The Terrain Ruggedness Index (TRI) is used to express the amount of elevation difference between adjacent cells of a DEM. This raster function template is used to generate a visual representation of the TRI with your elevation data. The results are interpreted as follows:0-80m is considered to represent a level terrain surface81-116m represents a nearly level surface117-161m represents a slightly rugged surface162-239m represents an intermediately rugged surface240-497m represents a moderately rugged surface498-958m represents a highly rugged surface959-4367m represents an extremely rugged surfaceWhen to use this raster function templateThe main value of this measurement is that it gives a relatively accurate view of the vertical change taking place in the terrain model from cell to cell. The TRI provides data on the relative change in height of the hillslope (rise), such as the side of a canyon.How to use this raster function templateIn ArcGIS Pro, search ArcGIS Living Atlas for raster function templates to apply them to your imagery layer. You can also download the raster function template, attach it to a mosaic dataset, and publish it as an image service. The output is a visual TRI representation of your imagery. This index supports elevation data.References:Raster functionsApplicable geographiesThe index is a standard index which is designed to work globally.

The Topographic Roughness Index (TRI) of AZ HUC8 watersheds generated using a custom R script utilizing the package terra. TRI is defined as the square root of the summation of the difference between the eight neighboring cells and the center cell squared, in a DEM 3x3 focal neighborhood. Put simply, it is a unitless index of surface roughness which incorporates change in surface elevation in all directions, unlike slope which is unidirectional.

The raster dataset comprises of morphological derived variables from global bathymetry dataset (GEBCO 2014). Slope, Bathymetric Position Index (BPI) at fine and broad moving windows, Terrain Ruggedness Index (TRI), Roughness, Aspect (Eastness and Northness), Curvature (General, Planar, Profile) are provided individually and at a resolution of 90 metres.

Eighteen lakebed areas in northern Lake Michigan known to be used by adult fishes for spawning were surveyed between 2021 and 2023 using a Norbit® iWBMSh multibeam echosounder to support fisheries habitat assessment. More than 3500 kilometers of high resolution multibeam data were collected with 0.05m horizontal and 0.5m vertical positional accuracy, with 100% overlap between survey track-lines. Raw echosounder data were processed into bathymetry and backscatter raster layers gridded at 0.5-m and 1-m horizontal resolutions to allow for comparison of reef extents and support development of additional derived products including slope, aspect, roughness, Topographical Roughness Index (TRI), Topographical Position Index (TPI), Hillshade, and 10 Class geomorphic classification. These high-resolution map products can be used to accurately compare reefs and support additional analyses and geographic targeting of future research and restoration efforts.

The dataset for PM10 concentration prediction was compiled from 25 predictor variables and PM10 measurements from 11 monitoring stations in Addis Ababa. The final dataset consists of 38,408 observations spanning two years, from August 2021 to August 2023. The predictor variables encompass various factors impacting PM10 levels, including atmospheric conditions such as atmospheric density (AD), temperature (Ta), wind exposure (WEX), wind speed (WS), and rainfall (RF); urban features like night light (NL), land surface temperature (LST), urban index (UI), road density (RD), and building density (BD); terrain characteristics such as elevation (ELV), slope (SLP), topographic wetness index (TWI), terrain roughness index (TRI), and Soil-Adjusted Vegetation Index (SAVI); and proximity to potential PM10 sources including airports, petrol stations, roads, quarries, railways, markets, waste sites, bus stations, construction sites, and industries. This dataset facilitates the development of robust predictive models and supports the creation of effective air quality management strategies for Addis Ababa and other urban areas facing similar challenges.