Airborne LiDAR and SAR estimation of forest top height

Transcription

1 Airborne LiDAR and SAR estimation of forest top height E. D. Wallington 1* and J.C. Suárez 2 1 Forestry Commission, Silvan House, 231 Corstorphine Road, Edinburgh, EH12 7AT, UK. * edward.wallington@forestry.gsi.gov.uk 2 Forest Research, Northern Research Station, Roslin, Midlothian, EH25 9SY, UK. Abstract Top height is an important parameter in production forestry and ecological studies. This paper demonstrates the use of two commercially available remote sensing systems for estimating forest top height, and discusses the operational use of such systems with regard to monitoring sustainable forestry indicators through enhancement of traditional inventory methods. Airborne LiDAR and X-band interferometric Synthetic Aperture Radar (InSAR) systems were used to retrieve forest stand top height with comparison to ground measurements to three test sites in the UK. Top height estimation was achieved with errors of around 2-6% from LiDAR and 18-24% from SAR. Potential reasons for errors are discussed, and the use of remote sensing is discussed in relation to operational sustainable forest management at a range of scales. Keywords: LiDAR, InSAR, Forest parameter retrieval, Top Height. Introduction Monitoring and information reporting to promote adaptive and sustainable forest management has emerged as an important component in the field of forest management (Hickey et al., 2005). Sustainable forest management is a complex process, requiring input and collaboration from a number of forest sectors (e.g. management for timber, biodiversity, conservation, recreation, health etc) and related organisations using a wide range of data sources and inventory techniques. A set of key indicators and guidelines have been developed in the UK, as in other countries, to help guide sustainable forestry management practices (FC, 2002; FC, 2004). Earth observation (EO), through the use of remote sensing systems, has the capability to provide valuable information for sustainable forestry management (Rosenqvist et al., 2003; Hall, 2000). This is achieved through input into management decisions and continued monitoring towards meeting, assessing and maintaining the indicators of sustainable forestry. Top height is an important parameter in traditional production forestry (West, 2004) that can also be used as an indicator in studies looking at, for example, forest stand structure, growth dynamics, bio-fuel estimations or carbon sequestration - all of which need to be assessed and monitored as contributions towards sustainable forest management. This paper demonstrates the use of commercially available airborne remote sensing systems in the UK to provide cost-effective estimates of forest top height (defined in this study as the average height of the 100 tallest trees/ha, H 100 ; Philip, 1994). The work presented is a comparative study between commercial LiDAR (Light Detection and Ranging; Aronoff and

2 Petrie, 2005) and Interferometric Synthetic Aperture Radar (InSAR; Henderson and Lewis, 1998) data validated with ground reference data, as a supplement to traditional field inventory. Previous studies have estimated forest height to within 7-30% for LiDAR (e.g. Nilsson, 1996; Zimble et al., 2003) and 4-90% for radar (e.g. Yong et al. 2003; Walker et al. 2004) dependent on system and wavelength used. The perceived advantages of these two airborne systems are the high resolution of the data and their versatility to capture data over a large geographical area at will. Additional advantages are the increasingly competitive costs and the possibility to process the data into a product compatible with many Geographical Information Systems (GIS). Study areas and data sets Study sites Three study sites were considered in the UK: Coed Y Brenin Forest District in North Wales (N W lat/long), Kielder Forest District in North England (N W2 32,11 lat/long) and Aberfoyle in SW of Scotland (N W lat/long). Forest stands consisted of Sitka spruce (Picea sitchensis) plantation. The SAR data were analysed over the three study sites: Kielder, Coed Y Brenin and Aberfoyle, whilst the LiDAR were analysed over selected stands in Kielder and Aberfoyle. Data sets InSAR Digital Surface Model The InSAR (Henderson and Lewis, 1998) derived Digital Surface Model (DSM) was supplied by Intermap Technologies (Intermap, 2005), and was geo-referenced to the UK national grid (OSGB36) prior to delivery. The DSM represents the first surface the signal came into contact with, whether ground or vegetation canopy. The DSM is presented with a pixel size of 5x5 m and a vertical RMSE of between m, dependent on flying height (Intermap, 2003). Ordnance Survey DEM The Ordnance Survey (OS) Profile 10m Digital Elevation Model (OSDEM) was used as a ground reference surface. The OSDEM has a pixel size of 10x10 m, with a stated accuracy of ± 5m (OS, 2001), but this is expected to be significantly improved over small areas. As this is the only high resolution wide area coverage ground surface for the UK, it is assumed to be a representation of the true ground surface for the purposes of this study. LiDAR Airborne LiDAR was obtained by the Environment Agency using an Optech ALTM2033 scanner (Optech, 2005). The first survey was undertaken in Aberfoyle in September 2002 at a flying altitude of 1,000m a.s.l. with a sampling intensity was 3-4 returns per m 2, a beam divergence of 10 cm and scanning angle was 20 degrees. A second area in Kielder forest was surveyed in April 2003 with a sampling intensity varied from 6 to 23 returns per m 2, a beam divergence of up to 1 m and a scanning angle of 10 degrees. Data were distributed in ASCII XYZ format, with first and last returns, and their corresponding intensities, located to the OS national grid. RMSE were ± 40 cm in X and Y and ± 9-15 cm in Z. Ground reference data Standard forest inventory techniques (Philip, 1994; Husch et al., 2003; Hamilton, 1975) were used to establish the top height of the sample stands. This height was then used as the true

3 top height of the stand. Individual tree locations were mapped using differential GPS and a total station, with x/y positional accuracy of <1m; these locations were subsequently used for locating individual trees in the LiDAR imagery for analysis. Top height retrieval from remote sensing INSAR In all the stands, height values from the DSM and OSDEM were retrieved from 50 x 50m plots (¼ ha). Care was taken to extract values away from potential error sources, for example edge effects which have a significant impact on height retrieval (Woodhouse et al., In press). Subtraction of the OSDEM from the DSM was performed to recover the height per pixel within the plot (Wallington et al., 2004). Retrieved stand top height (Hr 100 ) was estimated by averaging the highest 25 pixel heights. This techniques follows standard forest practice of taking the tallest 100 trees/ha (Philip, 1994). Retrieved top height was then compared to the measured top height per stand (Figure 1). Retrieved top height Wales Kielder Aberfoyle Aberfoyle Wales Kielder y = 0.82x R 2 = 0.90 y = 0.79x R 2 = 0.84 y = 0.76x R 2 = Measured top height Figure 1. Retrieved top height from SAR in Wales (Coed Y Brenin), Kielder and Aberfoyle. 1:1 line is shown. Top height retrieval for the three test sites gave consistent results (Figure 1) with underestimations of 18% (Aberfoyle), 21% (Wales) and 24% (Kielder). The average underestimation for the three data sets combined was 23% (Table 1). The retrieved height was an underestimation as expected. This was attributed to signal interaction with scatterers within the canopy of a similar size to the signal wavelength, and the resultant effect on penetration and attenuation through the canopy. At shorter wavelengths (X-band), penetration is limited to the upper canopy, and as such the resultant height of the scattering phase centre is predominately dominated by scattering from the smaller scatterers in the canopy. The results show that X-band does not easily penetrate the dense canopy of Sitka spruce stands and this is attributed to the compactness of their canopies. In contrast, other species, such as Scots pine, present less dense canopies and will allow more penetration of the signal through them (Wallington et al., 2005). Further to the above mentioned penetration, a number of other factors may also be affecting the height retrieval, thus producing underestimations. The aforementioned edge effects have been shown to significantly reduce height retrieval (Woodhouse et al., In press) due to the relative contributions of canopy and ground scattering to the resulting scattering phase

4 centre, with increased ground contribution resulting in a lower height. Other sources of potential error include the density of the stand, the height of the trees, canopy shape and the slope of the underlying ground surface in relation to the sensor position (Izzawati et al., 2004). The OSDEM has an accuracy of ± 5m, and it is reasonable to assume that an error in the ground surface may be contributing to the height underestimation. LIDAR A normalised forest canopy model is obtained by subtracting bare ground values from the canopy layer. The canopy layer is retrieved from the first laser return that measures the intensity of the signal as it first encounters an object in the ground. The last return will provide information about the location and height of the mid-point of the last strong waveform that is normally associated with the terrain. In order to approximate a model of the ground surface, the last returns were filtered to eliminate those hits being intercepted by the forest canopy and, therefore, not reaching the underlying terrain. The method involved an initial selection of points within a kernel of 10x10 m according to the local minima (Suárez et al., 2005a). Then, a basic DTM model was constructed using an interpolation method based on Kriging with no anisotropy. After, this a second selection of ground hits was performed by comparing LiDAR hits to this model within an empirically defined threshold of +/- 30 cm. This operation densified the initial selection of points allowing the construction of a higher resolution DTM using the same interpolation method. A normalised canopy height model was calculated for every laser hit as the difference between each first return and the resulted terrain model. Individual tree heights were accurately predicted in 73% of the cases within ±1m and 96% within ±2m. Generally, individual tree heights were 7-8% shorter than observed due to the low number of laser hits intercepted by the apices. In Kielder, the higher density of returns per m 2 (6 to 23) reduced underestimations to 2% (Figure 2, Table 1). 35 Aberfoyle Kielder 30 Estimated top height Kielder Aberfoyle y = 0.98x R 2 = 0.99 y = 0.93x R 2 = Measured top height Figure 2. Retrieved top height from LIDAR in Aberfoyle and Kielder. 1:1 line is shown.

5 Table 1. Summary of errors in estimations for retrieved top height (H 100 ). Kielder Aberfoyle Plot No. Measured Retrieved H 100 Measured Retrieved H 100 H 100 (m) LiDAR InSAR H 100 (m) LiDAR InSAR Average Combined average Summary and Conclusion This paper has demonstrated the use of commercially available airborne LiDAR and InSAR remote sensing systems to estimate stand top height, the following results were found: LiDAR gave an overall underestimation of between 2-6%, resulting in an average error of 4.54%. Likewise, SAR gave an overall underestimation of between 18-24%, with an average error of 22.82%. These results indicate that both airborne LiDAR and short wavelength SAR interferometry have the capability to provide accurate estimates of height. The ability of remote sensing products to cover large areas rapidly and to provide a sampling intensity that approaches full coverage as opposed to traditional sampling strategies (Suárez, et al., 2005b) will allow more detailed assessment and monitoring for sustainable forest management and related industries. Both of the airborne data sets discussed in this paper are available commercially, and as such, the techniques described are nearing operational status, with the cost of the data and the availability of data providers being the driving factors that will determine their business use. Further potential is foreseen with the ever increasing availability and use of satellite systems for data capture, allowing even greater areas to be rapidly mapped. Acknowledgments The authors would like to thank Intermap Technologies and the Environment Agency for kindly supplying the data for the test sites. Thanks are also due to Stuart Snape (UK Forest Enterprise), Daniel Donoghue and Pete Watt (both of Durham University) for providing supplementary ground data, and to Iain Woodhouse and Izzawati of Edinburgh University for assistance with the interpretation of radar modelling results. This study was funded by the NERC and the UK Forestry Commission.

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