Abstract
The following proposal was submitted to the National Science Foundation’s Major Research Instrumentation program (NSF#0319145). The proposal was a collaborative initiative that built on the existing strengths of the entire academic unit and explicitly details how an award would complement current departmental infrastructures and expand existing initiatives. The key for instrumentation grants is to avoid writing proposals that appear to be “wish lists” that indicate what researchers “might” do with a new toy. Instead, instrumentation grants must be closely associated with current research, unit infrastructures, and individual (as well as unit) capacities. Beyond the current research agenda, this proposal clearly identifies linkages between research and the undergraduate and graduate curriculum. Additionally, the proposal also outlines an outreach component and provides for public data sharing.
While this proposal was funded, it was funded on the third submission. Ironically, the subsequent submissions were revised only slightly. While some grant writers would have significantly re-tooled their applications in response to reviewer and panel feedback, the structure of NSF programs is such that every competition is a new competition with a restructured panel and often entirely new reviewers. For this reason, the authors believed the original submission was competitive and chose to only slightly revise the later two submissions. The revisions were minor in nature and included only staffing and figure updates. This approach was successful in that each competition is new, NSF programs are fund dependent, and the prevailing principle is “first past the post”. That is to say, NSF does not approach grant competitions as developmental exercises. However, not all grant competitions are the same and grant writers should be attentive to the policies, procedures, and practices of agencies and foundations. cr]
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References
Al, M. 1993. Urban morphological changes in Hofuf, Saudi Arabia: impact of western planning ideas in traditional housing. Dissertation, Indiana State University.
Clark, R.N., T.V.V. King, C. Ager, and G.A. Swayze. 1995. Initial vegetation species and senescence/stress mapping in the San Luis Valley, Colorado, using imaging spectrometer data. In Proceedings of the Summitville Forum 95, Summitville, Colorado, 64–69.
Clark, C.D., S.M. Garrod, and M. Parker Pearson. 1998. Landscape archaeology and remote sensing in southern Madagascar. International Journal of Remote Sensing 19:1461–1477.
Clevers, J.G.P.W. 1997. A simplified approach for yield prediction of sugar beet based on optical remote sensing data. Remote Sensing of Environment 61:221–228.
Cochrane, M.A. 2000. Using vegetation reflectance variability for species level classification of hyperspectral data. International Journal of Remote Sensing 21:2075–2087.
Elvidge, C.D., Z. Chen, and D.P. Groenveld. 1993. Detection of trace quantities of green vegetation in 1990 AVIRIS data. Remote Sensing of Environment 44:271–279.
Franklin, S.E., C.F. Blodgett, S. Mah, and C. Wrightson. 1991. Sensitivity of CASI data to anisotropic reflectance, terrain aspect and deciduous forest species. Canadian Journal of Remote Sensing 14:314–321.
Gao, B.C. and A.F.H. Goetz. 1995. Retrieval of equivalent water thickness and information related to bio-chemical components of vegetation canopies from AVIRIS data. Remote Sensing of Environment 51:155–162.
Gatrell, J.G. and R.R. Jensen, 2002. Growth through greening: developing and assessing alternative economic development programs. Applied Geography 22(4): 331–350.
Gong, P., P. Ruiliang, and J.R. Miller. 1992. Correlating leaf area index of ponderosa pine with hyperspectral CASI data. Canadian Journal of Remote Sensing, 18:275–282.
Gong, P. P. Ruiliang, and J.R. Miller. 1995. Coniferous forest leaf area index estimation along the Oregon Transect using Compact Airborne Spectrographic Imager data. Photogrammetric Engineering and Remote Sensing 61: 1107–1117.
Jackson, M. W. and J. R. Jensen, 2001. Quantifying Environmental Change from Remotely Sensed Imagery Using A Composite Ecosystem Degradation Index. Remote Sensing for Agriculture, Ecosystems, and Hydrology, (SPIE), 4171: 214–222.
Jensen, J. R., Halls, J. N. and J. Michel, 1998. A Systems Approach to Environmental Sensitivity Index (ESI) Mapping for Oil Spill Contingency Planning and Response. Photogrammetric Engineering & Remote Sensing, 64(10):1103–1014.
Jensen, J. R. and D. Cowen, 1999. Remote Sensing Urban/Suburban Infrastructure and Socio-economic Attributes. Photogrammetric Engineering & Remote Sensing, 65(5):611–622.
Jensen, J. R., Qiu, F. and M. Ji, 2000. Predictive Modeling of Coniferous Forest Age Using statistical and Artificial Neural Network Approaches Applied to Remote Sensor Data. International Journal of Remote Sensing, 20(14):2805–2822.
Jensen, R.R. 2000. Measurement, comparison, and use of remotely derived leaf area index (LAI) predictors. Dissertation, University of Florida.
Jensen, R.R. and A. Carson. 2001. Longleaf Pine / Turkey Oak Sandhill Loss in a North Central Florida Preserve, 1972–1997. Southeastern Geographer 41: 306–311.
Jensen, R.R. and M.W. Binford. 2001. Using low-resolution satellite data to quantify terrestrial carbon in tropical areas. Geocarto International 16(2): 17–22.
Jensen, R.R. 2002. Spatial and temporal leaf area index dynamics in a north central Florida, USA preserve. Geocarto International 17(4): 45–52.
Jensen, R.R., 2002. Teaching GIS and remote sensing integration using fire ecology in longleaf pine sandhills. Journal of Geoscience Education 50(3): 292–295.
Jensen, J.R. and R.R. Jensen. 2002. Remote sensing digital image processing system hardware and software considerations. In The Manual of GeoSpatial Science and Technology. J.D. Bossler, ed. Taylor and Francis, London. p. 325–348.
Jensen, R.R., J.R. Boulton, and B.T. Harper (a). In Review. The Relationship Between Urban Leaf Area and Household Energy Usage in Terre Haute, Indiana, USA. Submitted to Journal of Arboriculture.
Jensen, R.R., J.D. Gatrell, J.R. Boulton, and B.T. Harper (b). In Review. Greenness as an indicator of socioeconomic conditions in Terre Haute, Indiana, USA. Submitted to Society and Natural Resources.
Jensen, J.R. 1996. Introductory Digital Image Processing: A Remote Sensing Perspective. Prentice-Hall, Englewood Cliffs, NJ.
Jensen, J.R. 2000. Remote Sensing of the Environment: An Earth Resources Perspective. New Jersey, Prentice-Hall.
Larson, R.C. and W.H. Carnahan. 1997. The influence of surface characteristics on urban radiant temperatures. Geocarto International 12:5–16.
Lelong, C.C.D., P.C. Pinet, and H. Poilve. 1998. Hyperspectral imaging and stress mapping in agriculture: a case study on wheat in France. Remote Sensing of Environment 66:179–191.
Mausel, P., Y. Wu, Y. Li, E. Moran, and E. Brondizio. 1993. Spectral identification of succession stages following deforestation in Amazonia. Geocarto International 8:11–20.
Moran, E., E. Brondizio, and P. Mausel. 1994. Secondary succession. Research and Exploration 10: 459–476.
Nam, K. 1996. Impact of service industrial development of Terre Haute on the economy of the Wabash Valley. Thesis, Indiana State University.
Pax-Lenney, M. and C.E. Woodcock. 1997. The effect of spatial resolution on the ability to monitor the status of agricultural lands. Remote Sensing of Environment 61:210–220.
Resmini, R.G., M.E. Kappus, W.S. Aldrich, J.C. Harsanyi, and M. Anderson. 1997. Mineral mapping with Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at cuprite, Nevada, U.S.A. International Journal of Remote Sensing 18:1553–1570.
Rudibaugh, M.A. 1995. Gender differences in commuting patterns in Indianapolis, Indiana. Thesis, Indiana State University.
Treitz, P.M. and P.J. Howarth. 1999. Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems. Progress in Physical Geography 23:359–390.
Van Der Meer, F. 2000. Geophysical inversion of imaging spectrometer data for geologic modeling. International Journal of Remote Sensing 21:397–393.
Yoder, B.J. and R.E. Pettigrew-Crosby. 1995. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500nm) at leaf and canopy scales. Remote Sensing of Environment 53: 199–211.
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(2005). Extramural Grant II: Instrumentation. In: Research Design and Proposal Writing in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27953-9_16
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DOI: https://doi.org/10.1007/3-540-27953-9_16
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