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At least 415 records · Page 23Linked to original sources

Assessing giant sequoia mortality and regeneration following high-severity wildfire

Fire is a critical driver of giant sequoia ( Sequoiadendron giganteum [Lindl.] Buchholz) regeneration. However, fire suppression combined with the effects of increased temperature and severe drought has resulted in fires of an intensity and size outside of the historical norm. As a result, recent mega-fires have killed a significant portion of the world's sequoia population (13%–19%), and uncertainty surrounds whether severely affected groves will be able to recover naturally, potentially leading to a loss of grove area. To assess the likelihood of natural recovery, we collected spatially explicit data assessing mortality, crown condition, and regeneration within four giant sequoia groves that were severely impacted by the SQF- (2020) and KNP-Complex (2021) wildfires within Sequoia and Kings Canyon National Parks. In total, we surveyed 5.9 ha for seedlings and assessed the crown condition of 1104 giant sequoias. To inform management, we used a statistical methodology that robustly quantifies the uncertainty in inherently “noisy” seedling data and takes advantage of readily available remote sensing metrics that would make our findings applicable to other recently burned groves. A loss of giant sequoia grove area would be a consequence of giant sequoia tree mortality followed by a failure of natural regeneration. We found that areas that experienced very high-severity fire (above ~800 RdNBR) are at substantial risk for the loss of grove area, with tree mortality rapidly increasing and giant sequoia seedling density simultaneously decreasing with fire severity. Such high-severity areas comprised 17.8, 142.0, 14.6, 1.6 ha and ~90%, ~14%, ~53%, and ~27% of Board Camp, Redwood Mountain, Suwanee, and New Oriole Lake groves, respectively. In all sampling areas, we found that seedling densities fell far below the average density measured after prescribed fires, where seedling numbers were almost certainly adequate to maintain giant sequoia populations and postfire conditions were more in keeping with historical norms. Importantly, spatial pattern is also important in assessing the risk of grove loss, and in two groves, Suwanee and New Oriole Lake, the high-severity patches were not always contiguous, potentially making some areas more resilient to regeneration failure due to the proximity of surviving trees.

Ecosphere↗

Developing a strategy for the national coordinated soil moisture monitoring network

Soil moisture is a critical land surface variable, affecting a wide variety of climatological, agricultural, and hydrological processes. Determining the current soil moisture status is possible via a variety of methods, including in situ monitoring, remote sensing, and numerical modeling. Although all of these approaches are rapidly evolving, there is no cohesive strategy or framework to integrate these diverse information sources to develop and disseminate coordinated national soil moisture products that will improve our ability to understand climate variability. The National Coordinated Soil Moisture Monitoring Network initiative has developed a national strategy for network coordination with NOAA's National Integrated Drought Information System. The strategy is currently in review within NOAA, and work is underway to implement the initial milestones of the strategy. This update reviews the goals and steps being taken to establish this national-scale coordination for soil moisture monitoring in the United States.

Vadose Zone Journal↗

Ecosystem services from converted land: the importance of tree cover in Amazonian pastures

Deforestation is responsible for a substantial fraction of global carbon emissions and changes in surface energy budgets that affect climate. Deforestation losses include wildlife and human habitat, and myriad forest products on which rural and urban societies depend for food, fiber, fuel, fresh water, medicine, and recreation. Ecosystem services gained in the transition from forests to pasture and croplands, however, are often ignored in assessments of the impact of land cover change. The role of converted lands in tropical areas in terms of carbon uptake and storage is largely unknown. Pastures represent the fastest-growing form of converted land use in the tropics, even in some areas of rapid urban expansion. Tree biomass stored in these areas spans a broad range, depending on tree cover. Trees in pasture increase carbon storage, provide shade for cattle, and increase productivity of forage material. As a result, increasing fractional tree cover can provide benefits land managers as well as important ecosystem services such as reducing conversion pressure on forests adjacent to pastures. This study presents an estimation of fractional tree cover in pasture in a dynamic region on the verge of large-scale land use change. An appropriate sampling interval is established for similar studies, one that balances the need for independent samples of sufficient number to characterize a pasture in terms of fractional tree cover. This information represents a useful policy tool for government organizations and NGOs interested in encouraging ecosystem services on converted lands. Using high spatial resolution remotely sensed imagery, fractional tree cover in pasture is quantified for the municipality of Rio Branco, Brazil. A semivariogram and devolving spatial resolution are employed to determine the coarsest sampling interval that may be used, minimizing effects of spatial autocorrelation. The coarsest sampling interval that minimizes spatial dependence was about 22 m. The area-weighted fractional tree cover for the study area was 1.85 %, corrected for a slight bias associated with the coarser sampling resolution. The pastures sampled for fractional tree cover were divided between ‘high’ and ‘low’ tree cover, which may be the result of intentional incorporation of arboreal species in pasture. Further research involving those ranchers that have a higher fractional tree cover may indicate ways to promote the practice on a broader scale in the region.

Urban Ecosystems↗

Assessment of impacts of proposed coal-resource and related economic development on water resources, Yampa River basin, Colorado and Wyoming: A summary

Expanded mining and use of coal resources in the Rocky Mountain region of the western United States will have substantial impacts on water resources, environmental amenities, and social and economic conditions. The U.S. Geological Survey has completed a 3-year assessment of the Yampa River basin, Colorado and Wyoming, where increased coal-resource development has begun to affect the environment and quality of life. Economic projections of the overall effects of coal-resource development were used to estimate water use and the types and amounts of waste residuals that need to be assimilated into the environment. Based in part upon these projections, several physical-based models and other semiquantitative assessment methods were used to determine possible effects upon the basin's water resources. Depending on the magnitude of mining and use of coal resources in the basin, an estimated 0.7 to 2.7 million tons (0.6 to 2.4 million metric tons) of waste residuals may be discharged annually into the environment by coal-resource development and associated economic activities. If the assumed development of coal resources in the basin occurs, annual consumptive use of water, which was approximately 142,000 acre-feet (175 million cubic meters) during 1975, may almost double by 1990. In a related analysis of alternative cooling systems for coal-conversion facilities, four to five times as much water may be used consumptively in a wet-tower, cooling-pond recycling system as in once-through cooling. An equivalent amount of coal transported by slurry pipeline would require about one-third the water used consumptively by once-through cooling for in-basin conversion. Current conditions and a variety of possible changes in the water resources of the basin resulting from coal-resource development were assessed. Basin population may increase by as much as threefold between 1975 and 1990. Volumes of wastes requiring treatment will increase accordingly. Potential problems associated with ammonia-nitrogen concentrations in the Yampa River downstream from Steamboat Springs were evaluated using a waste-load assimilative-capacity model. Changes in sediment loads carried by streams due to increased coal mining and construction of roads and buildings may be apparent only locally; projected increases in sediment loads relative to historic loads from the basin are estimated to be 2 to 7 percent. Solid-waste residuals generated by coal-conversion processes and disposed of into old mine pits may cause widely dispersed ground-water contamination, based on simulation-modeling results. Projected increases in year-round water use will probably result in the construction of several proposed reservoirs. Current seasonal patterns of streamflow and of dissolvedsolids concentrations in streamflow will be altered appreciably by these reservoirs. Decreases in time-weighted mean-annual dissolved-solids concentrations of as much as 34 percent are anticipated, based upon model simulations of several configurations of proposed reservoirs. Detailed statistical analyses of water-quality conditions in the Yampa River basin were made. Regionalized maximum waterquality concentrations were estimated for possible comparison with future conditions. Using Landsat imagery and aerial photographs, potential remote-sensing applications were evaluated to monitor land-use changes and to assess both snow cover and turbidity levels in streams. The technical information provided by the several studies of the Yampa River basin assessment should be useful to regional planners and resource managers in evaluating the possible impacts of development on the basin's water resources.

Colorado, Wyoming↗

Characterizing the spatial structure of endangered species habitat using geostatistical analysis of IKONOS imagery

Our study used geostatistics to extract measures that characterize the spatial structure of vegetated landscapes from satellite imagery for mapping endangered Sonoran pronghorn habitat. Fine spatial resolution IKONOS data provided information at the scale of individual trees or shrubs that permitted analysis of vegetation structure and pattern. We derived images of landscape structure by calculating local estimates of the nugget, sill, and range variogram parameters within 25 ?? 25-m image windows. These variogram parameters, which describe the spatial autocorrelation of the 1-m image pixels, are shown in previous studies to discriminate between different species-specific vegetation associations. We constructed two independent models of pronghorn landscape preference by coupling the derived measures with Sonoran pronghorn sighting data: a distribution-based model and a cluster-based model. The distribution-based model used the descriptive statistics for variogram measures at pronghorn sightings, whereas the cluster-based model used the distribution of pronghorn sightings within clusters of an unsupervised classification of derived images. Both models define similar landscapes, and validation results confirm they effectively predict the locations of an independent set of pronghorn sightings. Such information, although not a substitute for field-based knowledge of the landscape and associated ecological processes, can provide valuable reconnaissance information to guide natural resource management efforts.

Arizona↗

Disaster response and the international charter program

In a meeting held in Vienna, Austria in 1999, a small group of space agencies conceived and approved a program to provide emergency response satellite data to those affected by disasters anywhere in the world. The purpose of this group, which came to be known as the “International Charter - Space and Major Disasters”, is to promote cooperation among space agencies in the use of satellite data to manage crises during and after disasters. When tropical storms, floods, oil spills, earthquakes, landslides, volcanoes or fires endanger human life, the Charter member agencies provide valuable information about these events’ extent and impact.

Photogrammetric Engineering and Remote Sensing↗

Actual evapotranspiration modeling using the operational Simplified Surface Energy Balance (SSEBop) approach

Remote-sensing technology and surface-energy-balance methods can provide accurate and repeatable estimates of actual evapotranspiration ( ETa ) when used in combination with local weather datasets over irrigated lands. Estimates of ETa may be used to provide a consistent, accurate, and efficient approach for estimating regional water withdrawals for irrigation and associated consumptive use (CU), especially in arid cropland areas that require supplemental water due to insufficient natural supplies from rainfall, soil moisture, or groundwater. ETa in these areas is considered equivalent to CU, and represents the part of applied irrigation water that is evaporated and/or transpired, and is not available for immediate reuse. A recent U.S. Geological Survey study demonstrated the application of the remote-sensing-based Simplified Surface Energy Balance (SSEB) model to estimate 10-year average ETa at 1-kilometer resolution on national and regional scales, and compared those ETa values to the U.S. Geological Survey’s National Water-Use Information Program’s 1995 county estimates of CU. The operational version of the operational SSEB (SSEBop) method is now used to construct monthly, county-level ETa maps of the conterminous United States for the years 2000, 2005, and 2010. The performance of the SSEBop was evaluated using eddy covariance flux tower datasets compiled from 2005 datasets, and the results showed a strong linear relationship in different land cover types across diverse ecosystems in the conterminous United States (correlation coefficient [r] ranging from 0.75 to 0.95). For example, r for woody savannas (0.75), grassland (0.75), forest (0.82), cropland (0.84), shrub land (0.89), and urban (0.95). A comparison of the remote-sensing SSEBop method for estimating ETa and the Hamon temperature method for estimating potential ET ( ETp ) also was conducted, using regressions of all available county averages of ETa for 2005 and 2010, and yielded correlations of r = 0.60 and r = 0.71, respectively. Correlations generally are stronger in the Southeast where ETa is close to ETp . SSEBop ETa provides more spatial detail and accuracy in the Southwest where irrigation is practiced in a smaller proportion of the region.

Scientific Investigations Report↗

Aquatics Systems Branch: transdisciplinary research to address water-related environmental problems

The Aquatic Systems Branch at the Fort Collins Science Center is a group of scientists dedicated to advancing interdisciplinary science and providing science support to solve water-related environmental issues. Natural resource managers have an increasing need for scientific information and stakeholders face enormous challenges of increasing and competing demands for water. Our scientists are leaders in ecological flows, riparian ecology, hydroscape ecology, ecosystem management, and contaminant biology. The Aquatic Systems Branch employs and develops state-of-the-science approaches in field investigations, laboratory experiments, remote sensing, simulation and predictive modeling, and decision support tools. We use the aquatic experimental laboratory, the greenhouse, the botanical garden and other advanced facilities to conduct unique research. Our scientists pursue research on the ground, in the rivers, and in the skies, generating and testing hypotheses and collecting quantitative information to support planning and design in natural resource management and aquatic restoration.

Fact Sheet↗

Combining remote sensing techniques with simulation modelling for assessing rangeland resources

Simulation modelling and remote sensing techniques can be used to describe ecological processes. When used together, these tools can increase our understanding of the spatial variability of ecosystems. Remotely sensed data can be combined with simulation models for predicting the accumulation of plant biomass, as surrogate variables within the models, or as correlative information for monitoring entire eco‐regions.

Geocarto International↗

Artisanal mining river dredge detection using SAR: A method comparison

Challenges exist in monitoring artisanal and small-scale mining (ASM) activities, given their dynamic and often informal nature. ASM takes form through various techniques and scales, including riverine dredging, which often targets the abundant alluvial gold deposits in South America. Remote sensing offers a solution to improve data collection, regulation, and monitoring of the more mobile and elusive ASM activities and their impacts. Mapping ASM riverine dredges using Synthetic Aperture Radar (SAR) is one of the application areas least explored. Three semi-automated detection approaches using Sentinel-1 SAR are compared on their ability to identify dredges with minimal false positives. The methods are: (i) Search for Unidentified Maritime Objects (SUMO), an established method for large ocean ship detection; and two techniques specifically developed for riverine environments that are introduced in this paper: (ii) a local detection method; and (iii) a global threshold method. A visual interpretation of SAR data with the inclusion of optical high-resolution data are used to generate a validation dataset. Results show it is possible to semi-automatically detect riverine dredge using SAR and that a local detection method provides the best balance between sensitivity and precision and has the lowest risk of error. Future improvements may consider further automation, more discriminatory variables, and analyzing the methods in different environments and at higher spatial resolutions.

Remote Sensing↗

EXPERIMENTS IN LITHOGRAPHY FROM REMOTE SENSOR IMAGERY.

Imagery from remote sensing systems such as the Landsat multispectral scanner and return beam vidicon, as well as synthetic aperture radar and conventional optical camera systems, contains information at resolutions far in excess of that which can be reproduced by the lithographic printing process. The data often require special handling to produce both standard and special map products. Some conclusions have been drawn regarding processing techniques, procedures for production, and printing limitations.

Conference Paper↗

River bathymetry retrieval from Landsat-9 images based on neural networks and comparison to SuperDove and Sentinel-2

The Landsat mission has kept an eye on our planet, including water bodies, for 50 years. With the launch of Landsat-9 and its onboard Operational Land Imager 2 (OLI-2) in September 2021, more subtle variations in brightness (14-bit dynamic range) can be captured than previous sensors in the Landsat series (e.g., 12-bit Landsat-8). The enhanced radiometric resolution of OLI-2 appeals to the aquatic remote sensing community because the instrument might be capable of resolving smaller differences in water-leaving radiance. This study evaluates the potential to map river bathymetry from Landsat-9 imagery. We employ a neural network (NN)-based regression model for bathymetry retrieval and compare the results with optimal band ratio analysis (OBRA). The effect of Landsat-9 pan-sharpening on depth retrieval is also examined. In addition, we perform an intersensor comparison with Sentinel-2 and newly available 8-band SuperDoves from the PlanetScope constellation. Depth retrieval results from the Colorado and Potomac Rivers imply that Landsat-9 provided more accurate bathymetry across a range of depths up to 20 m, particularly when pan-sharpened. Downsampling the SuperDove data improved bathymetry retrieval due to enhanced signal-to-noise ratio, most notably in deep waters (maximum detectable depth increased from ∼15 to ∼20 m). Similarly, the enhanced spectral resolution of 8-band SuperDoves improved depth retrieval relative to 4-band Doves. The NN-based model outperformed OBRA by incorporating more spectral information.

Journal of Selected Topics in Applied Earth Observ↗

Preliminary Surficial Geologic Map of the Mesquite Lake 30' X 60' Quadrangle, California and Nevada

The Quaternary surficial geologic map of the Mesquite Lake, California-Nevada 30'X60' quadrangle depicts deposit age and geomorphic processes of erosion and deposition, as identified by a composite of remote sensing investigations, laboratory analyses, and field work, in the arid to semi-arid Mojave Desert area, straddling the California-Nevada border. Mapping was motivated by the need to address pressing scientific and social issues such as understanding and predicting the effects of climate and associated hydrologic changes, human impacts on landscapes, ecosystem function, and natural hazards at a regional scale. As the map area lies just to the south of Las Vegas, Nevada, a rapidly expanding urban center, land use pressures and the need for additional construction materials are forecasted for the region. The map contains information on the temporal and spatial patterns of surface processes and hazards that can be used to model specific landscape applications. Key features of the geologic map include: (1) spatially extensive Holocene alluvial deposits that compose the bulk of Quaternary units (~25%), (2) remote sensing and field studies that identified fault scarps or queried faults in the Kingston Wash area, Shadow Mountains, southern Pahrump Valley, Bird Spring Range, Lucy Gray Mountains and Piute Valley, (3) a lineament indicative of potential fault offset is located in Mesquite Valley, (4) active eolian dunes and sand ramps located on the east side of Mesquite, Ivanpah, and Hidden Valleys adjacent to playas, (4) groundwater discharge deposits in southern Pahrump Valley, Spring Mountains, and Lucy Gray Mountains and (5) debris-flow deposits spanning almost the entire Quaternary period in age.

Open-File Report↗

The 1 km resolution global data set: needs of the International Geosphere Biosphere Programme

Examination of the scientific priorities for the International Geosphere Biosphere Programme (IGBP) reveals a requirement for global land data sets in several of its Core Projects. These data sets need to be at several space and time scales. Requirements are demonstrated for the regular acquisition of data at spatial resolutions of 1 km and finer and at high temporal frequencies. Global daily data at a resolution of approximately 1 km are sensed by the Advanced Very High Resolution Radiometer (AVHRR), but they have not been available in a single archive. It is proposed, that a global data set of the land surface is created from remotely sensed data from the AVHRR to support a number of IGBP's projects. This data set should have a spatial resolution of 1 km and should be generated at least once every 10 days for the entire globe. The minimum length of record should be a year, and ideally a system should be put in place which leads to the continuous acquisition of 1 km data to provide a base line data set prior to the Earth Observing System (EOS) towards the end of the decade. Because of the high cloud cover in many parts of the world, it is necessary to plan for the collection of data from every orbit. Substantial effort will be required in the preprocessing of the data set involving radiometric calibration, atmospheric correction, geometric correction and temporal compositing, to make it suitable for the extraction of information.

International Journal of Remote Sensing↗

Hyperspectral imaging of river bathymetry using an ensemble of regression trees

Remote sensing has emerged as an effective tool for characterizing river systems, and machine learning (ML) techniques could make this approach even more powerful. To explore this possibility, we developed an ML-based workflow for hyperspectral imaging of river bathymetry using an ensemble of regression trees (HIRBERT). This approach involves using paired observations of depth and reflectance to select wavelength bands as predictors and then train a depth retrieval model; applying the model to the image yields a spatially continuous bathymetric map. We used data from five rivers with diverse morphologies and optical characteristics to assess whether HIRBERT can (1) provide more accurate depth estimates than a band ratio-based algorithm and (2) extend the range of depths detectable via remote sensing. Relative to single band combinations identified via optimal band ratio analysis (OBRA), regression tree ensembles improved depth retrieval performance, with observed versus predicted (OP) regression R 2 values increasing for all five sites. Similarly, HIRBERT provided more reliable depth estimates than OBRA over the full range of depths present along each river. These results suggest that by incorporating additional spectral information from multiple wavelength bands, ML could enhance bathymetric mapping across a range of river environments. In addition, we show how graphical tools can facilitate interpretation of ML-based depth retrieval models and yield insight regarding relationships between depth and reflectance. The HIRBERT workflow is packaged in free, standalone software developed to support applications in river research and management. Although ML can enhance remote sensing of river bathymetry, the limitations of this approach must also be acknowledged: Field measurements of water depth are required to train a depth retrieval model and the resulting model should only be applied to the image from which the training data were derived. The inherently image-specific nature of this approach implies that developing generalized regression tree ensembles that could be applied at larger scales would require additional research.

California, Idaho, Nebraska, Oregon, Wyoming↗

GeoImageNet: A multi-source natural feature benchmark dataset for GeoAI and supervised machine learning

The field of GeoAI or Geospatial Artificial Intelligence has undergone rapid development since 2017. It has been widely applied to address environmental and social science problems, from understanding climate change to tracking the spread of infectious disease. A foundational task in advancing GeoAI research is the creation of open, benchmark datasets to train and evaluate the performance of GeoAI models. While a number of datasets have been published, very few have centered on the natural terrain and its landforms. To bridge this gulf, this paper introduces a first-of-its-kind benchmark dataset, GeoImageNet, which supports natural feature detection in a supervised machine-learning paradigm. A distinctive feature of this dataset is the fusion of multi-source data, including both remote sensing imagery and DEM in depicting spatial objects of interest. This multi-source dataset allows a GeoAI model to extract rich spatio-contextual information to gain stronger confidence in high-precision object detection and recognition. The image dataset is tested with a multi-source GeoAI extension against two well-known object detection models, Faster-RCNN and RetinaNet. The results demonstrate the robustness of the dataset in aiding GeoAI models to achieve convergence and the superiority of multi-source data in yielding much higher prediction accuracy than the commonly used single data source.

GeoInformatica↗

Comparative alteration mineral mapping using visible to shortwave infrared (0.4-2.4 μm) Hyperion, ALI, and ASTER imagery

Advanced Land Imager (ALI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Hyperion imaging spectrometer data covering an area in the Central Andes between Volcan Socompa and Salar de Llullaillaco were used to map hydrothermally altered rocks associated with several young volcanic systems. Six ALI channels in the visible and near-infrared wavelength range (0.4-1.0 ??m) were useful for discriminating between ferric-iron alteration minerals based on the spectral shapes of electronic absorption features seen in continuum-removed spectra. Six ASTER channels in the short wavelength infrared (1.0-2.5 ??m) enabled distinctions between clay and sulfate mineral types based on the positions of band minima related to Al-OH vibrational absorption features. Hyperion imagery embedded in the broader image coverage of ALI and ASTER provided essential leverage for calibrating and improving the mapping accuracy of the multispectral data. This capability is especially valuable in remote areas of the earth where available geologic and other ground truth information is limited.

IEEE Transactions on Geoscience and Remote Sensing↗

Removing rural roads from the National Land Cover Database to create improved urban maps for the United States, 1992-2011

Quantifying change in urban land provides important information to create empirical models examining the effects of human land use. Maps of developed land from the National Land Cover Database ( NLCD ) of the conterminous United States include rural roads in the developed land class and therefore overestimate the amount of urban land. To better map the urban class and understand how urban lands change over time, we removed rural roads and small patches of rural development from the NLCD developed class and created four wall-to-wall maps (1992, 2001, 2006, and 2011) of urban land. Removing rural roads from the NLCD developed class involved a multi-step filtering process, data fusion using geospatial road and developed land data, and manual editing. Reference data classified as urban or not urban from a stratified random sample was used to assess the accuracy of the 2001 and 2006 urban and NLCD maps. The newly created urban maps had higher overall accuracy (98.7 percent) than the NLCD maps (96.2 percent). More importantly, the urban maps resulted in lower commission error of the urban class (23 percent versus 57 percent for the NLCD in 2006) with the trade-off of slightly inflated omission error (20 percent for the urban map, 16 percent for NLCD in 2006). The removal of approximately 230,000 km 2 of rural roads from the NLCD developed class resulted in maps that better characterize the urban footprint. These urban maps are more suited to modeling applications and policy decisions that rely on quantitative and spatially explicit information regarding urban lands.

Photogrammetric Engineering and Remote Sensing↗