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LANDFIRE 2015 Remap – Utilization of Remotely Sensed Data to Classify Existing Vegetation Type and Structure to Support Strategic Planning and Tactical Response
The LANDFIRE Program produces national scale vegetation, fuels, fire regimes, and landscape disturbance data for the entire U.S. These data products have been used to model the potential impacts of fire on the landscape [1], the wildfire risks associated with land and resource management [2, 3], and those near population centers and accompanying Wildland Urban Interface zones [4], as well as many other applications . The initial LANDFIRE National Existing Vegetation Type (EVT ) and vegetation structure layers, including vegetation percent cover and height, were mapped circa 2001 and released in 2009 [5]. Each EVT is representative of the dominant plant community within a given area. The EVT layer has since been updated by identifying areas of landscape change and modifying the vegetation types utilizing a series of rules that consider the disturbance type, severity of disturbance, and time since disturbance [6, 7]. Non-disturbed areas were adjusted for vegetation growth and succession. LANDFIRE vegetation structure layers also have been updated by using data modeling techniques [see 6 for a full description]. The subsequent updated versions of LANDFIRE include LANDFIRE 2008, 2010, 2012 , and LANDFIRE 2014 is being incrementally released, with all data being released in early 2017. Additionally, a comprehensive remap of the baseline data, LANDFIRE 2015 Remap , is being prototyped, and production is tentatively planned to begin in early 2017 to provide a more current baseline for future updates.
Remote sensing and geospatial support to burned area emergency response teams
A major concern of land managers in the United States is the response of watersheds to weather after a wildfire. With an ever-expanding wildland-urban interface (WUI), land managers must be cognizant of potential damage to private property and other values at risk. In the United States, land-management agencies from the U.S. Department of Agriculture (USDA) and the U.S. Department of the Interior (DOI) deploy Burned Area Emergency Response (BAER) teams to address these concerns and to “prescribe and implement emergency treatments to minimize threats to life or property or to stabilize and prevent unacceptable degradation to natural and cultural resources resulting from the effects of a fire” (USDA Forest Service 2004, p. 17). BAER teams’ objective is emergency stabilization of burned areas, rather than long-term restoration of the landscape after a fire.
Environmental consequences of the Persian Gulf War, 1990-1991: Remote sensing datasets of Kuwait and environs
No abstract available.
Hyperspectral remote sensing of wetland vegetation
Chapter 11 by Ramsey and Rangoonwala provides an overview of how hyperspectral imaging (HSI) advances the mapping of coastal wetlands that comprise a unique variety of plant species, forms, and associations. Each description begins by seeking to uncover the relationship between canopy hyperspectral reflectance and one or more of the aggregated biophysical properties of the wetland canopy: leaf spectral properties, canopy structure, or background reflectance. Examples incorporate the application of radiative transfer equations, direct measurements, and above the top of canopy reflectance and photography. First demonstrated is how HSI can elucidate relations observed with broadband mapping of mangroves as well as enhance change detection. Similar uses of HSI are suggested for the mapping of cypress and bottomland hardwood forests. Next, the mapping of invasive plants is used to demonstrate how HSI can be used to transfer high-spatial-resolution broadband information to spatial resolutions amenable to regional mapping. The demonstration also includes the fusion of HSI and broadband mapping for added-value regional risk assessment of invasive establishment. Illustrations and discussion then show that the proper interpretation of HSI canopy reflectance can require measurement of both the marsh canopy structure and its compositional biomass. The final HSI application demonstrates the capability to detect and track abnormal marsh change at the leaf and canopy levels within variable backgrounds. It further shows how necessary HSI spectral information can be identified and captured in a limited number of narrowbands for advancing operational mapping.
Remote sensing of water quality
Water property data were collected within 3 cooling water reservoirs (active to inactive, large [1,068 ha] to small [68.6 ha]. oligotrophic to eutrophic) at 31 locations. A description of the water characteristics was obtained including algal pigments, total suspended particles, dissolved and particulate organic matter, and total particle absorption spectra. Field data included generated water volume reflectance spectra and secchi depth. Specialized regression techniques were used to derive specific absorption coefficients from measured water property concentrations and total particle absorption spectra. Subsequently, using first order approximations relating water volume reflectance to water intrinsic properties, generated absorption coefficients were combined with an optimization procedure that minimized the difference between the observed and predicted water volume reflectances resulting in specific backscatter coefficient estimates.
The role of remote sensing and GIS for wildland fire hazard assessment
No abstract available.
Multiple temporal and spatial remote sensing data as inputs into a landcover resource geographic information system
No abstract available.
Monitoring Hurricane Andrew damage and recovery of the coastal Louisiana marsh using satellite remote sensing data
No abstract available.
Dust emission and deposition in the southwestern United States - Integrated field, remote sensing, and modeling studies to evaluate response to climatic variability and land use
No abstract available.
Pacific Continental Shelf Environmental Assessment (PaCSEA): Characterization of Seasonal Water Masses within the Northern California Current System Using Airborne Remote Sensing off Northern California, Oregon, and Washington, 2011–2012
Here, we use ocean color measurements (Figure 1) and sea surface temperature (SST) data collected using sensors mounted on low-flying aircraft to characterize NCCS water masses and identify patterns among seasons and between years. To accomplish this, we applied k-means clustering to measured and derived ecologically-relevant physical and bio-optical variables (SST, Chla, absorbance by colored dissolved organic matter [aCDOM], proxy particle load). These classifications will be used in the future to evaluate species habitat distributions in the NCCS.
Remote sensing to petroleum exploration in the Qaidam Basin, China
No abstract available.
Update on the use of remote sensing in oil and gas exploration
No abstract available.