The Arizona Remote Sensing Center (ARSC) was established in 1972 and since its inception, ARSC has worked on a wide range of international, national, regional, and local projects. ARSC researchers utilize advanced airborne-based and satellite-based sensors along with other geospatial information technologies to help address both fundamental and applied issues in natural resource management. ARSC’s mission is to employ remote sensing and geospatial technologies to solve natural, agricultural, and cultural resource problems in the arid and semi-arid regions of the world. This mission involves both basic and applied research in support of the operational application of geospatial technologies and their extension to stakeholders through the integration, analysis, and modeling of field data and remotely sensed imagery, modeling of coupled human and natural systems, and the deployment of decision support systems.ARSC develops and maintains scientific web sites to provide critical hydrologic and vegetation data to various agencies, including two publically available decision support tools: DroughtView and SnowView. DroughtView is a web mapping tool that makes available a variety of vegetation data layers derived from the MODIS and VIIRS satellite sensors, along with other climate, fire, and contextual data layers across the United States. The web mapping and timeseries analyses tools in DroughtView allow users to better understand rangeland impacts of things like fire and drought. Meanwhile, SnowView provides hydrologic (precipitation, snow, and streamflow) information for river basins across the United States. Both tools allow for the easy contextualization of data by allowing for quick and convenient comparisons between different products and for different times.
Current and Recent Projects
- Snowpack and streamflow monitoring and modelling for the Salt River Project
- Assessing impacts of Bighorn Fire on vegetation communities and flood potential. Partners include Pima County Flood control and Arizona Geological Survey.
- Rangeland Brush Estimation Toolbox (RaBET). Partners include USDA Agricultural Research Service and National Ecological Observation Network (NEON).
- Cultural Resources Vulnerability Assessment (CREVAT). Partners include US National Park Service and the Vanishing Treasures Program.
- Vegetation change along the Lower Gila River, Arizona. Partners include US Bureau of Land Management.
- Assessing Biodiversity in the Eastern Mojave Desert. Partners include the Eastern Mojave Conservation Collaborative.
- Detecting cotton root rot in Pecan orchards using drone imagery.
- Earth fissure mapping.
- Identifying urban mosquito habitat in Tucson, AZ.
- Mapping Pygmy Owl Habitat near the U.S. Mexico Border.
- Using drone-based hyperspectral/lidar imagery to investigate dryland productivity.
For additional information on ARSC projects and recent publications, please visit our StoryMap page.
Arizona Remote Sensing Center
Environment and Natural Resources 2 Room N460
1064 East Lowell Street
Tucson, AZ 85721
ARSC People
Willem J.D. van Leeuwen, Ph.D. – ARSC Director and Professor
Kyle Hartfield, M.A. – Assistant Professor of Practice
Patrick Broxton, Ph.D. – Associate Research Scientist
Jeffrey Gillan, Ph.D. – Data Scientist III
Cynthia Norton, M.S. – Research Technologist III
Kangsan Lee – Ph.D. Student School of Geography and Development
Angie Chambers – Undergraduate Student in GIST
The Bighorn fire (June 2020) consumes the Santa Catalina mountains just north of Tucson, captured by Sentinel-2 satellite imagery. Image is shown as a traditional false color composite with vegetation displayed as red. ARSC is using satellite imagery to assess vegetation cover changes caused by the wildfire.
ARSC is using drone imagery to identify pecan trees infected by cotton root rot in San Simon, AZ.
Using hundreds of drone images and photogrammetry software, we can make detailed 3D maps known as point clouds. The graphic depicts the mixed conifer forest on Mt. Bigelow in the Santa Catalina mountains. ARSC is exploring how forest structure contributes to carbon exchange.
The graphic depicts 3D lidar data of a northern Arizona pine forest. A time-series of lidar was used to estimate snow depth.