Welcome to ROSA P | Satellite remote sensing of submerged aquatic vegetation distribution and status in the Currituck Sound, NC. - 23392 | US Transportation Collection >
Stacks Logo
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.
 
 
Help
Clear All Simple Search
Advanced Search
Satellite remote sensing of submerged aquatic vegetation distribution and status in the Currituck Sound, NC.
  • Published Date:
    2012-11-01
  • Language:
    English
Filetype[PDF-10.48 MB]


This document cannot be previewed automatically as it exceeds 5 MB
Please click the thumbnail image to view the document.
Satellite remote sensing of submerged aquatic vegetation distribution and status in the Currituck Sound, NC.
Details:
  • Report Number:
    FHWA/NC/2010-14
  • Resource Type:
  • Geographical Coverage:
  • NTL Classification:
    NTL-ENERGY AND ENVIRONMENT-ENERGY AND ENVIRONMENT ; NTL-GEOGRAPHIC INFORMATION SYSTEMS-GEOGRAPHIC INFORMATION SYSTEMS ;
  • Format:
  • Description:
    Submerged Aquatic Vegetation (SAV) is an important component in any estuarine ecosystem. As such, it is regulated by federal and state agencies as a jurisdictional resource, where impacts to SAV are compensated through mitigation. Historically, traditional detection methodologies have been proven to be ineffective or inappropriate for SAV mitigation over very large areas. These tasks are further complicated in that the location and density of SAV can change from year to year depending on variances in weather and water quality. Satellite remote sensing holds great promise for providing a labor and cost-effective means of monitoring and quantifying SAV distribution. For this analysis, sensor specific models based on multinomial logit procedures proved to be the best approach for predicting SAV presence or absence. No models could be developed for low distribution occurrence categories due to a low ratio of events to non-events. Statistical automated selection methods were developed to produce the final models we selected for each sensor. The use of the automated best-subsets method allowed for exploration of a number of potential candidate models based on the number of variables input in the model. The automated stepwise selection method led to the final, most reasonable model as decided upon in the best-subset procedure. For a variable to enter into or remain in the model, a p-value of <0.01 was necessary. A model was considered fit if the Hosmer and Lemeshow test yielded an insignificant difference in groups (p>0.05). Sensor specific models were developed for both the Quickbird and Worldview-II sensors, however LANDSAT 5 specific models were inconclusive largely due to quality of the data.

  • Supporting Files:
    No Additional Files
No Related Documents.
You May Also Like:
Submit Feedback >