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Results for “Remote Sensing and the Environment”

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USGS2020

Keeping up with the times: Mapping range-wide habitat suitability for endangered species in a changing environment

Biologists and policy-makers have the difficult task of allocating limited resources to habitat conservation and management for endangered species in the face of changing environmental conditions. Satellite remote sensing can inform conservation because it is an efficient means to obtain environmental data over broad spatial and temporal extents. Yet, the challenges of accessing, processing, and analyzing remote sensing data hinder wider application of these techniques in conservation planning. We used Landsat data and hierarchical statistical models to link satellite-derived habitat measurements with abundance of endangered Yuma Ridgway's rails ( Rallus obsoletus yumanensis ) within the Lower Colorado River Basin and Salton Sink, USA. We addressed many of the challenges facing the application of remote sensing techniques by using the web-based, freely-available Google Earth Engine to process Landsat datasets, apply habitat models, and generate maps to predict habitat suitability at a fine spatial grain (30 m) across the range of the species. These maps are shareable, interactive, and easy to update annually as habitat conditions change using a Google Earth Engine App we developed. Thus, we provide a framework for building habitat suitability models and maps to help target adaptive habitat management over broad extents for sensitive species, enabling biologists to improve conservation and restoration efforts regularly as conditions change in highly variable ecosystems. We demonstrate this approach for Yuma Ridgway's rails, but our methods for merging hierarchical statistical models with open-source mapping software to describe spatial-temporal heterogeneity in habitat quality are applicable to any species, and are especially helpful to species inhabiting highly variable ecosystems.

Arizona, California, Nevada↗

USGS2025

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↗

USGS2006

Mountain hydrology of the western United States

Climate change and climate variability, population growth, and land use change drive the need for new hydrologic knowledge and understanding. In the mountainous West and other similar areas worldwide, three pressing hydrologic needs stand out: first, to better understand the processes controlling the partitioning of energy and water fluxes within and out from these systems; second, to better understand feedbacks between hydrological fluxes and biogeochemical and ecological processes; and, third, to enhance our physical and empirical understanding with integrated measurement strategies and information systems. We envision an integrative approach to monitoring, modeling, and sensing the mountain environment that will improve understanding and prediction of hydrologic fluxes and processes. Here extensive monitoring of energy fluxes and hydrologic states are needed to supplement existing measurements, which are largely limited to streamflow and snow water equivalent. Ground‐based observing systems must be explicitly designed for integration with remotely sensed data and for scaling up to basins and whole ranges.

Water Resources Research↗

USGS2019

A preliminary assessment of hyperspectral remote sensing technology for mapping submerged aquatic vegetation in the Upper Delaware River National Parks

Hyperspectral remote sensing of submerged aquatic vegetation is a complex and difficult process that is affected by unique constraints on the energy flow profile near and below the water surface. In addition, shallow, winding, lotic systems, such as the Upper Delaware River, present additional remote sensing problems in the form of specular reflectance, variable depth and constituents in the water column and sometimes extremely weak signal strength due to absorption and scattering in the water column that can be statistically overwhelmed by the reflectance from upland vegetation in any individual image scene. Here we test hyperspectral imagery from the Civil Air Patrol’s (CAP), Airborne Real-time Cueing Hyperspectral Enhanced Recon (ARCHER) system in the scenic waters of two National Parks on the Upper Delaware River. A number of unique image processing problems were encountered, including specular reflectance from winding lotic systems, variable depth and flow dynamics of the riverine environment, and disproportionate signal strength from surface reflectance in this riverine environment. This were solved by applying a specular reflectance removal algorithm, applying field data collections to classification results and masking upland vegetation so as to not statistically overwhelm the weak reflectance signal from surface and near-surface water. Much was learned about conducting imaging spectroscopy in such difficult conditions. Significant results include successful mapping of SAV presenece/absence, advantages of upland masking of the reflectance signal, and a numkber of processing approaches that are unique to this environment. In this paper we summarize our results and identify unique issues that must be addressed in this environment

New Jersey, New York, Pennsylvania↗

USGS2025

Advancing broadscale spatial evapotranspiration modelling by incorporating sun-induced chlorophyll fluorescence measurements

Evapotranspiration (ET) describes the sum of water transfer from the ground surface through soil evaporation and water loss from leaf stomata into the atmosphere − critical factors linking the global water and carbon cycles. Myriad ET models based on remote sensing data provide spatially continuous estimates of ET; however, leaf photosynthetic information is critical to ensure accurate ET estimates, which are difficult to measure from space. Remotely sensed sun-induced chlorophyll fluorescence (SIF) provides a proxy of stomatal conductance activity with high performance in predicting plant transpiration, which can account for a large proportion of terrestrial and riverine ET. This study aims to improve estimates of tree water use in semi-arid to arid environments. In this study, a fixed stomatal conductance model and three SIF-driven canopy conductance (g sc ) models were applied to model potential ET (PET). The models estimated PET using the Penman-Monteith equation with: (1) a constant leaf stomatal conductance; (2) a transpiration-driven g sc model; (3) a g sc model based on electron-transfer rate and vapor pressure deficit, and a (4) Ball-Berry stomatal conductance model. A machine learning model was then applied to scale PET to actual ET (AET) using remote sensing and climate data. Accordingly, four AET models were cross-validated with in-situ measured AET at 52 sites, including 21 eddy covariance flux tower sites, and 31 sap-flow measurement sites (semi-arid and plantation area), for various plant functional types in Australia. This study demonstrated that SIF effectively captured seasonal variations of g sc , finding that AET models with SIF-driven g sc models correlated well with in-situ measured AET (R 2 = 0.64). Modelled AET with dynamic variations of g sc generated lower prediction error (0.85 mm day −1 ), while the AET model with fixed stomatal conductance tended to overestimate AET in floodplains and underestimate it in evergreen broadleaf forests, indicating using fixed stomatal conductance results in unstable performance when modelling AET. This study demonstrated that SIF-driven AET models improved broadscale estimation of ET. Our findings provide vital broadscale hydrological data to assist catchment and regional water management, particularly over unmonitored areas at risk of future climate-driven reductions in rainfall.

Journal of Hydrology↗

USGS2018

Status of tidal marsh mapping for blue carbon inventories

Remote-sensing-based maps of tidal marshes, both of their extents and carbon stocks, will play a key role in conducting greenhouse gas (GHG) inventories. The U.N. Environment Programme World Conservation Monitoring Centre has produced a new Global Distribution of Salt Marsh dataset that estimates global salt marsh area at 5.5 Mha. A Tier 1–2 GHG Inventory of U.S. Coastal Wetlands has been developed using the NOAA Coastal-Change Analysis Program Landsat-based land cover maps as a primary dataset. 180Successful mapping of tidal marsh biomass with optical satellite images provides opportunity to improve GHG Inventories. Further work is needed to map tidal marsh salinity gradients, the extent of tidal vs. non-tidal marshes, methane emissions, and high-resolution elevation.

Book chapter↗

USGS2002

Monitoring dust storms and mapping landscape vulnerability to wind erosion using satellite and ground-based digital images

Wind-induced dust emission in the southwestern United States is important regionally because of its impact on human health and safety and its influence on ecosystem dynamics. Factors that control dust emission include wind velocity, sediment availability, and surface conditions (e.g., vegetation type and degree of cover, surface crusts and armoring, and soil moisture - Gillette and Passi, 1988; Gillette and Hanson 1989; Marticorena and others, 1997). Emission of dust from the land surface is a process of degradation that depletes fine-grained minerals needed for optimum vegetation growth, creates potentially hazardous air quality for humans on a local and regional scale, and can affect climate on a regional and global scale. Future climatic change may lead to increased aridification of southwestern deserts, reducing protective vegetation and enhancing dust emissions, thereby increasing the impacts of dust in this region. It is currently not well understood how climate change in the Southwest will affect dust emission and, in turn, how dust emission will affect climate and human health. As part of a study of landscape vulnerability to wind erosion and the potential impacts of dust, we are investigating remotely sensed satellite, airborne, and ground-based image data to determine their ability to detect and monitor active dust storms, as well as to map areas vulnerable to wind erosion. A main objective has been to investigate the use of high temporal resolution digital images collected by satellite and a long-term, ground-based digital camera station, along with wind data collected at our field sites, to detect, monitor, and analyze the location, size, frequency, duration, and transport patterns of dust storms in the Mojave Desert of the southwestern United States. Generally, many current methods and instruments are not developed to the level required to routinely detect and monitor dust storms or to develop models that accurately predict total dust flux and emission rates. Improving these capabilities is critical to generating baseline datasets for assessments of landscape vulnerability to future climate change. On going modeling efforts are attempting to identify meteorological parameters and soil surface roughness parameters needed to predict the vulnerability of various geomorphic substrates to wind erosion. Datasets to calibrate model results are difficult to collect and typically not available. Results of our studies, incorporating digital image maps and new methods using remotely sensed images to identify and monitor dust sources, will be useful for the calibration of dust-emission models of the Mojave Desert and other arid environments. At the same time, we are exploring new methods to map the amount, sizes, and spatial distribution of particles at the surface to enable detailed wind-erosion vulnerability mapping at a regional scale.

California↗

USGS1979

Progress in remote sensing as it applies to missions of Committee for Coordination of Joint Prospecting for Mineral Resources in Asian Offshore Areas

The major thrusts of investigations of the use of space data for understanding our Earth continue to focus on the land and near-shore environments. This is expectable; people live on the land, draw most of their resources from the land or near-shore areas, and in these areas environmental degradation or improvement are most clearly observable. An exception to this focus of investigation was the launch of Seasat, a satellite dedicated to marine investigations. Seasat was short lived but the data produced strongly supported the use of imaging radar for land investigation, and, although studies are incomplete, suggest eventual benefits for the navigator, searchers for marine resources, and mariners--provided that the data and/or interpretations can be available to the marine users shortly after collection. In this regard, the positive steps now being taken in Southeast Asia to establish reception, processing, and interpretation centers are a major development.

Open-File Report↗

USGS2020

Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing

Landslides modify the natural landscape and cause fatalities and property damage worldwide. Quantifying landslide dynamics is challenging due to the stochastic nature of the environment. With its large area of ~1 km 2 and perennial motions at ~10–20 mm per day, the Slumgullion landslide in Colorado, USA, represents an ideal natural laboratory to better understand landslide behavior. Here, we use hybrid remote sensing data and methods to recover the four-dimensional surface motions during 2011–2018. We refine the boundaries of an area of ~0.35 km 2 below the crest of the prehistoric landslide. We construct a mechanical framework to quantify the rheology, subsurface channel geometry, mass flow rate, and spatiotemporally dependent pore-water pressure feedback through a joint analysis of displacement and hydrometeorological measurements from ground, air and space. Our study demonstrates the importance of remotely characterizing often inaccessible, dangerous slopes to better understand landslides and other quasi-static mass fluxes in natural and industrial environments, which will ultimately help reduce associated hazards.

Colorado↗

USGS2025

Scalable environmental DNA methods reveal strong associations between landscape-scale forest habitat and insect richness

While aquatic environmental DNA (eDNA) methods have reached relative maturity, terrestrial eDNA methods are nascent and have yet to reach widespread use. Field-ready applications require eDNA survey methods where samples are easy to collect by inexperienced practitioners, easy to transport between the field and lab, and easy to process thereafter. Here, we demonstrate methods that satisfy these requirements and show strong potential for characterizing diverse terrestrial eDNA samples collected from flower and leaf surfaces. We used novel methods to collect and process 236 flower eDNA samples and 21 leaf surface eDNA samples, obtaining 2,228 Arthropoda eDNA detections spanning 175 families using amplicon sequencing of two genetic markers. Detected taxa were diverse and included numerous groups of conservation concern, such as bees (Hymenoptera; Anthophila, 32 genera spanning 5 families) and Lepidoptera (209 genera from 21 families). Data reveal strong associations between insect community richness and remotely sensed measures of forest habitat, providing a quantitative perspective of relevance to insect conservation. It is increasingly clear that a variety of organisms readily disperse eDNA throughout the environment, supporting the notion that eDNA will be a powerful tool for characterizing species distributions and monitoring at-risk species. However, we conclude that researchers seeking to characterize fine-scale habitat associations or plant-pollinator interactions using eDNA will need to carefully design studies with appropriate field controls, such as the leaf surface eDNA samples collected here.

BioRxiv↗

USGS2007

The U.S. Geological Survey Land Remote Sensing Program

The fundamental goals of the U.S. Geological Survey's Land Remote Sens-ing (LRS) Program are to provide the Federal Government and the public with a primary source of remotely sensed data and applications and to be a leader in defining the future of land remote sensing, nationally and internationally. Remotely sensed data provide information that enhance the understand-ing of ecosystems and the capabilities for predicting ecosystem change. The data promote an understanding of the role of the environment and wildlife in human health issues, the requirements for disaster response, the effects of climate variability, and the availability of energy and mineral resources. Also, as land satellite systems acquire global coverage, the program coordinates a network of international receiving stations and users of the data. It is the responsibility of the program to assure that data from land imaging satellites, airborne photography, radar, and other technologies are available to the national and global science communities.

Fact Sheet↗

USGS2018

AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data

In this work we describe the AutoCNet library, written in Python, to support the application of Computer Vision techniques for n-image correspondence identication in remotely sensed planetary images and subsequent bundle adjustment. The library is designed to support exploratory data analysis, algorithm and processing pipeline development, and application at scale in High Performance Computing (HPC) environments for processing large data sets and generating foundational data products. We also present a brief case study illustrating high level usage for the Apollo 15 Metric camera.

SoftwareX↗

USGS2017

The impact of lidar elevation uncertainty on mapping intertidal habitats on barrier islands

While airborne lidar data have revolutionized the spatial resolution that elevations can be realized, data limitations are often magnified in coastal settings. Researchers have found that airborne lidar can have a vertical error as high as 60 cm in densely vegetated intertidal areas. The uncertainty of digital elevation models is often left unaddressed; however, in low-relief environments, such as barrier islands, centimeter differences in elevation can affect exposure to physically demanding abiotic conditions, which greatly influence ecosystem structure and function. In this study, we used airborne lidar elevation data, in situ elevation observations, lidar metadata, and tide gauge information to delineate low-lying lands and the intertidal wetlands on Dauphin Island, a barrier island along the coast of Alabama, USA. We compared three different elevation error treatments, which included leaving error untreated and treatments that used Monte Carlo simulations to incorporate elevation vertical uncertainty using general information from lidar metadata and site-specific Real-Time Kinematic Global Position System data, respectively. To aid researchers in instances where limited information is available for error propagation, we conducted a sensitivity test to assess the effect of minor changes to error and bias. Treatment of error with site-specific observations produced the fewest omission errors, although the treatment using the lidar metadata had the most well-balanced results. The percent coverage of intertidal wetlands was increased by up to 80% when treating the vertical error of the digital elevation models. Based on the results from the sensitivity analysis, it could be reasonable to use error and positive bias values from literature for similar environments, conditions, and lidar acquisition characteristics in the event that collection of site-specific data is not feasible and information in the lidar metadata is insufficient. The methodology presented in this study should increase efficiency and enhance results for habitat mapping and analyses in dynamic, low-relief coastal environments.

Alabama↗

USGS2007

Remote sensing and GIS technology in the Global Land Ice Measurements from Space (GLIMS) Project

Global Land Ice Measurements from Space (GLIMS) is an international consortium established to acquire satellite images of the world's glaciers, analyze them for glacier extent and changes, and to assess these change data in terms of forcings. The consortium is organized into a system of Regional Centers, each of which is responsible for glaciers in their region of expertise. Specialized needs for mapping glaciers in a distributed analysis environment require considerable work developing software tools: terrain classification emphasizing snow, ice, water, and admixtures of ice with rock debris; change detection and analysis; visualization of images and derived data; interpretation and archival of derived data; and analysis to ensure consistency of results from different Regional Centers. A global glacier database has been designed and implemented at the National Snow and Ice Data Center (Boulder, CO); parameters have been expanded from those of the World Glacier Inventory (WGI), and the database has been structured to be compatible with (and to incorporate) WGI data. The project as a whole was originated, and has been coordinated by, the US Geological Survey (Flagstaff, AZ), which has also led the development of an interactive tool for automated analysis and manual editing of glacier images and derived data (GLIMSView). This article addresses remote sensing and Geographic Information Science techniques developed within the framework of GLIMS in order to fulfill the goals of this distributed project. Sample applications illustrating the developed techniques are also shown. ?? 2006 Elsevier Ltd. All rights reserved.

Computers & Geosciences↗

USGS2022

Can we accurately estimate sediment budgets on Mars?

Sediment budgets are fundamentally important for planetary science. However, only one primary method, based on remote sensing, is currently available for determining extraterrestrial sediment budgets. For determining sediment budgets on Earth, both in-situ and remote sensing methods are available. Despite the widespread use of the two methods, there has been surprisingly little research on how well the sediment budgets produced by these two approaches reconcile with one another, which highlights the lack of quantitative understanding of errors for sediment budgets measured with remote sensing in planetary research. Therefore, there is a general need to expand our knowledge of sediment budgets. Here we use a background review and analog case study of an aeolian dunefield in Grand Canyon, Earth to frame a path forward for addressing shortcomings of remote sensing sediment budgets on Mars. We estimate a 53% percent difference in the sediment budget determined with remote sensing relative to in-situ methods for a simple endmember scenario of a dunefield within a unimodal wind directional regime and no external sediment supply. However, when we incorporated key sources of uncertainty in remote sensing change detection following methods commonly used by geomorphologists on Earth, the estimates of sediment budget differences relative to the in-situ method spanned a much larger range, from 3% to 138%. Our case study also suggests that sediment budget errors could be much larger under more complex wind direction, sediment supply, and physiographic settings, and that variability in those landscape characteristics might be used to better estimate errors for dunefield sediment budgets. We conclude that by comparing sediment budgets derived from in-situ measurements of sediment fluxes and from remote sensing measurements at many more analog sites on Earth, the aeolian research community, and the geomorphology discipline, could gain an understanding of the errors of the remote sensing method, which is used by investigators on other planetary bodies such as Mars. This could improve the ability to quantify sediment budgets on Mars – and, in the future, other planetary environments where high-resolution topographic data are available – as well as directly improve our ability to interpret extraterrestrial landscape evolution related to climate, weather, and geologic history.

Earth and Planetary Science Letters↗

USGS2018

Characteristics of tropical tree species in hyperspectral and multispectral data

Remote sensing has been hailed as a promising technology to provide spatially explicit information on tree species distribution. Such information is of high value for ecologists and forest managers, particularly in tropical environments in which it is acquired by costly field inventories performed at the plot level (∼1 ha). Over the last decade, hyperspectral sensors, usually on board airborne platforms, have been successfully employed for tropical tree species classification. Most of the studies focused on the improvement of the classification accuracy, without examining the sources of variability in canopy reflectance that contribute to discriminate among species. Trees of different species usually feature distinct crown structural and chemical characteristics defined by size, shape, leaf density, branching, pigment and nonpigment biochemical constituents, among others. Understanding the role of these characteristics on tree species discrimination is important to improve and optimize the use of hyperspectral data. Here, we show how spectral characteristics of tree species, from highly diverse tropical forests, are related to their chemical and structural properties. These spectral characteristics were retrieved by spectral mixture and feature analysis, using narrow-band data acquired in the optical domain (450–2400 nm). Finally, we assessed how the spectral resolution affects the classification accuracy and spectral separability.

Book chapter↗

USGS2022

Mapping the probability of freshwater algal blooms with various spectral indices and sources of training data

Algal blooms are pervasive in many freshwater environments and can pose risks to the health and safety of humans and other organisms. However, monitoring and tracking of potentially harmful blooms often relies on in-person observations by the public. Remote sensing has proven useful in augmenting in situ observations of algal concentration, but many hurdles hinder efficient application by end users. First, numerous approaches to estimate aquatic chlorophyll-a are available and can produce inconsistent results. Second, lack of quantitative in situ observations limits opportunities to train models for specific waterbodies, such that models developed for other systems must be used instead. We (1) implement univariate and multivariate logistic regression models to estimate the probability that aquatic chlorophyll-a concentrations exceed an accepted threshold beyond which harmful effects become likely and (2) evaluate the use of visually classified bloom/no-bloom satellite imagery to augment in situ training data. Using a binary classification of aquatic chlorophyll-a exceeding 10 μg / L, we found that (1) logistic regression models were ∼80 % accurate, (2) univariate models trained with visually classified data produce nearly the same accuracy (79%) as models trained with in situ observations (80%), and (3) augmenting in situ chlorophyll-a observations with visual classifications outperformed (82% accuracy) models trained on in situ observations alone (80% accuracy). These results provide a framework for evaluating multiple spectral indices in retrieving algal bloom presence or absence and illustrate that training data derived directly from satellite imagery can be useful in augmenting in situ observations.

Idaho, Oregon↗

USGS2020

Volcanological applications of unoccupied aircraft systems (UAS): Developments, strategies, and future challenges

Unoccupied aircraft systems (UAS) are developing into fundamental tools for tackling the grand challenges in volcanology; here, we review the systems used and their diverse applications. UAS can typically provide image and topographic data at two orders of magnitude better spatial resolution than space-based remote sensing, and close-range observations at temporal resolutions down to those of video frame rates. Responsive deployments facilitate dense time-series measurements, unique opportunities for geophysical surveys, sample collection from hostile environments such as volcanic plumes and crater lakes, and emergency deployment of ground-based sensors (and robots) into hazardous regions. UAS have already been used to support hazard management and decision-makers during eruptive crises. As technologies advance, increased system capabilities, autonomy and availability, supported by more diverse and lighter-weight sensors, will offer unparalleled potential for hazard monitoring. UAS are expected to provide opportunities for pivotal advances in our understanding of complex physical and chemical volcanic processes.

Volcanica↗