Trends in Lake Ontario water levels cm above annual mean Upper Midwest Regional Earth Science Applications Center “Applying NASA earth science to key regional issues” The Upper Midwest RESAC is a consortium of researchers from three universities: the University of Minnesota, the University of Wisconsin  Madison, and Michigan State University. With partners from government, private industry and resource agencies, we are developing remote sensing, geospatial analysis methods, and biophysical process models for regional applications in agriculture, forestry, land cover and cover change and water resources. The key to our success is the end- to-end involvement of our partners in all stages of development and application. The Upper Midwest RESAC’s goal is to provide advanced information and tools to a variety of decision makers in our area of the country. Such decision makers can be individual growers, who must integrate information from many sources in order to make farm operations profitable. Decision makers can be private industry, who need to account for fluctuating markets, potential environmental impacts and government regulatory policies in making land-use decisions. Lastly decision-makers can be governmental agencies, who when making policy decisions must balance the sector demands of recreation, business, housing, environmental and aesthetic interests and others. Examples of some of our work are presented below. Some of our partners are  Minnesota Department of Natural Resources -- ForNet  Michigan Department of Natural Resources  Wisconsin Department of Natural Resources  State Cartographer's Office, Wisconsin  USDA, Forest Service  USDA, Natural Resources Conservation Service  USGS, Upper Midwest Environmental Sciences Center  Case New Holland Advanced Farming Systems Services  Champion International Our main web site is located at You can also contact our two Principal Investigators directly Dr. George R. Diak, University of Wisconsin-Madison (608) Dr. Marvin E. Bauer, University of Minnesota (612) Due to the diverse agriculture of the region and the changing patterns of land use in agricultural and other areas, an inventory of the spatial position and characteristics of crop production in the region is needed to assess the changing dynamics. We use a variety of satellite sensors including AVHRR, MODIS, and Landsat 7 ETM+ to classify and to create a spatial inventory of different agricultural commodities. This information will be assembled into a digital atlas available on the internet. Web-based prediction models for productivity are also being developed to utilize these data layers and assess long term changes in agricultural production. Some of the data layers in the atlas and an example image of web- based modeling are shown above. Corn Yield, 1991 Vegetation percent cover, 1991 Land Cover Classification Ecosystem Classification ElevationWeb-based modeling Our Precision Agricultural-Landscape Model (PALM) provides agricultural consultants with decision-support information. PALM simulates the effects of environmental conditions on soil moisture, runoff, plant growth, grain yield, and grain moisture. PALM is a precision-scale model, meaning that it simulates conditions on individual grid cells over the field. These figures show soil moisture. Dark red areas of the field are impassible for heavy farm machinery. Areas in shades of orange are passable, but vulnerable to compaction (soil compaction can cause a 10% yield loss by inhibiting root growth). Shades of blue indicate drier soil at little risk for compaction. With such maps, farmers can quickly identify which fields are ready for tillage and which should be allowed to dry further. This is particularly important for logistics on larger farms, where fields can be spread out over many miles. May 17 May 22 May 27 No Compaction ~10% Yield Loss Not Passable To assist the classification and estimation of forests, our RESAC is developing forest cover maps from Landsat ETM+ and SPOT 4 VEGETATION sensors. An archive of SPOT 4 imagery (1km) acquired from April-November 1999 has been compiled weekly. A “nearest neighbor” data analysis approach allows us to use remote sensing imagery to fill in gaps left by manual inventories. Classification and fractional cover estimation of agricultural and forest vegetation produces maps showing the temporal variation in percent vegetation cover. The RESAC also sponsors the Great Lakes Forest Information Center (GLFIC) at MSU. GLIFIC provides internet access of information and remote sensing data to researchers and policy makers. Great Lakes Forest Information Center SPOT 4 percent vegetation cover, 1999 Nearest neighbor forest inventory The Land Transformation Model uses many levels of information from natural features of the land to man-made features and land ownership. Using a neural network approach, the model is “trained” with prior information to relate data layer changes to land-use changes. The relationships developed then can predict future land-use changes based on current knowledge. The panel to the right shows a prediction of land- use change around Saginaw Bay MI. Similar predictions are now being made for the entire MN-WI-MI area. Land use and natural resource managers can use these predictions to guide long-term planning. Land Transformation Model (LTM) LTM uses these data layers and others 1938 air photo near Alba, MI Land cover classification of air photo We are compiling an archive of land cover data created by the Multi-resolution Land Characteristics Consortium. A variety of techniques are used to classify land cover types from historical air photos, airborne sensor data and satellite remote sensing imagery. This data is valuable in tracing land use and land cover history, analyzing patterns of current land use, and providing inputs for modeling techniques, such as the LTM, described below. Observers take Secchi readings on an overpass day Our Satellite Lake Observatory Initiative (SLOI) involves volunteer observers throughout the state of Wisconsin. These volunteers coordinate their Secchi disk observations with days of Landsat overpass. We then derive a relation between brightness values from the Landsat images and Secchi readings over time. This relationship allows us to calculate the Trophic State Index (TSI), which is a measure of water quality. The regression can be calculated for all the lakes in the Landsat image, not just lakes where Secchi measurements were performed, allowing the water quality of many other lakes to be estimated. Landsat imageLandsat - Secchi regression Trophic State Index Who we are What we do Our Partners How to contact us We are studying the role of reforestation as an offset to rising atmospheric CO 2 concentrations. The objective is to quantify the ecological and economic potential of different land-use strategies (agriculture, native forest and plantation forests) on carbon sequestration in the soil and vegetation. One study site is in the highly erodable Driftless area of Southern Wisconsin (right). The two graphs below show measured versus simulated (IBIS) Above-Ground Net Primary Productivity (ANPP) and Leaf-Area Index (LAI). Simulated annual runoff (cm) The lakes and rivers of the Great Lakes region are a unique resource and are potentially sensitive to changes in climate and land cover. Our RESAC is using a combination of observational data analysis and computer modeling to understand the regional climate and hydrology of the Upper Midwest. We have found that each of the Great Lakes has undergone significant changes in seasonal hydrology over the past 140 years, such as an earlier rise and fall of the level of Lake Ontario. Computer modeling tools have been developed to help understand these changes as well as future impacts on the region. We are using a regional land surface model (the Integrated Biosphere Simulator, or IBIS) to simulate evaporation, snow cover, soil moisture, and runoff over the Upper Midwest. Together with a hydrologic routing algorithm (HYDRA), which simulates river discharge and lake level, the modeling system provides a powerful tool for understanding and predicting various impacts on the water resources of the Great Lakes region. We are completing the model evaluation phase and will soon be initiating simulations of historical and future changes in climate.