Abstract

The Leica Geosystems CountryMapper hybrid system has the potential to collect data that satisfy the U.S. Geological Survey (USGS) National Geospatial Program (NGP) and 3D Elevation Program (3DEP) and the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) requirements in a single collection. This research will help 3DEP determine if this sensor has the potential to meet current and future 3DEP topographic lidar collection requirements. We performed an accuracy analysis and assessment on the lidar point cloud produced from CountryMapper. The boresighting calibration and co-registration by georeferencing correction based on ground control points are assumed to be performed by the data provider. The scope of the accuracy assessment is to apply the following variety of ways to measure the accuracy of the delivered point cloud to obtain the error statistics. Intraswath uncertainty from a flat surface was computed to evaluate the point cloud precision. Intraswath difference between opposite scan directions and the interswath overlap difference were evaluated to find boresighting or any systematic errors. Absolute vertical accuracy over vegetated and non-vegetated areas were also assessed. Both horizontal and vertical absolute errors were assessed using the 3D absolute error analysis methodology of comparing conjugate points derived from geometric features. A three-plane feature makes a single unique intersection point. Intersection points were computed from ground-based lidar and airborne lidar point clouds for comparison. The difference between two intersection points form one error vector. The geometric feature-based error analysis was applied to intraswath, interswath, and absolute error analysis. The CountryMapper pilot data appear to satisfy the accuracy requirements suggested by the USGS lidar specification, based upon the error analysis results. The focus of this research was to demonstrate various conventional accuracy measures and novel 3D accuracy techniques using two different error computation methods on the CountryMapper airborne lidar point cloud.

Highlights

  • The U.S. Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) commissioned the acquisition of imagery and lidar using the Leica Geosystems CountryMapper hybrid sensor

  • This paper reports on several different methods used to measure the accuracy of the lidar point cloud data collected for the National Agriculture Imagery Program (NAIP)/3D Elevation Program (3DEP) pilot project

  • All the results appeared to satisfy the U.S. Geological Survey (USGS) Lidar Base Specification (LBS) quality level (QL) 2 requirements, some of the point cloud vector-based methods presented in this paper are different from the raster-based methods suggested in LBS

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Summary

Introduction

The USDA Natural Resources Conservation Service (NRCS) commissioned the acquisition of imagery and lidar using the Leica Geosystems CountryMapper hybrid sensor. Two pilot areas of interest were flown in north-central Colorado over two different physical settings to evaluate the system’s performance. The western area of interest (AOI) included land managed by the Bureau of Land Management and the U.S Forest Service. The eastern AOI included agricultural and urban areas. USGS scientists conducted field data collection efforts during the weeks of 9–13 September and 18–22 November 2019, using a combination of technologies to map and validate topography, vegetation, and features in the two AOIs. The work was initiated as an effort to test and evaluate the Leica Geosystems CountryMapper sensor

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