LiDAR-derived digital elevation models can reveal road networks located beneath dense forest canopy. This study tests the accuracy of forest road characteristics mapped using LiDAR in the Santa Cruz Mountains, CA. The position, gradient, and total length of a forest haul road were accurately extracted using a 1 m DEM. In comparison to a field-surveyed centerline, the LiDAR-derived road exhibited a positional accuracy of 1.5 m, road grade measurements within 0.53% mean absolute difference, and total road length within 0.2% of the field-surveyed length. Airborne LiDAR can provide thorough and accurate road inventory data to support forest management and watershed assessment activities.
Aerial and satellite imagery are widely used to assess the severity and impact of wildfires. Light detection and ranging (LiDAR) is a newer remote sensing technology that has demonstrated utility in measuring vegetation structure. Combined use of imagery and LiDAR may improve the assessment of wildfire impacts compared to imagery alone. Estimation of tree mortality at the plot scale could serve for more rapid, broad-scale, and lower cost post-fire assessments than feasible through field assessment. We assessed the accuracy of classifying color-infrared imagery in combination with post-fire LiDAR, and with differenced (pre-and post-fire) LiDAR, in estimating plot percent mortality in a second-growth coast redwood forest near Santa Cruz, CA. Percent mortality of trees greater than 25.4 cm DBH in 47 permanent 0.08 ha plots was categorized as low (<25%), moderate (25%-50%), or high (>50%). The model using Normalized Difference Vegetation Index (NDVI) from National Agricultural Imagery Program (NAIP) was 74% accurate; the model using NDVI and post-fire LiDAR was 85% accurate, while the model using NDVI and differenced LiDAR was 83% accurate. The addition of post-fire LiDAR data provided a modest increase in accuracy compared to imagery alone, which may not OPEN ACCESS Remote Sens. 2014, 6 1955 justify the substantial cost of data acquisition. The method demonstrated could be applied to rapidly estimate tree mortality resulting from wildfires at fine to moderate scale.
Programs for geospatial support at academic libraries have evolved over the past decade in response to changing campus needs and developing technologies. Geospatial applications have matured tremendously in this time, emerging from specialty tools to become broadly used across numerous disciplines. At many universities, the library has served as a central resource allowing students and fac ulty across academic departments access to GIS resources. Today, as many academic libraries evaluate their spaces and services, GIS and data services are central in discussions on how to further en gage with patrons and meet increasingly diverse researcher needs. As library programs evolve to support increasingly technical data and GIS needs, many universities are faced with similar challenges and opportunities. To explore these themes, data and GIS services librarians and GIS specialists from five universities-the
Accuracy of Forest Road and Stream Channel CharacteristicsDerived from LiDAR in Forested Mountain Conditions Russell White Forest roads and stream channels are mapped using a variety of remote sensing and ground-based techniques. In densely forested areas, conventional remote sensing methods provide limited terrain information, while ground-based surveys can be time-consuming, difficult, and expensive. Light Detection and Ranging (LiDAR) is an airborne remote sensing technology used to create high-resolution digital elevation models (DEMs) of the earth's surface.This study tests the accuracy of forest road and stream channel features mapped using LiDAR in the steep, forested terrain of California's Santa Cruz Mountains. A conventional total station survey was used to determine centerline position and elevations along a four-kilometer forest road, and along six thirty-meter stream channel study reaches. A 1.5 m LiDAR DEM was suitable to accurately map the location of the forest road and channel features. Ninety five percent of the LiDAR-derived road length was located within 2.2 m normal to the field-surveyed centerline and LiDAR-derived road slopes were not significantly different from field-surveyed slopes. Stream channel features derived from the LIDAR DEM were located within 2.7 m normal to the field-surveyed thalweg, while the LiDAR-derived slopes measured within 0.49 percent of field-surveyed slopes. These findings indicate that LiDAR can provide accurate terrain measurements that are suitable for resource management and assessment.
Distribution of neighborhoods and services in Philadelphia, Pennsylvania, by density of ethnic Chinese residents, from 2014-2018 estimates. Map A shows percentages by neighborhood, highlighting those with a density of 8.7% or more. Map B shows locations of 6 types of community resources for Chinese residents overlaid on Map A to illustrate resource distribution in relation to population density. Geographic proximity of resources corresponds overall to neighborhood density of Chinese residents. However, not all types of resources are equally distributed, indicating they are unavailable to residents of some neighborhoods. Data sources: Chinese demographic data are from the American Community Survey 2018 (5-Year Estimates), prepared by Social Explorer (1). Boundaries for Philadelphia neighborhoods data are from OpenDataPhilly, developed by Azavea Inc (2). Community resource data are from the Chinese Philadelphia Yellow Pages (3).
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