Nancy F. Glenn

A review of remote sensing of invasive weeds and example of the early detection of spotted knapweed (Centaurea maculosa) and babysbreath (Gypsophila paniculata) with a hyperspectral sensor

Abstract Remote sensing technology is a tool for detecting invasive species affecting forest, rangeland, and pasture environments. This article provides a review of the technology, and algorithms used to process remotely sensed data when detecting weeds and a working example of the detection of spotted knapweed and babysbreath with a hyperspectral sensor. Spotted knapweed and babysbreath frequently invade semiarid rangeland and irrigated pastures of the western United States. Ground surveys to identify the extent of invasive species infestations should be more efficient with the use of classified images from remotely sensed data because dispersal of an invasive plant may have occurred before the discovery or treatment of an infestation. Remote sensing data were classified to determine if infestations of spotted knapweed and babysbreath were detectable in Swan Valley near Idaho Falls, ID. Hyperspectral images at 2-m spatial resolution and 400- to 953-nm spectral resolution with 12-nm increments were used to identify locations of spotted knapweed and babysbreath. Images were classified using the spectral angle mapper (SAM) algorithm at 1, 2, 3, 4, 5, and 10° angles. Ground validation of the classified images established that 57% of known spotted knapweed infestations and 97% of known babysbreath infestations were identified through the use of hyperspectral imagery and the SAM algorithm. Nomenclature: Babysbreath, Gypsophila paniculata L. GYPPA; spotted knapweed, Centaurea maculosa Lam. CENMA.

Mapping Sagebrush Distribution Using Fusion of Hyperspectral and Lidar Classifications

The applicability of high spatial resolution hyperspectral data and small-footprint Light Detection and Ranging (lidar) data to map and describe sagebrush in a semi-arid shrub steppe rangeland is demonstrated. Hyperspectral processing utilized a spectral subset (605 nm to 984 nm) of the reflectance data to classify sagebrush presence to an overall accuracy of 74 percent. With the inclusion of co-registered lidar data, this accuracy increased to 89 percent. Furthermore, lidar data were utilized to generate stand specific descriptive information in areas of sagebrush presence and sagebrush absence. The methods and results of this study lay the framework for utilizing co-registered hyperspectral and lidar data to describe semi-arid shrubs in greater detail than would be feasible using either dataset independently or by most ground based surveys.

Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle

In the summer of 2010, an Unmanned Aerial Vehicle (UAV) hyperspectral calibration and characterization experiment of the Resonon PIKA II imaging spectrometer was conducted at the US Department of Energy’s Idaho National Laboratory (INL) UAV Research Park. The purpose of the experiment was to validate the radiometric calibration of the spectrometer and determine the georegistration accuracy achievable from the on-board global positioning system (GPS) and inertial navigation sensors (INS) under operational conditions. In order for low-cost hyperspectral systems to compete with larger systems flown on manned aircraft, they must be able to collect data suitable for quantitative scientific analysis. The results of the in-flight calibration experiment indicate an absolute average agreement of 96.3%, 93.7% and 85.7% for calibration tarps of 56%, 24%, and 2.5% reflectivity, respectively. The achieved planimetric accuracy was 4.6 m (based on RMSE) with a flying height of 344 m above ground level (AGL).

Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area

Separating ground from nonground laser returns from airborne light detection and ranging (LiDAR) data is a key step in creating digital terrain models (DTMs). In this letter, bare-earth and forested surfaces are classified from LiDAR intensity data in a data set from central Idaho, U.S. Next, a Gaussian fitting (GF) method is applied to determine ground elevations from LiDAR elevation data according to the land-cover information. In comparison to ground-based reference data, the GF method generated an accurate DTM in this study area. Overall, the DTM underestimated the ground observations by approximately 31 cm. A combination of LiDAR intensity and elevation data may be effectively used to develop DTMs in similar terrain of relatively simple land-cover classes.

Subpixel abundance estimates in mixture-tuned matched filtering classifications of leafy spurge (Euphorbia esula L.)

Two demonstration sites in southeast Idaho, USA were used to extend remote sensing of leafy spurge research to fine-scale detection for abundance mapping using matched filtering (MF) scores. Linear regression analysis was used to quantify the relationship between MF estimates and calibrated ground estimates of leafy spurge abundance. The two sites had r 2 values of 0.46 and 0.64. Both the slope of the regressions and the scaling behaviour of MF scores indicate that the technique consistently underestimated true abundance (defined here as percentage canopy cover) by roughly one-third. This underestimation may be influenced by field estimation bias and algorithm confusion between target and background signal. Further results indicate that MF exhibits linear scaling behaviour in six locations containing dense, uniform infestations. At these locations, where canopy cover was held relatively constant, high spatial resolution (3 m) estimates were not significantly different from coarser spatial resolution estimates (up to 16 m). Given the mathematically unconstrained nature of the estimation technique, MF is not a straightforward method for estimating leafy spurge canopy cover.

Small-Footprint Lidar Estimations of Sagebrush Canopy Characteristics

The height and shape of shrub canopies are critical measurements for characterizing shrub steppe rangelands. Remote sensing technologies might provide an efficient method to acquire these measurements across large areas. This study compared point-cloud and rasterized lidar data to field-measured sagebrush height and shape to quantify the correlation between field-based and lidar-derived estimates. The results demonstrated that discrete return, small-footprint lidar with high point density (9.46 points/m 2 ) can provide strong predictions of true sagebrush height (R 2 of 0.84 to 0.86), but with a consistent underestimation of approximately 30 percent. Our results provided the first successful lidar-based descriptors of sagebrush shape with R 2 values of 0.65, 0.74, and 0.78 for respective predictions of shortest canopy diameter, longest canopy diameter, and canopy area. Future studies can extend lidar-derived shrub height and shape measurements to canopy volume, cover, and biomass estimates.

A Comparison of Two Open Source LiDAR Surface Classification Algorithms

With the progression of LiDAR (Light Detection and Ranging) towards a mainstream resource management tool, it has become necessary to understand how best to process and analyze the data. While most ground surface identification algorithms remain proprietary and have high purchase costs; a few are openly available, free to use, and are supported by published results. Two of the latter are the multiscale curvature classification and the Boise Center Aerospace Laboratory LiDAR (BCAL) algorithms. This study investigated the accuracy of these two algorithms (and a combination of the two) to create a digital terrain model from a raw LiDAR point cloud in a semi-arid landscape. Accuracy of each algorithm was assessed via comparison with >7,000 high precision survey points stratified across six different cover types. The overall performance of both algorithms differed by only 2%; however, within specific cover types significant differences were observed in accuracy. The results highlight the accuracy of both algorithms across a variety of vegetation types, and ultimately suggest specific scenarios where one approach may outperform the other. Each algorithm produced similar results except in the ceanothus and conifer cover types where BCAL produced lower errors.

A Linear Regression Method for Tree Canopy Height Estimation Using Airborne Lidar Data

Tree canopy height is one of the most fundamental measurements in forest inventory and is a critical variable in the quantitative assessment of tree (or stand) volume, forest biomass, carbon stocks, growth, and site productivity. In this study, we analyzed two traditional methods for tree canopy height estimation and designed a new linear regression method for improved tree canopy height estimation using airborne light detection and ranging (lidar) data. Examples of two typical crown shapes were used, and theoretical analysis was performed on simulated datasets with varying crown shape, unit penetrability, and laser-missed canopy layer(s). The final result derived from the simulated lidar data illustrates that the linear regression method can improve canopy height estimation. This method was also applied to lidar data covering a tall pine forest in Idaho, USA. An average error of 0.51 m was obtained from a comparison of the lidar-derived tree canopy heights and 79 field measurements. This error was also compared with the estimation error resulting from the use of two traditional methods. Results indicate our method produced more accurate tree canopy height estimates, with a mean error and root mean square error (RMSE) ranging between 25% and 50% lower than those from the two traditional methods.

Vegetation and Slope Effects on Accuracy of a LiDAR-Derived DEM in the Sagebrush Steppe

This study analysed the errors associated with vegetation cover type and slope in a Light Detection and Ranging (LiDAR) derived digital elevation model (DEM) in a semiarid environment in southwest Idaho, USA. Reference data were collected over a range of vegetation cover types and slopes. Reference data were compared to bare-ground raster values and root mean square error (RMSE) and mean signed error (MSE) were used to quantify errors. Results indicate that vegetation cover type and slope have statistically significant effects on the accuracy of a LiDAR-derived bare-earth DEM. RMSE and MSE ranged from 0.072 to 0.220 m and from −0.154 to 0.017 m, respectively, with the largest errors associated with herbaceous cover and steep slopes. The lowest errors were associated with low sagebrush and low-slope environments. Although the RMSEs in this study were lower than those reported by others, further refinement of the accuracy of LiDAR systems may be needed for fine-scale vegetation and terrain applications in rangeland environments.

What is the effect of LiDAR-derived DEM resolution on large-scale watershed model results? ☆

This paper examines the effect of raster cell size on hydrographic feature extraction and hydrological modeling using LiDAR derived DEMs. LiDAR datasets for three experimental watersheds were converted to DEMs at various cell sizes. Watershed boundaries and stream networks were delineated from each DEM and were compared to reference data. Hydrological simulations were conducted and the outputs were compared. Smaller cell size DEMs consistently resulted in less difference between DEM-delineated features and reference data. However, minor differences been found between streamflow simulations resulted for a lumped watershed model run at daily simulations aggregated at an annual average. These findings indicate that while higher resolution DEM grids may result in more accurate representation of terrain characteristics, such variations do not necessarily improve watershed scale simulation modeling. Hence the additional expense of generating high resolution DEMs for the purpose of watershed modeling at daily or longer time steps may not be warranted.