Michael E. Hodgson

Accuracy of Airborne Lidar-Derived Elevation: Empirical Assessment and Error Budget

As part of a countywide large-scale mapping effort for Richland County, South Carolina, an accuracy assessment of a recently acquired lidar-derived data set was conducted. Airborne lidar (2-m nominal posting) was collected at a flying height of 1207 meters above ground level (AGL) using an Optech ALTM (Airborne Laser Terrain Mapper) 1210 system. Unique to this study are the reference point elevations. Rather than using an interpolation approach for gathering observed elevations at reference points, the x-y coordinates of lidar points were located in the field and these elevations were surveyed. Using both total-station-based and rapid-static GPS techniques, observed vertical heights were measured at each reference lidar posting. The variability of vertical accuracy was evaluated for six land-cover categories. Root-meansquared error (RMSE) values ranged from a low of 17 to 19 cm (pavement, low grass, and evergreen forests) to a high of 26 cm (deciduous forests). The unique error assessment of lidar postings also allowed for the creation of an error budget model. The observed lidar elevation error was decomposed into errors from lidar system measurements, horizontal displacement, interpolation error, and surveyor error. A crossvalidation approach was used to assess the observed interpolated lidar elevation error for each field-verified reference point. In order of decreasing importance, the lidar system measurements were the dominant source of error followed by interpolation error, horizontal displacement error, and surveyor error. Observed elevation error in steeper slopes (e.g., 25°) was estimated to be twice as large as those on low slopes (e.g., 1.5°).

An evaluation of LIDAR- and IFSAR-derived digital elevation models in leaf-on conditions with USGS Level 1 and Level 2 DEMs

Abstract An assessment of four different remote sensing based methods for deriving digital elevation models (DEMs) was conducted in a flood-prone watershed in North Carolina. New airborne LIDAR (light detecting and ranging) and IFSAR (interferometric synthetic aperture radar (SAR)) data were collected and corresponding DEMs created. These new sources were compared to two methods: Gestalt Photomapper (GPM) and contour-to-grid, used by the U.S. Geological Survey (USGS) for creating DEMs. Survey-grade points (1470) for five different land cover classes were used as reference points. One unique aspect of this study was the LIDAR and IFSAR data were collected during leaf-on conditions. Analyses of absolute elevation accuracy and terrain slope were conducted. The LIDAR- and contour-to-grid derived DEMs exhibited the highest overall absolute elevation accuracies. Elevation accuracy was found to vary with land cover categories. Elevation accuracy also decreased with increasing slopes—but only for the scrub/shrub land cover category. Appreciable terrain slope errors for the reference points were found with all methods.

Synergistic use of lidar and color aerial photography for mapping urban parcel imperviousness

The imperviousness of land parcels was mapped and evaluated using high spatial resolution digitized color orthophotography and surface-cover height extracted from multiple-return lidar data. Maximum-likelihood classification, spectral clustering, and expert system approaches were used to extract the impervious information from the datasets. Classified pixels (or segments) were aggregated to parcels. The classification model based on the use of both the orthophotography and lidar-derived surface-cover height yielded impervious surface results for all parcels that were within 15 percent of reference data. The standard error for the rule-based per-pixel model was 7.15 percent with a maximum observed error of 18.94 percent. The maximum-likelihood per-pixel classification yielded a lower standard error of 6.62 percent with a maximum of 14.16 percent. The regression slope (i.e., 0.955) for the maximum-likelihood per-pixel model indicated a near perfect relationship between observed and predicted imperviousness. The additional effort of using a per-segment approach with a rule-based classification resulted in slightly better standard error (5.85 percent) and a near-perfect regression slope (1.016).

Tree invasion within a pine/grassland ecotone: an approach with historic aerial photography and GIS modeling

Abstract In previous studies, evidence of tree invasion into grasslands has mainly been through comparison of historical terrestrial photographs and/or tree age data. The goal of this paper is to provide a quantitative description of the tree invasion process at a landscape scale using historical aerial photography, image processing and geographic information systems (GIS) approaches. Various map interpretive techniques provided evidence of shifts in the ponderosa pine-grassland ecotone along the Colorado Front Range since the late 1930s. Historical aerial photos were digitally scanned and the outlines of tree invasions into the grassland were determined based on gray tone density slicing. Image processing of digitized aerial photography identified areas of change in tree cover and quantified locations and total hectares of tree invasions into grassland areas. Overall, the results clearly show an increase in woodland areas where there formerly existed grasslands. GIS modeling was used to relate tree invasion patterns to topographic orientation and changes in settlement patterns. The importance of terrain aspect on rate of tree invasion is clearly shown by the greater rate of tree invasion on north-facing slopes (generally moister with less heat stress) versus south-facing slopes. The most dramatic change in the controls of vegetation patterns over the past one or two centuries has been the decline in fire frequency due to fire suppressing policy since ca. 1920. However, changes in grazing regimes may also have played an important role. When comparing these results to the instrumental climate record of the area, periods of favorable climatic conditions for seedling establishment generally correspond to periods of increased rate of tree invasion into grassland areas.

An Evaluation of Lidar-derived Elevation and Terrain Slope in Leaf-off Conditions

The effects of land cover and surface slope on lidar-derived elevation data were examined for a watershed in the piedmont of North Carolina. Lidar data were collected over the study area in a winter (leaf-off) overflight. Survey-grade elevation points (1,225) for six different land cover classes were used as reference points. Root mean squared error (RMSE) for land cover classes ranged from 14.5 cm to 36.1 cm. Land cover with taller canopy vegetation exhibited the largest errors. The largest mean error (36.1 cm RMSE) was in the scrub-shrub cover class. Over the small slope range (0° to 10°) in this study area, there was little evidence for an increase in elevation error with increased slopes. However, for low grass land cover, elevation errors do increase in a consistent manner with increasing slope. Slope errors increased with increasing surface slope, under-predicting true slope on surface slopes � 2°. On average, the lidarderived elevation under-predicted true elevation regardless of land cover category. The under-prediction was significant, and ranged up to � 23.6 cm under pine land cover.

Impact of Lidar Nominal Post-spacing on DEM Accuracy and Flood Zone Delineation

Lidar data have become a major source of digital terrain information for use in many applications including hydraulic modeling and flood plane mapping. Based on established relationships between sampling intensity and error, nominal post-spacing likely contributes significantly to the error budget. Post-spacing is also a major cost factor during lidar data collection. This research presents methods for establishing a relationship between nominal post-spacing and its effects on hydraulic modeling for flood zone delineation. Lidar data collected at a low post-spacing (approximately 1 to 2 m) over a piedmont study area in North Carolina was systematically decimated to simulate datasets with sequentially higher post-spacing values. Using extensive first-order ground survey information, the accuracy of each DEM derived from these lidar datasets was assessed and reported. Hydraulic analyses were performed utilizing standard engineering practices and modeling software (HEC-RAS). All input variables were held constant in each model run except for the topographic information from the decimated lidar datasets. The results were compared to a hydraulic analysis performed on the un-decimated reference dataset. The sensitivity of the primary model outputs to the variation in nominal post-spacing is reported. The results indicate that base flood elevation does not statistically change over the post-spacing values tested. Conversely, flood zone boundary mapping was found to be sensitive to variations in post-spacing.

A GIS‐Based Model to Determine Site Suitability of Emergency Evacuation Shelters

In recent years, the increase in the number of hurricanes and other costal hazards in the US pose a tremendous threat to the residents of coastal states. According to the National Hurricane Center, Florida is the most vulnerable coastal state to hurricanes. Mitigation policies have been formulated to reduce mortality and provide emergency services by evacuating people from the hazard zone. Many of these evacuees, particularly the elderly or lower income populations, rely on evacuation shelters for temporary housing. Because of the cost and limited use, evacuation shelters are almost exclusively dual use shelters where the primary purpose of the facility is for some other public function (e.g. school, hospital, etc.). In 2000, the estimated shortage of public shelter spaces in Florida was about 1.5 million. The purpose of this study was to rank the existing and candidate shelters (schools, colleges, churches and community centers) available in the state based on their site suitability. The research questions examined in this study include: (1) How many candidate shelters are located in physically suitable areas (e.g. not in a flood prone area, not near hazardous facilities, etc.)?; (2) How many existing shelters are located in physically unsuitable areas, but in socially suitable areas (situated in areas with demand)?; (3) How many alternative existing and/or candidate shelters with high/very high physical suitability are located near physically unsuitable existing shelters and thus, may be better choices for a shelter?; and (4) How many existing shelters located in physically unsuitable areas are not near alternative existing and/or candidate shelters? A Geographic Information System-based suitability model integrating Weighted Linear Combination (WLC) with a Pass/Fail screening technique was implemented for the 17 counties of Southern Florida. It was found that 48% of the existing shelters are located in

Object-Based Land Cover Classification Using High-Posting-Density LiDAR Data

This study introduces a method for object-based land cover classification based solely on the analysis of LiDAR-derived information—i.e., without the use of conventional optical imagery such as aerial photography or multispectral imagery. The method focuses on the relative information content from height, intensity, and shape of features found in the scene. Eight object-based metrics were used to classify the terrain into land cover information: mean height, standard deviation (STDEV) of height, height homogeneity, height contrast, height entropy, height correlation, mean intensity, and compactness. Using machine-learning decision trees, these metrics yielded land cover classification accuracies > 90%. A sensitivity analysis found that mean intensity was the key metric for differentiating between the grass and road/parking lot classes. Mean height was also a contributing discriminator for distinguishing features with different height information, such as between the building and grass classes. The shape- or texture-based metrics did not significantly improve the land cover classifications. The most important three metrics (i.e., mean height, STDEV height, and mean intensity) were sufficient to achieve classification accuracies > 90%.

Effects of lidar post‐spacing and DEM resolution to mean slope estimation

This paper examines the effect of scale (exhibited by spatial sampling) in modeling mean slope from lidar data using two representations of scale: lidar posting density (i.e. post‐spacing) and DEM resolution (i.e. cell size). The study areas selected include six small (i.e. approximately 3 km2) urban drainage basins in Richland County, SC, USA, which share similar hydrologic characteristics. This research spatially sampled an airborne lidar dataset collected in 2000 at a 2 m nominal posting density to simulate lidar posting density at various post‐spacings, from 2 m through 10 m. DEMs were created from the lidar observations at a corresponding cell size using spatial interpolation. Finally, using these DEMs, a sensitivity analysis between modeled terrain slope and lidar post‐spacing was conducted. Results of the sensitivity analyses showed that the deviation between mean slope and modeled mean slope decreases with finer posting density and DEM resolution. The relationship of mean slope with varying cell sizes and post‐spacing suggests a linear and a logarithmic function, respectively, for all study areas. More importantly, cell size has a greater effect on mean slope than lidar posting density. Implications of these results for lumped hydrologic modeling are then postulated.

Forest impact estimated with NOAA AVHRR and Landsat TM data related to an empirical hurricane wind-field distribution

Abstract An empirical model was used to relate forest type and hurricane-impact distribution with wind speed and duration to explain the variation of hurricane damage among forest types along the Atchafalaya River basin of coastal Louisiana. Forest-type distribution was derived from Landsat Thematic Mapper image data, hurricane-impact distribution from a suite of transformed advanced very high resolution radiometer images, and wind speed and duration from a wind-field model. The empirical model explained 73%, 84%, and 87% of the impact variances for open, hardwood, and cypress–tupelo forests, respectively. These results showed that the estimated impact for each forest type was highly related to the duration and speed of extreme winds associated with Hurricane Andrew in 1992. The wind-field model projected that the highest wind speeds were in the southern basin, dominated by cypress–tupelo and open forests, while lower wind speeds were in the northern basin, dominated by hardwood forests. This evidence could explain why, on average, the impact to cypress–tupelos was more severe than to hardwoods, even though cypress–tupelos are less susceptible to wind damage. Further, examination of the relative importance of wind speed in explaining the impact severity to each forest type showed that the impact to hardwood forests was mainly related to tropical-depression to tropical-storm force wind speeds. Impacts to cypress–tupelo and open forests (a mixture of willows and cypress–tupelo) were broadly related to tropical-storm force wind speeds and by wind speeds near and somewhat in excess of hurricane force. Decoupling the importance of duration from speed in explaining the impact severity to the forests could not be fully realized. Most evidence, however, hinted that impact severity was positively related to higher durations at critical wind speeds. Wind-speed intervals, which were important in explaining the impact severity on hardwoods, showed that higher durations, but not the highest wind speeds, were concentrated in the northern basin, dominated by hardwoods. The extreme impacts associated with the cypress–tupelo forests in the southeast corner of the basin intersected the highest durations as well as the highest wind speeds.