Research Projects                                                  Home

                                                                                                                                     

 

 

 

 

Research projects undertaken through the OSU Center for Applications of Remote Sensing (CARS)

·         Land Use and Land Cover (LULC) Mapping of the Upper Washita River Subwatersheds. USDA-ARS, EL Reno, OK,  05/06-12/06. $21,600

·         Using a Multi-Resolution GIS-Modeling Approach to Evaluate the Carbon Sequestration Potential in Texas County, Oklahoma, Oklahoma NASA EPSCoR, Feb. 2005-July 2005. $21,000. PI: Mahesh Rao; Co-PI: Guoliang Fan, ECEN and Johnson Thomas, CS

·         Shoreline Mapping and Vegetation Inventory around Hudson Lake, Oklahoma,  AtkinsAmericas Environmental, Inc. August 2003 –June 2004. $80,091. PI: Mahesh Rao

·         Development of a Web-GIS Decision Support System for Environmental Water Quality and Resource Management. University Center for Water Research (UCWR). July 1, 2003-June 30, 2004. Co-PI with Johnson Thomas, CS

·         Toward an Integrated Web-GIS Decision Support System for Evaluating USDA’s Conservation Reserve Program (CRP). Oklahoma NASA-EPSCoR. Research Initiation Grant (RIG) Jan 2003- June 2003. $21,306. Co-PI with Guoliang Fan, ECEN

·         Developing a GIS-based Tool for Automated Feature Information Retrieval from Multisource Geospatial Data: Application to CRP Mapping at Texas County, Oklahoma. University Center for Water Research (UCWR). July 2002 – June 2003. $24,575. Co-PI with Guoliang Fan, ECEN

·         Radiometric Studies to Characterize the Onset of Greenbug-Induced Stress in Wheat. University Center for Water Research (UCWR). July 2001- June 2002. $24,979. PI: Mahesh Rao

·         Evaluating Spatio-Temporal Changes in CRP tracts Using Multi-Seasonal Satellite Imagery. University Center for Water Research (UCWR). July 1, 2000 – June 30, 2001. $25,000. PI: Mahesh Rao

·         EPIC-View: Developing Calibration and Validation Tools. Oklahoma Center for Advancement of Science and Technology (OCAST). $50,573. March 2000- March 2002.PI: Mahesh Rao

 Ft.Cobb Output

 

 

Land Use and Land Cover (LULC) Mapping of the Upper Washita River Subwatersheds

 

Understanding the effects of land use, land management and climate variability on hydrologic responses can greatly help improve the predictive capability of watershed models that are applied for multitude of applications in natural resource management. Satellite remote sensing provides an effective mechanism for regional and statewide land use and land cover mapping.

The goal of this project is to develop a land use and land cover map of the Upper Washita River subwatersheds over time (1970 to present). The specific objectives include:

1.    Analyze the mutliseasonal Landsat TM imagery for 2005 using maximum likelihood classification procedures.

2.    Evaluate the accuracy of the LULC map produced.

 

 

 

Using a Multi-Resolution GIS-Modeling Approach to Evaluate the Carbon Sequestration Potential in Texas County, OK

One of the major goals of USDA’s Conservation Reserve Program (CRP) is to reduce water and wind erosion through the establishment of perennial grass cover on more than 17 million ha (45 million ac) of highly erodible and environmentally sensitive croplands (FSA 2004). Once established, however, these new grasslands also may have the potential to concomitantly reduce atmospheric CO2 levels and increase SOC levels due to accumulation and incorporation of litter into surface soils and the relatively large amounts of net primary production allocated toward root growth. The overall goal of our project is to estimate long-term trends in carbon sequestration potential and water quality due to CRP using a multi-resolution remote sensing and GIS modeling approaches. Specifically, this pilot project aims to scale MODIS EVI based on Landsat data with the intent to refine the carbon predictive capability of CASA model. A related goal of our project is to design and develop an interactive geospatial and non geospatial query system. We believe such mapping, modeling and querying tools will greatly benefit USDA-FSA to better manage the CRP program in terms of effective targeting of lands for enrollment in the program as well as to evaluate the long-term environmental benefits.

  

 

 

Development of a Web-GIS Decision Support System for Environmental Water Quality and Resource Management

This project aims at developing a prototype web-GIS Decision Support System (DSS), CRP-DSS, for use in resource management and assessment of environmental quality. Specifically, the DSS is targeted toward aiding USDA/FSA to better manage and plan the Conservation Reserve Program (CRP). The DSS is based on the emerging industry-standard ArcIMS GIS platform and integrates a mapping component AFIRS (Automated Feature Information Retrieval System) and a modeling component SWAT (Soil and Water Assessment Tool). Our novel integrated web-GIS DSS will be implemented using web server and Java Servlet technology over an ArcIMS platform to support data access and processing in a distributed environment. AFIRS functions as a feature information and extraction protocol that uses multisource geospatial data sets, and SWAT serves to simulate long-term trends of soil and water quality. Specifically, AFIRS enhancements will provide advanced spatial statistical models for multispectral remote sensing data to enable more accurate and precise feature mapping. SWAT will be made available through ArcIMS and will essentially compare the long-term soil and water quality benefits of CRP.

 

 

 

Toward an Integrated Web-GIS Decision Support System for Evaluating USDA’s Conservation Reserve Program (CRP)

CRP is a voluntary program to provide incentives for farmers and ranchers to strengthen environmental stewardship of their lands, and gives producers additional resources to reduce topsoil erosion, increase wildlife habitat and improve air and water quality on these lands. However, CRP has been criticized for administrative shortcomings and failure to achieve ancillary environmental objectives, such as improving wildlife habitat and promoting water quality. Key to the inefficiency of current CRP procedures is the lack of automated decision-support tools. The objective of this project is to develop accurate and timely decision-support aids and research tools to delineate and evaluate CRP. The evaluation of CRP is based on a GIS-based environmental modeling approach. More importantly, we will propose a prototype CRP-DSS that will be interfaced with the Internet and be capable of accessing databases in a distributed environment. Our long-term goal is to develop an integrated Web-GIS DSS to help USDA manage, plan, and prioritize CRP enrollments, leading to maximum environmental benefit within the budget constraints.

 

 

Developing a GIS-based Tool for Automated Feature Information Retrieval from Multisource Geospatial Data: Application to CRP Mapping at Texas County, Oklahoma

This project aims at developing a GIS-based tool, Automated Feature Information Retrieval System, to delineate USDA's Conservation Reserve Program (CRP) tracts. In addition to the satellite imagery, AFIRS will involve multisource geospatial data to achieve accurate and robust feature extractions. Specifically, a GIS database consisting of multisource data, including Landsat TM imagery, soils, elevation, and slope data, etc., will be developed for Texas County, Oklahoma, which ranks first in the state for CRP enrollments. Based on this database and reference data, AFIRS will be trained, developed, and verified. Two machine learning approaches, i.e., decision tree classifier (DTC) and support vector machine (SVM), are applied to develop AFIRS in this work. It is shown that both DTC and SVM are capable of handling the complex problem of CRP mapping with high accuracy and robustness when limited training samples are available. Simulation results show that significant improvements can be obtained by incorporating GIS ancillary data and other derived features. This work validates the applicability of machine learning approaches to the complex real-world remote sensing applications.

 

 

 

Evaluating Spatio-Temporal Changes in CRP tracts Using Multi-Seasonal Satellite Imagery

This project focuses on evaluating the spatial and temporal changes associated with CRP tracts using multi-seasonal satellite imagery. Under the Conservation Reserve Program (CRP), agricultural producers voluntarily retire environmentally sensitive land for 10 to 15 years. In return, USDA’s Commodity Credit Corporation makes annual rental payments to producers and shares the cost of establishing approved conservation practices. This contractual program encourages farmers to plant long-term resource-conserving covers to improve soil, water and wildlife resources.The main goal of this study is to evaluate the spatio-temporal changes in CRP lands between two important time-periods in the CRP program i.e. 1989 and 1999 years. These two years mark important landmarks in the CRP program and provide important information in terms of the sign-up acreage and the take-out acreage under the program. Texas County located in the Panhandle region of Oklahoma will be selected as the study area of this project. Multi-seasonal Landsat Thematic Mapper (TM) data will be used for delineation of the CRP lands. Based on the land cover classification of the satellite imagery, CRP maps will be produced for two time periods i.e. 1989 and 1999. The difference between these two maps produced will reveal important characteristics of CRP lands in both spatial and temporal dimensions.

 

 

Radiometric Studies to Characterize the Onset of Greenbug-Induced Stress in Wheat

A critical component of integrated pest management (IPM) involves a method for monitoring fields to determine whether there is a pest problem. Remote sensing techniques can identify pest infestations in agricultural fields. Field-based remote sensing techniques could provide a vital tool to study such stress in crop plants.

This project aims at characterizing stress induced by greebugs on wheat using a field radiometer. Currently, spectral radiance data is being collected using a field radiometer (Exotech – 100BX) from a wheat crop infested with different intensities of greenbug populations. Based on the preliminary data results, a proposal will be prepared for submission to Sustainable Agriculture Research and Extension (SARE) for funding. Preparation of the proposal is funded by the A&S Dean’s Incentive Grant.

 

 

 

 

EPIC-View: Developing Calibration and Validation Tools

This project aims at developing model calibration and validation tools for EPIC-View. EPIC-View is a user-friendly, GIS-based modeling system developed integrating EPIC with ArcView GIS. EPIC, an acronym for Environment Policy Integrated Climate (previously, Erosion-Productivity Impact Calculator) is a hydrologic/crop growth/water quality model that can be used to determine the effect of management strategies on agricultural production and soil and water resources.

EPIC-View provides: