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Remote Sensing & Management
Dr. van Leeuwen and Aaryn Olsson ground reference satellite data on Tumamoc Hill.
Researchers in SNRE’s Office of Arid Land Studies are looking down from above on the landscapes of Arizona. Remote sensing technology has advanced rapidly in the last two decades due to improved quality, spatial resolution and availability of data, and is being used for managing land and water resources as land use patterns and climate are changing. The Arizona Remote Sensing Center (ARSC), founded in 1972, processes and stores remotely sensed information – creating and sharing decision support tools and raw data through its web portal.
The Office of Arid Lands Studies is active in all aspects of generating science and solutions on real landscapes in real time – from gathering and synthesizing information to returning applications to the public, either directly or through resource management agencies. One example of an ongoing ARSC project, RangeView, enables natural resource managers, land owners, educators and researches to monitor vegetation dynamics through visualizations and analysis of satellite imagery. Data and tools supplied by the easy-to-use RangeView site can be used to:
- Track droughts
- Inform annual pasture rotation plans
- Map and model potential and current fires
- Post-analysis of prescribed burns – comparing the impact of the burn request with the actual area burned
- Distinguish vegetation changes on rangeland due to natural conditions from those due to management decisions
- Monitor impacts of urban encroachment on riparian areas
OALS Assistant Professor Wim van Leeuwen has been involved in the development of RangeView and also works on other web-based decision support tools and applications for agriculture and natural resource management in the US and Africa (Senegal River Valley; below).
In his research program, Dr. van Leeuwen’s is working on the development of tools and methods for characterizing and monitoring post wildfire vegetation recovery and species habitats, land degradation, invasive species mapping, land cover classification and land surface phenology in response to climate variability and human impacts, employing remote sensing and geospatial tools.
Phenology is the study of the life cycles of plants and animals – and Dr. van Leeuwen’s focus is on vegetation dynamics and green up, the timing of photosynthesis in plants due to seasonal precipitation and temperature patterns. Plant community growth can be detected when he examines successive satellite images of a single place. He focuses on the relative usefulness of several types of remotely sensed data for determining vegetation response to the environment. For example, he is monitoring and assessing vegetation response to drought with remotely sensed indices that can detect “brown down” and green up. He is using field observations and repeat photography (shown below) to interpret and corroborate satellite based information. The vegetation response of the biologically diverse sky island in Saguaro National Park in Arizona can be monitored through time and inform management decisions.
Dr. van Leeuwen believes that phenology is a great tool for engaging the larger public in citizen science. He is a key player in the Southwestern Regional Phenology Network, part of the USA National Phenology Network’s efforts to encourage people of all ages and backgrounds to observe and record plant and animal phenology as a tool to discover and explore the nature and pace of our dynamic world.
van Leeuwen, Willem J. D., G. M. Casady, D. G. Neary, S. Bautista, J. A. Alloza, Y. Carmel, L. Wittenberg, D. Malkinson, B. J. Orr, 2009. Monitoring post-wildfire vegetation recovery with remotely sensed time-series data in Spain, USA and Israel. International Journal of Wildland Fire, (Accepted).
van Leeuwen, W.J.D. 2009. “Visible, Near-IR & Shortwave IR Spectral Characteristics of Terrestrial Surfaces.” In: T. Warner, D. Nellis and G. Foody (eds) Handbook of Remote Sensing. SAGE
Huang, C., Geiger, E., W.J.D. van Leeuwen, and Marsh, S., 2009. Discrimination of invaded and native species sites in a semi-desert grassland using MODIS multi-temporal data. International Journal of Remote Sensing, 30:897-91.
Michael A. White, Kirsten M. de Beurs, Kamel Didan, David W. Inouye, Andrew D. Richardson, Olaf P. Jensen, John Magnuson, John O’Keefe, Gong Zhang, Ramakrishna R. Nemani, Willem J.D. van Leeuwen, Jesselyn F. Brown, Allard de Wit, Michael Schaepman, Xioamao Lin, Michael Dettinger, Amey Bailey, John Kimball, Mark D. Schwartz, Dennis D. Baldocchi, John T. Lee, William K. Lauenroth, 2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982 to 2006. Global Change Biology, In Press.
van Leeuwen, W.J.D. 2008. Monitoring the Effects of Forest Restoration Treatments on Post-Fire Vegetation Recovery with MODIS Multitemporal Data. Sensors, 8:2017-2042.
van Leeuwen, W.J.D. and B.J. Orr. 2006. Spectral vegetation indices and uncertainty: insights from a user’s perspective. IEEE Transactions on Geoscience and Remote Sensing, 44(7):1931-1933.
van Leeuwen, W.J.D., B.J. Orr, S.E. Marsh, and S. Herrmann. 2006. Multi-sensor NDVI data continuity: uncertainties and implications for vegetation monitoring applications. Remote Sensing of Environment, 100(1):67-81.
van Leeuwan, W.J.D. and J. Kariyeva. 2009. An Assessment of Land Surface Phenology for Detecting Spatio-Temporal Landscape Change Patterns: Arizona and its National Parks. 33rd International Symposium on Remote Sensing of Environment, May 4 - 8, 2009, Stresa, Lago Maggiore, Italy.
NASA, USGS, IALC, NSF
Dr. Craig Allen, USGS
Dr. Susana Bautista, University of Alicante
Dr. Grant Casady, OALS, SNRE
Dr. Mike Crimmins, SWES
Dr. Kamel Didan, ECE
Dr. Dan Malkinson, University of Haifa
Dr. Stuart Marsh, OALS, SNRE
Dr. Dan Neary, USDA Forest Service
Dr. Barron Orr, OALS, SNRE
Dr. Lea Wittenberg, University of Haifa