AUTOMATED DEM VALIDATION USING ICESAT GLAS DATA INTRODUCTION



Similar documents
White Paper. PlanetDEM 30. PlanetObserver 25/11/ Update

Notable near-global DEMs include

LIDAR and Digital Elevation Data

TerraColor White Paper

Lidar 101: Intro to Lidar. Jason Stoker USGS EROS / SAIC

COMPARISON OF SRTM AND 25K TOPOGRAPHIC MAPS IN TURKEY

On the CyberShARE site you will find this interpolated version (labeled SRTMv4_90m)

Automated Spacecraft Scheduling The ASTER Example

DEM products from TerraSAR-X & TanDEM-X. Nora Meyer zu Erpen //

EO based glacier monitoring

3D VISUALIZATION OF GEOTHERMAL WELLS DIRECTIONAL SURVEYS AND INTEGRATION WITH DIGITAL ELEVATION MODEL (DEM)

Earth Coordinates & Grid Coordinate Systems

Maintaining High Accuracy in Modern Geospatial Data

1 laser altimeter. Background & Instructions. exploration extension. Instructions. Background

GNSS and Heighting, Practical Considerations. A Parker National Geo-spatial Information Department of Rural Development and Land Reform

Reprojecting MODIS Images

LiDAR for vegetation applications

Estimating Firn Emissivity, from 1994 to1998, at the Ski Hi Automatic Weather Station on the West Antarctic Ice Sheet Using Passive Microwave Data

Earth Data Science in The Era of Big Data and Compute

The USGS Landsat Big Data Challenge

Development of new hybrid geoid model for Japan, GSIGEO2011. Basara MIYAHARA, Tokuro KODAMA, Yuki KUROISHI

Obtaining and Processing MODIS Data

Temporal variation in snow cover over sea ice in Antarctica using AMSR-E data product

Request for Proposals for Topographic Mapping. Issued by: Teton County GIS and Teton County Engineering Teton County, Wyoming

Create a folder on your network drive called DEM. This is where data for the first part of this lesson will be stored.

Introduction to GIS (Basics, Data, Analysis) & Case Studies. 13 th May Content. What is GIS?

American-Eurasian Journal of Sustainable Agriculture

GEOGRAPHIC INFORMATION SYSTEMS CERTIFICATION

VIIRS-CrIS mapping. NWP SAF AAPP VIIRS-CrIS Mapping

Geospatial Positioning Accuracy Standards Part 3: National Standard for Spatial Data Accuracy

Vertical Datums: An Introduction and Software Review

The Map Grid of Australia 1994 A Simplified Computational Manual

A remote sensing instrument collects information about an object or phenomenon within the

Landsat Monitoring our Earth s Condition for over 40 years

How To Monitor Sea Level With Satellite Radar

CBERS Program Update Jacie Frederico dos Santos Liporace AMS Kepler

SPOT Satellite Earth Observation System Presentation to the JACIE Civil Commercial Imagery Evaluation Workshop March 2007

Online GPS processing services: an initial study

Digital Terrain Model Grid Width 10 m DGM10

APPLICATION OF TERRA/ASTER DATA ON AGRICULTURE LAND MAPPING. Genya SAITO*, Naoki ISHITSUKA*, Yoneharu MATANO**, and Masatane KATO***

Near Real Time Blended Surface Winds

Remote Sensing, GPS and GIS Technique to Produce a Bathymetric Map

Terrain-Related Gravimetric Quantities Computed for the Next EGM

Jason-2 GDR Quality Assessment Report. Cycle / M. Ablain, CLS. P. Thibaut, CLS

High Resolution Information from Seven Years of ASTER Data

GEOENGINE MSc in Geomatics Engineering (Master Thesis) Anamelechi, Falasy Ebere

The Status of Geospatial Information Management in China

Visualizing of Berkeley Earth, NASA GISS, and Hadley CRU averaging techniques

Managing Lidar (and other point cloud) Data. Lindsay Weitz Cody Benkelman

Examination Space Missions and Applications I (AE2103) Faculty of Aerospace Engineering Delft University of Technology SAMPLE EXAM

The RapidEye optical satellite family for high resolution imagery

WHAT YOU NEED TO USE THE STATE PLANE COORDINATE SYSTEMS

Information Contents of High Resolution Satellite Images

APPLICATION OF GOOGLE EARTH FOR THE DEVELOPMENT OF BASE MAP IN THE CASE OF GISH ABBAY SEKELA, AMHARA STATE, ETHIOPIA

EPSG. Coordinate Reference System Definition - Recommended Practice. Guidance Note Number 5

Using Remotely Sensed Data From ASTER to Look Impact of Recent Earth Quakes in Gujarat, India.

High Resolution RF Analysis: The Benefits of Lidar Terrain & Clutter Datasets

3D Capabilities of SPOT 6

Lidar Remote Sensing for Forestry Applications

Review for Introduction to Remote Sensing: Science Concepts and Technology

Precipitation Remote Sensing

Operating Instructions. 3DEM Software for Terrain Visualization and Flyby Animation. Version 20

A CONCEPT OUTLINE ESTABLISHING THE

NASA Earth System Science: Structure and data centers

Map World Forum Hyderabad, India Introduction: High and very high resolution space images: GIS Development

Cloud Model Verification at the Air Force Weather Agency

Monitoring a Changing Environment with Synthetic Aperture Radar. Alaska Satellite Facility National Park Service Don Atwood

Artificial Satellites Earth & Sky

Model Virginia Map Accuracy Standards Guideline

NJDEP GPS Data Collection Standards For GIS Data Development

Remote sensing is the collection of data without directly measuring the object it relies on the

Submitted to: Submitted by: Department of Geology and Mineral Industries 800 NE Oregon Street, Suite 965 Portland, OR 97232

Geomatics Guidance Note 3

Solving for Camera Exterior Orientation on Historical Images Using Imagine/LPS Version 1.0

GPS Precise Point Positioning as a Method to Evaluate Global TanDEM-X Digital Elevation Model

'Developments and benefits of hydrographic surveying using multispectral imagery in the coastal zone

Dawn - Overview, Science Objectives, Mission Progress. Hap McSween For PI Chris Russell

Marine route optimization. Jens Olaf Pepke Pedersen Polar DTU / DTU Space

Adaptation of High Resolution Ikonos Images to Googleearth for Zonguldak Test Field

Compilation of Digital Elevation Model for Turkey in 3-Arc-Second Resolution by Using SRTM Data Supported with Local Elevation Data

Data Processing Flow Chart

Norwegian Satellite Earth Observation Database for Marine and Polar Research USE CASES

DIGITAL ELEVATION MODEL DATABASE W42 - A SCALABLE SYSTEM FOR SPATIAL DATA

Geospatial Positioning Accuracy Standards Part 2: Standards for Geodetic Networks

PLOTTING SURVEYING DATA IN GOOGLE EARTH

Photogrammetric Point Clouds

STATE OF NEVADA Department of Administration Division of Human Resource Management CLASS SPECIFICATION

Robot Perception Continued

Digital Remote Sensing Data Processing Digital Remote Sensing Data Processing and Analysis: An Introduction and Analysis: An Introduction

Passive Remote Sensing of Clouds from Airborne Platforms

Transcription:

AUTOMATED DEM VALIDATION USING ICESAT GLAS DATA Mary Pagnutti Robert E. Ryan Innovative Imaging and Research Corp. Building 1103, Suite 140C Stennis Space Center, MS 39529 mpagnutti@i2rcorp.com rryan@i2rcorp.com ABSTRACT Digital Elevation Model (DEM) vertical accuracy evaluations have historically been difficult to validate especially in regions of the world where there are limited ground control points due to either harsh environmental conditions or dangerous political situations. As a result, elevations defined by DEMs in these geographic locations may have errors as large as tens to even hundreds of meters and these errors may significantly impact user applications. To address this potential issue, an automated method utilizing NASA s Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimetry System (GLAS) global land surface altimetry data product was developed to evaluate the accuracy of DEMs worldwide. The new automated method pre-processes the ICESat data by removing any saturated laser returns, identifying and removing outliers and interpolating the DEM data set to the ICESAT laser spot locations. The method has been used to evaluate commercial, SRTM and ASTER-based DEMs. Since the entire ICESat archive was employed, on average over 10,000 GLAS data points were utilized for each tile evaluated. This paper describes the new automated DEM validation method and showcases some results. The results presented are compared to current methods. Key Words: DEM, vertical accuracy, ICESat, GLAS INTRODUCTION There are many places throughout the world where accurate validated Digital Elevation Models (DEMs) do not exist. This is particularly true in regions where there are limited ground control points due to either difficult weather conditions, such as extreme northern and southern latitudes, mountainous regions, or dangerous and politically unstable situations. These DEMs have yielded errors as large as tens to even hundreds of meters, which have the possibility of significantly affecting the results of applications that rely on them. To address this potential issue, Innovative Imaging and Research Corporation (I2R) developed an automated method that utilizes NASA s Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimetry System (GLAS) global land surface altimetry data product to evaluate the accuracy of commercially and government provided DEMs worldwide. ICESat is a satellite mission, shown in Figure 1, which was launched in 2003 to measure ice sheet mass balance, cloud and aerosol heights, as well as land topography and vegetation characteristics. GLAS is a space-based full waveform LIDAR and sole instrument onboard ICESat. The Nd:YAG laser operates in the near infrared at 1064 nm and transmits 40 5-nanosecond duration pulses per second. The laser beam spot sizes are approximately 65 meters in diameter and Figure 1. ICESat Mission. are separated by nearly 172 meters along the spacecraft's ground track. The instrument records a full waveform for each laser pulse to reach the Earth s surface and return back to the receiver. This waveform is combined with the instrument s position in space, established using a GPS receiver, star tracker and gyroscopes to estimate a height of the Earth s surface (UT Austin and NASA GSFC 2006). Published ICESat GLAS altimetry data product accuracy specifications are shown in Table 1 below (Carabajal and Harding 2005). The ICESat GLAS altimetry data product is geolocated using the Jason Topex Poseidon ellipsoid which is slightly different than WGS84 and EGM96.

Table 1. ICESat GLAS Land Surface Altimetry Data Product Accuracy Specifications CE90 LE90 RMSExy RMSEz Posting Horizontal Elevation Ellipsoid GLAS Elevation Product Nominal Terrain Rough Terrain 172 meters linear 172 meters linear *Indicates derived accuracy 7.5 8.0 16.1 13.2-15.0 16.1* 24.7* TOPEX Poseidon- Jason Ellipsoid TOPEX Poseidon- Jason Ellipsoid Prior to developing the automated DEM evaluation method, the accuracy of the GLAS ICESat altimetry product was validated by comparing it to the National Elevation Dataset (NED), which represents the highest resolution, best quality dataset available across the US (USGS NED). The NED data product was developed by merging the highest-resolution, best quality elevation data available across the US into a seamless raster to provide 1:24,000 scale, or approximately 30 meter resolution DEM data for the conterminous US and 1:63,360 scale or approximately 60 meter resolution DEM data for Alaska. The NED has a consistent projection (geographic), resolution (1 arcsecond over the continental US) and elevation units. The NED horizontal datum is the North American Datum 1983 (NAD83) while the NED vertical datum is the North American Vertical Datum of 1988 (NAVD88). In the 2006 timeframe, NED accuracy over the continental US was determined through bilinear interpolation of the NED to an array of over 13,000 high precision surveyed geodetic control points that the National Geodetic Survey (NGS) uses for gravity and geoid modeling. The overall absolute vertical accuracy of the circa 2003 NED expressed as an average RMSE was found to be 2.4 meters. In that same assessment, the absolute vertical accuracy as measured by a linear error with a 90% confidence interval (LE90) was estimated to be 4.0 meters (Maune 2006). Nine US NED tiles were selected to validate the GLAS ICESat elevation product and are shown graphically, with the exception of an additional point in Hawaii, in Figure 2 below. Sixty-nine GLA14 land centroid elevation product granules, each containing 14 orbital passes, totaling over 20,000 laser shots were selected to validate the vertical accuracy of the ICESat GLAS land surface altimetry data product. These granules were acquired by the instrument between Feb 17 and June 20, 2004 and were obtained through the National Snow and Ice Data Center Search N Order Web Interface (SNOWI) Website http://nsidc.org/data/snowi/index.html. The results of the ICESat GLAS accuracy assessment are Figure 2. NED tile locations. shown in Table 2 for a variety of locations and ground covers. Each of the NED tiles used in this evaluation is identified by the latitude and longitude of its lower left corner. Between approximately 900 and 3700 ICESat GLAS ICESat laser shots, identified as points, were compared to geographically corresponding NED product elevation values for each of the nine NED tile used in the assessment. The following equations were used to calculate the values identified within the table. LE90 was calculated empirically by determining the elevation difference at which 90% of the data point pairs evaluated fell within. Elevation Difference = Δ Z = i Z REF i Z GLAS i n i Average Delta Z = = i= 1 n STD Delta = Standard Deviation of the Average Delta STD Delta Z = σ = n i= 1 ( i ( n 1) ) 2

n 2 ( i ) RMSE = i= 1 n Where: n is the total number of points evaluated within a tile Z GLAS i is the elevation value of point I within the ICESat GLAS elevation data Z is the elevation value of point i within the reference tile REF i NED File Table 2. Comparison of GLAS Elevation Data to the NED No. of ICESat Points Average (meter) STD (meter) RMSE (meter) Empirical LE90 (meter) NED_n19w156 891-3.4 7.7 8.4 13.5 NED_n28w082 1577-1.4 2.9 3.2 5.1 NED_n29w100 1783-0.2 4.4 4.4 7.5 NED_n38w122 3982 0.8 2.3 2.4 3.4 NED_n42w076 1324-4.2 7.6 8.7 14.9 NED_n43w109 2742 1.0 5.6 5.6 9.8 NED_n43w118 2579 1.2 4.5 4.7 6.9 NED_n44w085 1673-0.8 2.3 2.4 4.1 NED_n46w069 3544-2.3 3.5 4.2 6.8 Mean Values -1.0 4.9 8.0 Standard Deviation 2.0 2.3 4.0 The results show that the ICESat GLAS overall mean vertical accuracy expressed in terms of LE90 is 8.0 meters ± 4.0 meters (1σ) when compared to the NED. Taking into account the NED absolute vertical accuracy expressed as LE90 of 4.0 meters (Gesch 2006), I2R estimated the GLAS absolute vertical LE90 accuracy to be 6.9 meters ± 3.8 meters (1σ). This value exceeds the 13.2 meter LE90 GLAS data specification derived from published values. Assuming a normal distribution, the ICESat GLAS data product absolute vertical linear error with a 95% confidence interval (LE95) accuracy is approximately 8.1 meters. The ICESat GLAS elevation data product therefore is an ideal product to use to evaluate DEMs in areas of the world where absolute control is limited or nonexistent. ALGORITHM An algorithm was developed to determine the vertical accuracy of a given DEM using the ICESat GLAS data product as a reference. The software was written using MATLAB software. MATLAB is a high-level programming language and interactive environment that enables computationally intensive code to be developed faster than with traditional programming languages. The algorithm reads the ICESat GLAS data files a record at a time to extract the latitude, longitude, elevation, SRTM DEM elevation (if available), geoid, and peak amplitude voltage. Scaling factors are applied to convert the data to appropriate units. The SRTM DEM elevation is used to perform a validity check on the ICESat GLAS surface altimetry data. If the absolute value of the difference between the SRTM DEM elevation and the ICESat GLAS elevation is greater than 100 meters, the current data point is considered an outlier (possible cloud) and the data is removed (Carabajal and Harding 2005). The algorithm also checks the peak amplitude voltage value for each data point. If the peak amplitude voltage is greater than or equal to 1.4 (saturated) (Maune 2006), the current data point is considered invalid and is not used. Each data record also contains the height of the geoid above the ellipsoid for the first and last shot in the record. The average of the geoid values is calculated and subtracted from the ICESat GLAS elevation. Once the data points have been processed, the latitude, longitude, elevation, SRTM DEM elevation, elevation difference (SRTM DEM ICESat GLAS elevation), and peak amplitude voltage data are saved. The algorithm reads a given DEM and performs an elevation assessment using the ICESat GLAS data as a reference data product. Bi-linear interpolations are performed to locate the ICESat GLAS laser shot on to the given DEM. The same analysis used to validate the ICESat GLAS product described above is used to determine the

accuracy of a given DEM. In addition, the software automatically generates histograms of elevation differences found by comparing the ICESat GLAS laser shot to interpolated DEM elevation values and plots a trace of the ICESat GLAS data points used in the assessment over an image of the DEM. The software is written to read in and evaluate multiple DEMs within a single software execution step. SAMPLE RESULTS This software has been used to evaluate both commercial and government provided DEMS. Most recently this algorithm was applied in support of the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) Global DEM (GDEM) product validation (ASTER GDEM Validation Team 2009). The GDEM was created by stereo-correlating the 1.3 million scene ASTER VNIR archive, covering the Earth s land surface between 83N and 83S latitudes. The GDEM is produced with 30 meter postings, and is formatted in 1 x 1 degree tiles as GeoTIFF files. The product was released for public download on June 29, 2009. Since the ASTER GDEM and ICESat GLAS land surface altimetry data products are both optical-based data sets with comparable spatial resolution, it is reasonable to assume that the ICESat GLAS data would be a valuable product to use to validate the ASTER GDEM. I2R evaluated three different ASTER GDEM tiles using the methods described above. These tiles were selected because they were in geographical regions known to contain poorly validated AST14DEM ASTER DEMS and are shown in Table 3. Table 3. ASTER GDEMs Evaluated Location Lower Left Coordinate Elevation Variation No. of ICESat Points Central Alaska North 65 deg, West 151 deg Approx. 0-1800 meters 12,416 Eastern Russia North 63 deg, East 134 deg Approx. 0-1800 meters 18,735 Southern India North 10 deg, East 76 deg Approx. 0-1800 meters 13,627 I2R utilized 522 ICESat GLAS granules, each containing 14 orbital passes that were acquired between February 2003 and March 2008 for this analysis. These granules contain over 365 million laser shots. At the time the assessment was performed, these granules comprised the entire GLA14 archive. GLAS data acquired before October 2007 was processed using Release 28 software, while GLAS data acquired after that time was processed using Release 29 software (Zwally et al. 2006-2008). Figures 3-5 below show the ASTER GDEM tiles along with the ICESat GLAS laser shot traces used for to perform the DEM accuracy assessment.

Figure 3. Central Alaska ASTER GDEM tile with ICESat GLAS data point location overlaid on top. Figure 4. Eastern Russia ASTER GDEM tile with ICESat GLAS data point location overlaid on top.

Figure 5. Southern India ASTER GDEM tile with ICESat GLAS data point location overlaid on top. The following table summarizes the accuracy results of the three ASTER GDEM tiles evaluated. Elevation differences were obtained by subtracting the ICESat GLAS elevation value from the ASTER GDEM elevation value (Z DEM Z REF ). The ASTER GDEM Validation Team requested that accuracy be provided in terms of LE95. LE95 was calculated empirically by determining the elevation difference at which 95% of the data point pairs evaluated fell within. Location Min Diff Table 4. ASTER GDEM Elevation Accuracy Summary Max Diff Mean Diff STD Diff RMSE LE95 Alaska -149.0 920.0-8.6 12.8 15.4 23.6 Russia -205.3 40.0-10.4 8.0 13.1 22.7 India -229.5 137.6-2.5 11.4 11.7 21.9 Histograms was generated which show the elevation difference (ASTER GDEM ICESat GLAS) distribution across the scenes and are shown in Figures 6-8 below. In all three cases the histograms show a fairly normal Gaussian distribution of elevation difference. No effort was made to remove ASTER GDEM outliers in this assessment, which can be clearly seen as shifts along the x-axis in the histogram plots. I2R developed an algorithm to systematically and rapidly evaluate DEMs using ICESat GLAS global surface altimetry data as a reference. The ICESat GLAS data product has been shown to have an absolute vertical accuracy as measured by a linear error with a 95% confidence interval (LE95) of 8.1 meters using the NED. This technique has successfully been employed to validate the newly released ASTER GDEM product as well as other commercial DEMs.

Figure 6. Histogram of Central Alaska ICESat GLAS ASTER GDEM elevation difference. Figure 7. Histogram of Eastern Russia ICESat GLAS ASTER GDEM elevation difference.

Figure 8. Histogram of Southern India ICESat GLAS ASTER GDEM elevation difference. REFERENCES ASTER GDEM Validation Team: METI/ERSDAC, NASA/LPDAAC, USGS/EROS 2009. ASTER Global DEM Validation Summary Report. Carabajal, C.C., and D.J. Harding, 2005. ICESat validation of SRTM C-band digital elevation models, Geophysical Research Letters, 32, L22S01, doi:10.1029/2005gl023957. Gesch, D. 2006. The National Elevation Dataset, Presentation to the NRC Committee on Floodplain Mapping Technologies. Maune, D.F., 2006. Digital Elevation Model Technologies and Applications: The DEM Users Manual, The National Elevation Dataset chapter, 2nd edition, ASPRS. http://gis.utah.gov/images/sgidraster/ned_accuracy_assessment.doc University of Texas Center for Space Research and NASA Goddard Space Flight Center, 2006. ICESat/GLAS Algorithm Theoretical Basis Documents (ATBD), www.csr.utexas.edu/glas/atbd.html. USGS National Elevation Dataset website description http://ned.usgs.gov/ Zwally, H.J., R. Schutz, C. Bentley, J. Bufton, T. Herring, J. Minster, J. Spinhirne, and R. Thomas, 2006-2008. GLAS/ICESat L2 Global Land Surface Altimetry Data V028-V029, Boulder, CO: National Snow and Ice Data Center, Digital media.