Dataset Summary: This datasheet describes three 2-foot resolution rasters that cover the extent of Santa Cruz County – slope percent, slope degrees, and aspect. These rasters are derived directly from the 2-foot resolution Santa Cruz County Digital Terrain Models (DTMs), which were derived from 2020 QL1 lidar. These rasters represent the state of the landscape when countywide lidar data was collected in 2020. QL1 lidar was collected in western Santa Cruz County by Quantum Spatial and in eastern Santa Cruz County by the Sanborn Map Company. Figure 1 shows the respective areas of lidar collection. This deliverable is a combination of these two lidar datasets. The horizontal coordinate system of these rasters, State Plane CA Zone III (WKID 6420), is the native horizontal resolution of the 2020 point clouds. Figure 1. Sources of lidar data for Santa Cruz County

Table 1 provides links to download these lidar derived rasters.
These three lidar derivatives provide information about the surface of the earth. The two slope rasters depict the steepness of the ground for each 2-foot x 2-foot cell of the raster surface. One of the slope rasters represents slope in degrees, the other in percent. Aspect (or ‘slope direction’) shows the downslope direction of the maximum rate of change in elevation value from each cell to its neighbors.
The two slope and 1 aspect rasters were derived from the Santa Cruz County Digital Terrain Model using the ‘slope’ and ‘aspect’ functions in ArcGIS Pro Spatial Analyst. Table 1. lidar derivatives for Santa Cruz County

Dataset

Description

Link to Datasheet

Link to Data

Slope (Percent)

Steepness of the ground in percent for each 2-foot x 2-foot raster cell. Units in percent.

https://vegmap.press/scz_slope_and_aspect_datasheet

https://vegmap.press/scz_slope_percent

Slope (Degrees)

Steepness of the ground in degrees for each 2-foot x 2-foot raster cell. Units in degrees.

https://vegmap.press/scz_slope_and_aspect_datasheet

https://vegmap.press/scz_slope_degrees

Aspect

Aspect (or ‘slope direction’) shows the downslope direction of the maximum rate of change in elevation value from each cell to its neighbors.

https://vegmap.press/scz_slope_and_aspect_datasheet

https://vegmap.press/scz_aspect

Related Datasets: Other related lidar derived topography derivatives are available for Santa Cruz County. The following is a list of those rasters, with links to their datasheets: HillshadeDigital Terrain Model (western areas of the county)Digital Terrain Model (eastern areas of the county)5-meter resolution slope and aspect

In Puerto Rico, tens of thousands of landslides, slumps, debris flows, rock falls, and other slope failures were triggered by Hurricane María, which made landfall on 20 September 2017. “Landslide” is used here and below to represent all types of slope failures. This dataset is a point shapefile of landslide headscarps identified across Puerto Rico using georeferenced aerial and satellite imagery recorded following the hurricane. The imagery used includes publicly available aerial imagery obtained by the Federal Emergency Management Agency (FEMA; Quantum Spatial, Inc., 2017), aerial imagery obtained by the National Oceanic and Atmospheric Administration (NOAA; NOAA, 2017), and several WorldView satellite imagery datasets available from DigitalGlobe, Inc. The FEMA imagery was recorded by Sanborn and Quantum Spatial, Inc. between 25 September and 27 October 2017, has a pixel resolution of approximately 15 cm, and includes over 6,000 image tiles that cover approximately 97% of the large island and 100% of Vieques. The NOAA imagery was recorded 22-26 September 2017, also has a resolution of approximately 15 cm, and covers about 10% of the large island, 60% of Vieques, and 100% of Culebra. The DigitalGlobe imagery used in this project was recorded during September-November 2017, has a pixel resolution of approximately 50 cm, and covers approximately 99% of the large island and 35% of Vieques. DigitalGlobe images were acquired via the DigitalGlobe Open Data Program, the DigitalGlobe Foundation imagery grant, and via partnership with the U.S. Geological Survey. No imagery was examined for Desecheo, Mona, Monito, Caja de Muertos, or other smaller islands.
The FEMA imagery was usually used first for landslide mapping due to its high resolution and more accurate georeferencing. For almost every location, there were multiple images available due to overlap in each dataset and overlap between different datasets. This overlap was helpful when clouds or shadows obscured the view of the ground surface in one or more images for a given location. Additional oblique and un-georeferenced aerial imagery recorded by the Civil Air Patrol (ArcGIS, 2017) was consulted, if needed. Comparing the post-event imagery with pre-event imagery available through the ESRI ArcGIS basemap layer and/or Google Earth was useful to accurately identify sites that failed during September 2017; such comparisons were made for landslides that appeared potentially older. Some landslides in our inventory may have occurred prior to Hurricane María—potentially triggered by Hurricane Irma which passed northeast of Puerto Rico two weeks earlier—or between the time of the hurricane and when photographs were taken. UTM Zone 19N projection with WGS 84 datum was used throughout the mapping process. The inventory process began with creation of a first draft by a team of 15 people. This draft was subsequently checked for quality and revised by the three leaders of the mapping effort. Each identified landslide is represented by a point located at the center of its headscarp. The horizontal position of headscarp points was carefully selected using multiple overlapping images (usually available) and other geospatial datasets including lidar acquired during 2015 and available from the U.S. Geological Survey 3DEP program, the U.S. Census Bureau TIGER road shapefile, and the National Hydrology Dataset flowline shapefile. Mapping was generally performed at 1:1000 scale. Given errors in georeferencing and landslides poorly resolved in imagery, we conclude that headscarp point locations are generally accurate within 3 m. Municipality (municipio) and barrio names in which each landslide occurred are included in the attribute table of the shapefile, as are the geographic coordinates of each point in decimal degrees (WGS 84 datum). Landslides were identified in 72 of the 78 municipalities of Puerto Rico. No landslides were documented on the island municipalities of Culebra or Vieques. On the main island of Puerto Rico, 64% of land experienced 0-3 landslides per square kilometer, 26% experienced 3-25 landslides per square kilometer, and 10% experienced more than 25 landslides per square kilometer. Concentrated zones of more than 100 landslides per square kilometer are in the municipalities of Maricao, Utuado, Jayuya, and Corozal. Of the ten barrios where more than 100 landslides per square kilometer were catalogued, eight are in Utuado. The drainage basins with the highest density of landslides are the Rio Grande de Arecibo and Rio Grande de Añasco watersheds, each with over 30 landslides per square kilometer. Six out of the seven sub-basins with more than 50 landslides per square kilometer are in the Rio Grande de Arecibo basin. We identified and mapped 71,431 landslides in total. The College of Arts and Sciences at the University of Puerto Rico in Mayagüez is thanked for providing release time to K.S. Hughes to permit partial development of this dataset. References ArcGIS, 2017, CAP Imagery – Hurricane Maria: https://www.arcgis.com/home/webmap/viewer.html?webmap=3218d1cb022d4534be0c7d6833c0adf1. Last accessed 18 June 2019. NOAA, 2017, Hurricane MARIA Imagery: https://storms.ngs.noaa.gov/storms/maria/index.html. Last accessed 18 June 2019. Quantum Spatial, Inc., 2017, FEMA PR Imagery: https://s3.amazonaws.com/fema-cap-imagery/Others/Maria. Last accessed 18 June 2019.