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STARFM

    The STARFM algorithm uses comparisons of one or more pairs of observed Landsat/MODIS maps, collected on the same day, to predict maps at Landsat-scale on other MODIS observation dates. STARFM was initially developed at the NASA Goddard Space Flight Center by Dr. Feng Gao. This version (v1.2) has been greatly improved in computing efficiency (e.g. one run for multiple dates and parallel computing) for large-area processing (Gao et al., 2015). Additional improvements (e.g. Landsat and MODIS images co-registration, daily MODIS nadir BRDF-adjusted reflectance) in the operational data fusion system (Wang et al., 2014) are beyond the STARFM program and are not included in this package. Improvement and continuous maintenance are being undertaken in the USDA-ARS Hydrology and Remote Sensing Laboratory (HRSL), Beltsville, MD by Dr. Feng Gao.

    Resampling Validation of Sample Plans (RVSP)

      Sets of tools for sample plan evaluation originally released in 1997, these Monte Carlo simulations can be used to evaluate sampling models during the developmental phase; however, they may not be adequate for testing model validity and performance under field conditions. This is primarily due to the assumption of an underlying statistical distribution (e.g., negative-binomial, normal) which may not adequately represent the actual distributions of insects in all instances. Here we present a method in which actual field data is resampled to evaluate sample plan performance. We originally developed DOS-based computer software for this purpose.

      Stream Temperature Modeling and Monitoring: Air Temperature Based Thermal Stream Habitat Model

        The Air Temperature Based Thermal Stream Habitat Model was originally developed from weather station information across the Columbia River basin in the Pacific Northwest. Multiple regression was used to predict mean annual air temperatures from elevation, latitude, and longitude with good success R^2 ~ 0.89). The model was developed as an alternative to PRISM data interpolations based on spline surface smoothing and should more accurately represent thermal conditions in stream valleys.

        Stream Temperature Modeling and Monitoring: Multiple Regression Stream Temperature Model

          This simple Stream Temperature Modeling and Monitoring approach uses thermograph data and geomorphic predictor variables from GIS software and digital elevation models (DEM). Multiple regression models are used to predict stream temperature metrics throughout a stream network with moderate accuracy (R^2 ~ 0.65). The models can provide basic descriptions of spatial patterns in stream temperatures, suitable habitat distributions for aquatic species, or be used to assess temporal trends related to climate or management activities if multiple years of temperature data are available.

          The Aquatic eDNAtlas Project: Lab Results Map - USFS RMRS

            The eDNA samples in the eDNAtlas database describe species occurrence locations and were collected by the U.S. Forest Service and numerous agencies that have partnered with the National Genomics Center for Wildlife and Fish Conservation (NGC) throughout the United States. The eDNAtlas is accessed via an interactive ArcGIS Online (AGOL) map that allows users to view and download sample site information and lab results of species occurrence for the U.S. The results are primarily based on samples analyzed at the National Genomics Center for Wildlife and Fish Conservation (NGC) and associated with geospatial attributes created by the Boise Spatial Streams Group (BSSG).

            Data from: Range size, local abundance and effect inform species descriptions at scales relevant for local conservation practice

              This study describes how metrics defining invasions may be more broadly applied to both native and invasive species in vegetation management, supporting their relevance to local scales of species conservation and management. A sample monitoring dataset is used to compare range size, local abundance and effect as well as summary calculations of landscape penetration (range size × local abundance) and impact (landscape penetration × effect) for native and invasive species in the mixed-grass plant community of western North Dakota, USA.

              RF-CLASS: Remote-sensing-based Flood Crop Loss Assessment Service System

                The Remote-sensing-based Flood Crop Loss Assessment Service System (RF-CLASS) is an Earth Observation (EO) based flood crop loss assessment cyber-service system operated by the Center for Spatial Information Science and Systems (CSISS), George Mason University. RF-CLASS supports flood-related crop statistics and insurance decision-making.