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USGS2009

Sand resources, regional geology, and coastal processes of the Chandeleur Islands Coastal System: An evaluation of the Breton National Wildlife Refuge

Breton National Wildlife Refuge, the Chandeleur Islands chain in Louisiana, provides habitat and nesting areas for wildlife and is an initial barrier protecting New Orleans from storms. The U.S. Geological Survey (USGS) in partnership with the University of New Orleans Pontchartrain Institute for Environmental Sciences undertook an intensive study that included (1) an analysis of island change based on historical maps and remotely sensed shoreline and topographic data; (2) a series of lidar surveys at 3- to 4-month intervals after Hurricane Katrina to determine barrier island recovery potential; (3) a discussion of sea level rise and effects on the islands; (4) an analysis of sea floor evolution and sediment dynamics in the refuge over the past 150 years; (5) an assessment of the local sediment transport and sediment resource availability based on the bathymetric and subbottom data; (6) a carefully selected core collection effort to groundtruth the geophysical data and more fully characterize the sediments composing the islands and surrounds; (7) an additional survey of the St. Bernard Shoals to assess their potential as a sand resource; and (8) a modeling study to numerically simulate the potential response of the islands to the low-intensity, intermediate, and extreme events likely to affect the refuge over the next 50 years. Results indicate that the islands have become fragmented and greatly diminished in subaerial extent over time: the southern islands retreating landward as they reorganize into subaerial features, the northern islands remaining in place. Breton Island, because maintenance of the Mississippi River-Gulf Outlet (MRGO) outer bar channel requires dredging, is deprived of sand sufficient to sustain itself. Regional sediment transport trends indicate that large storms are extremely effective in transporting sand and controlling the shoreline development and barrier island geometry. Sand is transported north and south from a divergent zone near Monkey Bayou at the southern end of the Chandeleur Islands. Numerical simulation of waves and sediment transport supports the geophysical results and indicates that vast areas of the lower shoreface are affected and are undergoing erosion during storm events, that there is little or no fair weather mechanism to rework material into the littoral system, and that as a result, there is a net loss of sediment from the system. Lidar surveys revealed that the island chain immediately after Hurricane Katrina lost about 84 percent of its area and about 92 percent of its prestorm volume. Marsh platforms that supported the islands’ sand prior to the storm were reduced in width by more than one-half. Repeated lidar surveys document that in places the shoreline has retreated about 100 m under the relatively low-energy waves since Hurricanes Katrina and Rita; however, this retreat is nonuniform. Recent high-resolution geophysical surveys of the sea floor and subsurface within 5–6 km of the Chandeleur Islands during 2006 and 2007 show that, in addition to the sand that is rebuilding portions of the island chain, a large volume of sand is contained in Hewes Point, in an extensive subtidal spit platform that has formed at the northern end of the Chandeleur Islands. Hewes Point appears to be the depositional terminus of the alongshore transport system. In the southern Chandeleurs, sand is being deposited in a broad tabular deposit near Breton Island called the southern offshore sand sheet. These two depocenters account for approximately 70 percent of the estimated sediment volume located in potential borrow sites. An additional large potential source of sand for restoration lies in the St. Bernard Shoals, which are estimated to contain approximately 200 × 10 6 m 3 of sand. Successful restoration planning for the Breton National Wildlife Refuge should mimic the natural processes of early stages of barrier island evolution including lateral transport to the flanks of the island chain from a centralized sand source that will ultimately enhance the ability of the islands to naturally build backbarrier marsh, dunes, and a continuous sandy shoreline. Barrier island sediment nourishment should be executed with the understanding that gulf shoreline erosion is inevitable but that island area can be maintained and enhanced during retreat (thus significantly prolonging the life of the island chain) with strategic sand placement.

Louisiana↗

USGS2006

Trend analysis of time-series phenology of North America derived from satellite data

Remote sensing information has been used in studies of the seasonal dynamics (phenology) of the land surface since the 1980s. While our understanding of remote sensing phenology is still in development, it is regarded as a key to understanding land-surface processes over large areas. Phenologic metrics, including start of season, end of season, duration of season, and seasonally integrated greenness, were derived from 8 km advanced very high resolution radiometer (AVHRR) data over North America spanning the years 1982-2003. Trend analysis was performed on annual summaries of the metrics to determine areas with increasing or decreasing growing season trends for the time period under study. Results show a trend toward earlier starts of season in limited areas of the mixed boreal forest, and a trend toward later end of season in well-defined areas of New England and southeastern Canada. Results in Saskatchewan, Canada, include a trend toward longer duration of season over a well-defined area, principally as a result of regional changes in land use practices. Changing seasonality appears to be an integrated response to a complex of factors, including climate change, but also, in many places, changes in land use practices. Copyright ?? 2006 by V. H. Winston & Son, Inc. All rights reserved.

GIScience and Remote Sensing↗

USGS1987

Summary mineral resource appraisal of the Richfield 1° x 2° quadrangle, west-central Utah

The mineral resource potential of the Richfield 1° x 2° quadrangle, Utah, has been appraised using geological, geophysical, geochemical, and remote-sensing techniques. These studies have led to many publications giving basic data and interpretations; of these, a series of 18 maps at 1:250,000 and 1:500,000 scales summarizing aspects of the geology, geophysics, geochemistry, and remote sensing is designated the CUSMAP (Conterminous United States Mineral Appraisal Program) folio. This circular uses the data shown on these maps to appraise the mineral resource potential of the quadrangle. The oldest rocks exposed in the Richfield quadrangle are small patches of Early Proterozoic (1.7 billion years old) gneiss and schist on the west side of the Mineral Mountains. These rocks presumably formed the basement on which many thousands of meters of Late Proterozoic, Paleozoic, and lower Mesozoic sedimentary strata were deposited. These rocks were deformed during the Late Cretaceous Sevier orogeny when Precambrian and Paleozoic strata in the western part of the quadrangle were thrust relatively eastward across Paleozoic and Mesozoic strata in the eastern part of the quadrangle. Late Cretaceous and early Tertiary highlands above the overthrust belt were eroded and much of the debris was deposited in broad basins east of the belt. Volcanism in Oligocene and earliest Miocene time formed an east-northeast-trending belt of calcalkalic volcanoes across the southern half of the quadrangle. In early Miocene time, the composition of the volcanic rocks changed to a bimodal assemblage of mafic rocks and high-silica alkali rhyolite that has been erupted episodically ever since. Syngenetic mineral resources developed during formation of both sedimentary and volcanic rocks. These include limestone and dolomite, silica-rich sandstone, metalliferous black shale, evaporite deposits, zeolite deposits, pumice, cinders and scoria, and evaporitic or diagenetic deposits in playa environments. Most of these deposits need to have markets established, or extraction and fabrication techniques developed, for them to be utilized. Most epigenetic deposits are of volcanogenic-hydrothermal origin. Deposits associated with calc-alkalic igneous activity largely contain Cu, Pb, Zn, Au, and Ag, and occur in a variety of types zoned around core intrusions. Younger deposits are mostly associated with silicic igneous centers belonging to the bimodal mafic-silicic igneous association. Resources associated with this latter group are likely to contain one or more of the elements Mo, W, U, Sn, Be, and F, as well as Pb, Zn, Au, and Ag. Alunite and kaolinite deposits are found at many mineralized centers. Most epigenetically mineralized areas expose only the upper, near-surface parts of the different hydrothermal systems; most of whatever mineral deposits formed in these systems probably still exist at depth, awaiting discovery. Our conclusion is that many mineralized areas have excellent possibilities for the occurrence of mineral resources. Each of the many identified centers of mineralization is discussed briefly in this report and an estimate made of its resource potential.

Utah↗

USGS2014

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of ≥95% for cultivated croplands and ≥76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.

California↗

USGS2017

Remote measurement of surface-water velocity using infrared videography and PIV: a proof-of-concept for Alaskan rivers

Thermal cameras with high sensitivity to medium and long wavelengths can resolve features at the surface of flowing water arising from turbulent mixing. Images acquired by these cameras can be processed with particle image velocimetry (PIV) to compute surface velocities based on the displacement of thermal features as they advect with the flow. We conducted a series of field measurements to test this methodology for remote sensing of surface velocities in rivers. We positioned an infrared video camera at multiple stations across bridges that spanned five rivers in Alaska. Simultaneous non-contact measurements of surface velocity were collected with a radar gun. In situ velocity profiles were collected with Acoustic Doppler Current Profilers (ADCP). Infrared image time series were collected at a frequency of 10Hz for a one-minute duration at a number of stations spaced across each bridge. Commercial PIV software used a cross-correlation algorithm to calculate pixel displacements between successive frames, which were then scaled to produce surface velocities. A blanking distance below the ADCP prevents a direct measurement of the surface velocity. However, we estimated surface velocity from the ADCP measurements using a program that normalizes each ADCP transect and combines those normalized transects to compute a mean measurement profile. The program can fit a power law to the profile and in so doing provides a velocity index, the ratio between the depth-averaged and surface velocity. For the rivers in this study, the velocity index ranged from 0.82 – 0.92. Average radar and extrapolated ADCP surface velocities were in good agreement with average infrared PIV calculations.

Conference Paper↗

USGS2012

State of the Earth’s cryosphere at the beginning of the 21st century: Glaciers, global snow cover, floating ice, and permafrost and periglacial environments

This chapter is the tenth in a series of 11 book-length chapters, collectively referred to as “this volume,” in the series U.S. Geological Survey Professional Paper 1386, Satellite Image Atlas of Glaciers of the World. In the other 10 chapters, each of which concerns a specific glacierized region of Earth, the authors used remotely sensed images, primarily from the Landsat 1, 2, and 3 series of spacecraft, in order to analyze that glacierized region and to monitor changes in its glaciers. Landsat images, acquired primarily during the period 1972 through 1981, were used by an international team of glaciologists and other scientists to study the various glacierized regions and (or) to discuss related glaciological topics. In each glacierized region, the present distribution of glaciers within its geographic area is compared, wherever possible, with historical information about their past areal extent. The atlas provides an accurate regional inventory of the areal extent of glacier ice on our planet during the 1970s as part of an expanding international scientific effort to measure global environmental change on the Earth’s surface. However, this chapter differs from the other 10 in its discussion of observed changes in all four elements of the Earth’s cryosphere (glaciers, snow cover, floating ice, and permafrost) in the context of documented changes in all components of the Earth System. Human impact on the planet at the beginning of the 21st century is pervasive. The focus of Chapter A is on changes in the cryosphere and the importance of long-term monitoring by a variety of sensors carried on Earth-orbiting satellites or by a ground-based network of observatories in the case of permafrost. The chapter consists of five parts. The first part provides an introduction to the Earth System, including the interrelationships of the geosphere (cryosphere, hydrosphere, lithosphere, and atmosphere), the biosphere, climate processes, biogeochemical cycles, and the critically important hydrologic cycle, in which glacier ice is the second largest reservoir of water after the oceans. The second part assesses the state of glaciers in all of the glacierized regions of the planet, primarily as drawn in the other 10 chapters. It includes sections on ice cores and the climate record they contain, volumetric changes in glaciers, harnessing spaceborne sensors to measure changes in glaciers, and related topics. The third part summarizes trends in global snow cover. The fourth part summarizes long-term changes in area and thickness of floating ice, including polar sea ice and freshwater (lake and river) ice. The fifth part assesses the loss of permafrost and changes in periglacial environments at high latitudes and high altitudes.

Professional Paper↗

USGS2011

Timing constraints on remote sensing of wildland fire burned area in the southeastern US

Remote sensing using Landsat Thematic Mapper (TM) satellite imagery is increasingly used for mapping wildland fire burned area and burn severity, owing to its frequency of collection, relatively high resolution, and availability free of charge. However, rapid response of vegetation following fire and frequent cloud cover pose challenges to this approach in the southeastern US. We assessed these timing constraints by using a series of Landsat TM images to determine how rapidly the remotely sensed burn scar signature fades following prescribed burns in wet flatwoods and depression swamp community types in the Apalachicola National Forest, Florida, USA during 2006. We used both the Normalized Burn Ratio (NBR) of reflectance bands sensitive to vegetation and exposed soil cover, as well as the change in NBR from before to after fire (dNBR), to estimate burned area. We also determined the average and maximum amount of time following fire required to obtain a cloud-free image for burns in each month of the year, as well as the predicted effect of this time lag on percent accuracy of burn scar estimates. Using both NBR and dNBR, the detectable area decreased linearly 9% per month on average over the first four months following fire. Our findings suggest that the NBR and dNBR methods for monitoring burned area in common southeastern US vegetation community types are limited to an average of 78–90% accuracy among months of the year, with individual burns having values as low as 38%, if restricted to use of Landsat 5 TM imagery. However, the majority of burns can still be mapped at accuracies similar to those in other regions of the US, and access to additional sources of satellite imagery would improve overall accuracy.

Remote Sensing↗

USGS2023

Integration of remote sensing and field observations in evaluating DSSAT model for estimating maize and soybean growth and yield in Maryland, USA

Crop models are useful for evaluating crop growth and yield at the field and regional scales, but their applications and accuracies are restricted by input data availability and quality. To overcome difficulties inherent to crop modeling, input data can be enhanced by the incorporation of remotely sensed and field observations into crop growth models. This approach has been recognized to be an important way to monitor crop growth conditions and to predict yield at the field and regional scale. In recent years, satellite remote sensing has provided high-temporal and high-spatial-resolution data that allow for generating continuous time series of biophysical parameters such as vegetation indices, leaf area index, and phenology. The objectives of this study were to use remote sensing along with field observations as inputs to the Decision Support System for Agro-Technology (DSSAT) model to estimate soybean and maize growth and yield. The study used phenology and leaf area index (LAI) data derived from Planet Fusion (daily, 3 m) satellite imagery along with field observation data on crop growth stage, LAI and yield collected at the United State Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center (BARC), Beltsville, Maryland. For maize, a total of 17 treatments (site years) were used (ten treatments for model calibration and seven treatments for validation), while for soybean (maturity groups three and four), a total of 18 treatments were used (nine for calibration and nine for validation). The calibrated model was tested against an independent, multi-location and multi-year set of phenology and yield data (2017–2020) from BARC fields. The model accurately simulated maize and soybean days to flowering and maturity and produced reasonable yield estimates for most fields and years. Model run for independent locations and years produced good results for phenology and yields for both maize and soybean, as indicated by index of agreement (d) values ranging from 0.65 to 0.93 and normalized root-mean-squared error values ranging from 1 to 20%, except for soybean maturity group four. Overall, model performances with respect to phenology and grain yield for maize and soybean were good and consistent with other DSSAT evaluation studies. The inclusion of remote sensing along with field observations in crop-growth model inputs can provide an effective approach for assessing crop conditions, even in regions lacking ground data.

Maryland↗

USGS2011

Detection, emission estimation and risk prediction of forest fires in China using satellite sensors and simulation models in the past three decades-An overview

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction，have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status.

International Journal of Environmental Research an↗

USGS2021

Hyperspectral narrowband data propel gigantic leap in the earth remote sensing

Hyperspectral narrowbands (HNBs) capture data as nearly continuous “spectral signatures” rather than a “few spectral data points” along the electromagnetic spectrum as with multispectral broadbands (MBBs). Almost all of satellite remote sensing of the Earth in the twentieth century was conducted using MBB data from sensors such as the Landsat-series, Advanced Very High-Resolution Radiometer (AVHRR), SPOT (Système Pour l’Observation de la Terre), and the Indian Remote Sensing (IRS) satellites. These systems typically provide 4 to 9 broad spectral wavebands spread from 400 to 2500 nm, often with one or two additional bands in the thermal range. Significant advances in the study of the Earth have been made based on these data [Thenkabail et al., 2018a,b,c,d; Thenkabail et al., 2015a,b,c]. Possibilities of great advances that can be made using HNB data over MBB data are well established based on studies conducted using hyperspectral sensors such as the hand-held spectroradiometers, the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and spaceborne Earth Observing -1 (EO-1) Hyperion [Thenkabail 2018a,b,c,d]. The twenty-first century is already seeing the dawn of hyperspectral imaging data from sensors such as the German Aerospace Center’s (DLR’s) DESIS (DLR Earth Sensing Imaging Spectrometer) onboard the MUSES (Multi-User System for Earth Sensing) platform on the International Space Station (ISS), the polar-orbiting Italian Space Agency’s (ASI) PRISMA (PRecursore IperSpettrale della Missione Applicativa), and many other upcoming sensors such as the NASA Surface Biology and Geology (SBG) [Thenkabail et al., 2018a,b,c,d]. These satellites acquire data in hundreds of narrow spectral bands of 1 to 10 nm width, typically between 400 to 2500 nm; also future planned missions will be extending HNBs to the thermal (9,000 to 14,000 nm) electromagnetic spectrum. This expansion creates a quantum leap in new data, new information, and myriad possible new applications in the study of the Earth in addition to great advances in existing applications. Given the above, the objective of this article is to provide insights on the gigantic leap in our understanding, modeling, mapping, and monitoring of the Earth that can be made using HNB relative to MBB by focusing on agricultural and vegetation applications. We will address this in four aspects: 1. Comparison between HNB and MBB data; 2. Spectral libraries of agricultural crops; 3. HNB data analysis in general; and 4. HNB analysis using machine learning (ML) and cloud computing.

Photogrammetric Engineering and Remote Sensing.↗

USGS2016

Dynamic reusable workflows for ocean science

Digital catalogs of ocean data have been available for decades, but advances in standardized services and software for catalog search and data access make it now possible to create catalog-driven workflows that automate — end-to-end — data search, analysis and visualization of data from multiple distributed sources. Further, these workflows may be shared, reused and adapted with ease. Here we describe a workflow developed within the US Integrated Ocean Observing System (IOOS) which automates the skill-assessment of water temperature forecasts from multiple ocean forecast models, allowing improved forecast products to be delivered for an open water swim event. A series of Jupyter Notebooks are used to capture and document the end-to-end workflow using a collection of Python tools that facilitate working with standardized catalog and data services. The workflow first searches a catalog of metadata using the Open Geospatial Consortium (OGC) Catalog Service for the Web (CSW), then accesses data service endpoints found in the metadata records using the OGC Sensor Observation Service (SOS) for in situ sensor data and OPeNDAP services for remotely-sensed and model data. Skill metrics are computed and time series comparisons of forecast model and observed data are displayed interactively, leveraging the capabilities of modern web browsers. The resulting workflow not only solves a challenging specific problem, but highlights the benefits of dynamic, reusable workflows in general. These workflows adapt as new data enters the data system, facilitate reproducible science, provide templates from which new scientific workflows can be developed, and encourage data providers to use standardized services. As applied to the ocean swim event, the workflow exposed problems with two of the ocean forecast products which led to improved regional forecasts once errors were corrected. While the example is specific, the approach is general, and we hope to see increased use of dynamic notebooks across the geoscience domains.

Journal of Marine Science and Engineering↗

USGS2007

Integrating remote sensing and ground methods to estimate evapotranspiration

Evapotranspiraton (ET) is the second largest term in the terrestrial water budget after precipitation, and ET is expected to increase with global warming. ET studies are relevant to the plant sciences because over 80% of terrestrial ET is due to transpiration by plants. Remote sensing is the only feasible means for projecting ET over large landscape units. In the past decade or so, new ground and remote sensing tools have dramatically increased our ability to measure ET at the plot scale and to scale it over larger regions. Moisture flux towers and micrometeorological stations have been deployed in numerous natural and agricultural biomes and provide continuous measurements of actual ET or potential ET with an accuracy or uncertainty of 10–30%. These measurements can be scaled to larger landscape units using remotely-sensed vegetation indices (VIs), Land Surface Temperature (LST), and other satellite data. Two types of methods have been developed. Empirical methods use time-series VIs and micrometeorological data to project ET measured on the ground to larger landscape units. Physically-based methods use remote sensing data to determine the components of the surface energy balance, including latent heat flux, which determines ET. Errors in predicting ET by both types of methods are within the error bounds of the flux towers by which they are calibrated or validated. However, the error bounds need to be reduced to 10% or less for applications that require precise wide-area ET estimates. The high fidelity between ET and VIs over agricultural fields and natural ecosystems where precise ground estimates of ET are available suggests that this might be an achievable goal if ground methods for measuring ET continue to improve.

Critical Reviews in Plant Science↗

USGS2024

Detecting trajectories of regime shifts and loss of resilience in coastal wetlands using remote sensing

Many freshwater forested wetlands along the southeastern United States coastline are rapidly transitioning from forest to marsh or open water, due to climate change-related disturbances. Recent studies have found early warning signals (EWS) of regime shifts in other ecosystems, but it is unclear if these can be detected for coastal wetlands. In this study, we examined the ability to detect EWS of regime shifts in coastal wetlands within the Albemarle Pamlico peninsula (APP), North Carolina, U.S.A. We used the Landsat record (1985–2021) to examine trends of normalized difference vegetation index (NDVI) time series for selected areas known to have undergone regime shifts. We found that while 77% of the APP was either stable or revegetating, 22% of the landscape underwent a decrease in NDVI that would indicate a transition from forest to marsh or open water. Of the areas that transitioned, about half (11%) experienced an abrupt decrease in NDVI and 10% experienced a gradual decline. Increasing standard deviation and skewness of time series could serve as EWS of abrupt transitions, but can also provide false negative and positives. Our results suggest that ecosystem transitions from a forest to a marsh or open water can occur both rapidly and slowly, and remote sensing of NDVI time series can help identify EWS for some areas, but not all. Our results allow for prioritization of conservation/restoration of coastlines which will become important in the face of climate change and sea level rise.

North Carolina↗

USGS2025

Satellite assessment of winter cover crop and conservation tillage outcomes to support adaptive management in working landscapes

The use of winter cover crops and conservation tillage are agricultural practices promoted to reduce nutrient and sediment loss from cropland, improve soil health, increase infiltration, and support farm nutrient cycling and ecosystem services. However, environmental performance of these practices is variable in the working farm landscape. The Lower Chesapeake Bay research project within the USDA Long-Term Agroecosystem Research (LTAR) network has collaboratively developed satellite remote sensing algorithms to measure the performance and phenology of winter cover crops (aboveground biomass, nitrogen content, fractional cover, and emergence and termination dates) using no-cost Harmonized Landsat and Sentinel-2 multispectral satellite imagery. This research supports annual operational assessment of >28,000 fields per year in four states. Results document the impacts of agronomic management on conservation outcomes, support adaptive management of incentive payment structures, and can reduce the workload for conservation district staff by remotely verifying cover crop management. Additionally, super-spectral satellite applications have been developed to accurately map crop residue cover by measuring lignocellulose absorption in shortwave infrared wavelengths, producing a 7-year time series of tillage intensity maps for the Delmarva Peninsula. These remote sensing products can be used in decision support and modeling to estimate changes in nutrient, sediment, and carbon cycling resulting from conservation practice implementation in the working farm landscape. This manuscript provides an overview of remote sensing research findings and applications associated with the USDA LTAR and Conservation Effects Assessment Projects (CEAP), documenting a variety of previously published outcomes with update and expansion of techniques using additional unpublished data and analyses as appropriate.

Maryland↗

USGS2016

Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data

There are many types of changes occurring over the Earth's landscapes that can be detected and monitored using Landsat data. Here we focus on monitoring “within-state,” gradual changes in vegetation in contrast with traditional monitoring of “abrupt” land-cover conversions. Gradual changes result from a variety of processes, such as vegetation growth and succession, damage from insects and disease, responses to shifts in climate, and other factors. Despite the prevalence of gradual changes across the landscape, they are largely ignored by the remote sensing community. Gradual changes are best characterized and monitored using time-series analysis, and with the successful launch of Landsat 8 we now have appreciable data continuity that extends the Landsat legacy across the previous 43 years. In this study, we conducted three related analyses: (1) comparison of spectral values acquired by Landsats 7 and 8, separated by eight days, to ensure compatibility for time-series evaluation; (2) tracking of multitemporal signatures for different change processes across Landsat 5, 7, and 8 sensors using anniversary-date imagery; and (3) tracking the same type of processes using all available acquisitions. In this investigation, we found that data representing natural vegetation from Landsats 5, 7, and 8 were comparable and did not indicate a need for major modification prior to use for long-term monitoring. Analyses using anniversary-date imagery can be very effective for assessing long term patterns and trends occurring across the landscape, and are especially good for providing insights regarding trends related to long-term and continuous trends of growth or decline. We found that use of all available data provided a much more comprehensive level of understanding of the trends occurring, providing information about rate, duration, and intra- and inter-annual variability that could not be readily gleaned from the anniversary date analyses. We observed that using all available clear Landsat 5–8 observations with the new Continuous Change Detection and Classification (CCDC) algorithm was very effective for illuminating vegetation trends. There are a number of potential challenges for assessing gradual changes, including atmospheric impacts, algorithm development and visualization of the changes. One of the biggest challenges for studying gradual change will be the lack of appropriate data for validating results and products.

Remote Sensing of Environment↗

USGS2020

Spatially explicit reconstruction of post-megafire forest recovery through landscape modeling

Megafires are large wildfires that occur under extreme weather conditions and produce mixed burn severities across diverse environmental gradients. Assessing megafire effects requires data covering large spatiotemporal extents, which are difficult to collect from field inventories. Remote sensing provides an alternative but is limited in revealing post-fire recovery trajectories and the underlying processes that drive the recovery. We developed a novel framework to spatially reconstruct the post-fire time-series of forest conditions after the 1987 Black Dragon fire of China by integrating a forest landscape model (LANDIS) with remote sensing and inventory data. We derived pre-fire (1985) forest composition and the megafire perimeter and severity using remote sensing and inventory data. We simulated the megafire and the post-megafire forest recovery from 1985 to 2015 using the LANDIS model. We demonstrated that the framework was effective in reconstructing the post-fire stand dynamics and that it is applicable to other types of disturbances.

Environmental Modelling and Software↗

USGS2016

Extraordinary sediment delivery and rapid geomorphic response following the 2008–2009 eruption of Chaitén Volcano, Chile

The 10 day explosive phase of the 2008–2009 eruption of Chaitén volcano, Chile, draped adjacent watersheds with a few cm to >1 m of tephra. Subsequent lava-dome collapses generated pyroclastic flows that delivered additional sediment. During the waning phase of explosive activity, modest rainfall triggered an extraordinary sediment flush which swiftly aggraded multiple channels by many meters. Ten kilometer from the volcano, Chaitén River channel aggraded 7 m and the river avulsed through a coastal town. That aggradation and delta growth below the abandoned and avulsed channels allow estimates of postdisturbance traction-load transport rate. On the basis of preeruption bathymetry and remotely sensed measurements of delta-surface growth, we derived a time series of delta volume. The initial flush from 11 to 14 May 2008 deposited 0.5–1.5 × 10 6 m 3 of sediment at the mouth of Chaitén River. By 26 May, after channel avulsion, a second delta amassed about 2 × 10 6 m 3 of sediment; by late 2011 it amassed about 11 × 10 6 m 3 . Accumulated sediment consists of low-density vesicular pumice and lithic rhyolite sand. Rates of channel aggradation and delta growth, channel width, and an assumed deposit bulk density of 1100–1500 kg m −3 indicate mean traction-load transport rate just before and shortly after avulsion (&sim;14–15 May) was very high, possibly as great as several tens of kg s −1 m −1 . From October 2008 to December 2011, mean traction-load transport rate declined from about 7 to 0.4 kg −1 m −1 . Despite extraordinary sediment delivery, disturbed channels recovered rapidly (a few years).

Chaitén Volcano↗

USGS2020

Legacy effects of hydrologic alteration in playa wetland responses to droughts

Wetland conservation increasingly must account for climate change and legacies of previous land-use practices. Playa wetlands provide critical wildlife habitat, but may be impacted by intensifying droughts and previous hydrologic modifications. To inform playa restoration planning, we asked: (1) what are the trends in playa inundation? (2) what are the factors influencing inundation? (3) how is playa inundation affected by increasingly severe drought? (4) do certain playas provide hydrologic refugia during droughts, and (5) if so, how are refugia patterns related to historical modifications? Using remotely sensed surface-water data, we evaluated a 30-year time series (1985–2015) of inundation for 153 playas of the Great Basin, USA. Inundation likelihood and duration increased with wetter weather conditions and were greater in modified playas. Inundation probability was projected to decrease from 22% under average conditions to 11% under extreme drought, with respective annual inundation decreasing from 1.7 to 0.9 months. Only 4% of playas were inundated for at least 2 months in each of the 5 driest years, suggesting their potential as drought refugia. Refugial playas were larger and more likely to have been modified, possibly because previous land managers selected refugial playas for modification. These inundation patterns can inform efforts to restore wetland functions and to conserve playa habitats as climate conditions change.

Nevada, Oregon↗