Geology topics

Elijah W. Ramsey

Publications and source records attributed to Elijah W. Ramsey.

42 records · Page 3Linked to original sources

USGS1999

Using multiple-polarization L-band radar to monitor marsh burn recovery

Aircraft L-band VV-, HH-, and VH-polarizations were examined as tools for monitoring burn recovery in a coastal marsh. Significant relationships were observed between time-since-burn (difference between burn and image collection dates; 550-900 days after burn) and returns related to all polarizations. As marsh burn recovery progressed, VV returns decreased while HH and VH returns increased. Radar returns extracted from control sites adjacent to each burn-simulated nonburn marsh and were not individually or in combination significantly related to the timesince-burn. Normalized by the control data, VH-polarization explained up to 83% of the total variations. Overall, the L-band multipolarization radars estimated time-since-burn within ±59 to ±92 days.

Florida↗

USGS1998

Classifying coastal resources by integrating optical and radar imagery and color infrared photography

A progressive classification of a marsh and forest system using Landsat Thematic Mapper (TM), color infrared (CIR) photograph, and ERS-1 synthetic aperture radar (SAR) data improved classification accuracy when compared to classification using solely TM reflective band data. The classification resulted in a detailed identification of differences within a nearly monotypic black needlerush marsh. Accuracy percentages of these classes were surprisingly high given the complexities of classification. The detailed classification resulted in a more accurate portrayal of the marsh transgressive sequence than was obtainable with TM data alone. Individual sensor contribution to the improved classification was compared to that using only the six reflective TM bands. Individually, the green reflective CIR and SAR data identified broad categories of water, marsh, and forest. In combination with TM, SAR and the green CIR band each improved overall accuracy by about 3% and 15% respectively. The SAR data improved the TM classification accuracy mostly in the marsh classes. The green CIR data also improved the marsh classification accuracy and accuracies in some water classes. The final combination of all sensor data improved almost all class accuracies from 2% to 70% with an overall improvement of about 20% over TM data alone. Not only was the identification of vegetation types improved, but the spatial detail of the classification approached 10 m in some areas.

Mangroves and Salt Marshes↗

USGS1998

Generation of coastal marsh topography with radar and ground-based measurements

A topographic surface of a low lying coastal marsh was created by using three flood extent vectors digitized from ERS-1 SAR images and two elevation contours from U.S. Geological Survey topographic quadrangles. Point measurement of water depth at the times of the SAR collections allowed conversion of the radar measured flood extent vectors to topographic contours. Generation of the topographic surface was accomplished with a surface gridding algorithm, SAR and on-site measures. Errors in the generated topography were mainly associated with the lack of input contours covering narrow to broad plateaus and topographic highs and lows. The misplacement of SAR derived flood extent vectors also caused errors in sparsely vegetated high marsh, at convoluted marsh-forest boundaries, and at topographic depressions. Overall, the standard deviation of differences between measured and predicted elevations at 747 points was 19 cm. Excluding the above mentioned abrupt boundaries and topographic highs and lows outside the range of available contours, standard deviation differences averaged about 14 cm, but most often averaged about 8 cm. This suggested a 5 to 9 factor improvement over the 150 cm topographic resolution currently available for this area.

Journal of Coastal Research↗

USGS1997

Using remote sensing to monitor global change

To properly respond to natural and human-induced stresses to wetlands, resource managers must consider their functions and values. Remote sensing is an important tool for monitoring wetland responses to changes in the hydrologic regime and water quality caused by global climate change and sea-level rise.

St. Marks National Wildlife Refuge↗

USGS1997

Generation of coastal marsh topography with radar and ground-based measurements

A topographic surface of a low lying coastal marsh was created by using three flood extent vectors digitized from ERS-1 SAR images and two elevation contours from U.S. Geological Survey topographic quadrangles. Point measurement of water depth at the times of the SAR collections allowed conversion of the radar measured flood extent vectors to topographic contours. Generation of the topographic surface was accomplished with a surface gridding algorithm. SAR and on-site measures. Errors in the generated topography were mainly associated with the lack of input contours covering narrow to broad plateaus and topographic highs and lows. The misplacement of SAR derived flood extent vectors also caused errors in sparsely vegetated high marsh at convoluted marsh-forest boundaries, and at topographic depressions. Overall, the standard deviation of differences between measured and predicted elevations at 747 points was 19 cm. Excluding the above mentioned abrupt boundaries and topographic highs and lows outside the range of available contours, standard deviation differences averaged about 14 cm, but most often averaged about 8 cm. This suggested a 5 to 9 factor improvement over the 150 cm topographic resolution currently available for this area.

Journal of Coastal Research↗

USGS1997

AVHRR imagery used to identify hurricane damage in a forested wetland of Louisiana

Certain events provide a unique opportunity to test the monitoring capability of AVHBR imagery. On 26 August 1992, Hurricane Andrew passed through Louisiana, impacting a large area of forested wetlands. One response to the widespread defoliation resulting from the hurricane impact was an abnormal bloom of new leaves and new growth in the underlying vegetation between September and October. To capture this atypical phenology, a time sequence of AVHRR images was transformed into a normalized difference vegetation index, NDVI, as an indicator of vegetation changes in the forest impacted by the passage of a hurricane. Using geographic information system functions, three sites in the impacted forest were vectorized as polygons, and the inclusive pixels were extracted for subsequent graphical and univariate statistical analysis. Temporal curves of mean NDVIs for the three sites for before, during, and after the hurricane passage, and aggregate curves of the impacted forest to an undisturbed forest, were compared. These comparisons corroborated the atypical phenology of the impacted forested wetland and directly related the cause to the hurricane passage.

Louisiana↗