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USGS2014

Effects of disturbance and climate change on ecosystem performance in the Yukon River Basin boreal forest

A warming climate influences boreal forest productivity, dynamics, and disturbance regimes. We used ecosystem models and 250 m satellite Normalized Difference Vegetation Index (NDVI) data averaged over the growing season (GSN) to model current, and estimate future, ecosystem performance. We modeled Expected Ecosystem Performance (EEP), or anticipated productivity, in undisturbed stands over the 2000–2008 period from a variety of abiotic data sources, using a rule-based piecewise regression tree. The EEP model was applied to a future climate ensemble A1B projection to quantify expected changes to mature boreal forest performance. Ecosystem Performance Anomalies (EPA), were identified as the residuals of the EEP and GSN relationship and represent performance departures from expected performance conditions. These performance data were used to monitor successional events following fire. Results suggested that maximum EPA occurs 30–40 years following fire, and deciduous stands generally have higher EPA than coniferous stands. Mean undisturbed EEP is projected to increase 5.6% by 2040 and 8.7% by 2070, suggesting an increased deciduous component in boreal forests. Our results contribute to the understanding of boreal forest successional dynamics and its response to climate change. This information enables informed decisions to prepare for, and adapt to, climate change in the Yukon River Basin forest.

Alaska↗

USGS2019

UAV-derived estimates of forest structure to inform ponderosa pine forest restoration

Restoring forest ecosystems has become an increasingly high priority for land managers across the American West. Millions of hectares of forest are in need of drastic yet strategic reductions in density (e.g., basal area). Meeting the restoration and management goals requires quantifying metrics of vertical and horizontal forest structure, which has relied upon field‐based measurements, manned airborne or satellite remote sensing datasets. We used unmanned aerial vehicle (UAV ) image‐derived Structure‐from‐Motion (SfM) models and high‐resolution multispectral orthoimagery in this study to quantify vertical and horizontal forest structure at both the fine‐ (<4 ha) and mid‐scales (4–400 ha) across a forest density gradient. We then used these forest structure estimates to assess specific objectives of a forest restoration treatment. At the fine‐scale, we found that estimates of individual tree height and canopy diameter were most accurate in low‐density conditions, with accuracies degrading significantly in high‐density conditions. Mid‐scale estimates of canopy cover and forest density followed a similar pattern across the density gradient, demonstrating the effectiveness of UAV image‐derived estimates in low‐ to medium‐density conditions as well as the challenges associated with high‐density conditions. We found that post‐treatment conditions met a majority of the prescription objectives and demonstrate the UAV image application in quantifying changes from a mechanical thinning treatment. We provide a novel approach to forest restoration monitoring using UAV ‐derived data, one that considers varying density conditions and spatial scales. Future research should consider a more spatially extensive sampling design, including different restoration treatments, as well as experimenting with different combinations of equipment, flight parameters, and data processing workflows.

Western United States↗

USGS2011

LiDAR: Providing structure

Since the days of MacArthur, three-dimensional (3-D) structural information on the environment has fundamentally transformed scientific understanding of ecological phenomena (MacArthur and MacArthur 1961). Early data on ecosystem structure were painstakingly laborious to collect. However, as reviewed and reported in recent volumes of Frontiers(eg Vierling et al. 2008; Asner et al.2011), advances in light detection and ranging (LiDAR) remote-sensing technology provide quantitative and repeatable measurements of 3-D ecosystem structure that enable novel ecological insights at scales ranging from the plot, to the landscape, to the globe. Indeed, annual publication of studies using LiDAR to interpret ecological phenomena increased 17-fold during the past decade, with over 180 new studies appearing in 2010 (ISI Web of Science search conducted on 23 Mar 2011: [{lidar AND ecol*} OR {lidar AND fores*} OR {lidar AND plant*}]).

Frontiers in Ecology and the Environment↗

USGS2009

The National Map customer requirements: Findings from interviews and surveys

The purpose of this study was to receive customer feedback and to understand data and information requirements for The National Map. This report provides results and findings from interviews and surveys and will guide policy and operations decisions about data and information requirements leading to the development of a 5-year strategic plan for the National Geospatial Program. These findings are based on feedback from approximately 2,200 customers between February and August 2008. The U.S. Geological Survey (USGS) conducted more than 160 interviews with 200 individuals. The American Society for Photogrammetry and Remote Sensing (ASPRS) and the International Map Trade Association (IMTA) surveyed their memberships and received feedback from over 400 members. The Environmental Systems Research Institute (ESRI) received feedback from over 1,600 of its U.S.-based software users through an online survey sent to customers attending the ESRI International User Conference in the summer of 2008. The results of these surveys were shared with the USGS and have been included in this report.

Open-File Report↗

USGS2011

Hyperspectral analysis of rocky surfaces on the Earth and other planetary systems: Chapter 27

This book is focused on studies of vegetation on Earth using hyperspectral remote sensing methods. However, it is appropriate to extend the application of these methods out to other rocky bodies in our Solar System for a variety of reasons. First, minerals, soils, and rocks form the substrate on which vegetation grows on Earth. Compositional analyses of these components with hyperspectral data provide essential background information for distinguishing, identifying, and removing their effects on vegetation spectra. Second, variation in distribution, chemical and physical properties among soil and rock, has been demonstrated to have a significant effect on factors such as moisture retention, dust production, and the presence and distribution of biological species ranging from bacteria, fungi, grasses, shrubs, trees, and small mammals to humans [1]. These factors in turn can have profound influences on human health.

Book chapter↗

USGS2010

The use of multi-temporal Landsat Normalized Difference Vegetation Index (NDVI) data for mapping fuels in Yosemite National Park, USA

The objective of this study was to test the applicability of using Normalized Difference Vegetation Index (NDVI) values derived from a temporal sequence of six Landsat Thematic Mapper (TM) scenes to map fuel models for Yosemite National Park, USA. An unsupervised classification algorithm was used to define 30 unique spectral-temporal classes of NDVI values. A combination of graphical, statistical and visual techniques was used to characterize the 30 classes and identify those that responded similarly and could be combined into fuel models. The final classification of fuel models included six different types: short annual and perennial grasses, tall perennial grasses, medium brush and evergreen hardwoods, short-needled conifers with no heavy fuels, long-needled conifers and deciduous hardwoods, and short-needled conifers with a component of heavy fuels. The NDVI, when analysed over a season of phenologically distinct periods along with ancillary data, can elicit information necessary to distinguish fuel model types. Fuels information derived from remote sensors has proven to be useful for initial classification of fuels and has been applied to fire management situations on the ground.

International Journal of Remote Sensing↗

USGS2015

Classification of rocky headlands in California with relevance to littoral cell boundary delineation

Despite extensive studies of hydrodynamics and sediment flux along beaches, there is little information on the processes, pathways and timing of water and sediment transport around rocky headlands. In this study, headlands along the California coast are classified to advance understanding of headland dynamics and littoral cell boundaries in support of improved coastal management decisions. Geomorphological parameters for 78 headlands were quantified from geological maps, remote-sensing imagery, navigational charts, and shoreline geospatial databases. K -means cluster analysis grouped the headlands into eight distinct classes based on headland perimeter, bathymetric slope ratio, and the headland apex angle. Wave data were used to investigate the potential for sediment transport around the headland types and determine the efficacy of the headland as a littoral cell boundary. Four classes of headland appear to function well as littoral cell boundaries, with headland size (e.g., perimeter or area) and a marked change in nearshore bathymetry across the headland being relevant attributes. About half of the traditional California littoral cell boundaries align with headland classes that are expected to perform poorly in blocking alongshore sediment transport, calling into question these boundaries. Better definition of these littoral cell boundaries is important for regional sediment management decisions.

California↗

USGS2010

Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing

This study evaluates the efficacy of remote sensing technology to monitor species composition, areal extent and density of aquatic plants (macrophytes and filamentous algae) in impoundments where their presence may violate water-quality standards. Multispectral satellite (IKONOS) images and more than 500 in situ hyperspectral samples were acquired to map aquatic plant distributions. By analyzing field measurements, we created a library of hyperspectral signatures for a variety of aquatic plant species, associations and densities. We also used three vegetation indices. Normalized Difference Vegetation Index (NDVI), near-infrared (NIR)-Green Angle Index (NGAI) and normalized water absorption depth (DH), at wavelengths 554, 680, 820 and 977 nm to differentiate among aquatic plant species composition, areal density and thickness in cases where hyperspectral analysis yielded potentially ambiguous interpretations. We compared the NDVI derived from IKONOS imagery with the in situ, hyperspectral-derived NDVI. The IKONOS-based images were also compared to data obtained through routine visual observations. Our results confirmed that aquatic species composition alters spectral signatures and affects the accuracy of remote sensing of aquatic plant density. The results also demonstrated that the NGAI has apparent advantages in estimating density over the NDVI and the DH. In the feature space of the three indices, 3D scatter plot analysis revealed that hyperspectral data can differentiate several aquatic plant associations. High-resolution multispectral imagery provided useful information to distinguish among biophysical aquatic plant characteristics. Classification analysis indicated that using satellite imagery to assess Lemna coverage yielded an overall agreement of 79% with visual observations and >90% agreement for the densest aquatic plant coverages. Interpretation of biophysical parameters derived from high-resolution satellite or airborne imagery should prove to be a valuable approach for assessing the effectiveness of management practices for controlling aquatic plant growth in inland waters, as well as for routine monitoring of aquatic plants in lakes and suitable lentic environments. ?? 2010 Blackwell Publishing Ltd.

Freshwater Biology↗

USGS2009

Hazards management in Grand County, Colorado— Fire fuels characterization

The USGS Fire Science Initiative is designed to identify potential wildfire risks and related hazards and to mitigate their effects on people, property, and natural resources. The USGS Rocky Mountain Geographic Science Center (RMGSC) plays an integral role in the fire science demonstration project targeting Grand County, Colo., which uses remote sensing imagery, other geospatial data, and advanced classification techniques to produce inventories and assessments of the current state of the ecosystem. The data gathered - extent of tree mortality and insect infestation, changes in fire fuels, susceptibility to post-fire effects, distribution of wildland-urban interface areas, etc. - will give much needed information to decisionmakers on the Federal, State, and local levels.

Colorado↗

USGS2008

Airborne Hyperspectral Survey of Afghanistan 2007: Flight Line Planning and HyMap Data Collection

Hyperspectral remote sensing data were acquired over Afghanistan with the HyMap imaging spectrometer (Cocks and others, 1998) operating on the WB-57 high altitude NASA research aircraft (http://jsc-aircraft-ops.jsc.nasa.gov/wb57/index.html). These data were acquired during the interval of August 22, 2007 to October 2, 2007, as part of the United States Geological Survey (USGS) project 'Oil and Gas Resources Assessment of the Katawaz and Helmand Basins'. A total of 218 flight lines of hyperspectral remote sensing data were collected over the country. This report describes the planning of the airborne survey and the flight lines that were flown. Included with this report are digital files of the nadir tracks of the flight lines, including a map of the labeled flight lines and corresponding vector shape files for geographic information systems (GIS).

Open-File Report↗

USGS1987

Water-resources activities of the U.S. Geological Survey in South Dakota; fiscal years 1986-87

In South Dakota, the first collection of streamflow data by the U.S. Geological Survey (USGS) was in 1903. Despite its early beginning, it was not until October 16, 1944, that the Bismarck District, comprising the states of North Dakota and South Dakota , was created to assess the water resources of the two states. The next major increase in collection of surface water records occurred during the mid-1940 's as a result of the Pick-Sloan Plan for Missouri Basin development. Since 1944, about 98 water resources studies have been made in South Dakota. These range from reconnaissance-type studies of counties and Indian reservations to research on small basin runoff and toxic wastes, the quality of water in lakes, the use of remote sensing for defining aquifers, and studies using digital models to describe the groundwater regimen and surface water hydraulics such as those currently underway in the James River basin and the Big Sioux River basin. During the past 20 years, 140 formal reports describing the studies and results of investigations have been prepared to inform the public and the scientific community. The location of surface water stations and observation wells in bedrock are tabulated. Brief (1 page) descriptions of current water resources projects in South Dakota include information on the location, purpose, period of performance, cooperating agencies, project leader, and completed reports. (Lantz-PTT)

Open-File Report↗

USGS1980

Detection and modeling of subsurface coal oxidation

The oxidation and sustained ignition of coal and coaly wastes within surface coal mine spoils in the southwestern U.S. have hampered the success of reclamation efforts at these locations. To assess better the magnitude, depth, geometry, and dynamics of the oxidation process thermal infrared remote sensing data have been used. Digital thermal imagery was found to be useful for this purpose and was integrated with finite different heat transfer models to yield predictions of several characteristics of the thermal source. In addition to thermal infrared imagery, aerial color and false color infrared imagery were found to provide useful information for the interpretation of oxidation phenomena by means of variations in surface vegetation, color of the surface material, subsidence, etc. The combined use of thermal infrared imagery and thermal modeling techniques are well suited for use in exploration and interpretation of other thermal targets.

Sixth Annual Pecora Symposium and Exposition↗

USGS2018

Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system

Cropland fallows are the next best-bet for intensification and extensification, leading to increased food production and adding to the nutritional basket. The agronomical suitability of these lands can decide the extent of usage of these lands. Myanmar’s agricultural land (over 13.8 Mha) has the potential to expand by another 50% into additional fallow areas. These areas may be used to grow short-duration pulses, which are economically important and nutritionally rich, and constitute the diets of millions of people as well as provide an important source of livestock feed throughout Asia. Intensifying rice fallows will not only improve the productivity of the land but also increase the income of the smallholder farmers. The enhanced cultivation of pulses will help improve nutritional security in Myanmar and also help conserve natural resources and reduce environmental degradation. The objectives of this study was to use remote sensing methods to identify croplands in Myanmar and cropland fallow areas in two important agro-ecological regions, delta and coastal region and the dry zone. The study used moderate-resolution imaging spectroradiometer (MODIS) 250-m, 16-day normalized difference vegetation index (NDVI) maximum value composite (MVC), and land surface water index (LSWI) for one 1 year (1 June 2012–31 May 2013) along with seasonal field-plot level information and spectral matching techniques to derive croplands versus cropland fallows for each of the three seasons: the monsoon period between June and October; winter period between November and February; and summer period between March and May. The study showed that Myanmar had total net cropland area (TNCA) of 13.8 Mha. Cropland fallows during the monsoon season account for a meagre 2.4% of TNCA. However, in the winter season, 56.5% of TNCA (or 7.8 Mha) were classified as cropland fallows and during the summer season, 82.7% of TNCA (11.4 Mha) were cropland fallows. The producer’s accuracy of the cropland fallow class varied between 92 and 98% (errors of omission of 2 to 8%) and user’s accuracy varied between 82 and 92% (errors of commission of 8 to 18%) for winter and summer, respectively. Overall, the study estimated 19.2 Mha cropland fallows from the two major seasons (winter and summer). Out of this, 10.08 Mha has sufficient moisture (either from rainfall or stored soil water content) to grow short-season pulse crops. This potential with an estimated income of US\$ 300 per hectare, if exploited sustainably, is estimated to bring an additional net income of about US\$ 1.5 billion to Myanmar per year if at least half (5.04 Mha) of the total cropland fallows (10.08 Mha) is covered with short season pulses.

GIScience and Remote Sensing↗

USGS2007

Spectral reflectance and emissivity features of broad leaf plants: Prospects for remote sensing in the thermal infrared (8.0-14.0 μm)

Field emissivity measurements were made of leaves collected from nine deciduous tree and agricultural plant species. The data show, for the first time, that it is possible to discriminate subtle spectral emissivity features of leaves from the natural background emission. Under conditions of controlled measurement geometry (leaves arranged to cover a flat surface), the field emissivity spectra agreed fairly well with emissivity values calculated from laboratory directional hemispherical reflectance measurements. Spectral features associated with a variety of leaf chemical constituents, including cellulose, cutin, xylan, silica, and oleanolic acid could be identified in the field emissivity data. Structural aspects of leaf surfaces also influenced spectral behavior, notably the abundance of trichomes, as well as wax thickness and texture. Field spectral measurements made at increasing distances from natural plant canopies showed progressive attenuation of the spectral emissivity features. This attenuation is ascribed to increased multiple scattering that superimposes an opposite-in-sign reflected component on the emittance, and to the increasing number of canopy voids within the instrument field of view. Errors associated with the removal of atmospheric features and with the non-isotropic thermal characteristics of canopies also contribute to the loss of spectral information at greater measurement distances. In contrast to visible and short-wave infrared data, thermal infrared spectra of broad leaf plants show considerable spectral diversity, suggesting that such data eventually could be utilized to map vegetation composition. However, remotely measuring the subtle emissivity features of leaves still presents major challenges. To be successful, sensors operating in the 8–14 μm atmospheric window must have high signal-to-noise and a small enough instantaneous field of view to allow measurements of only a few leaf surfaces. Methods for atmospheric compensation, temperature–emissivity separation, and spectral feature analysis also will need to be refined to allow the recognition, and perhaps, exploitation of leaf thermal infrared spectral properties.

Virginia↗

USGS2019

Where’s the rock: Using convolutional neural networks to improve land cover classification

While machine learning techniques have been increasingly applied to land cover classification problems, these techniques have not focused on separating exposed bare rock from soil covered areas. Therefore, we built a convolutional neural network (CNN) to differentiate exposed bare rock ( rock ) from soil cover ( other ). We made a training dataset by mapping exposed rock at eight test sites across the Sierra Nevada Mountains (California, USA) using USDA’s 0.6 m National Aerial Inventory Program (NAIP) orthoimagery. These areas were then used to train and test the CNN. The resulting machine learning approach classifies bare rock in NAIP orthoimagery with a 0.95 F 1 "> F 1 score. Comparatively, the classical OBIA approach gives only a 0.84 F 1 "> F 1 score. This is an improvement over existing land cover maps, which underestimate rock by almost 90%. The resulting CNN approach is likely scalable but dependent on high-quality imagery and high-performance algorithms using representative training sets informed by expert mapping. As image quality and quantity continue to increase globally, machine learning models that incorporate high-quality training data informed by geologic, topographic, or other topical maps may be applied to more effectively identify exposed rock in large image collections.

California↗

USGS2019

Monitoring drought impact on annual forage production in semi-arid grasslands: A case study of Nebraska sandhills

Land management practices and disturbances (e.g. overgrazing, fire) have substantial effects on grassland forage production. When using satellite remote sensing to monitor climate impacts, such as drought stress on annual forage production, minimizing land management practices and disturbance effects sends a clear climate signal to the productivity data. This study investigates the effect of this climate signal by: (1) providing spatial estimates of expected biomass under specific climate conditions, (2) determining which drought indices explain the majority of interannual variability in this biomass, and (3) developing a predictive model that estimates the annual biomass early in the growing season. To address objective 1, this study uses an established methodology to determine Expected Ecosystem Performance (EEP) in the Nebraska Sandhills, US, representing annual forage levels after accounting for non-climatic influences. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000–2016) of annual EEP was calculated using piecewise regression tree models of site potential and climate data. Expected biomass (EB), EEP converted to biomass in kg*ha −1 *yr −1 , was then used to examine the predictive capacity of several drought indices and the onset date of the growing season. Subsets of these indices were used to monitor and predict annual expected grassland biomass. Independent field-based biomass production data available from two Sandhills locations were used for validation of the EEP model. The EB was related to field-based biomass production (R 2 = 0.66 and 0.57) and regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the U.S. Drought Monitor (USDM), which represented moisture conditions during May, June and July, explained the majority of the interannual biomass variability in this grassland system (three-month ESI explained roughly 72% of the interannual biomass variability). A new model was developed to use drought indices from early in the growing season to predict the total EB for the whole growing season. This unique approach considers only climate-related drought signal on productivity. The capability to estimate annual EB by the end of May will potentially enable land managers to make informed decisions about stocking rates, hay purchase needs, and other management issues early in the season, minimizing their potential drought losses. View Full-Text

Nebraska↗

USGS2018

Barrier-island and estuarine-wetland physical-change assessment after Hurricane Sandy

Introduction The Nation’s eastern coast is fringed by beaches, dunes, barrier islands, wetlands, and bluffs. These natural coastal barriers provide critical benefits and services, and can mitigate the impact of storms, erosion, and sea-level rise on our coastal communities. Waves and storm surge resulting from Hurricane Sandy, which made landfall along the New Jersey coast on October 29, 2012, impacted the U.S. coastline from North Carolina to Massachusetts, including Assateague Island, Maryland and Virginia, and the Delmarva coastal system. The storm impacts included changes in topography, coastal morphology, geology, hydrology, environmental quality, and ecosystems. In the immediate aftermath of the storm, light detection and ranging (lidar) surveys from North Carolina to New York documented storm impacts to coastal barriers, providing a baseline to assess vulnerability of the reconfigured coast. The focus of much of the existing coastal change assessment is along the ocean-facing coastline; however, much of the coastline affected by Hurricane Sandy includes the estuarine-facing coastlines of barrier-island systems. Specifically, the wetland and back-barrier shorelines experienced substantial change as a result of wave action and storm surge that occurred during Hurricane Sandy (see also USGS photograph, http://coastal.er.usgs.gov/hurricanes/sandy/photo-comparisons/virginia.php ). Assessing physical shoreline and wetland change (land loss as well as land gains) can help to determine the resiliency of wetland systems that protect adjacent habitat, shorelines, and communities. To address storm impacts to wetlands, a vulnerability assessment should describe both long-term (for example, several decades) and short-term (for example, Sandy’s landfall) extent and character of the interior wetlands and the back-barrier-shoreline changes. The objective of this report is to describe several new wetland vulnerability assessments based on the detailed physical changes estimated from observations. The scope includes understanding changes caused by both short- and long-term processes using both remotely sensed and in situ observations to characterize changes to the wetland in terms of accretion/expansion and erosion/contraction. Accretion may be due to net vertical and (or) horizontal deposition, including estuarine-shoreline change due to overwash. Wetland erosion may be due to elevated waves and water levels in the estuary itself. We included additional information based on wave runup and storm-surge elevations based on models and elevation data. We then developed a predictive assessment for wetland vulnerability that describes the likelihood of changes of the estuarine shoreline and the landward extent of sand overwash driven by processes occurring on the ocean-facing shoreline. This assessment is intended to be linked to the beach and dune vulnerability assessments that have been developed previously.

Open-File Report↗

USGS2014

Thermal measurements of dark and bright surface features on Vesta as derived from Dawn/VIR

Remote sensing data acquired during Dawn’s orbital mission at Vesta showed several local concentrations of high-albedo (bright) and low-albedo (dark) material units, in addition to spectrally distinct meteorite impact ejecta. The thermal behavior of such areas seen at local scale (1-10 km) is related to physical properties that can provide information about the origin of those materials. We use Dawn’s Visible and InfraRed (VIR) mapping spectrometer hyperspectral data to retrieve surface temperatures and emissivities, with high accuracy as long as temperatures are greater than 220 K. Some of the dark and bright features were observed multiple times by VIR in the various mission phases at variable spatial resolution, illumination and observation angles, local solar time, and heliocentric distance. This work presents the first temperature maps and spectral emissivities of several kilometer-scale dark and bright material units on Vesta. Results retrieved from the infrared data acquired by VIR show that bright regions generally correspond to regions with lower temperature, while dark regions correspond to areas with higher temperature. During maximum daily insolation and in the range of heliocentric distances explored by Dawn, i.e. 2.23-2.54 AU, the warmest dark unit found on Vesta rises to a temperature of 273 K, while bright units observed under comparable conditions do not exceed 266 K. Similarly, dark units appear to have higher emissivity on average compared to bright units. Dark-material units show a weak anticorrelation between temperature and albedo, whereas the relation is stronger for bright material units observed under the same conditions. Individual features may show either evanescent or distinct margins in the thermal images, as a consequence of the cohesion of the surface material. Finally, for the two categories of dark and bright materials, we were able to highlight the influence of heliocentric distance on surface temperatures, and estimate an average temperature rate change of 1% following a variation of 0.04 AU in the solar distance.

Icarus↗