SAR forest canopy penetration depth as an indicator for forest health monitoring based on leaf area index (LAI)

Transcription

1 SAR forest canopy penetration depth as an indicator for forest health monitoring based on leaf area index (LAI) Svein Solberg 1, Dan Johan Weydahl 2, Erik Næsset 3 1 Norwegian Forest and Landscape Institute, P.O.Box 115, 1431 Ås, Norway svein.solberg@skogoglandskap.no 2 Norwegian Defence Research Establishment, Land and Air Systems Division, P O Box 25, NO-2027 Kjeller, Norway Dan-Johan.Weydahl@ffi.no 3 Norwegian University of Life Sciences, 1432 Ås, Norway erik.naesset@umb.no ABSTRACT Forest health monitoring may be done with remote sensing. Satellite based SAR is one promising technology as it works day and night and with cloud cover, and because it is sensitive to 3D properties. We here apply an interferometry based XDEM approach, where we assumed that an increasing defoliation would cause an increasing X band penetration downwards into the canopy layer, and that the penetration depth is a function of the amount of leaf area index (LAI) penetrated. We had at hand data for a 4 km 2 forest area, having an SRTM X and C band SAR data set from 2000; a discrete-return laser scanning data set from 2003; and ground based measurements of some hundred trees and a forest stand map from We initially adjusted the XDEM and CDEM using elevation data from some agricultural fields nearby the forest using an official, Norwegian DTM data base having a 25mx25m spatial resolution. All further analyses were carried out on a 10mx10m grid. With the laser data we obtained a DTM and a canopy surface model (CSM), where the latter was set to the 75 percentile of the DZ data in each grid cell. The X band penetrated about six m downwards into the canopy layer, which means that for all grid cells having a forest canopy lower than six m, the XDEM was around zero. With an increasing DSM from six m upwards, the DSM could be approximated by the linear function DSM = *XDEM, having a RMSE of 4.0 m. The laser data provided the possibility to estimate LAI in every grid cell and at any height in that cell. For every grid cell, an LAI value was estimated for the forest canopy being above the XDEM height, using the method of Solberg et al (2006), where LAI = C * ln(n/nb), where LAI is effective LAI above a given height; C is a constant calibrated from ground based measurements with the value 2.0, N is the total number of laser pulses; and Nb is the number of laser pulses below the given height. The median LAI abovex value was 1.42, and percentile values being Also, in order to have a more homogeneous data set we redid the analyses using only spruce dominated stands, and excluding all grid cells at stand borders. The latter was set as grid cells that had neighbour grid cells in a neighbour stand. This had however, only a minor influence on the results. Keywords: SAR, LAI, forest, interferometry INTRODUCTION Forest health monitoring has been carried out in Europe and North-America for more than 20 years based on visual assessment of defoliation. Remote sensing may provide alternative tools with the advantage of providing data that are not influenced by subjective observer effects and that have complete aerial coverage where variations in leaf area index (LAI) may serve as a measure of defoliation. It has been demonstrated that the SAR microwaves penetrates down into the canopy layer and the penetration depth depends on the wavelength [Anon 1986]. The X-band is dominated by leaf interaction and the penetration is minor, while the C-band is dominated by twigs and small branches and penetrates clearly downwards into the canopy. The basic idea of this study is to utilize the sensitivity of the X-band to foliage for monitoring defoliation. The penetration depth into the canopy layer of healthy, fully foliated trees will be minor, while with defoliated trees the penetration will be more pronounced. We hypothesize that the penetration depth of the X-band is a function of the amount of foliage passed by the microwaves, i.e., that the X-band is able to penetrate a given amount of LAI before it is reflected back to the SAR sensor. We here combine the Shuttle Radar Topography Mission (SRTM) DEM [Rabus et al. 2003] with airborne laser scanning data, and use theoretical models to test this.

2 MATERIALS The test area is a 4 km 2 forest area southeast of Oslo in Norway dominated by Norway spruce, and a forest stand map was available for the study. The SRTM mission was carried out in February At this time the deciduous trees are leaf-off, however, this is of minor importance as evergreen conifer tree species dominate. This mission applied the interferometric SAR technique using C-band and X-band radar antennas. The results are digital elevation models (DEMs) with a grid spacing of either 90 m or 30 m for large parts of the Earth surface. These maps did also cover our test site in south Norway. See Figure 1 a) for an example of the SRTM X-band DEM from our test site. The SRTM DEM absolute vertical accuracy on non-vegetated terrain is reported to better than 5.2 m and 6.5 m for the X-band and C-band data respectively [Weydahl 2005, Weydahl et al. 2007]. This is better than the SRTM pre-flight specifications. One should notice that the SRTM system refers its elevations to the reflective surface computed from the interferometric SAR returns from the Earth surface. An SRTM DEM may therefore be referred to as a digital surface model (DSM) rather that a digital terrain model (DTM). As a result, the SRTM DEMs will include man-made features and vegetation canopy elevations. Examples from previous work show that dense forest areas are mapped with elevations meters above the true ground [Weydahl et al. 2007]. The laser scanner data were acquired in 2003, using the ALTM 1233 laser scanning system produced by Optech, Canada. The scan was accomplished at a flying altitude of 600 m above the ground with a scan angle of maximum 11, and the 37 million laser pulses produced a dataset of about five first-return echoes per m 2 having a footprint diameter of 18 cm. The last return data were used to model the terrain surface DTM using a triangulated irregular network method (TIN), see Figure 1 b). All laser first return echoes were recalculated to heights above the DTM. a) b) Figure 1 Digital elevation models from the 4 km2 large forest test area southeast of Oslo in Norway. The DEMs are resampled to a grid of 10 m x 10 m. a) SRTM X-band DEM. b) Airborne laser scanner DTM. METHODS A digital map of the canopy height was made with a 10 m x 10 m spatial resolution. The height of every laser echo was subtracted from the laser DTM. The heights of the first returns were ranked within each 10m x 10m grid cell, and the 75 percentile value of these heights were used as a crude estimate of the canopy surface height model (CSM).

3 We use a DEM produced from interferometric processing of an X-band SAR data set. We calibrated the SRTM DEM elevations for our test site by using elevation data from an official Norwegian DTM with a grid spacing of 25 m x 25 m over four agricultural fields nearby the forest. These agricultural fields are fairly flat. The SRTM elevation offset was estimated to -0.2 m and -3.6 m for the X-band and C-band respectively. The calibrated SRTM DEMs were then resampled to a grid of 10 m x 10 m using bicubic convolution. This resampling works well for the 30 m X-band data, but may be a bit oversampled for the 90 m C-band data. In the following analysis, we are therefore focusing our attention on the X-band data set. Also, X-band DEMs are quite interesting when preparing for the upcoming TerraSAR-X Tandem mission. Nevertheless, the SRTM C-band data should be analysed in more detail when larger areas of homogeneous forest stands are included in the test area. First, we focussed on the penetration depth of the X-band down into the forest canopy layer. A canopy surface height model was also derived from the X-band by subtracting the laser DTM and the SRTM DSM: DEM = DSM + Offset DTM difference SRTM We are now left with an elevation map ranging from zero to 28 meters, i.e. a canopy surface height model. This difference DEM is subsequently used in connection with the LAI estimates. Second, we describe the amount of foliage above this X-band DEM, i.e. the amount of LAI. Here the laser data was again used, and LAI above the X-band DEM was calculated for every grid cell using the method of Solberg et al (2006): SRTM laser N LAI = C*ln N b where LAI is effective LAI above a given height; C is a constant calibrated from ground based measurements with the value 2.0, N is the total number of laser pulses; and N b is the number of laser pulses below the given height. The analyses were done with both the total area, and second with a careful selection of observations. The latter was Norway spruce only, grid cells at stand borders were excluded, and only a forestv reserve with no forest management was used. RESULTS The X-band penetrated about six m downwards into the canopy layer, which means that for all grid cells having a forest canopy lower than six meters, the XDEM was around zero. This corresponds to findings reported elsewhere [Kellndorfer et al. 2004]. With an increasing DSM from six meters upwards, the DSM could be approximated by the linear function: DSM = *XDEM, having a RMSE of 4.0 m.

4 Figure 2. LIDAR based canopy surface heights plotted against the X-band elevation. It was a moderate support only for the hypothesis. The median LAI abovex value was 1.42, and percentile values being Hence, a considerable variation in LAI above the X-band remained. With a careful selection of cases being spruce stands only, excluding forest stand edges, and using only the forest reserve, only a minor improvement was seen. Table 1. Major results for LAI above the SAR DEM. The careful selection is forest reserve only, spruce only, no stand edge pixels, and DEM>DTM height selection N Mean Std.D Min Max x-band DEM DEM>DTM x-band DEM careful m above ground all m above ground careful DISCUSSION The results indicated that the X-band penetrates down into the canopy layer. The mean value was six meters, however, a large residual variation was present. This is likely to result from effects of geometry, i.e. both stand density and terrain effects such as slope and aspect. Further studies, in particular residual analyses may elucidate such effects. The amount of penetrated foliage, i.e. LAI above the X-band was on average 1.7 m2/m2. However, also for LAI there was a consoderable ressidual variation, and again further analyses seems necessary before the idea can be verified or rejected. CONCLUSION In conclusion, these preliminary findings did not verify the hypothesis. However, further studies of this seems necessary and LIDAR data can be very useful in these. ACKNOWLEDGEMENTS

5 We like to thank the Norwegian Space Centre for financing this study, and the German Aerospace Center (DLR) for the SRTM X-band DEM that was supplied under AO-038. REFERENCES Anon Shuttle imaging RADAR-C science Plan. Jet propulsion laboratory Referred by Franklin, S.E Remote sensing for sustainable forest management. Lewis, Boca Raton. 407pp. Gens, R., and Van Genderen, J. L., 1996, Review Article: SAR interferometry issues, techniques, applications, International Journal of Remote Sensing, 17(10), Kellndorfer, J., Walker, W., Pierce, L., Dobson, C., Fites, J. A., Hunsaker, C., Vona, J., and Clutter, M., 2004, Vegetation height estimation from Shuttle Radar Topography Mission and National Elevation Datasets, Remote Sensing of Environment, 93, Rabus, B., Eineder, M., Roth, A., and Bambler, R., 2003, The shuttle radar topography mission a new class of digital elevation models acquired by spaceborne radar, ISPRS Journal of Photogrammetry & Remote Sensing, 57, Solberg, S., Næsset, E. & Bollandsås, O.M Single-tree segmentation using airborne laser scanner data in a structurally heterogeneous spruce forest. Photogrammetric Engineering & Remote Sensing. 72: Solberg, S., Næsset, E., Hanssen, K.H. & Christiansen, E. 2006a. Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning. Remote Sensing of Environment. 102: Weydahl, D. J., Sagstuen, J., Dick, Ø. B., and Rønning, H., 2007, SRTM DEM accuracy assessment over vegetated areas in Norway, International Journal of Remote Sensing, 28(15-16), Weydahl, D.J., 2005, Validation of SRTM elevation data in Norway, FFI/RAPPORT-2005/02600, 128 pages, Norwegian Defence Research Establishment, Kjeller, Norway, 6 September 2005.