Monitoring Agricultural Practices in Rwanda

By Langley Rwanda Agriculture Team , posted on August 13th, 2012 in DEVELOP Virtual Poster Session

A comparison chart showing the difference between an unclassified and classified image.  The unclassified image is displayed in three color palettes, and the classified image is shown at the bottom right.

A comparison chart showing the difference between an unclassified and classified image. The unclassified image is displayed in three color palettes, and the classified image is shown at the bottom right.

Team Location: NASA Langley Research Center, Hampton, Virginia

Authors: Sarah Lewis-Gonzales, Texas Tech University; Jeff Kent, Colorado State University; Keith Jaszka, Ball State University; Christopher O’Brien, Troy University; Benjamin Wykes, Embry Riddle Aeronautical University; Suzanne Wong, Miami Lakes Educational Center.

Advisors/Mentors: Dr. Kenton Ross, NASA Langley Research Center; Lauren Childs, NASA Langley Research Center.

Other Acknowledgements: Dr. Rick van Remortel, Innovate! Inc.; Dr. Shawana Johnson, Global Market Insights Inc.

Abstract: Rwanda’s mountainous landscape is a challenge for the republic’s citizens, who depend on agriculture to make a living and survive, especially in the Western Province. In addition to problems that occur naturally, such as high precipitation during the rainy seasons and the extreme topography of the Western Province, under-regulated farming practices and deforestation cause high levels of erosion, which intensifies the degradation of the soil. The Republic of Rwanda is taking steps to improve agricultural practices, but is in need of an efficient means of monitoring their success. NASA’s Earth Observations System, if used in conjunction with remote-sensing software like ERDAS, ArcGIS, and MultiSpec, can facilitate land-use evaluations so that the Rwandan government can assess the efficacy of agricultural policies. Outputs of this project include detailed methodologies for classifying land cover through use of remote-sensing data and the previously mentioned software. In addition, an erosion susceptibility map was generated using digital elevation data with the Revised Universal Soil Loss Equation (RUSLE). Using factors such as slope length, soil type, and rainfall, the map classified the Western Province into varying degrees of erosion susceptibility.

Summer VPS > Agriculture

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