Abstract—LANDFIRE is a large interagency project designed to provide nationwide spatial data for fire management applica-tions. As part of the effort, many 2000 vintage Landsat Thematic Mapper and Enhanced Thematic Mapper plus data sets were used in conjunction with a large volume of field information to generate detailed vegetation type and structure data sets for the entire United States. In order to keep these data sets current and relevant to resource managers, there was strong need to develop an approach for updating these products. We are using three dif-ferent approaches for these purposes. These include: 1) updating using Landsat-derived historic and current fire burn information derived from the Monitoring Trends in Burn Severity project; 2) incorporating vegetation disturbance information derived from time series Landsat data analysis using the Vegetation Change Tracker; and 3) developing data products that capture subtle intra-state disturbance such as those related to insects and disease using either Landsat or the Moderate Resolution Imaging Spec-troradiometer (MODIS). While no one single approach provides all of the land cover change and update information required, we believe that a combination of all three captures most of the disturbance conditions taking place that have relevance to the fire community. Index Terms—Landscape monitoring, LANDFIRE, Landsat, MODIS, time series analyses.

Abstract.-We examined the influence of landscape alteration and in situ stream habitat variables on brook trout Salvelinus fontinalis by using a landscape-scale, space-for-time substitution analysis and a smaller data set that tracked long-term changes in land use over time. Forested land cover within a catchment was the overall best landscape-scale predictor of brook trout occurrence at a given site; measures of impervious land cover and urbanization were also important predictors. Brook trout were almost never found in watersheds where impervious land cover exceeded 4%, as assessed from the 2001 National Land Cover Dataset (2001 NLCD); the single exception was in a stream that displayed consistently low water temperatures. Landscapescale analyses indicated that increases in water temperature and erosion were associated with increasing percentages of urbanization and imperviousness and decreasing percentage of forested land cover. Three of six brook trout populations that were followed over time were extirpated within the last 15 years (between 1990 and 2005), coinciding with increases in urbanization and impervious land cover. At these sites, water temperatures were substantially greater than at the three sites with extant brook trout. Land use amounts derived from high-resolution aerial photography showed substantially greater amounts of urbanization and particularly impervious land cover than did amounts derived from the 2001 NLCD. The differences in measured land cover between imagery types warrant caution when stating upper threshold limits of land cover, because use of imagery methods interchangeably may produce inconsistent results. Our findings suggest that brook trout are very sensitive to landscape alterations in Maryland and disappear at low levels of impervious land cover regardless of the specific mechanism involved. Populations near the periphery of a species' geographic distribution may be particularly sensitive to relatively minor anthropogenic perturbations. Brook trout Salvelinus fontinalis in the Piedmont physiographic province of Maryland are near the southeastern edge of the species' native distribution. Water temperature is vitally important Declines in brook trout distribution and abundance have frequently been attributed to the degradation of streams' physical and chemical habitat conditions resulting from landscape alterations, such as forest clearing and agriculture.

Terrestrial ecosystems in the southern United States (SUS) have experienced a complex set of changes in climate, atmospheric CO2 concentration, tropospheric ozone (O3), nitrogen (N) deposition, and land-use and land-cover change (LULCC) during the past century. Although each of these factors has received attention for its alterations on ecosystem carbon (C) dynamics, their combined effects and relative contributions are still not well understood. By using the Dynamic Land Ecosystem Model (DLEM) in combination with spatially explicit, longterm historical data series on multiple environmental factors, we examined the century-scale responses of ecosystem C storage and flux to multiple environmental changes in the SUS. The results indicated that multiple environmental changes

This study evaluates rainfall estimates from the Next GenerationWeather Radar (NEXRAD), operational rain gauges, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) in the context as inputs to a calibrated, distributed hydrologic model. A high-density Micronet of rain gauges on the 342-km2 Ft. Cobb basin in Oklahoma was used as reference rainfall to calibrate the National Weather Service’s (NWS) Hydrology Laboratory Research Dis-tributedHydrologicModel (HL-RDHM) at 4-km/l-h and 0.258/3-h resolutions. The unadjusted radar product was the overall worst product, while the stage IV radar product with hourly rain gauge adjustment had the best hydrologic skill with a Micronet relative efficiency score of 20.5, only slightly worse than the reference simulation forced by Micronet rainfall. Simulations from TRMM-3B42RT were better than PERSIANN-CCS-RT (a real-time version of PERSIANN-CSS) and equivalent to those from the operational rain gauge network. The high degree of hydrologic skill with TRMM-3B42RT forcing was only achievable when the model was calibrated at TRMM’s 0.258/3-h resolution, thus highlighting the importance of considering rainfall product resolution during model calibration. 1.

Wind energy, if improperly sited, can impact wildlife through direct mortality and habitat loss and fragmentation, in contrast to its environmental benefits in the areas of greenhouse gas, air quality, and water quality. Fortunately, risks to wildlife from wind energy may be alleviated through proper siting and mitigation offsets. Here we identify areas in Kansas where wind development is incompatible with conservation, areas where wind development may proceed but with compensatory mitigation for impacts, and areas where development could proceed without the need for compensatory mitigation. We demonstrate that approximately 10.3 million ha in Kansas (48 percent of the state) has the potential to provide 478 GW of installed capacity while still meeting conservation goals. Of this total, approximately 2.7 million ha would require no compensatory mitigation and could produce up to 125 GW of installed capacity. This is 1,648 percent higher than the level of wind development needed in Kansas by 2030 if the United States is to get 20 percent of its electricity from wind. Projects that avoid and offset impacts consistent with this analysis could be awarded ‘‘Green Certification.’ ’ Certification may help to expand and sustain the wind industry by facilitating the completion of individual projects sited to avoid sensitive areas and protecting the industry’s reputation as an ecologically friendly source of electricity.

Empirical analysis of the influence of forest extent on annual and seasonal surface temperatures for the continental United States

Exploring the benefits of satellite remote sensing for flood prediction across scales

Effective roughness length s u m m a r y Parameterizing surface roughness is a key element in the application of tidal and storm surge inundation models. In this context, surface roughness refers to the ability of the terrain to act as a momentum sink to the overland water flow and also the prevailing winds that help drive this flow. These effects are typically parameterized using estimates of Manning's n, surface canopy coverage and effective aerodynamic roughness length which vary spatially across the modeling domain as a function of the physical landscape. The current methodology for coastal inundation in the United States assigns these parameters based on published land use/land cover data such as the National Land Cover Dataset. This paper compares those assigned values to values computed based on field measurements at 24 sites in Florida that are representative of land use/land cover classes affected by storm surge. It is shown that while the land use/land cover method is capable of automatically parameterizing surface roughness over a large model domain, parameter prediction errors due to variability within land cover types, misclassification, and parameter value selection for specific land cover classes at the local level are significant which could result in inaccurate estimates of inundation extent and duration.

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remote sensing