Post-Katrina Land-Cover, Elevation, and Volume Change Assessment along the South Shore of Lake Pontchartrain, Louisiana, U.S.A.

Abstract Advances in remote-sensing technology have led to its increased use for posthurricane disaster response and assessment; however, the use of the technology is underutilized in the recovery phase of the disaster management cycle. This study illustrates an example of a postdisaster recovery assessment by detecting coastal land cover, elevation, and volume changes using 3 years of post-Katrina hyperspectral and light detection and ranging data collected along the south shore of Lake Pontchartrain, Louisiana. Digital elevation models and basic land-cover classifications were generated for a 34-km2 study area for 2005, 2006, and 2007. A change detection method was used to assess postdisaster land-cover, elevation, and volume changes. Results showed that the vegetation classes had area increases, whereas bare ground/roads and structures classes had area decreases. Overall estimated volume changes included a net volume decrease of 1.6 × 106 m3 in 2005 to 2006 and a net volume decrease of 2.1 × 106 m3 in 2006 to 2007 within the study area. More specifically, low vegetation and bare ground/roads classes had net volume increases, whereas medium and tall vegetation and structures classes had net volume decreases. These changes in land cover, elevation, and volume illustrate some of the major physical impacts of the disaster and ensuing recovery. This study demonstrates an innovative image fusion approach to assess physical changes and postdisaster recovery in a residential, coastal environment.

[2]  Investigating Recovery Patterns in Post Disaster Urban Settings: Utilizing Geospatial Technology to Understand Post-Hurricane Katrina Recovery in New Orleans, Louisiana , 2009 .

[3]  Carl R. Froede Changes to Dauphin Island, Alabama, Brought about by Hurricane Katrina (August 29, 2005) , 2008 .

[4]  Hermann M. Fritz,et al.  Hurricane katrina storm surge distribution and field observations on the Mississippi Barrier Islands , 2007 .

[5]  J. R. Jensen,et al.  Remote Sensing Change Detection in Urban Environments , 2007 .

[6]  Mark Hansen,et al.  Estimation of post-Katrina debris volume: An example from coastal Mississippi: Chapter 3E in Science and the storms-the USGS response to the hurricanes of 2005 , 2007 .

[7]  D. Lu,et al.  Change detection techniques , 2004 .

[8]  Budhendra L. Bhaduri,et al.  Rapid Damage Assessment from High Resolution Imagery , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[9]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[10]  Xianjun Hao,et al.  Post-hurricane forest damage assessment using satellite remote sensing , 2010 .

[11]  Christopher F. Barnes,et al.  Hurricane Disaster Assessments With Image-Driven Data Mining in High-Resolution Satellite Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Ibon Galparsoro,et al.  Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery , 2008 .

[13]  Adam W. Murrah,et al.  The Impact of Hurricane Katrina on the Coastal Vegetation of the Weeks Bay Reserve, Alabama from NDVI Data , 2009 .

[14]  J. Barras,et al.  Land area changes in coastal Louisiana after Hurricanes Katrina and Rita , 2007 .

[15]  C. Brodley,et al.  Decision tree classification of land cover from remotely sensed data , 1997 .

[16]  J. Clevers,et al.  Classification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data , 2007 .

[17]  Jacob T. Mundt,et al.  Mapping Sagebrush Distribution Using Fusion of Hyperspectral and Lidar Classifications , 2006 .

[18]  Paul M. Mather,et al.  An assessment of the effectiveness of decision tree methods for land cover classification , 2003 .

[19]  C. Oswalt,et al.  Relationships between common forest metrics and realized impacts of Hurricane Katrina on forest resources in Mississippi , 2008 .

[20]  A. Goetz,et al.  Software for the derivation of scaled surface reflectances from AVIRIS data , 1992 .

[21]  Ross A. Hill,et al.  Mapping woodland species composition and structure using airborne spectral and LiDAR data , 2005 .

[22]  V. Klemas,et al.  Remote Sensing of Coastal Resources and Environment , 2009 .

[23]  J. Barras Land Area Changes in Coastal Louisiana After the 2005 Hurricanes: A Series of Three Maps , 2006 .

[24]  S. Penland,et al.  Hurricane impact and recovery shoreline change analysis of the Chandeleur Islands, Louisiana, USA: 1855 to 2005 , 2009 .

[25]  Michael J. Oimoen,et al.  Integrating Disparate Lidar Datasets for a Regional Storm Tide Inundation Analysis of Hurricane Katrina , 2009 .

[26]  Jennifer L. Irish,et al.  Airborne Lidar and Airborne Hyperspectral Imagery: A Fusion of Two Proven Sensors for Improved Hydrographic Surveying , 2000 .

[27]  C. Guard,et al.  Tropical Cyclone Report , 1989 .

[28]  J. Barras Land Area Change and Overview of Major Hurricane Impacts in Coastal Louisiana, 2004-08 , 2009 .

[29]  Fugui Wang,et al.  Hurricane Katrina-induced forest damage in relation to ecological factors at landscape scale , 2009, Environmental monitoring and assessment.

[30]  Ashbindu Singh,et al.  Review Article Digital change detection techniques using remotely-sensed data , 1989 .

[31]  W. Cohen,et al.  Lidar Remote Sensing for Ecosystem Studies , 2002 .

[32]  Christopher L. Macon USACE National Coastal Mapping Program and the next generation of data products , 2009, OCEANS 2009.

[33]  A. Elaksher Fusion of hyperspectral images and lidar-based dems for coastal mapping , 2008 .

[34]  J. Wozencraft,et al.  JALBTCX Coastal Mapping for the USACE , 2006 .