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At least 397 records · Page 22Linked to original sources

Linkages between lake shrinkage/expansion and sublacustrine permafrost distribution determined from remote sensing of interior Alaska, USA

[1] Linkages between permafrost distribution and lake surface-area changes in cold regions have not been previously examined over a large scale because of the paucity of subsurface permafrost information. Here, a first large-scale examination of these linkages is made over a 5150 km 2 area of Yukon Flats, Alaska, USA, by evaluating the relationship between lake surface-area changes during 1979–2009, derived from Landsat satellite data, and sublacustrine groundwater flow-path connectivity inferred from a pioneering, airborne geophysical survey of permafrost. The results suggest that the shallow (few tens of meters) thaw state of permafrost has more influence than deeper permafrost conditions on the evolving water budgets of lakes on a multidecadal time scale. In the region studied, these key shallow aquifers have high hydraulic conductivity and great spatial variability in thaw state, making groundwater flow and associated lake level evolution particularly sensitive to climate change owing to the close proximity of these aquifers to the atmosphere.

Alaska↗

Automated integration of lidar into the LANDFIRE product suite

Accurate information about three-dimensional canopy structure and wildland fuel across the landscape is necessary for fire behaviour modelling system predictions. Remotely sensed data are invaluable for assessing these canopy characteristics over large areas; lidar data, in particular, are uniquely suited for quantifying three-dimensional canopy structure. Although lidar data are increasingly available, they have rarely been applied to wildland fuels mapping efforts, mostly due to two issues. First, the Landscape Fire and Resource Planning Tools (LANDFIRE) program, which has become the default source of large-scale fire behaviour modelling inputs for the US, does not currently incorporate lidar data into the vegetation and fuel mapping process because spatially continuous lidar data are not available at the national scale. Second, while lidar data are available for many land management units across the US, these data are underutilized for fire behaviour applications. This is partly due to a lack of local personnel trained to process and analyse lidar data. This investigation addresses these issues by developing the Creating Hybrid Structure from LANDFIRE/lidar Combinations (CHISLIC) tool. CHISLIC allows individuals to automatically generate a suite of vegetation structure and wildland fuel parameters from lidar data and infuse them into existing LANDFIRE data sets. CHISLIC will become available for wider distribution to the public through a partnership with the U.S. Forest Service’s Wildland Fire Assessment System (WFAS) and may be incorporated into the Wildland Fire Decision Support System (WFDSS) with additional design and testing. WFAS and WFDSS are the primary systems used to support tactical and strategic wildland fire management decisions.

Remote Sensing Letters↗

Earth observing data and methods for advancing water harvesting technologies in the semi-arid rain-fed environments of India

The paper develops approaches and methods of modeling and mapping land and water productivity of rain-fed crops in semi-arid environments of India using hyperspectral, hyperspatial, and advanced multispectral remote sensing data and linking the same to field-plot data and climate station data. The overarching goal is to provide information to advance water harvesting technologies in the agricultural croplands of the semi-arid environments of India by conducting research in a representative pilot site in Jodhpur, Rajasthan, India. ?? 2011 IEEE.

Conference Paper↗

Factors influencing the at-sea distribution of Cassin's Auklets (Ptychoramphus aleuticus) that breed in the Channel Islands, California

We used radiotelemetry to evaluate at-sea habitat use by Cassin's Auklets ( Ptychoramphus aleuticus ) that bred at Prince Island, off southern California, from 1999 through 2001. We used logistic regression to compare paired radiotelemetry (presence) with random (pseudo-absence) location-associated habitat variables derived from (1) satellite remote-sensing of sea surface temperature and chlorophyll- a concentration and (2) bathymetry. Compared with random locations within their foraging area and after controlling for distance to colony, odds ratios indicated that Cassin's Auklets with dependent young occurred in relatively shallower, warmer, and chlorophyll-rich water associated with chlorophyll fronts near the insular shelf break. These oceanographic features characterize habitats that support key euphausiid prey (e.g., Thysanoessa spinifera ) and also other krill predators. Radiotelemetry combined with satellite remote-sensing of the ocean provides an alternative to vessel-based surveys for evaluating seabird foraging habitats. In the absence of information on the actual distribution, abundance, and, hence, availability of Zooplankton prey for seabirds, environmental factors can serve as proxies to help elucidate distributional patterns of seabirds at sea.

California↗

Measuring phenological variability from satellite imagery

Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variability of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and predicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for large-area land cover mapping and monitoring. The utility of remotely sensed data as input to vegetation mapping is demonstrated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particularly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.

North Dakota↗

Declining American Goshawk (Accipiter atricapillus) nest site habitat suitability in a timber production landscape: Effects of abiotic, biotic, and forest management factors

Conservation of the American Goshawk ( Accipiter atricapillus ; hereafter goshawk) has been contentious in relation to forest management. Higher quality goshawk nesting habitat is generally considered to consist of contiguous tracts of mature forest, due to goshawks' large home ranges, territoriality, and food requirements. The large trees of mature forest have the greatest economic value to timber companies. We used long-term (1965–2019) data from 281 goshawk nest site locations in the Black Hills National Forest (BHNF), South Dakota, and Wyoming, USA, to evaluate (1) abiotic and biotic factors associated with goshawk nest site habitat suitability (hereafter habitat suitability); (2) changes in habitat suitability over time; and (3) the effect of anthropogenic activities and natural disturbances on habitat suitability. We evaluated forest attributes across five spatial scales relevant to goshawks, used information-theoretic methods to rank and select models, and assessed the predictive capability of the best-approximating models using the concordance statistic. The best-approximating model had excellent predictive capability (concordance = 0.821). Forest attributes at the 12-ha scale were a better predictor of goshawk habitat suitability than covariates evaluated at the point or >12-ha scales, indicating the importance of managing goshawk habitat beyond the nest tree, but within the nest stand. Goshawk habitat suitability was positively related to mean percent canopy cover and median canopy base height, and negatively related to variability in canopy base height within 12 ha of the location. As mean percent canopy cover within 12 ha of a location increased, goshawk habitat suitability increased more slowly in burned compared to unburned areas. Commercial thinning treatments were more likely to occur in closed canopy forest that already had a higher likelihood of goshawk nesting, and we documented a positive relationship between habitat suitability and the interaction of canopy cover with commercial thinning. Goshawk habitat suitability was negatively related to slope and distance to drainage bottoms, and positively related to distance to ridges, which may be related to microclimatic factors. Our results indicate goshawk habitat suitability decreased across the BHNF over the past three decades and much high-quality nesting habitat was lost during this period due to a combination of unsustainable timber harvest and natural disturbances. Minimizing forest management activities that decrease canopy cover and canopy base height, and increase variability in canopy base height in areas of high- and medium-quality goshawk habitat are likely to slow the loss of higher-quality habitat and allow development of future nesting habitat. In addition to informing management, this study demonstrates the value of using existing long-term legacy datasets in conjunction with time series of remotely sensed habitat attributes to evaluate changes in habitat suitability for raptors in heavily managed landscapes with extensive natural disturbances.

South Dakota, Wyoming↗

Need for new sensors to map lithologic units

One of the most important contributions that remote sensing can make to mineral energy explorations to provide data from satellites to augment regional geological mapping. Geologic maps, which show information on the subsurface, are the main basis for formulating models of resource genesis that guide exploration. However, conventional compilation procedures are time-consuming and therefore often slow the pace of exploration, especially in large, inaccessible areas. Landsat Multispectral Scanner (MSS) images have been applied to a wide variety of specific geological problems, including discrimination of lithologic and delineation of previously unrecognized tectonic features. However, these lithologic distinctions are based on brightness, spectral reflectance, and, less commonly, the morphology of the unit, which in the wavelength region of MSS images are only rarely diagnostic of specific mineralogical content. Limonite is the only lithological material that can be identified be analyzing MSS spectral radiance.

Sixth Annual Pecora Symposium and Exposition↗

Porphyry copper assessment of western Central Asia

The U.S. Geological Survey conducted an assessment of resources associated with porphyry copper deposits in the western Central Asia countries of Kyrgyzstan, Uzbekistan, Kazakhstan, and Tajikistan and the southern Urals of Kazakhstan and Russia as part of a global mineral resource assessment. The purpose of the study was to (1) delineate permissive areas (tracts) for undiscovered porphyry copper deposits; (2) compile a database of known porphyry copper deposits and significant prospects; (3) where data permit, estimate numbers of undiscovered deposits within those permissive tracts; and (4) provide probabilistic estimates the amounts of copper (Cu), molybdenum (Mo), gold (Au), and silver (Ag) that could be contained in those undiscovered deposits. Western Central Asia, a region diverse in its geologic complexity, is situated north of the Tarim and North China tectonic blocks and sandwiched between the East European and Siberian cratons. The Ural Mountains form the western margin of the region; the southern margin is formed by the high-standing ranges that make up the Tian Shan mountain range in the border regions of Kazakhstan, Kyrgyzstan, and western China, where the effects of collisional tectonics are well displayed. The tectonic collage that makes up the core of western Central Asia is perhaps the geologically least understood part of the region. There is broad agreement that the early Paleozoic is made up of tectonically juxtaposed blocks that vary from Precambrian-cored microcontinents to magmatic arc and related complexes to subduction-related accretionary complexes. The rudiments of an incipient, contiguous single Kazakhstan block were formed by the end of the Silurian. In the middle to late Paleozoic, the block was unconformably superposed by two large, nested magmatic-arc belts, one Devonian, the other Carboniferous. Both magmatic-arc complexes were folded into a horseshoe-shaped, southeast-opening orocline in response to the final collisions of the various surrounding cratonic blocks with the Kazakhstan block. Additional deformation in the upper Cenozoic derived from the collision of India and China significantly redistributed fragments of the various mosaicked blocks, particularly in the central and southern parts of the western Central Asian region. Porphyry copper deposits are associated with many of the magmatic-arc fragments and belts throughout the geologically complex region, and economically important deposits are found in arc sequences of all Paleozoic Periods. The economically most productive arcs are Carboniferous. The assessment includes a discussion of the tectonic and geologic setting of porphyry copper deposits in western Central Asia (chapter 1), an application of remote sensing data for hydrothermal alteration mapping as a tool for porphyry copper assessment in the region (chapter 2), and a probabilistic assessment of undiscovered porphyry copper resources in four areas that represent Ordovician and Late Paleozoic (Carboniferous-Permian) magmatic arcs (chapter 3). The principal litho-tectonic terrane concept used to delineate permissive tracts was that of a magmatic arc that formed in the subduction boundary zone above a subducting plate. Eight permissive tracts are delineated on the basis of mapped and inferred subsurface distributions of igneous rocks assigned to magmatic arcs of specified age ranges that define areas where the occurrence of porphyry copper deposits within 1 kilometer of the Earth’s surface is possible. These tracts range in area from about 8,000 to 200,000 square kilometers and host 18 known porphyry copper deposits that contain about 54 million metric tons of copper. Available data included geologic maps, the distribution of significant porphyry copper occurrences and potentially related deposit types, the distribution of hydrothermal alteration patterns that are consistent with porphyry copper mineralization, and information on possible subsurface extensions of permissive rocks. On the basis of analyses of these data, the assessment team estimated a mean of 25 undiscovered porphyry copper deposits for the study area. Estimates of numbers of undiscovered deposits were combined with grade and tonnage models in a Monte Carlo simulation to yield a mean estimate of 95 million metric tons of copper in undiscovered porphyry copper deposits; this represents about twice the amount of identified porphyry copper resources (54 million metric tons). Detailed descriptions of each permissive tract, including the rationales for delineation and assessment, are given in appendixes, along with a geographic information system (GIS) that includes permissive tract boundaries, point locations of known porphyry copper deposits and significant occurrences, and hydrothermal alteration data based on analysis of remote sensing data.

Ural Mountains↗

Remote sensing with simulated unmanned aircraft imagery for precision agriculture applications

An important application of unmanned aircraft systems (UAS) may be remote-sensing for precision agriculture, because of its ability to acquire images with very small pixel sizes from low altitude flights. The objective of this study was to compare information obtained from two different pixel sizes, one about a meter (the size of a small vegetation plot) and one about a millimeter. Cereal rye (Secale cereale) was planted at the Beltsville Agricultural Research Center for a winter cover crop with fall and spring fertilizer applications, which produced differences in biomass and leaf chlorophyll content. UAS imagery was simulated by placing a Fuji IS-Pro UVIR digital camera at 3-m height looking nadir. An external UV-IR cut filter was used to acquire true-color images; an external red cut filter was used to obtain color-infrared-like images with bands at near-infrared, green, and blue wavelengths. Plot-scale Green Normalized Difference Vegetation Index was correlated with dry aboveground biomass ( ${mbi {r}} = 0.58$ ), whereas the Triangular Greenness Index (TGI) was not correlated with chlorophyll content. We used the SamplePoint program to select 100 pixels systematically; we visually identified the cover type and acquired the digital numbers. The number of rye pixels in each image was better correlated with biomass ( ${mbi {r}} = 0.73$ ), and the average TGI from only leaf pixels was negatively correlated with chlorophyll content ( ${mbi {r}} = -0.72$ ). Thus, better information for crop requirements may be obtained using very small pixel sizes, but new algorithms based on computer vision are needed for analysis. It may not be necessary to geospatially register large numbers of photographs with very small pixel sizes. Instead, images could be analyzed as single plots along field transects.

IEEE Journal of Selected Topics in Applied Earth O↗

Streambed flux measurement informed by distributed temperature sensing leads to a significantly different characterization of groundwater discharge

Groundwater discharge though streambeds is often focused toward discrete zones, indicating that preliminary reconnaissance may be useful for capturing the full spectrum of groundwater discharge rates using point-scale quantitative methods. However, many direct-contact reconnaissance techniques can be time consuming, and remote sensing (e.g. thermal infrared) typically does not penetrate the water column to locate submerged seepages. In this study we tested whether dozens of groundwater discharge measurements made at "uninformed" (i.e., selected without knowledge on high-resolution temperature variations at the streambed) point locations along a reach would yield significantly different Darcy-based groundwater discharge rates when compared with “informed” measurements, focused at streambed thermal anomalies that were identified a-priori using fiber-optic distributed temperature sensing (FO-DTS). A non-parametric U-test showed a significant difference between median discharge rates for uninformed (0.05 m·d-1; n = 30) and informed (0.17 m·d-1; n = 20) measurement locations. Mean values followed a similar pattern (0.12 versus 0.27 m·d-1) and frequency distributions for uninformed and informed measurements were also significantly different based on a Kolmogorov-Smirnov test. Results suggest that even using a quick “snapshot-in-time” field analysis of FO-DTS data can be useful in streambeds with groundwater discharge rates <0.2 m·d-1, a lower threshold than proposed in a previous study. Collectively, study results highlight that FO-DTS is a powerful technique for identifying higher-discharge zones in streambeds, but the pros and cons of informed and uninformed sampling depend in part on groundwater/surface water exchange study goals. For example, studies focused on measuring representative groundwater and solute fluxes may be biased if high-discharge locations are preferentially sampled. However, identification of high-discharge locations may complement more randomized sampling plans and lead to improvements in interpolating streambed fluxes and upscaling point measurements to the stream reach scale.

Nebraska↗

Introduction to fire danger rating and remote sensing - Will remote sensing enhance wildland fire danger prediction?

While ‘Fire Danger’ per se cannot be measured, the physical properties of the biotic and abiotic world that relate to fire occurrence and fire behavior can. Today, increasingly sophisticated Remote Sensing methods are being developed to more accurately detect fuel properties such as species composition (fuel types), vegetation structure or plant water content - to name a few. Based on meteorological input data and physical, semi-physical or empirical model calculations, Wildland Fire Danger Rating Systems provide ‘indirect values’ - numerical indices - at different temporal scales (e.g., daily, weekly, monthly) denoting the physical conditions that may lead to fire ignition and support fire propagation. The results can be expressed as fire danger levels, ranging from ‘low’ to ‘very high’, and are commonly used in operational wildland fire management (e.g., the Canadian Fire Weather Index [FWI] System, the Russian Nesterov Index, or the U.S. National Fire Danger Rating System [NFDRS]). Today, fire danger levels are often turned into broad scale maps with the help of Geographical Information Systems (GIS) showing the areas with the different fire danger levels, and are distributed via the World Wide Web. In this chapter we will outline some key issues dealing with Remote Sensing and GIS techniques that are covered in the following chapters, and elaborate how the Fire Danger Rating concepts could be integrated into a framework that enables comprehensive and sustainable wildland fire risk assessment. To do so, we will first raise some general thoughts about wildland fires and suggest how to approach this extremely complex phenomenon. Second, we will outline a possible fire risk analysis framework and third we will give a short overview on existing Fire Danger Rating Systems and the principles behind them.

Series in Remote Sensing↗

The optical river bathymetry toolkit

Spatially distributed information on water depth is essential for many applications in river research and management and, under certain circumstances, can be inferred from remotely sensed data. Although fluvial remote sensing has emerged as a rapidly developing subdiscipline of the riverine sciences, more widespread adoption of these techniques has been hindered by a lack of accessible software. The Optical River Bathymetry Toolkit (ORByT) fills this void by providing a standalone package for mapping water depth from passive optical image data. The ORByT interface enables end users to import images and field‐based depth measurements, create and refine water masks, and perform spectrally based depth retrieval via an Optimal Band Ratio Analysis algorithm. The resulting bathymetric map can be exported as an image file, point cloud, and/or cross section; a thorough accuracy assessment also is incorporated into the workflow. In addition, image‐derived depth estimates can be subtracted from water surface elevations to obtain bed elevations suitable for input to a hydrodynamic model. Potential users of ORByT must bear in mind the inherent limitations of passive optical remote sensing: reliable bathymetry can only be inferred in clear‐flowing, shallow streams; this approach is not appropriate for more turbid, deeper rivers.

River Research and Applications↗

The 2006 eruption of Augustine Volcano, Alaska

Augustine Volcano, the most historically active volcano in Alaska&rsquo;s Cook Inlet region, again showed signs of life in April 2005. Escalating seismic unrest, ground deformation, and gas emissions culminated in an eruption from January 11 to mid-March of 2006, the fifth major eruption in 75 years. The eruption began with a series of 13 short-lived blasts over 20 days that sent pyroclastic flows; snow, rock, and ice avalanches; and lahars down the volcano&rsquo;s snow clad flanks; ash clouds drifted hundreds of kilometers downwind. Punctuated explosive activity gave way to effusion of lava and emplacement of thick block-and-ash flows on the volcano&rsquo;s north flank that continued through mid-February. In mid-March renewed extrusion resulted in the building of a new, higher summit lava dome and two blocky lava flows on the north and northeast flanks of the cone. The eruption resulted in ash fall on many south-central Alaskan communities and disrupted air traffic in the region. Augustine&rsquo;s frequent eruptions and relatively easy access have long drawn volcanologists to study the accumulation, ascent, and eruption of andesitic to dacitic magma. Studies of the most recent activity before 2006, in 1976 and 1986, revealed that the volcano lately produces explosive eruptions that are preceded by months of unrest and injection of new magma into a storage region in the upper several kilometers of the crust. Each of these eruptions then followed a similar progression from explosive to effusive behavior over several months. Petrologic and geophysical observations suggest that these three eruptions were triggered by similar magma mixing events and that the subsequent ascent and eruption of magma was governed by processes that were roughly constant from one eruption to the next. Geologic studies of the island show that in the more distant past parts of Augustine&rsquo;s edifice have failed repeatedly, resulting in debris avalanches that entered the sea and, at least once, in 1883, caused a tsunami that hit surrounding Cook Inlet coastlines. Such edifice failures and resultant local tsunamis should be expected in the future. Recognition of Augustine&rsquo;s frequent activity and hazardous nature led to the installation of a network of telemetered seismometers beginning in 1971, the establishment of a geodetic network in 1988, and the installation of other new instrumentation such as pressure sensors, broadband seismometers, and cameras by the Alaska Volcano Observatory (AVO), and the selection of Augustine for geodetic instrumentation through the EarthScope/Plate Boundary Observatory program in 2004. In addition, remote sensing techniques, such as airborne thermal imaging and the advanced spaceborne thermal emission and reflection radiometer (ASTER), provided novel and often critical information as the 2006 eruption progressed. The combination of a long-term seismic network and an array of new monitoring techniques has provided a breadth and depth of understanding of Augustine&rsquo;s most recent activity that was not possible in the past. This volume contains 28 chapters reporting on a diverse suite of new scientific observations and investigations that were motivated by the 2006 eruption. Understanding the magmatic processes that drive eruptions, identifying eruptive events, tracking the movement of ash clouds, and communicating the resultant hazards to other government agencies and the public are all critical tasks for AVO, and chapters touch upon all of these topics. One goal in this compilation is to synthesize the diverse information into as complete an understanding of the magmatic and eruptive processes as possible. An equally important goal is to provide a framework for diagnosing periods of unrest and formulating forecasts of eruptions that will certainly take place at Augustine in the future. This latter goal is especially important, as Augustine&rsquo;s frequent eruptive activity suggests that another eruption can be expected within the next several decades. Consequently, the investigations in this volume are intended to provide both a means to better forecast future eruptive episodes and also an opportunity to formulate and test future hypotheses for magmatic and eruptive processes. Future eruptions may follow a course similar to those observed in 1976, 1986, and 2006. However, a major perturbation that upsets conditions within the magmatic system could occur, owing perhaps to the rise of a much larger or different parental magma or to a large edifice failure similar to the 1883 sector collapse. In such events, the comprehensive study of past eruptions will provide data critical to assessing the current state of the magmatic system. In assembling this volume we have sought as consistent and accurate a portrayal of the 2006 eruption as possible. We have asked all authors to refer to the same basic eruption chronology, unless their observations and data require alternative explanations. Naturally, not all techniques or methodologies produce a completely consistent set of observations, nor do the precise conclusions in every paper support one another. We have grouped chapters on the basis of discipline. Papers that focus on specific techniques, methodology, or instrumentation are placed throughout the volume where they best fit with others that rely on their results.

Alaska↗

Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

Minnesota↗

Landslides across the United States: Occurrence, susceptibility, and data limitations

Detailed information about landslide occurrence is the foundation for advancing process understanding, susceptibility mapping, and risk reduction. Despite the recent revolution in digital elevation data and remote sensing technologies, landslide mapping remains resource intensive. Consequently, a modern, comprehensive map of landslide occurrence across the United States (USA) has not been compiled. As a first step toward this goal, we present a national-scale compilation of existing, publicly available landslide inventories. This geodatabase can be downloaded in its entirety or viewed through an online, searchable map, with parsimonious attributes and direct links to the contributing sources with additional details. The mapped spatial pattern and concentration of landslides are consistent with prior characterization of susceptibility within the conterminous USA, with some notable exceptions on the West Coast. Although the database is evolving and known to be incomplete in many regions, it confirms that landslides do occur across the country, thus highlighting the importance of our national-scale assessment. The map illustrates regions where high-quality mapping has occurred and, in contrast, where additional resources could improve confidence in landslide characterization. For example, borders between states and other jurisdictions are quite apparent, indicating the variation in approaches to data collection by different agencies and disparity between the resources dedicated to landslide characterization. Further investigations are needed to better assess susceptibility and to determine whether regions with high relief and steep topography, but without mapped landslides, require further landslide inventory mapping. Overall, this map provides a new resource for accessing information about known landslides across the USA.

Landslides↗

High resolution lidar data shed light on inter-island translocation of endangered bird species in the Hawaiian Islands

Translocation, often a management solution reserved for at-risk species, is a highly time-sensitive intervention in the face of a rapidly changing climate. The definition of abiotic and biotic habitat requirements is essential to the selection of appropriate release sites in novel environments. However, field-based approaches to gathering this information are often too time intensive, especially in areas of complex topography where common, coarse-scale climate models lack essential details. We apply a fine-scale remote sensing-based approach to study the 'akikiki ( Oreomystis bairdi ) and 'akeke'e ( Loxops caeruleirostris ), Hawaiian honeycreepers endemic to Kaua'i that are experiencing large-scale population declines due to warming-induced spread of invasive disease. We use habitat suitability modeling based on fine-scale light detection and ranging (lidar)-derived habitat structure metrics to refine coarse climate ranges for these species in candidate translocation areas on Maui. We found that canopy density was consistently the most important variable in defining habitat suitability for the two Kaua'i species. Our models also corroborated known habitat preferences and behavioral information for these species that are essential for informing translocation. We estimated a nesting habitat that will persist under future climate conditions on east Maui of 23.43 km 2 for 'akikiki, compared to the current Kaua'i range of 13.09 km 2 . In contrast, the novel nesting range for 'akeke'e in east Maui was smaller than its current range on Kaua'i (26.29 vs. 38.48 km 2 , respectively). We were also able to assess detailed novel competitive interactions at a fine scale using models of three endemic Maui species of conservation concern: 'ākohekohe ( Palmeria dolei ), Maui 'alauahio ( Paroreomyza montana ), and kiwikiu ( Pseudonestor xanthophrys ). Weighted overlap areas between the species from both islands were moderate (<12 km 2 ), and correlations between Maui and Kaua'i bird habitat were generally low, indicating limited potential for competition. Results indicate that translocation to east Maui could be a viable option for 'akikiki but would be more uncertain for 'akeke'e. Our novel multifaceted approach allows for the timely analysis of both climate and vegetation structure at informative scales for the effective selection of appropriate translocation sites for at-risk species.

Hawaii↗

CLICK: The new USGS center for LIDAR information coordination and knowledge

Elevation data is rapidly becoming an important tool for the visualization and analysis of geographic information. The creation and display of three-dimensional models representing bare earth, vegetation, and structures have become major requirements for geographic research in the past few years. Light Detection and Ranging (lidar) has been increasingly accepted as an effective and accurate technology for acquiring high-resolution elevation data for bare earth, vegetation, and structures. Lidar is an active remote sensing system that records the distance, or range, of a laser fi red from an airborne or space borne platform such as an airplane, helicopter or satellite to objects or features on the Earth&rsquo;s surface. By converting lidar data into bare ground topography and vegetation or structural morphologic information, extremely accurate, high-resolution elevation models can be derived to visualize and quantitatively represent scenes in three dimensions. In addition to high-resolution digital elevation models (Evans et al., 2001), other lidar-derived products include quantitative estimates of vegetative features such as canopy height, canopy closure, and biomass (Lefsky et al., 2002), and models of urban areas such as building footprints and three-dimensional city models (Maas, 2001).

Photogrammetric Engineering and Remote Sensing↗

Multi-temporal RADARSAT-1 and ERS backscattering signatures of coastal wetlands in southeastern Louisiana

Using multi-temporal European Remote-sensing Satellites (ERS-1/-2) and Canadian Radar Satellite (RADARSAT-1) synthetic aperture radar (SAR) data over the Louisiana coastal zone, we characterize seasonal variations of radar backscat-tering according to vegetation type. Our main findings are as follows. First, ERS-1/-2 and RADARSAT-1 require careful radiometric calibration to perform multi-temporal backscattering analysis for wetland mapping. We use SAR backscattering signals from cities for the relative calibration. Second, using seasonally averaged backscattering coefficients from ERS-1/-2 and RADARSAT-1, we can differentiate most forests (bottomland and swamp forests) and marshes (freshwater, intermediate, brackish, and saline marshes) in coastal wetlands. The student t-test results support the usefulness of season-averaged backscatter data for classification. Third, combining SAR backscattering coefficients and an optical-sensor-based normalized difference vegetation index can provide further insight into vegetation type and enhance the separation between forests and marshes. Our study demonstrates that SAR can provide necessary information to characterize coastal wetlands and monitor their changes.

Photogrammetric Engineering and Remote Sensing↗