An Esri® White Paper • June 2010 Lidar Analysis in ArcGIS® 9.3.1 for Forestry Applications Esri, 380 New York St., Redlands, CA 92373-8100 USA TEL 909-793-2853 • FAX 909-793-5953 • E-MAIL [email protected] • WEB www.esri.com Copyright © 2010 Esri All rights reserved. Printed in the United States of America. The information contained in this document is the exclusive property of Esri. This work is protected under United States copyright law and other international copyright treaties and conventions. No part of this work may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, or by any information storage or retrieval system, except as expressly permitted in writing by Esri. All requests should be sent to Attention: Contracts and Legal Services Manager, Esri, 380 New York Street, Redlands, CA 92373-8100 USA. The information contained in this document is subject to change without notice. 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J-9884 Lidar Analysis in ArcGIS 9.3.1 for Forestry Applications An Esri White Paper Contents Page Executive Summary.............................................................................. 1 Keywords.............................................................................................. 1 Author................................................................................................... 1 Introduction........................................................................................... 1 What Is Lidar?....................................................................................... 2 Advantages to the Forest Industry........................................................ 3 Managing and Understanding Lidar Data............................................. 4 Understanding Raw Lidar Data...................................................... 4 Point File Information Tool...................................................... 4 Lidar Classification in ArcGIS................................................. 6 Loading the Lidar Files to ArcGIS................................................. 7 LAS To Multipoint Tool........................................................... 8 Visualizing and Storing Lidar Data with ArcGIS................................. 11 Visualizing Lidar Data.................................................................... 11 Advantages of a Raster............................................................. 12 Advantages of a Geodatabase Terrain...................................... 12 Building and Delivering DEMs and DSMs from Lidar........................ 13 The Workflow to Create a Terrain and Deliver to Clients.............. 13 Building a Geodatabase Terrain...................................................... 14 Building a Raster DEM................................................................... 19 Analyzing Lidar Data for Foresters...................................................... 21 Calculating Vegetation Characteristics from Lidar Data................ 21 Tree Height Estimation............................................................. 21 Esri White Paper i Lidar Analysis in ArcGIS 9.3.1 for Forestry Applications J-9884 Contents Page Biomass Density Calculation.................................................... 22 Point to Raster..................................................................... 23 Replacing NoData Values as Zero Vegetation Density.............................................................................. 23 Merging the Aboveground and Ground Results................. 24 Creating a Floating Point Raster File.................................. 24 Calculating Density............................................................. 25 Distributing Large Lidar Datasets......................................................... 26 Preparing Raster DEM for Serving with the ArcGIS Server Image Extension............................................................................ 26 Serving an Elevation Service through the ArcGIS Server Image Extension............................................................................ 27 Creating an Elevation Image Service.............................................. 29 Visualizing an Elevation Service.................................................... 32 Estimating Tree Height Using Elevation Services.......................... 36 Preparing Image Service Data........................................................ 36 Creating the Height Estimation Service.......................................... 37 Adding .ISRef Files to the Height Estimation Service................... 38 Adding Algebraic Process to Service to Return Height Estimation..................................................................................... 40 Applying the Image Algebra Process............................................. 41 Conclusion............................................................................................ 44 Acknowledgments................................................................................. 45 June 2010 ii J-9884 Lidar Analysis in ArcGIS 9.3.1 for Forestry Applications Executive Summary Foresters use light detection and ranging (lidar) data to understand the forest canopy and terrain, which helps them with forest management and operational activities. Combining lidar data with Esri® ArcGIS® helps analysts assess forest health, calculate forest biomass, classify terrain, identify drainage patterns, and plan forest management activities such as fertilization, harvesting programs, development activities, and more. This paper will step through processes to convert lidar data into a format ArcGIS can process, explain methods to interpret the lidar data, and show how ArcGIS can disseminate the data to those who are not geospatial analysts. It will present methods for reading raw classified lidar data and demonstrate methods for (cid:132) Analyzing and validating raw lidar data with ArcGIS before any extensive processing occurs (cid:132) Storing and managing millions of lidar returns within the geodatabase in a seamless dataset, regardless of the number of original lidar files (cid:132) Processing to extract digital elevation models (DEMs) and digital surface models (DSMs) from the lidar data and store them as terrains in a geodatabase or as raster elevation files (cid:132) Extracting vegetation density estimates and tree height estimates from lidar, which aid in growth analysis, fertilization regimes, and logging operations (cid:132) Serving and analyzing large amounts of lidar data as a seamless dataset to geographic information system (GIS) clients In all areas, ArcGIS is an excellent tool for managing, storing, and analyzing lidar data. Coupling ArcGIS with the ArcGIS Server Image extension, the forestry professional is able to access large amounts of lidar data quickly and efficiently without the need to produce additional resultant datasets. Keywords Lidar, ArcGIS, terrains, geodatabase, ArcGIS Server Image extension Author Gordon Sumerling, Esri Australia Pty. Ltd., Adelaide, South Australia Introduction ArcGIS can be used to analyze and manipulate lidar data to provide useful results for the end user. This paper provides the processes to analyze and manipulate lidar data and details how to (cid:132) Check the supplied data. (cid:132) Read and separate the data into ground and canopy returns. Esri White Paper Lidar Analysis in ArcGIS 9.3.1 for Forestry Applications J-9884 (cid:132) Pass the resultant point clouds to a terrain that creates a viewable and displayable surface. (cid:132) Perform analysis on the terrain for tree height delineation and canopy density. (cid:132) Pass the terrain data to the ArcGIS Server Image extension for dissemination to a wider audience as a seamless viewable surface that can be accessed from GIS technology. What Is Lidar? Lidar stands for light detection and ranging. In its most common form, it is an airborne optical remote-sensing technology that measures scattered light to find range and other information on a distant target. Similar to radar technology, which uses radio waves, the range to an object is determined by measuring the time delay between transmission of a pulse and detection of a reflected signal. Instead of radio waves, lidar uses much shorter wavelengths of the electromagnetic spectrum, typically in the ultraviolet, visible, or near- infrared range. This technology allows the direct measurement of three-dimensional structures and the underlying terrain. Depending on the methodology used to capture the data, the resultant data can be very dense, for example, five points per meter. Such high resolution gives higher accuracy for the measurement of the height of features on the ground and above the ground. The ability to capture the height at such high resolution is lidar's principal advantage over conventional optical instruments, such as digital cameras, for elevation model creation. Also captured by the lidar sensors is the intensity of each return. The intensity value is a measure of the return signal strength. It measures the peak amplitude of return pulses as they are reflected back from the target to the detector of the lidar system. Intensity is often used as an aid in feature detection and, where conventional aerial photography is not available, can be used as a pseudoimage to provide context of the lidar acquisition area. See the image below: Figure 1 Lidar Intensity Image Lighter areas represent strong returns. Darker areas represent weaker returns. June 2010 2 Lidar Analysis in ArcGIS 9.3.1 for Forestry Applications J-9884 In forestry, lidar can be used to measure the three-dimensional structure of a forest stand and produce a model of the underlying terrain. The structure of the forest will typically generate a first return from the uppermost limit of the canopy, followed by less intense returns through the canopy, down to the underlying terrain. Returns are classified into ground and aboveground sources. The ground returns can generate a detailed terrain of the area of interest, while the canopy returns can be filtered to provide forest structure at the canopy and middle level of the forest. Advantages to the The ability to simultaneously visualize the ground and model the canopy structure Forest Industry provides significant advantages to the forest industry. Traditionally, foresters and land managers have relied on topographic maps for terrain classification and field-based surveys to obtain tree volumes and height information. Lidar data provides significant improvements over both these techniques. Existing topographic maps depict contours and rivers, which have been, for the most part, captured from aerial photography using stereographic terrain generation techniques. In areas where the tree canopy obscures the underlying terrain, interpretive methods are used to depict where streams and contours occur. Terrains generated from lidar data more accurately represent these geographic features. Lidar penetrates the tree canopy to return a more accurate interpretation of the ground surface. This increases the accuracy of terrain classification and thereby the resultant interpretation and analysis of the geographic features. Lidar has provided significant benefits for forest development and engineering operations including locating roads, harvest planning, forest regeneration, and more. The ability to identify suitable creek crossings, determine optimal routes, and locate previously unmapped historic roads aids in reducing costs and creating operational efficiencies. Lidar has also offered an improvement to existing forest inventory methods and procedures. Traditional field-based timber inventory methods are based on measurements derived from systematically sampling plots in forest stands. This statistical sampling method is most often used in forests where measuring every tree is impractical. Tree volumes and heights are calculated in each sample plot, then generalized throughout a forest stand that shares similar characteristics. Estimated results help describe stand characteristics but are inaccurate due to variability in growing conditions throughout the forest, sampling bias, and lack of precision. In addition, the time to collect such measurements is both lengthy and expensive, as many sample plots may be required to provide a reliable representation. Lidar can overcome these limitations. An increasing number of forestry and land management organizations are using lidar for forest inventory measurements. A wide range of information can be directly obtained from lidar including (cid:132) Digital elevation models (cid:132) Tree heights and digital surface models (cid:132) Crown cover (cid:132) Forest structure (cid:132) Crown canopy profile Esri White Paper 3 Lidar Analysis in ArcGIS 9.3.1 for Forestry Applications J-9884 Postprocessing of lidar data can reveal (cid:132) Volume—Canopy geometric volume (cid:132) Biomass—Canopy cover (cid:132) Density—Height-scaled crown openness index and counts of delineated crowns (cid:132) Foliage projected cover—Crown dimensions The forest industry is requiring increasingly precise inventories to guide forest management activities. Using lidar data, forest inventories can be conducted at nearly the single tree level, offering more accurate representations of the true forest stand structure. For forest inventory activities, lidar has been used primarily to retrieve basic structural tree attributes including height, canopy cover, and vertical profiles. These attributes can be used to derive other critical forestry measurements including basal area and timber volume, as well as biomass for alternative energy and carbon sequestration analysis. This paper will address these attributes. Managing and Understanding Lidar Data Understanding Raw Before any analysis is performed with lidar data, the data received must be checked for Lidar Data any inconsistencies. Lidar data can be delivered in either binary .las format or ASCII .xyz files. The LAS file format is a public binary file format that is an alternative to proprietary systems or a generic ASCII file interchange system used by many data providers. Details on the format can be found at http://www.asprs.org/society/committees/lidar/. Although a data provider will endeavor to provide the best quality data to its clients, there is always a chance a client will encounter anomalies in the data. These can be in the form of irregular minimum bounding shapes or holes in the sampling. It is therefore necessary to check the quality of the data before performing any analysis. The Point File Information tool in Esri's ArcGIS Desktop 3D Analyst™ assists in performing data quality assurance checks. Point File The Point File Information tool, found in the 3D Analyst toolbox in ArcGIS Information Tool (ArcToolbox\3D Analyst Tools\Conversion\From File\Point File Information), reports important statistics about the raw lidar files. June 2010 4 Lidar Analysis in ArcGIS 9.3.1 for Forestry Applications J-9884 The tool is designed to read the headers of LAS or scan ASCII files and summarize the file contents. As a single lidar file often contains millions of points and many lidar datasets contain more than one file, the Point File Information tool can accommodate reading one or more files by specifying either individual lidar files or folders. The result from this tool is a feature class that shows the (cid:132) Minimum bounding rectangle for each file (cid:132) Number of points recorded (cid:132) Average point spacing (cid:132) Minimum/Maximum z-values When the feature class is loaded into ArcMap™, the minimum bounding rectangle of each lidar file is drawn. Lidar data files are usually uniform in size, so if any of the feature shapes appear large or irregular compared to the majority of features from the feature class, ArcMap will flag the corresponding lidar data file for further investigation. The average point spacing is important and should be uniform throughout the data files. If any of the files have an average point spacing that is significantly larger than other files, this may indicate incorrect sampling. In addition, average point spacing is important when building geodatabase terrains and converting lidar files to feature classes. The average point spacing is a product of the total number of points divided by the area of the lidar data file. In cases where a lidar data file is only partially covered by points, such as along a coastline, the average point spacing will be calculated to be greater than the point spacing of the area sampled. These anomalous files would still be used in the dataset, but their calculations would be excluded from further processing. Esri White Paper 5 Lidar Analysis in ArcGIS 9.3.1 for Forestry Applications J-9884 The images that follow show three of the elements as reported by the Point File Information tool, including (cid:132) A uniform grid showing the extents of each lidar file. (cid:132) The attribute table associated with the lidar extents, showing the average point spacing, point count, min and max z-values, and originating file names. (cid:132) The average point spacing as indicated by the statistics from the Pt_Spacing column. In this example, the average point spacing tends to be approximately 0.6 meter. The lidar dataset used in this paper was captured at a sampling density of two returns per square meter; thus, 0.6 meter gives a good approximation to the ordered capture rate. Again, if there were any significant outliers in the files, these would be highlighted for further inspection. Lidar Classification in LAS files contain a classification field that identifies each point's return type. A ArcGIS classification describes the point return as Ground returns, Canopy returns, Building returns, or Unclassified. This is useful in determining the content of each lidar file. June 2010 6