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Potentially harmful cyanobacterial blooms are an emerging environmental concern in freshwater bodies worldwide. Cyanobacterial blooms are generally caused by high nutrient inputs and warm, still waters and have been appearing with increasing frequency in water bodies used for drinking water supply and recreation, a problem which will likely worsen with a warming climate. Cyanobacterial blooms are composed of genera with known biological pigments and can be distinguished and analyzed via hyperspectral image collection technology such as remote sensing by satellites, airplanes, and drones. Here, we utilize hyperspectral microscopy and imaging spectroscopy to characterize and differentiate several important bloom-forming cyanobacteria genera obtained in the field during active research programs conducted by US Geological Survey and from commercial sources. Many of the cyanobacteria genera showed differences in their spectra that may be used to identify and predict their occurrence, including peaks and valleys in spectral reflectance. Because certain cyanobacteria, such as Cylindrospermum or Dolichospermum, are more prone to produce cyanotoxins than others, the ability to differentiate these species may help target high priority waterbodies for sampling. These spectra may also be used to prioritize restoration and research efforts to control cyanobacterial harmful algal blooms (CyanoHABs) and improve water quality for aquatic life and humans alike.

Oil and gas development is changing the landscape in many regions of the United States and globally. However, the nature, extent, and magnitude of landscape change and development, and precisely how this development compares to other ongoing land conversion (e.g. urban/sub-urban development, timber harvest) is not well understood. In this study, we examine land conversion from oil and gas infrastructure development in the upper Susquehanna River basin in Pennsylvania and New York, an area that has experienced much oil and gas development over the past 10 years. We quantified land conversion in terms of forest canopy geometric volume loss in contrast to previous studies that considered only areal impacts. For the first time in a study of this type, we use fine-scale lidar forest canopy geometric models to assess the volumetric change due to forest clearing from oil and gas development and contrast this land change to clear cut forest harvesting, and urban and suburban development. Results show that oil and gas infrastructure development removed a large volume of forest canopy from 2006 to 2013, and this removal spread over a large portion of the study area. Timber operations (clear cutting) on Pennsylvania State Forest lands removed a larger total volume of forest canopy during the same time period, but this canopy removal was concentrated in a smaller area. Results of our study point to the need to consider volumetric impacts of oil and gas development on ecosystems, and to place potential impacts in context with other ongoing land conversions.

Increases in global populations and acceleration in land cover and land use change necessitate the rapid monitoring of such changes to address issues like global food security and global water security. Remote sensing has been instrumental in doing this over the past 50 years. There have also been many advances in remote sensing technology; although many of these advances have been limited to multispectral sensors, we have hyperspectral data from the recently decommissioned Hyperion sensor, and upcoming hyperspectral sensors such as Germany's EnMAP and the National Aeronautics Space Administration's (NASA's) HyspIRI. However, there is no standardized protocol for pre-processing hyperspectral data, such as the Hyperion imagery. We need to establish such protocols for hyperspectral data to facilitate the use of these large datasets efficiently to address ecological questions at global extents. In this chapter, we review methods available for pre-processing Hyperion data and suggest a workflow for Hyperion image pre-processing. Examples of these pre-processing steps are also provided for the Google Earth Engine (GEE) cloud-computing platform, which facilitates studies at a global extent by eliminating the need to store and process imagery on a personal computer. These hyperspectral datasets, once pre-processed, are useful for many applications including vegetation classification, biomass estimation, and crop water productivity estimation.

Due to a combination of factors, such as a new coastal/aerosol band and improved radiometric sensitivity of the Operational Land Imager aboard Landsat 8, the atmospherically-corrected Surface Reflectance product for Landsat data, and the growing availability of corrected fDOM data from U.S. Geological Survey gaging stations, moderate-resolution remote sensing of fDOM may now be achievable. This paper explores the background of previous efforts and shows preliminary examples of the remote sensing and data relationships between corrected fDOM and Landsat 8 reflectance values. Although preliminary results before and after Hurricane Sandy are encouraging, more research is needed to explore the full potential of Landsat 8 to continuously map fDOM in a number of water profiles.

The spatial footprint of unconventional (hydraulic fracturing) and conventional oil and gas development in the Marcellus Shale region of the State of Pennsylvania was digitized from high-resolution, ortho-rectified, digital aerial photography, from 2004 to 2010. We used these data to measure the spatial extent of oil and gas development and to assess the exposure of the extant natural resources across the landscape of the watersheds in the study area. We found that either form of development: (1) occurred in ~50% of the 930 watersheds that defined the study area, (2) was closer to streams than the recommended safe distance in ~50% of the watersheds, (3) was in some places closer to impaired streams and state-defined wildland trout streams than the recommended safe distance, (4) was within 10 upstream kilometers of surface drinking water intakes in ~45% of the watersheds that had surface drinking water intakes, (5) occurred in ~10% of state-defined exceptional value watersheds, (6) occurred in ~30% of the watersheds with resident populations defined as disproportionately exposed to pollutants, (7) tended to occur at interior forest locations, and (8) had &gt, 100 residents within 3 km for ~30% of the unconventional oil and gas development sites. Further, we found that exposure to the potential effects of landscape disturbance attributable to conventional oil and gas development was more prevalent than its unconventional counterpart.

We conducted a large-scale assessment of unconventional oil and gas (UOG) development effects on brook trout (Salvelinus fontinalis) distribution. We compiled 2231 brook trout collection records from the Upper Susquehanna River Watershed, USA. We used boosted regression tree (BRT) analysis to predict occurrence probability at the 1:24,000 stream-segment scale as a function of natural and anthropogenic landscape and climatic attributes. We then evaluated the importance of landscape context (i.e., pre-existing natural habitat quality and anthropogenic degradation) in modulating the effects of UOG on brook trout distribution under UOG development scenarios. BRT made use of 5 anthropogenic (28% relative influence) and 7 natural (72% relative influence) variables to model occurrence with a high degree of accuracy [Area Under the Receiver Operating Curve (AUC)=0.85 and cross-validated AUC=0.81]. UOG development impacted 11% (n=2784) of streams and resulted in a loss of predicted occurrence in 126 (4%). Most streams impacted by UOG had unsuitable underlying natural habitat quality (n=1220; 44%). Brook trout were predicted to be absent from an additional 26% (n=733) of streams due to pre-existing non-UOG land uses (i.e., agriculture, residential and commercial development, or historic mining). Streams with a predicted and observed (via existing pre- and post-disturbance fish sampling records) loss of occurrence due to UOG tended to have intermediate natural habitat quality and/or intermediate levels of non-UOG stress. Simulated development of permitted but undeveloped UOG wells (n=943) resulted in a loss of predicted occurrence in 27 additional streams. Loss of occurrence was strongly dependent upon landscape context, suggesting effects of current and future UOG development are likely most relevant in streams near the probability threshold due to pre-existing habitat degradation.

The development of unconventional oil and gas (UOG) involves infrastructure development (well pads, roads and pipelines), well drilling and stimulation (hydraulic fracturing), and production; all of which have the potential to affect stream ecosystems. Here, we developed a fine-scaled (1:24,000) catchment-level disturbance intensity index (DII) that included 17 measures of UOG capturing all steps in the development process (infrastructure, water withdrawals, probabilistic spills) that could affect headwater streams ( 50). Well density, often used as a proxy for development, only correlated strongly with well pad coverage and produced materials, and therefore may miss potential effects associated with roads and pipelines, water withdrawals and spills. When analyzed with a future development scenario, 91.1% of EV stream length, 68.7% of Class A streams and 80.0% of patches were in catchments with a moderate to high probability of development. Our method incorporated the cumulative effects of UOG on streams and can be used to identify catchments and reaches at risk to existing stressors or future development.

Precise monitoring of agricultural crop biomass and yield quantities is critical for crop production management and prediction. The goal of this study was to compare hyperspectral narrowband (HNB) versus multispectral broadband (MBB) reflectance data in studying irrigated cropland characteristics of five leading world crops (cotton, wheat, maize, rice, and alfalfa) with the objectives of: 1. Modeling crop productivity, and 2. Discriminating crop types. HNB data were obtained from Hyperion hyperspectral imager and field ASD spectroradiometer, and MBB data were obtained from five broadband sensors: Landsat-7 Enhanced Thematic Mapper Plus (ETM +), Advanced Land Imager (ALI), Indian Remote Sensing (IRS), IKONOS, and QuickBird. A large collection of field spectral and biophysical variables were gathered for the 5 crops in Central Asia throughout the growing seasons of 2006 and 2007. Overall, the HNB and hyperspectral vegetation index (HVI) crop biophysical models explained about 25% greater variability when compared with corresponding MBB models. Typically, 3 to 7 HNBs, in multiple linear regression models of a given crop variable, explained more than 93% of variability in crop models. The evaluation of λ 1 (400–2500 nm) versus λ 2 (400–2500 nm) plots of various crop biophysical variables showed that the best two-band normalized difference HVIs involved HNBs centered at: (i) 742 nm and 1175 nm (HVI742-1175), (ii) 1296 nm and 1054 nm (HVI1296-1054), (iii) 1225 nm and 697 nm (HVI1225-697), and (iv) 702 nm and 1104 nm (HVI702-1104). Among the most frequently occurring HNBs in various crop biophysical models, 74% were located in the 1051–2331 nm spectral range, followed by 10% in the moisture sensitive 970 nm, 6% in the red and red-edge (630–752 nm), and the remaining 10% distributed between blue (400–500 nm), green (501–600 nm), and NIR (760–900 nm). Discriminant models, used for discriminating 3 or 4 or 5 crop types, showed significantly higher accuracies when using HNBs (> 90%) over MBBs data (varied between 45 and 84%). Finally, the study highlighted 29 HNBs of Hyperion that are optimal in the study of agricultural crops and potentially significant to the upcoming NASA HyspIRI mission. Determining optimal and redundant bands for a given application will help overcoming the Hughes' phenomenon (or curse of high dimensionality of data).

Hyperspectral imaging (HSI) is an exciting and rapidly expanding area of instruments and technology in passive remote sensing. Due to quickly changing applications, the instruments are evolving to suit new uses and there is a need for consistent definition, testing, characterization and calibration. This paper seeks to outline a broad prescription and recommendations for basic specification, testing and characterization that must be done on Visible Near Infra-Red grating-based sensors in order to provide calibrated absolute output and performance or at least relative performance that will suit the user’s task. The primary goal of this paper is to provide awareness of the issues with performance of this technology and make recommendations towards standards and protocols that could be used for further efforts in emerging procedures for national laboratory and standards groups.

One of the critical global environmental problems is human and ecological exposure to hazardous wastes from agricultural, industrial, military and mining activities. These wastes often include heavy metals, hydrocarbons and other organic chemicals. Traditional field and laboratory detection and monitoring of these wastes are generally expensive and time consuming. The synoptic perspective of overhead remote imaging can be very useful for the detection and remediation of hazardous wastes. Aerial photography has a long and effective record in waste site evaluations. Aerial photographic archives allow temporal evaluation and change detection by visual interpretation. Multispectral aircraft and satellite systems have been successfully employed in both spectral and morphological analysis of hazardous wastes on the landscape and emerging hyperspectral sensors have permitted determination of the specific contaminants by processing strategies using the tens or hundreds of acquired wavelengths in the solar reflected and/or thermal infrared parts of the electromagnetic spectrum. This paper reviews the literature of remote sensing and overhead imaging in the context of hazardous waste and discusses future monitoring needs and emerging scientific research areas.

Cyanobacterial blooms are a nuisance and a potential hazard in freshwater systems worldwide. Remote sensing has been used to detect cyanobacterial blooms, but few studies have distinguished among genera of cyanobacteria. Because some genera are more likely to be toxic than others, this is a useful distinction. Hyperspectral imaging reflectance microscopy was used to examine cyanobacteria from Upper Klamath Lake, Oregon, at high spatial and spectral resolution to determine if two species found commonly in the lake, Aphanizomenon flos-aquae and Microcystis aeruginosa, can be separated spectrally. Of the analytical methods applied, a spectral shape algorithm applied to the derivative was found to be most successful in classifying these species in microscope scenes. Further work is required to determine if the spectral characterization of cyanobacterial genera can be scaled up to remote sensing applications.

Riparian ecosystems are important elements in landscapes that often provide a disproportionately wide range of ecosystem services and conservation benefits. Their protection and restoration have been one of the top environmental management priorities across the US over the last several years. Despite the level of concern, visibility and management effort, little is known about trends in riparian habitats. Moreover, little is known about whether or not cumulative efforts to restore and protect riparian zones and floodplains are affecting the rates of riparian habitat change nationwide. To address these issues, we analyzed riparian land cover change between the early 1970s and the late 1990s/early 2000s using existing spatial data on hydrography and land cover. This included an analysis of land cover changes within 180 m riparian buffer zones, and at catchment scales, for 42,363 catchments across 63 ecoregions of the continental US. The total amount of forest and natural land cover (forests, shrublands, wetlands) in riparian buffers declined by 0.7 and 0.9%, respectively across the entire study period. Gains in grassland/shrubland accounted for the 0.2% lower percentage of total natural land cover loss relative to forests. Conversely, urban and developed land cover (urban, agriculture, and mechanically disturbed lands) increased by more than 1.3% within riparian buffers across the entire study period. Despite these changes, we documented an opposite trend of increasing proportions of natural and forest land cover in riparian buffers versus the catchment scale. We surmise that this trend might reflect a combination of natural recovery and cumulative efforts to protect riparian ecosystems across the US. However, existing models limit our ability to assess the impacts of these changes on specific ecosystem services. We discuss the implications of changes observed in this study on the sustainability of ecosystem services. We also recommend opportunties for future riparian change assessments.

Over the past 20 years, hyperspectral microscopy has grown into a robust field of analysis for a number of applications. The visible to near-infrared (VNIR; 400 to 1000 nm) region of the spectrum has demonstrated utility for the characterization of healthy and diseased tissue and of biomolecular indicators at the cellular level. Here, we describe the development of a hyperspectral imaging (HSI) microscope that is aimed at material characterization to complement traditional stand-off, earth remote sensing with hyperspectral sensors. We combine commercial off the shelf technology to build an HSI microscope to collect spectral data with illumination provided by a tunable laser. Hyperspectral imaging microscopy (HIM) facilitates detailed examination of target materials at the subcentimeter spatial scale. The custom-built, laser illumination HSI microscope covers the NIR to shortwave infrared (NIR/SWIR; 900 to 2500 nm) solar-reflected spectral range. It is combined with a separate VNIR sensor (400 to 900 nm) that utilizes quartz–tungsten–halogen lamps for illumination. The combined sensors provide a means to collect >10,000 s of spectra in the full VNIR/SWIR spectral range from both pure substances and precisely engineered linear and nonlinear mixtures. The large abundance of spectra allows for a more detailed understanding of the variability and multivariate probability distributions of spectral signatures. This additional information aids in understanding the variability observed in ground truth spectra collected from portable spectrometers, and it greatly enhances sample description and metadata content. In addition, HIM data cubes can serve as proxies, as “microscenes,” for systems engineering applications such as trade studies for HSI acquired by air- and space-borne sensors.

Landscape indicators, derived from land use and land cover data as well as other data, were used to calculate the ecological consequences of land cover change in terms of nitrate loading and physical bird habitat. Both were modeled from 1973, 1992, and 2001 land cover data in the Mid-Atlantic region of the United States. Land cover statistics and trends are calculated for three time periods. In general, forest gain and agricultural loss was found in areas of improving landscape indicators and forest loss and agricultural gain was found to occur in areas of declining indicators, which was confirmed by high-resolution aerial photographic analysis.

The percentage of impervious surface area in a watershed has been widely recognized as a key indicator of terrestrial and aquatic ecosystem condition. Although the use of the impervious indicator i...

One of the emerging areas of scientific interest in the control of non‐point‐source pollution (NPS) is the detection and analysis of impervious surfaces within watersheds. NPS runoff from urban surfaces is now a leading threat to water quality and the percentage of impervious surface within a particular watershed has been recognized as a key indicator of the effects of non‐point runoff and of future water and ecosystem quality. Although the effect of land use, population and impervious surface cover on water quality has been generally known for thirty years, a basic problem exists in quantifying the detailed spatial extent and distribution of various classes of impervious surface phenomena. Remote sensing technology has been one of the primary methods for acquiring data on the impervious areas of watersheds for tax assessment, mapping and modeling applications and continues to be one of the most promising technologies for providing detailed mapping information as input into watershed‐level management deci...

Passive Sampling Devices (PSDs) have been successfully used by government and academic agencies to monitor common ambient air pollutants such as ozone and nitrogen dioxide (NO(2)). Most PSD studies have involved long-term (e.g. bi-weekly or monthly) sampling. But the Passive Ozone Network of Dallas (POND) studies of 1998 and 1999 showed that high quality 24-hour and 12-hour data using the Ogawa PSD could be collected for ambient ozone concentrations. This paper presents an evaluation of short-term passive sampling results for nitrogen oxides (NO(x)) in El Paso and Houston, Texas, using the Ogawa PSD. The Ogawa NO(x) PSDs were compared to both Federal Reference Method (FRM) monitors and a photolytic converter, with the photolytic converter designed to report closer concentrations to "true" NO(x) by more effectively limiting the interferences of other nitrogen species. Overall, good agreement was noted for all three monitor types in both cities, supporting the potential use of lower cost Ogawa PSDs for large multi-site episodic NO(x)/NO(2)/NO saturation screening studies. This evaluation was conducted during two separate six week periods of the cooler winter months so additional testing of the Ogawa PSDs during different seasons is recommended.

Nitrogen Dioxide (NO(2)) is a common urban air pollutant that results from the combustion of fossil fuels. It causes serious human health effects, is a precursor to the formation of ground level ozone, another serious air pollutant, and is one of the six criteria air pollutants established by the United States (U.S.) Clean Air Act (CAA). Ogawa Passive Sampling Devices (PSDs) for NO(2) were collocated and operated at six NO(2) Federal Reference Method (FRM) monitor locations in the El Paso, Texas area for the 2004 calendar year. Passive samples were taken at 2-week, 3-week, and 4-week intervals and compared against the continuously operating FRM monitors. Results showed that the collective NO(2) annual arithmetic mean for all passive monitors was identical to the NO(2) mean for all FRM monitors. Of the individual locations, three passive annual NO(2) means were identical to their corresponding FRM means, and three passive annual NO(2) means differed from their corresponding FRM means by only one part per billion (ppb). Linear correlation analysis between all readings of the individual NO(2) PSDs and FRM values showed an average absolute difference of 1.2 ppb with an r (2) of 0.95. Paired comparison between high and low concentration annual NO(2) sites, seasonal considerations, and interlab quality control comparisons all showed excellent results. The ease of deployment, reliability, and the cost-savings that can be realized with NO(2) PSDs could make them an attractive alternative to FRM monitors for screening purposes, and even possibly an equivalent method for annual NO(2) monitoring. More tests of the Ogawa NO(2) PSD are recommended for different ecosystem and climate regimes.

Automated orthorectification of raw image products is now possible based on the comprehensive metadata collected by Global Positioning Systems and Inertial Measurement Unit technology aboard aircraft and satellite digital imaging systems, and based on emerging pattern-matching and automated image-to-image and control point selection capabilities in many advanced image processing systems. Automated orthorectification of standard aerial photography is also possible if a camera calibration report and sufficient metadata is available. Orthorectification of historical imagery, for which only limited metadata was available, was also attempted and found to require some user input, creating a semi-automated process that still has significant potential to reduce processing time and expense for the conversion of archival historical imagery into geospatially enabled, digital formats, facilitating preservation and utilization of a vast archive of historical imagery. Over 90 percent of the frames of historical aerial photos used in this experiment were successfully orthorectified to the accuracy of the USGS 100K base map series utilized for the geospatial reference of the archive. The accuracy standard for the 100K series maps is approximately 167 feet (51 meters). The main problems associated with orthorectification failure were cloud cover, shadow and historical landscape change which confused automated image-to-image matching processes. Further research is recommended to optimize automated orthorectification methods and enable broad operational use, especially as related to historical imagery archives.