off-nadir angles, it is possible to calculate the
79
3D coordinates for that point. Even though SAR images are not stereo images in the same way that
80
the human eye perceives stereo information, the term stereoscopic measurement is sometimes
81
used.
82 83
The use of intensity and backscattering coefficient information in forest resources mapping has been
84
widely studied over the past few decades. In general, the longer radar wavelengths (L-band or P-
4
85
band) are more suitable for stem volume estimation that the shorter wavelengths of C-band or X-
86
band (Le Toan et al., 1992). The reason for this is that the interaction between radar waves and
87
forest structures in the L-band and P-band occurs on the trunks of trees. On the other hand, in the X-
88
band and C-band, the scattering takes place at the top of the forest canopy, on branches and foliage,
89
contributing apparently less to the information related to stem volume. Even though the
90
relationship between radar intensity and stem volume has been well studied, there still remain
91
practical challenges to be overcome. First, radar intensity data need to be radiometrically calibrated
92
taking into account the intensity variation due to topography. Second, seasonal variations in weather
93
(snow conditions and rain) affect the observed intensity, and these need to be taken into account
94
when applying wide-area image mosaics under variable weather conditions. In the case of satellite
95
images, the best results have typically been obtained using the L-band intensity. For example, Rauste
96
(2005) was able to obtain a correlation coefficient of 0.85 between stem volume and radiometrically
97
normalized L-band JERS-1 data in Finland. However, even after radiometric calibration, the L-band
98
intensity appears to saturate at some level of stem volume. Typically, stem volume levels beyond
99
100-200 m3/ha cannot be observed (Rauste, 2005).
100 101
The phase information provided by SAR images is used in polarimetric and/or interferometric
102
processing of radar data. SAR interferometry is a technique in which the pixel-by-pixel phase
103
difference between two SAR images acquired from slightly different perspectives can be converted
104
into elevation differences of the terrain. When the X-band or C-band of the radar are considered, the
105
scattering takes place near the top of the forest canopy (Le Toan et al., 1992). Therefore, if the
106
elevation of the ground surface is known (e.g. a Digital Terrain Model (DTM) is available), then the X-
107
band’s or C-band’s interferometric height compared to the ground surface elevation is related to the
108
forest canopy height and accordingly to the stem volume. An example of the use of interferometric
109
data for forest canopy height estimation has been provided by Kellndorfer et al. (2004), who used
5
110
the C-band’s interferometric heights from the Shuttle Radar Topography Mission (SRTM) to estimate
111
the forest canopy height. Similar results using the SRTM X-band data were presented by Solberg et
112
al. (2010), who also estimated the above-ground biomass based on SRTM elevation values. Hyde et
113
al. (2007) tested the capabilities of LIDAR and SAR interferometry in predicting above-ground forest
114
biomass, and they concluded that interferometric SAR data only slightly improved the estimation
115
capability when compared to LIDAR data alone. The interferometric processing of SAR data also
116
provides a coherence value. The coherence value describes the expected quality of the extracted
117
elevation values normalized to a range of 0 to 1. If the coherence is near to 1, the 2D phase patterns
118
of the two images are nearly identical. On the other hand, if the coherence value is close to 0, the
119
phase patterns do not match and the extracted DEM will be noisy. In the case of a forest, the
120
coherence value is correlated to stem volume if the time interval between image acquisitions is
121
suitable. For example, Askne et al. (2003) were able to obtain relatively good results using the
122
coherence information of the ERS-1 and ERS-2 tandem data (the time interval between image
123
acquisitions was 24 hours in the tandem mode). SAR polarimetry, which deals with the polarimetric
124
properties of radar data, can also be used to extract properties related to the target. For example,
125
the combination of polarimetry and interferometry can also be used in forest mapping
126
(Papathanassiou and Cloude, 2001). However, the techniques involved in combining polarimetry and
127
interferometry are still at the level of mere demonstration due to the lack of suitable and routinely
128
acquired SAR satellite images.
129 130
An alternative to interferometry when extracting elevation data from radar data is radargrammetry,
131
which is based on stereoscopic measurement of SAR images. Analogously to photogrammetric
132
spatial intersection, a stereo pair of SAR images with different off-nadir angles can be used to
133
calculate the 3D coordinates for corresponding points on the image pair. However, contrary to
134
interferometry, radargrammetry is based on the intensity values of SAR data and not on the phase
6
135
information. The foundations for the stereo-viewing capabilities of radar images were recognized
136
already in the 1960s (see, for example, La Prade, 1963). An example of research looking into the
137
mathematical foundations for calculating 3D coordinates and their expected accuracies is the work
138
by Leberl (1979). When the trajectory of a SAR antenna (position and velocity as functions of time) is
139
known accurately enough in relation to the object coordinate system and when a point target can be
140
clearly identified on two SAR images with different off-nadir angles, the 3D coordinates of the point
141
target can be calculated based on the range information. Typically, the so-called Range-Doppler
142
equation system is used as a sensor model, which describes accurately enough the propagation of
143
electro-magnetic radiation from the SAR image pixel to the point target and vice-versa (Leberl,
144
1979). The Canadian satellite, Radarsat-1, was one of the first SAR satellites to provide images with
145
variable off-nadir angles suitable for radargrammetric processing (Toutin, 2000). The ERS-1 and ERS-
146
2 satellites of the European Space Agency have also provided suitable stereo-pairs, but with limited
147
stereo overlap areas (Li et al., 2006). However, only a few studies related to the extraction of forest
148
information from radargrammetric DEMs have come to the authors’ knowledge. An example of
149
forest canopy extraction was given by Chen et al. (2007) based on Radarsat-1 SAR satellite images.
150 151
The radargrammetric processing of SAR satellite data has recently undergone a renaissance mostly
152
thanks to the German TerraSAR-X and Italian COSMO-SkyMed satellites. Firstly, the spatial resolution
153
of SAR data has improved to be of the order of 1 meter, which enables the extraction of more
154
detailed DEMs than was the case with earlier SAR satellite data. Secondly, TerraSAR-X enables image
155
acquisition with varying off-nadir angles over the target area. Moreover, the direct georeferencing
156
information of TerraSAR-X satellite images has proven to be accurate and reliable, and this enables
157
fairly effortless radargrammetric processing. Direct georeferencing refers to the solution of the
158
orientation parameters of the imaging sensor using the Global Navigation Satellite System and
159
Inertial Measurement Unit without ground control points. Ager and Bresnahan (2009) reported that
7
160
the direct georeferencing information provided with the TerraSAR-X image header files enables
161
coordinate measurements on the map plane with an accuracy of 1 meter when the elevation
162
information was used in the map projection. Similar results have been obtained by Raggam et al.
163
(2010), who also considered the aspects of stereoscopic measurements. Eineder et al. (2011)
164
reported of the range measurement accuracy of the order of centimeters of the TerraSAR-X system
165
once atmospheric and the Earth’s tidal effects had been corrected. Toutin (2010) reported that a 3D
166
measurement accuracy of 1-3 meters can also be achieved with fine-resolution SAR data of
167
Radarsat-2 satellite. Accordingly, highly accurate 3D coordinates can be obtained from stereoscopic
168
SAR measurements. However, the major challenge in the radargrammetry is automated seeking out
169
of corresponding points (also called tie-points) on stereo-pairs. This challenge has also been
170
recognized by Raggam et al. (2010). The seeking out of tie-points is challenging mainly due to the
171
speckle in SAR images. Another challenge is caused by the side-looking imaging geometry, which
172
causes image distortions especially in urban areas. A renaissance of radargrammetry can also be
173
anticipated in the case of forest mapping; the first results based on TerraSAR-X stereo data were
174
presented by Perko et al. (2010), who concluded that the elevation values of TerraSAR-X
175
radargrammetric DEMs and reference DTMs coincided in open areas. However, as was to be
176
expected, the elevation values of TerraSAR-X DEMs were somewhere between ground level and top
177
of the canopy, depending on the forest characteristics.
178 179
The results obtained by Perko et al. (2010) showed that X-band stereo SAR satellite data have
180
potential for application in the estimation of forests biomass. Modern SAR satellites provide images
181
with the spatial resolutions of up to 1 m, and so it appears to be possible to extract forest
182
information even at the plot level. The use of radargrammetry may also overcome the challenges
183
faced in the estimation of forest variables based on radar-intensity information in which the
184
radiometric normalization of image data and the saturation level in stem volume estimation are the
8
185
major challenges. In the present study, our objective was to study the use of elevation data
186
extracted from stereo SAR images (X-band TerraSAR-X satellite images) in the endeavor to predict
187
forest variables at the plot level. As far as the authors know, this is the first time that stereo SAR
188
data is used in plot level prediction. If the results turn out to be reliable, similar prediction methods,
189
some of which are already in use for ALS data, could be used in the case of stereo SAR data features
190
as well. Since SAR satellites enable mapping of wide areas, there could be potential in producing
191
detailed forest inventory data even at the national level. The structure of the paper is as follows. The
192
study area and the materials used (SAR images and reference data) are presented in Section 2. The
193
proposed approach and methods used in the study are described in Section 3. The results of the
194
study are provided in Section 4. Finally, conclusion and discussion are presented in Section 5.
195 196
2. Study area and material
197
2.1. Satellite SAR images and study area
198
Dual-polarization high-resolution Spotlight mode TerraSAR-X satellite images were used in this study.
199
TerraSAR-X is a German polar-orbiting satellite equipped with a modern SAR system using the X-
200
band microwave radiation carrier frequency having the wavelength of 3.1 cm. The satellite was
201
launched on June 15, 2007 and it is capable of acquiring high-resolution SAR images; its best spatial
202
resolution is about 1 m in the Spotlight imaging mode. (Düring et al., 2008)
203 204
The study area is located in Southern Finland, approximately 20 km west of the city of Helsinki. The
205
geographical coordinates of the center of the area are approximately 60°10'N and 24°36'E. In
206
general, the topography of the test area can be described as undulating, with only some tens of
207
meters of terrain elevation variation. The elevation values (geoidal heights) range from sea level (0
208
m) to approximately 50 m above sea level. The Bay of Espoonlahti is located in middle of the test
209
area. The test area, as seen by the TerraSAR-X satellite, is presented in Fig. 1.
9
210 211
[Fig. 1 about here]
212 213
The list of the used TerraSAR-X images is presented in Table 1. All images were ordered as Multilook
214
Ground Range Detected (MGD) products, i.e., non-interferometric data were used. According to the
215
ancillary data of the images, zero height from the WGS84 ellipsoid was used in the ground range
216
projection. This means that the stereoscopic measurements made of the SAR image pairs produce
217
elevation values from the WGS84 ellipsoid. All images covered the same area, and there were two
218
same-side stereo pairs (images 1+3 and 2+4) suitable for automated seeking of tie-points. The
219
intersection angles for both ascending and descending pairs were approximately 16°. All images
220
were acquired within a time period of two weeks in the spring, and there was little rain in the area at
221
that time. The weather conditions for each image are described in Table 1. However, it should be
222
noted that the weather conditions were based on the visual observations by the authors and were
223
therefore only rough estimates for the whole test area.
224 225
[Table 1 about here]
226 227
2.2. Reference data and Digital Terrain Model
228
In order to obtain accurate forest variable at the plot level, we carried out tree-by-tree field
229
measurements on a total of 110 circular test plots each with a radius of about 8 m. The field
230
measurement campaign was carried out in the fall of 2010, which was approximately a year later
231
than the dates of the image acquisitions. However, there were no forest management activities in
232
the test area during this time period and tree growth within the boreal forest zone is fairly minor
233
during one growing season. Therefore, we were able to safely use the field-measured forest
234
variables in the case of the TerraSAR-X images. All of the test plots were located with a hand-held
10
235
Global Navigation Satellite System device (GeoXT 2008 by Trimble Navigation Ltd., Sunnyvale, CA,
236
USA) supported by Virtual Reference Station (VRS) data and the locations were post-processed with
237
reference station data. The diameter-at-breast height (DBH) and tree species were determined for
238
all trees with a DBH of more than 5 cm. Moreover, the height of every fifth tree was measured using
239
a Haglöf Vertex clinometer (Haglöf Sweden AB, Långsele, Sweden) and the heights of all the trees
240
were then estimated. The stem volumes were calculated using standard Finnish models
241
(Laasasenaho, 1982). Finally, the plot-level data were obtained by summing the tree data of each
242
test plot. Considering all of the test plots, the mean stem volume (Vol) was 256.4 m3/ha, the mean
243
basal area (BA) was 30.6 m2/ha, the mean DBH (Dg) was 262 mm, and the mean height (Hg) was
244
17.7 m. The maximum and minimum stem volumes in the test plots were 503.1 m3/ha and 79.7
245
m3/ha. The distribution of the test plots in the stem volume classes of 50 m3/ha is shown in Fig. 2.
246
Dominant tree species in the test plots were Scots pine (Pinus Sylvestris, L.) and Norway spruce
247
(Picea Abies, L.) contributing 46.4% and 37.3% of the total volume respectively. Deciduous trees,
248
mainly birch (Betula, L.), contributed 16.3% of the total volume.
249 250
[Fig. 2 about here]
251 252
In order to obtain above-ground elevation values for the stereoscopically measured 3D points, an
253
accurate DTM was needed. In this study, we used the DTM produced by the National Land Survey of
254
Finland. This DTM was derived from ALS surveys (flights were carried out in 2008) with an average
255
point density of about 0.5 points/m2, and the final grid size of the DTM was 2 m. The derivation of
256
the ground surface was based on standard procedures used at the National Land Survey of Finland,
257
where stereoscopic photogrammetric observations are used to verify DTM accuracy. According to
258
the National Land Survey of Finland (2011), the vertical accuracy of ALS-based DTMs is 15 cm and
259
the horizontal accuracy is 60 cm.
11
260 261
3. Method
262
3.1. General workflow
263
The workflow of the proposed method for predicting plot-level forest variables for stereo SAR data is
264
presented in Fig. 3.
265 266
[Fig. 3 about here]
267 268
In the first stage of the workflow, the objective was to create a method for verifying the accuracy of
269
the stereoscopic SAR measurements so that plot-level accurate information can be extracted. In this
270
method, the verification is based on the use of check-points, which are manually measured from the
271
stereo SAR images. More details about the verification method are presented in Section 3.2.
272 273
The rest of the method was aimed at the automated extraction of elevation data from stereo SAR
274
images; these elevation data could be used in predicting the forest variables. The elevation data,
275
hereafter referred to as point clouds, consist of points having both horizontal coordinates (X and Y)
276
and height (Z) in the chosen object space coordinate system. In the present study, we used Universal
277
Transverse Mercator (UTM-35N) horizontal and geoidal height (FIN2000) vertical coordinate
278
systems. Statistical features were calculated from the point clouds for each test plot. Then, forest
279
variables were predicted from the features derived from the point cloud. The extraction of point
280
clouds from stereo SAR data, point cloud processing, and prediction of forest variables are presented
281
in Sections 3.3 and 3.4 in more detail.
282 283 284
3.2. Accuracy of 3D measurement from stereo SAR images
12
285
Well-defined 3D check-points were used in order to verify the accuracy of stereoscopic
286
measurements from SAR stereo pairs. The reference coordinates of the check-points were measured
287
with the aid of a digital topographic map and ALS data provided by the National Land Survey of
288
Finland. According to the National Land Survey of Finland (2011) the horizontal and vertical
289
accuracies of the check-points are expected to be less than 1 m, which should be sufficient for
290
estimating the accuracy of the 3D measurement of stereo SAR images. In the case of SAR images,
291
corner reflectors placed on the test area during image acquisitions are preferable for use as check-
292
points. However, in the present study, it was not possible to use corner reflectors. Therefore, we
293
used check-points, which were clearly identified on the SAR images. The stereoscopic measurements
294
were carried out using the Socet Set software package (version 5.5 by the BAE Systems). An example
295
of the check-point used in this study is presented in Fig. 4, which shows an artificial pond (centre of
296
the circle) measured stereoscopically from a SAR stereo pair.
297 298
[Fig. 4 about here]
299 300
The elevation values of the SAR measurements were converted into geoidal heights using the Finnish
301
geoid model (FIN2000), which is the same that was used in the processing of the ALS data. The
302
difference between the ellipsoidal (WGS84 reference ellipsoid) and geoidal heights in the test area
303
was 18.3 m. Finally, the accuracy of the 3D measurements was estimated using the Root Mean
304
Square Error (RMSE) and bias values between the measured and reference coordinates of the check-
305
points. The RMSE value in X coordinate was calculated using the following equation: n
å (X 306
RMSE X =
CP i
- X imeasured
)
2
i =1
n
,
(1)
13
307
where n is the number of check-points, XCP is the X coordinate of a check-point, and Xmeasured is the
308
corresponding X coordinate of the stereoscopic measurements. The RMSE values for the Y and Z
309
coordinates were calculated in the same way.
310 311
3.3. Automatic extraction of point clouds from SAR stereo data and point cloud processing
312
The most challenging part of the method was to find an automatic algorithm for seeking out the
313
corresponding points (i.e. the tie-points) from the SAR image pairs at the level of single pixels.
314
Stereoscopic SAR measurement is challenging even when performed manually due to the speckle
315
and image distortions. There are two approaches to automated seeking out of tie-points: 1) area-
316
based or 2) feature-based methods (Zitová and Flusser, 2003). Area-based methods typically use a
317
rectangular area of image pixels (a template window), for which the best matching location is sought
318
from within a pre-defined area on another image. In order to find out the location giving best
319
matching, a cross-correlation value of the pixels in the template window in the various locations in
320
the search window is typically calculated. If the cross-correlation value is higher than the selected
321
threshold value at some point within the area examined in the second image, this point is
322
considered to be a tie-point to the location shown in the template window of the first image. The
323
feature-based methods rely on basic mapping entities, i.e., points, lines, and polygons. However,
324
even though a considerable amount of effort has been put into feature-based methods, area-based
325
methods are still used the most when images with same sensors and similar imaging geometries are
326
considered. Therefore, an area-based method for extracting elevation data from stereo SAR images
327
was chosen for the proposed method. In calculation of the 3D object space coordinates of the tie-
328
points, a proper sensor model and the georeferencing information (orbit data) of the image data is
329
required. In the case of SAR images, the Range-Doppler model is typically used (see, for example,
330
Leberl, 1979). Recently, sensor models based on the Rational Polynomial Coefficients (RPC) have also
331
gained popularity (Zhang et al., 2011).
14
332 333
Because the aim of the present study was to study the predicting of forest variables, and not
334
automated seeking out of tie-points, a commercial state-of-the-art software package was selected
335
for the automated extraction of 3D data. In the present study, we used the Next Generation
336
Automatic Terrain Extraction (NGATE) module of the Socet Set software package (version 5.5) by
337
BAE Systems. The NGATE module uses standard image correlation to seek out tie-points between
338
images at the pixel level and the Range-Doppler model is used in the 3D coordinate calculation. In
339
the present study, the correlation threshold value for accepting tie-points was chosen manually in
340
order to ensure that there were enough successfully located tie-points for our test plots. We were
341
aware that a too low threshold value may increase the number of gross errors in the seeking out of
342
tie-points.
343 344
After the extraction of the elevation data, the elevation values of the 3D points were converted from
345
ellipsoidal into geoidal heights. Using the center coordinate of the test plots, the point clouds
346
representing the test plot in question were extracted from the point-cloud data. In this case, circles
347
with radiuses of 8 m and 15 m were used for each test plot (8 m corresponds to the radius used in
348
the field surveys). The reason for extending the radius of the test plots was that in this way more 3D
349
points could be obtained for each test plot. It should be noted that we can safely assume that the
350
forest characteristics did not significantly change when the radius of the circular area was extended.
351
The test plots were located so that they were definitely in the middle of the forest stands, each of
352
which represents a uniform area in terms of forest variables. Then, the Z coordinates of the points
353
were transformed as elevations above the ground surface. In this case, we used the ALS-based DTM
354
provided by the National Land Survey of Finland. The closest point on the DTM was located for each
355
point and this elevation value was subtracted from the Z coordinate. Points having an above-ground
356
elevation value of less than zero were deleted because they were expected to be gross errors due to
15
357
errors in the tie-point seeking algorithm. Finally, the following statistical features were calculated for
358
each test plot from the corresponding above-ground elevation point clouds: 1) the number of 3D
359
points within a test plot, 2) the mean above-ground elevation of 3D points, 3) the standard deviation
360
of the above-ground elevation values, and 4) the minimum and 5) the maximum above-ground
361
elevation values. At this stage, the above-ground elevation values higher than the mean above-
362
ground elevation plus two times the standard deviation (also known as a 2s rule for locating gross
363
errors) were excluded from the calculation of plot-specific statistics because they were considered
364
to be errors attributable to the tie-point seeking algorithm. In all the steps of the point cloud
365
processing in-house developed software components were used.
366 367
3.4. Predicting plot-level forest variables from stereo SAR-based features
368
We used the Nearest Neighbor (NN) approach in prediction of desired forest variables from
369
predictors (see, for example, Franco-Lopez et al., 2001). Plot-level forest variables measured in the
370
field were used as target observations (y values) and plot-specific features derived from stereo SAR
371
data were used as predictors (x values). The Random Forest (RF) technique developed by Breiman
372
(2001) was applied in the search of nearest neighbors. The RF method has been explained in detail,
373
for example, by Crookston and Finley (2008) and Falkowski et al. (2010). According to Hudak et al.
374
(2008) and Latifi et al. (2010) the RF method appears to be robust and flexible in forest variable
375
prediction when compared to other NN methods. In the RF method, several regression trees were
376
generated by randomly sampling the input data into training and testing sets. Two thirds of the data
377
was used in training in the present study. Measure of nearness in the RF method was defined based
378
on the probability of the observations to tend up to end in the same terminal node in regression
379
trees. The number of nearest neighbors was set to five based on previous knowledge (Vauhkonen et
380
al., 2010). In the present study, 500 regression trees per predicted variable were generated and two
381
predictors were picked randomly at the nodes of a regression tree (the number of predictors was 5).
16
382
Randomness was taken into account by running the RF method 100 times. The final result was the
383
average of these runs. In this study, x and y values were available for all of the test plots. This being
384
so, the error of the prediction was evaluated using the leave-one-out cross-validation method, in
385
which one of the observed values was selected as a validation data and the rest of the values were
386
used as test data.
387 388
4. Results
389
4.1. Accuracy of stereoscopic SAR measurement
390
We had a total of eleven well-defined check-points available to be used in the assessment of the
391
accuracy of stereoscopic measurement. All check-points were identified and stereoscopically
392
measured (X, Y, and Z coordinates in UTM-35N/WGS84 coordinate system) from two SAR stereo
393
pairs (TerraSAR-X images 1+3 and 2+4 in Table 1). The georeferencing information reported in the
394
header files of the TerraSAR-X images was used, i.e., no ground control points were applied. The
395
RMSE and bias values for two stereo pairs are given in Table 2. The bias values were all less than 1 m,
396
showing that un-biased coordinates can be measured from these SAR image pairs. The RMSE values
397
were less than 2 m, and mostly around 1 m, which implies that the accuracy of 3D measurement
398
should be sufficient for the purposes of this study. According to the results, it was not necessary to
399
refine the georeferencing information of the input SAR images. In fact, refining of the georeferencing
400
parameters would not have been even possible without the corner reflectors having known highly
401
accurate coordinates.
402 403
[Table 2 about here]
404 405
4.2. Point clouds from stereo SAR data
17
406
An example of ALS and stereo SAR point clouds for test plot #87 is shown in Fig. 5. In this case, the
407
SAR point cloud was derived by combining 3D points of the same-side image pairs. In Fig. 4, all of the
408
stereo SAR points were located between the ground surface and the top of the canopy. However, on
409
some of the test plots, gross errors existed, i.e., some of the stereo SAR points had negative above-
410
ground elevation values. These gross errors were deleted from the plot-specific point clouds as
411
described in Section 3.3. We also tested the use of opposite-side image pairs, but the results were
412
poor, i.e., there were only very few 3D points on our test plots.
413 414
[Fig. 5 about here]
415 416
When the radius of 8 m was used in the extraction of the plot-specific point clouds, there were on
417
average 5.5 stereo SAR points on our test plots when the point clouds of same-side image pairs were
418
combined. In the cases of ascending or descending image pairs only, the result was on average 2.5
419
and 3.0 points per test plot, respectively. However, there were a large number of test plots having
420
no stereo SAR points at all. For example, in the case of the combined point cloud, the number of test
421
plots having zero stereo SAR points was 21. When the radius of 15 m was used, the cases of the
422
ascending pair only, the descending pair only, and the combined point cloud resulted in 10.0, 10.7,
423
and 20.7 points per test plot on average. When using only ascending or descending image pairs,
424
there were still some test plots with no stereo SAR points. However, in the case of combined point
425
cloud, there was only one test plot, for which the number of stereo SAR points was zero. This test
426
plot was excluded from the forest variables prediction study because prediction is not possible
427
without any elevation values. Therefore, we used the combined point cloud and the radius of 15 m
428
in the forest variables prediction. The final number of test plots used in the forest variables
429
prediction was 109.
430
18
431
We also compared ALS-based forest canopy height to the mean elevation of the stereo SAR points
432
on our test plots. The comparison of ALS-based forest canopy height and the mean above-ground
433
elevation of stereo SAR points is presented in Fig. 6. According to these results, it appears that the
434
forest canopy height is underestimated by about 13 m in the stereo SAR data.
435 436
[Fig. 6 about here]
437 438
4.3. Prediction of plot-level forest variables
439
We had statistical features derived from stereo SAR point clouds for a total of 109 test plots for
440
predicting the plot-level forest variables. The k-NN method, which uses the RF technique to find
441
nearest neighbors, was applied. The relative errors of predicting the forest variables are given in
442
Table 3.
443 444
[Table 3 about here]
445 446
The RF method provides in-built importance scores for the predictors based on the increase in error,
447
when the predictors were considered in turn as random values. The most important predictors
448
according to the RF method were the mean and the maximum of the elevation values of the stereo
449
SAR points. A scatterplot of the predicted and observed stem volume values, which can be
450
considered as the most important forest variable, is shown in Fig. 7.
451 452
[Fig. 7 about here]
453 454
5. Conclusions and discussion
19
455
Promising results have been achieved recently in the matter of the automated processing of stereo
456
SAR satellite images in the endeavor to obtain elevation data. Perko et al. (2010) showed that
457
modern-day X-band SAR satellites with a spatial resolution of about 1 m can provide quite accurate
458
elevation data in open areas and they concluded that, in forested areas, stereoscopically measured
459
elevation data appears to be correlated with forest canopy height. The height of the forest canopy is
460
underestimated using X-band stereo measurements and the degree of underestimation depends on
461
the characteristics of the forest stand. These results encouraged us to study the predicting of plot-
462
level forest variables using elevation information obtained from stereo SAR data. In our case, using
463
test plot specific 3D point clouds and reference forest height obtained from ALS data, we estimated
464
that the underestimation was about 13 m on average in our test plots. These results agree with the
465
results of Perko et al. (2010) even though different types of forest were used.
466 467
In the present study, four TerraSAR-X Spotlight mode SAR satellite images were used. Two of the
468
images were from an ascending and the other two from a descending orbit, and they provided two
469
same-side stereo pairs for the automated extraction of elevation data. We also tested the use of
470
opposite-side stereo pairs, but as was to be expected, the number of successfully measured 3D
471
points was very low in these cases. Over bodies of water, the number of 3D points was very low even
472
for same-side stereo pairs. However, in the case of same-side stereo pairs, the number of
473
successfully measured points was in most cases acceptable in forested areas. Points clouds extracted
474
from the same-side stereo pairs were combined and this way stereo SAR points were obtained for
475
109 out of 110 test plots. It should also be noted that we had to increase the radius of the test plots
476
from 8 m to 15 m in order to obtain enough stereo SAR points.
477 478
According to the results obtained, the use of stereo SAR data in the predicting of plot-level forest
479
variables appears to be promising. A relative error (RMSE%) of 34.0% was obtained for stem volume
20
480
prediction. For the other forest variables, i.e., the mean basal area, mean diameter at breast height,
481
and mean forest canopy height, the accuracies were, 29.0%, 19.7%, and 14.0%, respectively.
482
Typically such a high level of prediction accuracy cannot be obtained using satellite-borne remote-
483
sensing at the plot-level data in the boreal forest zone. For example, Hyyppä et al. (2000) compared
484
SPOT XS, SPOT PAN, Landsat TM, and ERS SAR, and JERS SAR data, and the relative errors varied
485
from 45% to 65% in regard to stand-level stem volume estimation. The size of the forest area used in
486
the estimation significantly affects the accuracy of stem volume estimation. This accuracy increases
487
as the size of the area increases. According to Hyyppä and Hyyppä (2001a), stands larger than 1
488
hectare were needed to obtain a relative estimation accuracy level of about 40% for stem volume.
489
Consequently, the aforementioned relative RMSE of 34.0% is a good stem volume estimation result
490
for a test plot with an area of less than 0.1 hectares (radius of 15 m). However, when the results of
491
the stereo SAR data are compared to the ALS-based plot-level prediction presented in other studies,
492
the relative error in the case of stem volume is about 20% greater (Hyyppä et al., 2001b). Therefore,
493
ALS appears to be superior when compared to stereo SAR data; this is mainly due to the much higher
494
point density and lower penetration to the forest canopy. On the other hand, by adding more stereo
495
pairs to the process, the number of stereo SAR points could increase and slightly lower the relative
496
errors. Nevertheless, the above-ground elevation data derived from the stereo SAR data appears to
497
provide better results than SAR intensity data. For example, in the case of TerraSAR-X SAR intensity
498
images, the relative error obtained in the best case at the plot level was 55.8% (Holopainen et al.,
499
2010).
500 501
Accurate and up-to-date information is needed on forestry. According to the results of the present
502
study, fairly reliable forest variables describing forest resources can be obtained using X-band high-
503
resolution SAR satellite data. We believe that the proposed method can be developed to a very high
504
level of automation. Firstly, the spatial resolution of X-band SAR satellite images appears to be high
21
505
enough for extracting forest information at plot level. Secondly, the accuracy of stereoscopic
506
measurement of SAR images based on direct georeferencing information appears to be reliable
507
enough. However, it is advisable to check the accuracy of the georeferencing information using
508
corner reflectors or well-defined check-points. Thirdly, standard area-based tie-point seeking
509
algorithms for extracting elevation data from stereo SAR images and basic point cloud processing
510
techniques can be used. Moreover, the same prediction methods, such as the Random Forest
511
method, can be used to predict forest variables that are used in ALS data. Although ALS provides
512
more accurate estimates of forest variables, ALS surveys can be far more expensive to carry out
513
operatively. Moreover, SAR satellite images enable more frequent data acquisition. Therefore, it
514
seems possible to integrate stereo SAR data in updating forest inventories, for example, and in
515
detecting changes such as forest storm damage and clear-cuts.
516 517
The most crucial part of the proposed method is the automated seeking out of tie-points from
518
stereo SAR images. In the present study, we used same-side stereo SAR pairs (Spotlight mode) with
519
an intersection angle of 16° and stereo SAR points were acquired for the test plots. However, the
520
fact is that gaps still remained in the elevation data in places where the seeking out of tie-points
521
failed. These gaps can perhaps be filled by including more stereo SAR pairs in the process. Another
522
alternative could be the use of radar-intensity data or other remote-sensing data to obtain
523
information on the empty areas. Using the Spotlight mode images a limited area on the ground (up
524
to 10 by 10 km) can be processed. On the other hand, by using the Stripmap mode images, larger
525
areas can be covered, but we expect that the density of the extracted stereo SAR points might be
526
too low for plot-level analysis. However, the use of Stripmap images in the proposed methodology
527
should be tested.
528
22
529
The present study demonstrated only the use of spring-time images in Finland. In the case of winter-
530
time images with snow, the results may be slightly different. Therefore, more research is still needed
531
to verify the results in different seasons of the year and under variable weather conditions.
532
Moreover, tree species definitely impact on the accuracy of predicting, and this calls for more
533
profound test data. Nevertheless, the results are promising with the known limitations, the high cost
534
of high-resolution X-band SAR satellite data remains as the main obstacle for operational use. The
535
European Space Agency recently published its new free-of-costs data policy, which could facilitate
536
the use of stereo SAR data in forest inventories. However, we cannot predict if C-band data of the
537
Sentinel-1 SAR will provide result similar to those of TerraSAR-X data. The advances in SAR
538
interferometry and polarimetry (TanDEM-X and TerraSAR-X joint mission by German Aerospace
539
Center) will also be interest in the future from the forestry point of view.
540 541
Acknowledgments
542
The TerraSAR-X SAR images were acquired through the German Aerospace Center (DLR) prelaunch
543
Announcement of Opportunity scientific project (LAN-0049). The authors wish to use this
544
opportunity to express their appreciation to DLR for the image data.
545 546 547
References
548
Ager, T., & Bresnahan, P.A. (2009). Geometric Precision in Space Radar Imaging: Results from
549
TerraSAR-X. Proceedings of the ASPRS 2009 Annual Conference: Reflection of the Past, Vision of the
550
Future, Baltimore, 9-13 March 2009.
551
23
552
Askne, J., Santoro, M., Smith, G., & Fransson, J.E.S. (2003). Multitemporal Repeat-Pass SAR
553
Interferometry of Boreal Forests. IEEE Transactions on Geoscience and Remote Sensing, 43(6), 1219-
554
1228.
555 556
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
557 558
Chen, Y., Shi, P., Deng, L., & Li, J. (2007). Generation of a top-of-canopy Digital Elevation Model
559
(DEM) in tropical rain forest regions using radargrammetry. International Journal of Remote Sensing,
560
28(19), 4345-4349.
561 562
Crookston, N., & Finley, A. (2008). yaImpute: An R Package for k-NN Imputation. Journal of Statistical
563
Software, 23(10), 1-16.
564 565
Düring, R., Koudogbo, F.N., & Weber, M. (2008). TerraSAR-X and TanDEM-X Revolution in
566
Spaceborne Radar. The International Archives of the Photogrammetry, Remote Sensing and Spatial
567
Information Sciences, ISPRS Congress Beijing 2008, Volume XXXVII, Part B1, 227-234.
568 569
Eineder, M., Minet, C., Steigenberger, P., Cong, X., & Fritz, T. (2011). Imaging Geodesy—Toward
570
Centimeter-Level Ranging Accuracy With TerraSAR-X. IEEE Transactions on Geoscience and Remote
571
Sensing, 49(2), 661-671.
572 573
Falkowski, M., Hudak, A., Crookston, N., Gessler, P., Uebler, E. & Smith, A. (2010). Landscape-scale
574
parameterization of tree-level forest growth model: a k-nearest neighbor imputation approach
575
incorporation LiDAR data. Canadian Journal of Forest Research, 40(2), 184-199.
576
24
577
Finnish Forest Research Institute (2011). National Forest Inventory (NFI). URL:
578
http://www.metla.fi/ohjelma/vmi/info-en.htm, Available on-line, Last accessed: January 2011.
579 580
Franco-Lopez, H., Ek, A.R., & Bauer, M.E. (2001). Estimation and mapping of forest stand density,
581
volume, and cover type using the k-nearest neighbors method. Remote Sensing of Environment,
582
77(3), 251-274.
583 584
Holopainen, M., Haapanen, R., Karjalainen, M., Vastaranta, M., Hyyppä, J., Yu, X., Tuominen, S., &
585
Hyyppä, H. (2010). Comparing Accuracy of Airborne Laser Scanning and TerraSAR-X Radar Images in
586
the Estimation of Plot-Level Forest Variables. Remote Sensing, 2(2), 432-445.
587 588
Hudak, A.T., Crookston, N.L., Evans, J.S., Hall, D.E., & Falkowski, M.J. (2008). Nearest neighbor
589
imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sensing
590
of Environment, 112(5), 2232-2245.
591 592
Hyde, P., Nelson, R., Kimes, D., & Levine, E. (2007). Exploring LiDAR-RaDAR Synergy - Predicting
593
Aboveground Biomass in a Southwestern ponderosa pine forest using LiDAR, SAR, and InSAR.
594
Remote Sensing of Environment, 106(1), 28-38.
595 596
Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., & Zhu, Y.-H. (2000). Accuracy comparison
597
of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and
598
Management, 128(1), 109-120.
599 600
Hyyppä, H., & Hyyppä, J. (2001a). Effects of stand size on the accuracy of remote sensing-based
601
forest inventory. IEEE Transactions on Geoscience and Remote Sensing, 39(12), 2613-2621.
25
602 603
Hyyppä, J., Schardt, M., Haggrén, H., Koch, B., Lohr, U. Scherrer, H.U., Paananen, R., Luukkonen, H.,
604
Ziegler, M., Hyyppä, H., Pyysalo, U., Friedländer, H., Uuttera, J., Wagner, S., Inkinen, M., Wimmer, A.,
605
Kukko, A., Ahokas, E. & Karjalainen, M. (2001b). HIGH-SCAN: The first European-wide attempt to
606
derive single-tree information from laserscanner data. The Photogrammetric Journal of Finland,
607
17(2), 58-68.
608 609
Hyyppä, J., Hyyppä, H., Leckie, D., Gougeon, F., Yu, X., & Maltamo, M. (2008). Review of methods of
610
small-footprint airborne laser scanning for extracting forest inventory data in boreal forests.
611
International Journal of Remote Sensing, 29(5), 1339-1366.
612 613
Laasasenaho, J. (1982). Taper curve and volume functions for pine, spruce and birch.
614
Communicationes Institute Forestalis Fenniae, 108, 74 p.
615 616
La Prade, G. (1963). An analytical and experimental study of stereo for radar. Photogrammetric
617
Engineering, 29(2), 294-300.
618 619
Latifi , H., Nothdurft, A., & Koch, B. (2010). Non-parametric prediction and mapping of standing
620
timber volume and biomass in a temperate forest: application of multiple optical/LiDAR –derived
621
predictors. Forestry, 83(4), 395-407.
622 623
Leberl, F. (1979). Accuracy analysis of stereo side-looking radar. Photogrammetric Engineering and
624
Remote Sensing, 45(8), 1083-1096.
625
26
626
Le Toan, T., Beaudoin, A., Riom, J., & Guyon, D. (1992). Relating Forest Biomass to SAR Data. IEEE
627
Transactions on Geoscience and Remote Sensing, 30(2), 403-411.
628 629
Li, Z.L., Liu, G.X., & Ding, X.L. (2006). Exploring the generation of digital elevation models from same-
630
side ERS SAR images: Topographic and temporal effects. Photogrammetric Record, 21(114), 124-140.
631 632
Kellndorfer, J., Walker, W., Pierce, L., Dobson, C., Fites, J.A., Hunsaker, C., Vona, J., & Clutter, M.
633
(2004). Vegetation height estimation from Shuttle Radar Topography Mission and National Elevation
634
Datasets. Remote Sensing of Environment, 93(3), 339-358.
635 636
Koch, B. (2010). Status and future of laser scanning, synthetic aperture radar and hyperspectral
637
remote sensing data for forest biomass assessment. ISPRS Journal of Photogrammetry and Remote
638
Sensing, 65(6), 581-590.
639 640
The National Land Survey of Finland (2011). Description of Airborne Laser Scanning Data (in Finnish).
641
URL: http://www.maanmittauslaitos.fi/digituotteet/laserkeilausaineisto, Available on-line, Last
642
accessed: January 2011.
643 644
Papathanassiou, K.P., & Cloude, S.R. (2001). Single-baseline polarimetric SAR interferometry. IEEE
645
Transactions on Geoscience and Remote Sensing, 39(11), 2352-2363.
646 647
Parviainen, J., Västilä, S., & Suominen, S. (2007). State of Finland's Forests 2007 Based on the Criteria
648
and Indicators of Sustainable Forest Management. Publication 7a/2007 of the Ministry of Agriculture
649
and Forestry of Finland, 101 pages.
650
27
651
Perko, R., Raggam, H., Gutjahr, K., & Schardt, M. (2010). The capabilities of TerraSAR-X imagery for
652
retrieval of forest parameters. In the proceedings of ISPRS TC VII Symposium – 100 Years ISPRS,
653
Vienna, Austria, July 5–7, 2010, Vol. XXXVIII, Part 7B, 452-456.
654 655
Raggam, H., Gutjahr, K., Perko, R.,& Schardt, M. (2010). Assessment of the Stereo-Radargrammetric
656
Mapping Potential of TerraSAR-X Multibeam Spotlight Data. IEEE Transactions on Geoscience and
657
Remote Sensing, 48(2), 971-977.
658 659
Rauste, Y. (2005). Multi-temporal JERS SAR data in boreal forest biomass mapping. Remote Sensing
660
of Environment, 97(2), 263-275.
661 662
Solberg, S., Astrup, R., Bollandsås, O.M., Næsset, E., & Weydahl, D.J. (2010). Deriving forest
663
monitoring variables from X-band InSAR SRTM height. Canadian Journal of Remote Sensing, 36(1),
664
68-79.
665 666
Toutin T. (2000). Evaluation of radargrammetric DEM from RADARSAT images in high relief areas.
667
IEEE Transactions on Geoscience and Remote Sensing, 38(2), 782-789.
668 669
Toutin, T. (2010). Impact of Radarsat-2 SAR Ultrafine-Mode Parameters on Stereo-Radargrammetric
670
DEMs. IEEE Transactions on Geoscience and Remote Sensing, 48(10), 3816-3823.
671 672
Vauhkonen, J., Korpela, I., Maltamo, M., & Tokola, T., (2010). Imputation of single-tree attributes
673
using airborne laser scanning-based height, intensity, and alpha shape metrics. Remote Sensing of
674
Environment, 114(6), 1263-1276.
675
28
676
Zhang, G., Li, Z., Pan, H., Qiang, Q., Zhai, L., (2011). Orientation of Spaceborne SAR Stereo Pairs
677
Employing the RPC Adjustment Model, IEEE Transactions on Geoscience and Remote Sensing, 49(7):
678
2782-2792.
679 680
Zitová, B., & Flusser, J. (2003). Image Registration Methods: A Survey. Image and Vision Computing,
681
21(11), 977-1000.
682 683
Yu, X., Hyyppä, J., Holopainen, M., & Vastaranta, M. (2010). Comparison of Area-Based and
684
Individual Tree-Based Methods for Predicting Plot-Level Forest Attributes. Remote Sensing, 2(6),
685
1481-1495.
686
29
687 688
Table 1
689
The list of TerraSAR-X SAR satellite images used.
690 Image#
Acquisition
Polarization
date
1
28.4.2009
HH/VV
Orbit/ antenna
Incidence
Time
Resolution in
direction
angle at
(UTC)
ground
Ascending/Right
scene
range/azimuth
centre
(m)
35.8°
15:54
2.0/2.4
Weather
+19 °C, clear
2
29.4.2009
HH/VV
Descending/Right
36.1°
4:48
2.0/2.4
+10 °C, mostly cloudy
3
8.5.2009
HH/VV
Ascending/Right
51.7°
16:11
2.0/2.4
+16 °C, clear
4
11.5.2009
HH/VV
Descending/Right
52.0°
4:31
2.0/2.4
+9 °C, Overcast
691 692
30
693 694
Table 2
695
The RMSE and bias values of the measured versus reference coordinates.
696 Image pair
RMSE X (m) RMSE Y (m) RMSE Z (m) Bias X (m) Bias Y (m) Bias Z (m)
1+3 (ascending)
1.6
1.0
0.9
0.3
-0.6
-0.3
2+4 (descending) 1.1
1.3
1.2
-0.2
0.8
-0.3
697 698
31
699 700
Table 3
701
The relative RMSE and bias values (% of mean) of the predicted plot-level forest variables using the
702
stereo SAR data.
703 Bias
Bias%
RMSE
RMSE%
3.7
1.4
87.9
34.0
0.3
1.0
8.9
29.0
Mean height (dm)
1.5
0.8
25.0
14.0
Mean diameter at
3.6
1.4
51.7
19.7
Mean stem volume (m3/ha) Mean basal area (m2/ha)
breast height (mm) 704 705
32
706 707
List of figures:
708 709
Fig. 1. The test area as seen by TerraSAR-X SAR satellite (HH polarization). The input SAR images
710
were ortho-rectified for the UTM-N35/WGS84 map coordinate system using a DTM with a grid size
711
of 2 metres. (Original Data © 2009, German Aerospace Center).
712 713
Fig. 2. Distribution of the test plots in stem volume classes.
714 715
Fig. 3. Flow chart of the proposed method.
716 717
Fig. 4. An artificial pond shown as an example of the check-points used to verify the accuracy of the
718
stereoscopic SAR measurements. The check-point is located in the centre of the red circle
719
(measurement cursor). North arrow is show in yellow.
720 721
Fig. 5. Example of a point cloud at test plot #87. The blue dots represent the ALS data and the red
722
stars are 3D points derived from the stereo SAR data.
723 724
Fig. 6. Comparison of ALS-based forest canopy height and mean above-ground elevation of the
725
stereo SAR points (number of test plots is 109).
726 727
Fig. 7. A scatterplot showing the plot-level predicted and observed stem volume values (number of
728
test plots is 109).
729
33
Fig1 Click here to download high resolution image
Fig2 Click here to download high resolution image
Fig3 Click here to download high resolution image
Fig4 Click here to download high resolution image
Fig5 Click here to download high resolution image
Fig6 Click here to download high resolution image
Fig7 Click here to download high resolution image
