VALIDATION OF HEIGHTS FROM INTERFEROMETRIC SAR AND LIDAR OVER THE TEMPERATE FOREST SITE NATIONALPARK BAYERISCHER WALD

Transcription

1 VALIDATION OF HEIGHTS FROM INTERFEROMETRIC SAR AND LIDAR OVER THE TEMPERATE FOREST SITE NATIONALPARK BAYERISCHER WALD T. Aulinger (1,3), T. Mette (1), K.P. Papathanassiou (1), I. Hajnsek (1), M. Heurich (2), P. Krzystek (3) (1) German Aerospace Center, Microwaves and Radar Institute, D Wessling, Germany phone: +49-(0) (2) Bavarian Forest National Park, Department of Research (3) Munich University of Applied Sciences, Department of Geoinformatics Abstract Estimating forest parameters from remote sensing is becoming more relevant in these days as new sensor technologies allow to derive improved data quality with higher spatial resolution and higher accuracy. The determination of forest height has been shown to be feasible from both remote sensing systems Lidar and interferometric SAR, and can serve as an input parameter to estimate forest biomass, which is the most important forest structural parameter. Within the frame of the HTO project 33-8, different new remote sensing techniques were tested and analysed at the test site Nationalpark Bayerischer Wald in order to estimate different heights, the canopy top and the ground topography. The Nationalpark Bayerischer Wald is located in south-east Germany in a mountainous region ranging from m a.s.l and consists of three major forest zones dominated by Norway spruce. The stand structures are highly divergent ranging from natural to semi-natural to disturbed. The goal of this study was to validate and compare the forest and terrain heights retrieved from Lidar and from three interferometric SAR products: X-band digital elevation model (DEM) minus Lidar ground DEM, P-band DEM and X-minus-P-band DEM. The experimental Lidar and SAR data were acquired with airborne sensors over an area of 2 x 6 km 2. Inside this area, 34 test stands between ha were chosen, each one homogeneous in species composition, height distribution and density. As a reference height served the forestry standard parameter h100, the so-called upper canopy height or top height. 1. Introduction The motivation behind this work lies in the importance of forest height as a structural parameter. It is a standard parameter in every German forest inventory and yet very hard to measure from the ground. Further on forest height is a criterion to separate forest from non-forest vegetation, and to classify forest types and managements [2]. Remote Sensing can be a labour- and time-saving method to achieve this information. Providing tree heights has been shown feasible from remote sensing technologies like Photogrammetry and Lidar, both optical systems accounting for single trees. In contrast to this, forest height models derived from interferometric SAR are averaged forest heights and provides, depending on the sensor spatial resolution a mean forest height over a specific area. But since radar waves posses a different penetration capability into the forest (as a function of the wavelength and also forest structure), it is necessary to quantify this penetration with respect to a representative forest height reference, like the top height h100 that was used here, The goal of this study was to validate an interferometric X- and P-band DEM against high precision Lidar canopy and ground DEM on the basis of 34 test stands of the test site Nationalpark Bayerischer Wald In detail, the following three questions were addressed: How much is the X-band derived digital elevation model penetrating into the forest canopy? Does the P-band derived digital elevation model give an accurate estimate of the ground topography? How accurate is the forest height represented using the X-band subtracted from the P-band DEM? 2. Data and Methods 2.1 Test Site: The test site is located in the south east of Germany within the Nationalpark Bayerischer Wald in mountainous region ranging from m. Lidar, radar and ground data were acquired for test site C with an area of approximately 2 x 6 km 2. The stand structures are highly divergent ranging from natural to semi-natural to disturbed and are dominated by Norway spruce and European Beech. Forest heights vary from 1 m to more than 40 m. Characteristic are areas with very low vegetation and standing dead-wood due to bark beetle calamity.

2 2.2 Ground Data: The validation was based on 34 test stands with a size between 0.2 and 6 ha. The goal was to select areas with homogeneous forest conditions in regard to species composition, height distribution and density. The selection was made with a CIR-Orthophoto and verified in the field. (Tab. 1). Number Stage Area (ha) Forest Height Lidar (m) Number Stage Area (ha) Forest Height Lidar (m) 1 Juvenile stage 0,57 11,88 22 Mature stage 2,85 31,02 2 Juvenile stage 1,50 25,08 23 Juvenile stage 1,88 24,86 3 Growth stage 0,73 22,77 24 Heterogeneous stage 6,09 42,46 4 Growth stage 1,63 23,76 25 Mature stage 1,34 36,08 5 Growth stage 1,14 22,55 26 Heterogeneous stage 4,59 40,70 6 stage 0,30 20,79 27 Heterogeneous stage 2,74 37,95 7 Growth stage 0,47 20,79 28 Meadow 0,33 0,44 8 Regeneration stage 0,44 32,78 29 Mature stage 2,58 32,67 9 Mature stage 2,16 32,56 30 Mature stage 3,55 30,91 10 Coniferous stage 1,74 3,08 31 Mature stage 1,18 33,00 11 Mature stage 0,64 32,34 32 Heterogeneous stage 1,57 31,90 12 Mature stage 1,40 35,09 33 Mature stage 1,47 32,78 13 Mature stage 1,12 33,33 34 Growth stage 1,22 25,96 18 Meadow 0,93 0,99 35 Mature stage 1,68 23,87 19 Meadow 0,18 0,33 36 Juvenile stage 0,69 14,41 20 Growth stage 3,38 28,82 37 Mature stage 2,77 33,55 21 Growth stage 1,95 23,65 38 Regeneration stage. 1,36 34,98 Tab. 1: Summary of the 34 test stand within test site C. 2.3 Lidar Data: Lidar data were acquired with the airborne laser scanner system Toposys II from TopSys -System in spring (leaf-off) and summer 2002 (leaf-on). The Toposys System is based on two separate glass fibre arrays of 127 fibres each and produces a push broom measurement pattern on ground. The x,y,z coordinates of the aimed surface is estimated by the angle of the laser beam and the distance of the sensor to it. At the same time an ongoing estimation of the sensor position is running by GPS (Global Positioning System) and INS (Inertial Navigation System) [6]. The first reflection of laser pulse in the summer acquisition was used to calculate a DSM (Digital Surface Model), the last reflection in spring for the DTM (Digital Terrain Model/ ground DEM). By subtracting the two height models, a Digital Canopy Model (DCM) has been obtained (Fig. 9). The spatial resolution of all models is 1 m. In a previous analysis [4] the laser first reflection was shown to underestimate the tree top by 0.5 m. The height accuracy of the DTM was estimated to be 0.15 m [3]. 2.4 Interferometric Radar Data: Interferometric Radar data were acquired with the experimental airborne Synthetic Aperture Radar (E-SAR) system of DLR [5]) at X- and P-band (X-band 3 cm/ 10 GHz, P-band 67 cm/ 450 MHz) in August The X-Band (VV-pol.) height model was mosaicked from two directions (north/ south), the P-band (HH-pol.) model only from the south [7]. Due to its wavelength the X-band height model was supposed to lie close to a mean vegetation surface, the P-band model close to the ground topography. Two forest height models were generated: (1) by subtracting the Lidar DTM from X-Band DEM (Fig. 10), and (2) by subtracting P-band from X-band an X-/P-band forest height model was received (Fig. 12).

3 Fig. 1: h100-forest height for three different forest types 5 Areas 1:1 Linear (Areas) Lidar Height h100 3 y = 0,9025x 40 2 R = 0, Height (m) h 100 -calculated (Lidar DCM) h 100 -measured (ground) Percentage (%) 6 Fig. 2: Determination of the lidar-h100. In order to find an algorithm to determine the h100 from lidar data, the mean height of the highest 10 % of all lidar heights in one stand were averaged and plotted against the true (ground measured) h100. The regression shows that 10 % underestimation have to be corrected. 2.5 Reference height h100: A very important role for this work plays the forestry standard parameter h100, which was used as a reference height. The h100 or upper canopy height or top height refers to the basal area weighted average of the 100 highest trees per hectare. Fig. 1 shows three different forest scenarios where all trees that form the h100 are marked red. As can be seen, the h100 is similar for the three different forest types: the commercial forest where almost all trees reach the h100, the natural forest with a heterogeneous height structure and the open forest where the trees are spaced far apart. The independence of the h100 from the forest structure is an advantage and disadvantage at the same time. On the one hand, it makes the h100 a very convenient and easy-to-measure/use forest height, on the other hand it does not express any information about height distribution and forest density. Since the h100 was not available for all test stands, it was tried to find an algorithm how to determine the h100 from the Lidar data (lidar-h100). The problem is that the high resolution of the lidar system does not only measure the tree tops which is needed for the h100 but also lower parts of the crowns or even lower when hitting a canopy gap. An easy approximation of the actual h100 is to calculate the mean value of the highest 10% of the Lidar data in every test stands. Fig. 2 plots the true ground measured h100 against the lidar derived h100. Since this algorithm underestimates the true h100 by 10%, the calculated ones had to be corrected for this amount. The validation of the radar DEMs was carried out against the lidar-h100. It shall be remarked that in contrast to tree height, forest height is an abstract, statistical unit that integrates tree heights over a certain area. Especially, when variable tree heights lead to a heterogeneous canopy, a single forest height value looses its representatively. Even in eve-aged forest stands, the natural variability of tree heights may lead to a variability of the h100 of an estimated 15 %. 3. Results 3.1 X-band minus Lidar DTM: Fig. 3 compares the X-Band minus Lidar DTM with the (as true assumed) lidar-h100 in the test stands. The regression shows that the upper canopy height is underestimated by 35%. The correlation coefficient is very high (r2 = 0.93). Heterogeneous stands of the so-called Plenter -type and stands with standing dead-wood are excluded from trend calculation.

4 Mean height X-Band - Lidar DTM (m) Heterogeneous stand Stand with standing dead-wood 1:1 Trend (linear) y = 0,6647x R 2 = 0, h 100 Lidar DCM (m) Mean deviation P-Band DEM - Lidar DTM (m) 15,00 12,50 7,50 5,00 2,50-2,50-5,00-7,50 Deciduous forest Coniferous forest Mixed forest Trend (simulated) Heterogeneous Forest h100 Lidar DCM Fig. 3: Mean Height X-band-DTM vs. h 100 Lidar Fig. 4: Mean deviation P-band minus Lidar DTM vs. h 100 Lidar Fig. 5: Difference P-band DEM Lidar DTM Fig. 6: Fig. 8: Lidar Forest Heights (DCM) Mean deviation P-band - DTM (m) 15,00 5, m 20-25m 25-30m 30-35m >35m Difference X-Band - P-Band (m) 4 35, , ,00 5,00 Multilayererd stand Youth stand with vertikal dead-wood 1:1 Trend (simulated) -5, ,00 5,00 15, ,00 Slope ( ) Fig. 7: Mean deviation P-band DEM Lidar DTM vs. slope -5,00 5,00 15, , , ,00 h 100 Lidar DCM (m) Fig. 8: Difference X minus P-band vs. h 100 Lidar 3.2 P-Band DEM: P-Band DEM was assumed to represent the ground topography and therefore compared with the Lidar DTM (assumed as true ). As can be clearly seen from Fig. 11, the P-band model may over-or underestimate the true ground topography up to -20 m and +30 m. In Fig. 5 one section of the whole test site was zoomed in and overlaid with the stand borders. If we compare the difference between P-Band and lidar DTM with the Lidar forest height map (DCM, Fig. 6), we notice a remarkable congruence. The higher the forest in the lidar DCM, the higher the P-band DEM lies above the ground. Negative values occur in areas with low or without vegetation.

5 This effect also shows in our test stands. Excluding, again the heterogeneous Plenter -stands, the difference between P- Band DEM and ground increases with forest height. Assuming a relation in the form of a power function would indicate that especially forests above 10m increasingly shift the interferometric scattering center away from the ground. In this case also the absolute DEM heights lie 1-2 m below the true ground topography. Additionally a possible influence of the topography on P-band heights was examined by comparing the P-Band deviation with the slopes (in range direction). The comparison for the different test stands does not indicate to have an influence of the ground topography (Fig. 7). 3.3 X-minus-P-band: On the basis of the results from X- and P-band, we expect that the difference between X- and P-band underestimates forest height. As can be seen in Fig. 8, the bias from X-band to the real crown top and the bias from P-band to the real ground lead to an increasing underestimation with increasing heights, here approximately 40% for 35m high forest. Also here, a simulated trend was sketched excluding again the Plenter -type stands. 4. Conclusions The results showed that both X-band and P-band DEM were biased through vegetation. For X-band (VV-pol., single pass mosaic from two directions) the penetration into spruce forests was a linear trend that underestimated the upper canopy height h100 by 25 %. Although the relation had a high correlation coefficient, the results are not necessarily transferable to forest types of different species or densities or under different moisture conditions. Otherwise forest height could be very accurately determined from X-band supposed also a precise ground DEM is available. The P-band DEM (HH-pol., repeat pass, one direction) overestimated the ground topography with increasing forest height. These results contrast other studies [1], where the P-band DEM only varies little from the actual ground topography. Since double bounce between ground and stem plays a major role in locating the scattering center to the ground, the choice of the polarization (HH) and the variable terrain slopes may prevent the double bounce. But a relation between the P-band deviation and the terrain slope could not be proved from the data.finally, the difference between X- and P-band DEM underestimates the upper canopy height by the sum of the X-band and P-band deviation. This underestimation seems to increase with increasing forest height, because the P-band deviation from the ground was rather a power function than linear. Based on the results, forest height can be estimated with high accuracy both from an X-band DEM minus (an existing) ground DEM, and from X-minus P-band DEM. But in both cases, the upper canopy height is underestimated and a factor/ function have to be applied to get the true upper canopy height. How universal the factors/ function can be used is questionable, since forest structure, ground topography and moisture conditions can be assumed to alter the penetration depth. Also, the use of the h100 as a reference height only makes sense as long as the forest canopy is spatially homogeneous, meaning without high variations or large gaps. Such structural heterogeneities can only be detected if the sensor resolution is higher than the spatial resolution of the heterogeneity. 5. References 1. Andersen, H.-E., McGaughey, R., Carson, W., Reutebuch, S., Mercer, B. & Allan J., 2004: A Comparision of Forest Canopy Models Derived from Lidar and InSAR, Int. Arch. of Photogrammetry and Remote Sensing, Commision III, ISPRS 20th Congress, Istanbul, Turkey, 13th - 23rd July. 2. FAO 2001: State of the World s forest, 3. Fischer, F. & Knörzer, O., 2003: Statistische Analyse von digitalen Geländemodellen und Waldstrukturen im Nationalpark Bayerischer Wald mit Hilfe von hochaufgelösten Laserscanningdaten und GPS-Messungen, Diploma thesis Munich University of Applied Sciences 4. Heurich M., 2004: Baumhöhenmessung mit flugzeuggetragenen Laserscannern, AFZ-Der Wald /2004, pp Horn, R., 1997: The DLR airborne SAR project E-SAR, IEEE Transactions, No.4, pp Schnadt, K. & Katzenbeisser, R.: 2004: Unique Airborne Fiber Scanner Technique for Application-Oriented Lidar Products, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, VOL- UME XXXVI, PART 8/W2 7. Scheiber, R., 1998: Along- and Across-Track Interferometry using the E-SAR System, Proceedings of IGARSS, Vol., 1998, pp

6 Fig. 9: Digital Canopy Model (DCM) from difference Lidar DSM minus DTM Fig. 10: Forest Height from difference X-Band DEM minus Lidar DTM Fig. 11: Difference Values P-Band minus Lidar DTM Fig. 12: Forest Height from difference X-band DEM minus P-band DEM