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USGS2008

Numerical modeling of higher order magnetic moments in UXO discrimination

The surface magnetic anomaly observed in unexploded ordnance (UXO) clearance is mainly dipolar, and consequently, the dipole is the only magnetic moment regularly recovered in UXO discrimination. The dipole moment contains information about the intensity of magnetization but lacks information about the shape of the target. In contrast, higher order moments, such as quadrupole and octupole, encode asymmetry properties of the magnetization distribution within the buried targets. In order to improve our understanding of magnetization distribution within UXO and non-UXO objects and to show its potential utility in UXO clearance, we present a numerical modeling study of UXO and related metallic objects. The tool for the modeling is a nonlinear integral equation describing magnetization within isolated compact objects of high susceptibility. A solution for magnetization distribution then allows us to compute the magnetic multipole moments of the object, analyze their relationships, and provide a depiction of the anomaly produced by different moments within the object. Our modeling results show the presence of significant higher order moments for more asymmetric objects, and the fields of these higher order moments are well above the noise level of magnetic gradient data. The contribution from higher order moments may provide a practical tool for improved UXO discrimination. ?? 2008 IEEE.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS1986

Vegetation and terrain mapping in Alaska using Landsat MSS and digital terrain data

During the past 5 years, the U.S. Geological Survey's (USGS) Earth Resources Observation Systems (EROS) Data Center Field Office in Anchorage, Alaska has worked cooperatively with Federal and State resource management agencies to produce land-cover and terrain maps for 245 million acres of Alaska. The need for current land-cover information in Alaska comes principally from the mandates of the Alaska National Interest Lands Conservation Act (ANILCA), December 1980, which requires major land management agencies to prepare comprehensive management plans. The land-cover mapping projects integrate digital Landsat data, terrain data, aerial photographs, and field data. The resultant land-cover and terrain maps and associated data bases are used for resource assessment, management, and planning by many Alaskan agencies including the U.S. Fish and Wildlife Service, U.S. Forest Service, Bureau of Land Management, and Alaska Department of Natural Resources. Applications addressed through use of the digital land-cover and terrain data bases range from comprehensive refuge planning to multiphased sampling procedures designed to inventory vegetation statewide. The land-cover mapping programs in Alaska demonstrate the operational utility of digital Landsat data and have resulted in a new land-cover mapping program by the USGS National Mapping Division to compile 1:250,000-scale land-cover maps in Alaska using a common statewide land-cover map legend.

Alaska↗

USGS2015

Characterization of shrubland ecosystem components as continuous fields in the northwest United States

Accurate and consistent estimates of shrubland ecosystem components are crucial to a better understanding of ecosystem conditions in arid and semiarid lands. An innovative approach was developed by integrating multiple sources of information to quantify shrubland components as continuous field products within the National Land Cover Database (NLCD). The approach consists of several procedures including field sample collections, high-resolution mapping of shrubland components using WorldView-2 imagery and regression tree models, Landsat 8 radiometric balancing and phenological mosaicking, medium resolution estimates of shrubland components following different climate zones using Landsat 8 phenological mosaics and regression tree models, and product validation. Fractional covers of nine shrubland components were estimated: annual herbaceous, bare ground, big sagebrush, herbaceous, litter, sagebrush, shrub, sagebrush height, and shrub height. Our study area included the footprint of six Landsat 8 scenes in the northwestern United States. Results show that most components have relatively significant correlations with validation data, have small normalized root mean square errors, and correspond well with expected ecological gradients. While some uncertainties remain with height estimates, the model formulated in this study provides a cross-validated, unbiased, and cost effective approach to quantify shrubland components at a regional scale and advances knowledge of horizontal and vertical variability of these components.

California, Idaho, Nevada, Oregon↗

USGS2018

Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager

In this study, we demonstrated that the Landsat-8 Operational Land Imager (OLI) sensor is a powerful tool that can provide periodic and system-wide information on the condition of drinking water reservoirs. The OLI is a multispectral radiometer (30 m spatial resolution) that allows ecosystem observations at spatial and temporal scales that allow the environmental community and water managers another means to monitor changes in water quality not feasible with field-based monitoring. Using the provisional Land Surface Reflectance product and field-collected chlorophyll- a (chl- a ) concentrations from drinking water monitoring programs in North Carolina and Rhode Island, we compared five established approaches for estimating chl- a concentrations using spectral data. We found that using the three band reflectance approach with a combination of OLI spectral bands 1, 3, and 5 produced the most promising results for accurately estimating chl- a concentrations in lakes ( R 2 value of 0.66; root mean square error value of 8.9 µg l −1 ). Using this model, we forecast the spatial and temporal variability of chl- a for Jordan Lake, a recreational and drinking water source in piedmont North Carolina and several small ponds that supply drinking water in southeastern Rhode Island.

North Carolina↗

USGS2020

Regional crop water use assessment using Landsat-derived evapotranspiration

Reliable information on water use and availability at basin and field scales are important to ensure the optimized constructive uses of available water resources. This study was conducted with the specific objective to estimate Landsat-based actual evapotranspiration (ETa) using the Operational Simplified Surface Energy Balance (SSEBop) model across the state of South Dakota (SD), USA for the 1986–2018 (33-year) period. Validated ETa estimations (r 2 = 0.91, PBIAS = −4%, and %RMSE = 11.8%) were further used to understand the crop water-use characteristics and existing historic mono-directional (increasing/decreasing) trends over the eastern (ESD) and western (WSD) regions of SD. The crop water-use characteristics indicated that the annual cropland water uses across the ESD and WSD were more or less met by the precipitation amounts in the area. The ample water supply and distribution have led to high rainfed and low percentage of irrigated cropland (~2.5%) in the state. The WSD faced greater crop-water use reductions than the ESD during drought periods. The landscape ETa responses across the state were found to be more sensitive than precipitation for the drought impact assessments. The Mann Kendall trend analysis revealed the absence of a significant trend ( p > 0.05) in annual ETa at a regional scale due to the varying weather conditions in the state. However, about 12% and 9% cropland areas in the ESD and WSD, respectively, revealed a significant mono-directional trend at pixel scale ETa. Most of the pixels under significant trend showed an increasing trend that can be explained by the shift in agricultural practices, increased irrigated cropland area, higher productions, moisture regime shifts, and decreased risk of farming in the dry areas. The decreasing trend pixels were clustered in mid-eastern SD and could be the result of dynamic conversion of wetlands to croplands and decreased irrigation practices in the region. This study also demonstrates the tremendous potential and robustness of the SSEBop model, Landsat imagery, and remote sensing-based ETa modelling approaches in estimating consistent spatially distributed evapotranspiration.

South Dakota↗

USGS2009

Optical satellite data volcano monitoring: a multi-sensor rapid response system

In this chapter, the use of satellite remote sensing to monitor active geological processes is described. Specifically, threats posed by volcanic eruptions are briefly outlined, and essential monitoring requirements are discussed. As an application example, a collaborative, multi-agency operational volcano monitoring system in the north Pacific is highlighted with a focus on the 2007 eruption of Kliuchevskoi volcano, Russia. The data from this system have been used since 2004 to detect the onset of volcanic activity, support the emergency response to large eruptions, and assess the volcanic products produced following the eruption. The overall utility of such integrative assessments is also summarized. The work described in this chapter was originally funded through two National Aeronautics and Space Administration (NASA) Earth System Science research grants that focused on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument. A skilled team of volcanologists, geologists, satellite tasking experts, satellite ground system experts, system engineers and software developers collaborated to accomplish the objectives. The first project, Automation of the ASTER Emergency Data Acquisition Protocol for Scientific Analysis, Disaster Monitoring, and Preparedness , established the original collaborative research and monitoring program between the University of Pittsburgh (UP), the Alaska Volcano Observatory (AVO), the NASA Land Processes Distributed Active Archive Center (LP DAAC) at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, and affiliates on the ASTER Science Team at the Jet Propulsion Laboratory (JPL) as well as associates at the Earth Remote Sensing Data Analysis Center (ERSDAC) in Japan. This grant, completed in 2008, also allowed for detailed volcanic analyses and data validation during three separate summer field campaigns to Kamchatka Russia. The second project, Expansion and synergistic use of the ASTER Urgent Request Protocol (URP) for natural disaster monitoring and scientific analysis , has expanded the project to other volcanoes around the world and is in progress through 2011. The focus on ASTER data is due to the suitability of the sensor for natural disaster monitoring and the availability of data. The instrument has several unique facets that make it especially attractive for volcanic observations (Ramsey and Dehn, 2004). Specifically, ASTER routinely collects data at night, it has the ability to generate digital elevation models using stereo imaging, it can collect data in various gain states to minimize data saturation, it has a cross-track pointing capability for faster targeting, and it collects data up to ±85° latitude for better global coverage. As with any optical imaging-based remote sensing, the viewing conditions can negatively impact the data quality. This impact varies across the optical and thermal infrared wavelengths as well as being a function of the specific atmospheric window within a given wavelength region. Water vapor and cloud formation can obscure surface data in the visible and near infrared (VNIR)/shortwave infrared (SWIR) region due mainly to non-selective scattering of the incident photons. In the longer wavelengths of the thermal infrared (TIR), scattering is less of an issue, but heavy cloud cover can still obscure the ground due to atmospheric absorption. Thin clouds can be optically-transparent in the VNIR and TIR regions, but can cause errors in the extracted surface reflectance or derived surface temperatures. In regions prone to heavy cloud cover, optical remote sensing can be improved through increased temporal resolution. As more images are acquired in a given time period the chances of a clear image improve dramatically. The Advanced Very High Resolution Radiometer (AVHRR) routine monitoring, which commonly collects 4-6 images per day of any north Pacific volcano, takes advantage of this fact. The rapid response program described in this chapter also improves the temporal resolution of the ASTER instrument. ASTER has been acquiring images of volcanic eruptions since soon after its launch in December 1999. An early example included the observations of the large pyroclastic flow deposit emplaced at Bezymianny volcano in Kamchatka, Russia. The first images in March 2000, just weeks after the eruption, revealed the extent, composition, and cooling history of this large deposit and of the active lava dome (Ramsey and Dehn, 2004). The initial results from these early datasets spurred interest in using ASTER data for expanded volcano monitoring in the north Pacific. It also gave rise to the multi-year NASA-funded programs of rapid response scheduling and imaging throughout the Aleutian, Kamchatka and Kurile arcs. Since the formal establishment of the programs, the data have provided detailed descriptions of the eruptions of Augustine, Bezymianny, Kliuchevskoi and Sheveluch volcanoes over the past nine years (Wessels et al., in press; Carter et al., 2007, 2008; Ramsey et al., 2008; Rose and Ramsey, 2009). The initial research focus of this rapid response program was specifically on automating the ASTER sensor’s ability for targeted observational scheduling using the expedited data system. This urgent request protocol is one of the unique characteristics of ASTER. It provides a limited number of emergency observations, typically at a much-improved temporal resolution and quicker turnaround with data processing in the United States rather than in Japan. This can speed the reception of the processed data by several days to a week. The ongoing multi-agency research and operational collaboration has been highly successful. AVO serves as the primary source for status information on volcanic activity, working closely with the National Weather Service (NWS), Federal Aviation Administration (FAA), military and other state and federal emergency services. Collaboration with the Russian Institute of Volcanology and Seismology (IVS)/Kamchatka Volcanic Eruption Response Team (KVERT) is also maintained. Once a volcano is identified as having increased thermal output, ASTER is automatically tasked and the volcano is targeted at the next available opportunity. After the data are acquired, scientists at all the agencies have access to the images, with the primary science analysis carried out at the University of Pittsburgh and AVO. Results are disseminated to the responsible monitoring agencies and the global community through e-mail mailing lists.

Book chapter↗

USGS2016

Status and trends of land change in selected U.S. ecoregions - 2000 to 2011

U.S. Geological Survey scientists developed a dataset of 2006 and 2011 land-use and land-cover ( LULC ) information for selected 100-km 2 sample blocks within 29 U.S. Environmental Protection Agency ( EPA ) Level III ecoregions across the conterminous United States. The data can be used with the previously published Land Cover Trends Dataset: 1973 to 2000 to assess landuse/land-cover change across a 37-year study period. Results from analysis of these data include ecoregion-based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 km 2 in the Northern Basin and Range Ecoregion to a high of 78,782 km 2 in the Southeastern Plains Ecoregion. The Southeastern Plains continues to encompass one of the most intense forest harvesting and regrowth regions in the country, with 16.6 percent of the ecoregion changing between 2001 and 2011. These LULC change statistics provide a new, valuable resource that complements other reference data and field-verified LULC data. Researchers can use this resource to independently validate other land change products or to conduct regional land change assessments.

Photogrammetric Engineering and Remote Sensing↗

USGS2024

Classification of lakebed geologic substrate in autonomously collected benthic imagery using machine learning

Mapping benthic habitats with bathymetric, acoustic, and spectral data requires georeferenced ground-truth information about habitat types and characteristics. New technologies like autonomous underwater vehicles (AUVs) collect tens of thousands of images per mission making image-based ground truthing particularly attractive. Two types of machine learning (ML) models, random forest (RF) and deep neural network (DNN), were tested to determine whether ML models could serve as an accurate substitute for manual classification of AUV images for substrate type interpretation. RF models were trained to predict substrate class as a function of texture, edge, and intensity metrics (i.e., features) calculated for each image. Models were tested using a manually classified image dataset with 9-, 6-, and 2-class schemes based on the Coastal and Marine Ecological Classification Standard (CMECS). Results suggest that both RF and DNN models achieve comparable accuracies, with the 9-class models being least accurate (~73–78%) and the 2-class models being the most accurate (~95–96%). However, the DNN models were more efficient to train and apply because they did not require feature estimation before training or classification. Integrating ML models into benthic habitat mapping process can improve our ability to efficiently and accurately ground-truth large areas of benthic habitat using AUV or similar images.

Michigan, Wisconsin↗

USGS2016

Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin

Evapotranspiration (ET) mapping at the Landsat spatial resolution (100 m) is essential to fully understand water use and water availability at the field scale. Water use estimates in the Colorado River Basin (CRB), which has diverse ecosystems and complex hydro-climatic regions, will be helpful to water planners and managers. Availability of Landsat 8 images, starting in 2013, provides the opportunity to map ET in the CRB to assess spatial distribution and patterns of water use. The Operational Simplified Surface Energy Balance (SSEBop) model was used with 528 Landsat 8 images to create seamless monthly and annual ET estimates at the inherent 100 m thermal band resolution. Annual ET values were summarized by land use/land cover classes. Croplands were the largest consumer of “blue” water while shrublands consumed the most “green” water. Validation using eddy covariance (EC) flux towers and water balance approaches showed good accuracy levels with R 2 ranging from 0.74 to 0.95 and the Nash–Sutcliffe model efficiency coefficient ranging from 0.66 to 0.91. The root mean square error (and percent bias) ranged from 0.48 mm (13%) to 0.60 mm (22%) for daily (days of satellite overpass) ET and from 7.75 mm (2%) to 13.04 mm (35%) for monthly ET. The spatial and temporal distribution of ET indicates the utility of Landsat 8 for providing important information about ET dynamics across the landscape. Annual crop water use was estimated for five selected irrigation districts in the Lower CRB where annual ET per district ranged between 681 mm to 772 mm. Annual ET by crop type over the Maricopa Stanfield irrigation district ranged from a low of 384 mm for durum wheat to a high of 990 mm for alfalfa fields. A rainfall analysis over the five districts suggested that, on average, 69% of the annual ET was met by irrigation. Although the enhanced cloud-masking capability of Landsat 8 based on the cirrus band and utilization of the Fmask algorithm improved the removal of contaminated pixels, the ability to reliably estimate ET over clouded areas remains an important challenge. Overall, the performance of Landsat 8 based ET compared to available EC datasets and water balance estimates for a complex basin such as the CRB demonstrates the potential of using Landsat 8 for annual water use estimation at a national scale. Future efforts will focus on (a) use of consistent methodology across years, (b) integration of multiple sensors to maximize images used, and (c) employing cloud-computing platforms for large scale processing capabilities.

Arizona, California, Colorado, Nevada, New Mexico,↗

USGS2012

MODIS-informed greenness responsesto daytime land surface temperaturefluctuations and wildfire disturbancesin the Alaskan Yukon River Basin

Pronounced climate warming and increased wildfire disturbances are known to modify forest composition and control the evolution of the boreal ecosystem over the Yukon River Basin (YRB) in interior Alaska. In this study, we evaluate the post-fire green-up rate using the normalized difference vegetation index (NDVI) derived from 250 m 7 day eMODIS (an alternative and application-ready type of Moderate Resolution Imaging Spectroradiometer (MODIS) data) acquired between 2000 and 2009. Our analyses indicate measureable effects on NDVI values from vegetation type, burn severity, post-fire time, and climatic variables. The NDVI observations from both fire scars and unburned areas across the Alaskan YRB showed a tendency of an earlier start to the growing season (GS); the annual variations in NDVI were significantly correlated to daytime land surface temperature (LST) fluctuations; and the rate of post-fire green-up depended mainly on burn severity and the time of post-fire succession. The higher average NDVI values for the study period in the fire scars than in the unburned areas between 1950 and 2000 suggest that wildfires enhance post-fire greenness due to an increase in post-fire evergreen and deciduous species components

Alaska↗

USGS2003

The National Map: from geography to mapping and back again

When the means of production for national base mapping were capital intensive, required large production facilities, and had ill-defined markets, Federal Government mapping agencies were the primary providers of the spatial data needed for economic development, environmental management, and national defense. With desktop geographic information systems now ubiquitous, source data available as a commodity from private industry, and the realization that many complex problems faced by society need far more and different kinds of spatial data for their solutions, national mapping organizations must realign their business strategies to meet growing demand and anticipate the needs of a rapidly changing geographic information environment. The National Map of the United States builds on a sound historic foundation of describing and monitoring the land surface and adds a focused effort to produce improved understanding, modeling, and prediction of land-surface change. These added dimensions bring to bear a broader spectrum of geographic science to address extant and emerging issues. Within the overarching construct of The National Map, the U.S. Geological Survey (USGS) is making a transition from data collector to guarantor of national data completeness; from producing paper maps to supporting an online, seamless, integrated database; and from simply describing the Nation’s landscape to linking these descriptions with increased scientific understanding. Implementing the full spectrum of geographic science addresses a myriad of public policy issues, including land and natural resource management, recreation, urban growth, human health, and emergency planning, response, and recovery. Neither these issues nor the science and technologies needed to deal with them are static. A robust research agenda is needed to understand these changes and realize The National Map vision. Initial successes have been achieved. These accomplishments demonstrate the utility of The National Map to the Nation and give confidence in evolving its future applications.

Photogrammetric Engineering and Remote Sensing↗

USGS2014

Hydroclimate of the Spring Mountains and Sheep Range, Clark County, Nevada

Precipitation, potential evapotranspiration, and actual evapotranspiration often are used to characterize the hydroclimate of a region. Quantification of these parameters in mountainous terrains is difficult because limited access often hampers the collection of representative ground data. To fulfill a need to characterize ecological zones in the Spring Mountains and Sheep Range of southern Nevada, spatially and temporally explicit estimates of these hydroclimatic parameters are determined from remote-sensing and model-based methodologies. Parameter-elevation Regressions on Independent Slopes Model (PRISM) precipitation estimates for this area ranges from about 100 millimeters (mm) in the low elevations of the study area (700 meters [m]) to more than 700 mm in the high elevations of the Spring Mountains (> 2,800 m). The PRISM model underestimates precipitation by 7–15 percent based on a comparison with four high‑elevation precipitation gages having more than 20 years of record. Precipitation at 3,000-m elevation is 50 percent greater in the Spring Mountains than in the Sheep Range. The lesser amount of precipitation in the Sheep Range is attributed to partial moisture depletion by the Spring Mountains of eastward-moving, cool-season (October–April) storms. Cool-season storms account for 66–76 percent of annual precipitation. Potential evapotranspiration estimates by the Basin Characterization Model range from about 700 mm in the high elevations of the Spring Mountains to 1,600 mm in the low elevations of the study area. The model realistically simulates lower potential evapotranspiration on northeast-to-northwest facing slopes compared to adjacent southeast-to-southwest facing slopes. Actual evapotranspiration, estimated using a Moderate Resolution Imaging Spectroradiometer based water-balance model, ranges from about 100 to 600 mm. The magnitude and spatial variation of simulated, actual evapotranspiration was validated by comparison to PRISM precipitation. Estimated groundwater recharge, computed as the residual of precipitation depleted by actual evapotranspiration, is within the range of previous estimates. A climatic water deficit dataset and aridity-index-based climate zones are derived from precipitation and evapotranspiration datasets. Climate zones range from arid in the lower elevations of the study area to humid in small pockets on north- to northeast-facing slopes in the high elevations of the Spring Mountains. Correlative analyses between hydroclimatic variables and mean ecosystem elevations indicate that the climatic water deficit is the best predictor of ecosystem distribution (R 2 = 0.92). Computed water balances indicate that substantially more recharge is generated in the Spring Mountains than in the Sheep Range. A geospatial database containing compiled and developed hydroclimatic data and other pertinent information accompanies this report.

Nevada↗

USGS2021

Advancing cave detection using terrain analysis and thermal imagery

Since the initial experiments nearly 50 years ago, techniques for detecting caves using airborne and spacecraft acquired thermal imagery have improved markedly. These advances are largely due to a combination of higher instrument sensitivity, modern computing systems, and processor-intensive analytical techniques. Through applying these advancements, our goals were to: (1) Determine the efficacy of methods designed for terrain analysis and applied to thermal imagery; (2) evaluate the usefulness of predawn and midday imagery for detecting caves; and (3) ascertain which imagery type (predawn, midday, or the difference between those two times) was most informative. Using forward stepwise logistic (FSL) and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses for model selection, and a thermal imagery dataset acquired from the Mojave Desert, California, we examined the efficacy of three well-known terrain descriptors (i.e., slope, topographic position index (TPI), and curvature) on thermal imagery for cave detection. We also included the actual, untransformed thermal DN values (hereafter “unenhanced thermal”) as a fourth dataset. Thereafter, we compared the thermal signatures of known cave entrances to all non-cave surface locations. We determined these terrain-based analytical methods, which described the “shape” of the thermal landscape, hold significant promise for cave detection. All imagery types produced similar results. Down-selected covariates per imagery type, based upon the FSL models, were: Predawn— slope, TPI, curvature at 0 m from cave entrance, as well as slope at 1 m from cave entrance; midday— slope, TPI, and unenhanced thermal at 0 m from cave entrance; and difference— TPI and slope at 0 m from cave entrance, as well as unenhanced thermal and TPI at 3.5 m from cave entrance. We provide recommendations for future research directions in terrestrial and planetary cave detection using thermal imagery.

Remote Sensing↗

USGS2025

Towards seamless global 30-meter terrestrial monitoring: Evaluating 2022 cloud free coverage of harmonized Landsat and Sentinel-2 (HLS) V2.0

Global observations at 30-m ground sampling distance (GSD) are now possible at a cadence of 1-3 days by combining Landsat 8 and 9 with Sentinel-2A and -2B satellites. Previous studies characterizing pixel-level Landsat-class measurement frequency used data from different sources but offered little information on observation availability after rigorous quality screening. This study examined the coverage frequency of HLS V2.0 data for 2022, the first year all four satellites data were available. These data have had quality control filtering and harmonization, and therefore reflect the spatial-temporal distribution of usable observations. On average, HLS data provide observations every 1.6 days at the global scale, and 2.2 days in the data-scarce tropical regions, regardless of cloud cover. The global mean and median cloud-free observations were 69 and 64, respectively. The frequency of good-quality observations varies geographically and seasonally due to changes in satellite swath overlap, cloud frequency, and solar illumination. High latitudes (>~75°N) exhibit the highest number of cloud-free observations between March and September. However, data are unavailable during winter months due to low solar elevation angles and boreal regions have a lower number of clear observations in the summer months. The tropical regions have the lowest number of clear observations. More frequent HLS observations could improve terrestrial monitoring. We mapped the monthly and weekly number of clear observations globally to show where HLS data could support monthly or sub-weekly time series applications.

IEEE Geoscience and Remote Sensing Letters↗

USGS2025

Thirty years of the U.S. National Land Cover Database: Impacts and future direction

The National Land Cover Database (NLCD), developed through the Multi-Resolution Land Characteristics Consortium, was initiated 30 years ago and has continually provided critical, Landsat-based landcover and land-change information for the United States. Originally launched to address the lack of national-scale, moderate-resolution land-cover data, NLCD has evolved from the pioneering 1992 dataset into a comprehensive, annually updated product suite. Key innovations include the introduction of impervious surface mapping, forest canopy mapping, standardized Landsat mosaics, national-scale accuracy assessments, continual evolution of deep learning and artificial intelligence methodologies, and a transition toward operational, change-focused monitoring. The NLCD has become an essential resource for scientific research, land management, and policy development, with extensive adoption across federal, state, and local agencies; academia; and the private sector. The NLCD data underpin a wide array of applications, including biodiversity conservation, urban planning, hydrology, human health studies, and natural hazard assessment. As new global and high-resolution commercial land-cover products emerge, the NLCD continues to distinguish itself through its temporal depth, federal backing, and thematic consistency. Moving forward, the NLCD will maintain its niche as the leading, moderate-resolution, long-term land-cover and land-change dataset for the United States, ensuring continued support for broad national applications while complementing higher-resolution and global-mapping efforts.

Photogrammetric Engineering & Remote Sensing↗

USGS2025

Historical coast snaps: Using centennial imagery to track shoreline change

Understanding long-term coastal evolution requires historical data, yet accessing reliable information becomes increasingly challenging for extended periods. While vertical aerial imagery has been extensively used in coastal studies since the mid-20th century, and satellite-derived shoreline measurements are now revolutionizing shoreline change studies, ground-based images, such as historical photographs and picture postcards, provide an alternative source of shoreline data for earlier periods when other datasets are scarce. Despite their frequent use for documenting qualitative morphological changes, these valuable historical data sources have rarely supported quantitative assessments of coastal evolution. This study demonstrates the potential of historical ground-oblique images for quantitatively assessing shoreline position and long-term change. Using Conceição-Duquesa Beach (Cascais, Portugal) as a case study, we analyze shoreline evolution over 92 years by applying a novel methodology to historical photographs and postcards. The approach combines image registration, shoreline detection, coordinate transformation, and rectification while accounting for positional uncertainty. Results reveal a significant counterclockwise rotation of the shoreline between the 20th and 21st centuries, exceeding estimated uncertainty thresholds. This study highlights the feasibility of using historical ground-based imagery to reconstruct shoreline positions and quantify long-term coastal change. The methodology is straightforward, adaptable, and offers a promising avenue for extending the temporal range of shoreline datasets, advancing our understanding of coastal evolution.

Cascais↗

USGS2020

Training data selection for annual land cover classification for the LCMAP initiative

The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative characterizes changes in land cover, use, and condition with the goal of producing land change information that improves understanding of the earth system and provides insight into the impacts of land change on society. For LCMAP, all available high-quality data from the Landsat archive is used in a time series approach to detect land surface change. Annual thematic land cover maps are produced by classifying time series models. In this paper, we describe optimization of the classification method used to derive the thematic land cover product. We investigated the influences of auxiliary data, sample size, and training from different sources such as the U.S. Geological Survey’s Land Cover Trends project and National Land Cover Database (NLCD 2001 and NLCD 2011). Results were evaluated and validated based on independent data from the training dataset. We found that refining auxiliary data effectively reduced artifacts in the thematic land cover map that are related to data availability (i.e., SLC-off). The classification accuracy and stability were improved considerably by using a total of 20 million training pixels with a minimum of 600,000 and a maximum of 8 million training pixels per class. Finally, the NLCD 2001 training data delivered the best classification accuracy. Comparing to the original LCMAP classification strategy (Trends training data, 20,000 samples), the optimized classification strategy considerably improved the annual land cover map accuracy.

Remote Sensing↗

USGS2009

Influence of resolution in irrigated area mapping and area estimation

The overarching goal of this paper was to determine how irrigated areas change with resolution (or scale) of imagery. Specific objectives investigated were to (a) map irrigated areas using four distinct spatial resolutions (or scales), (b) determine how irrigated areas change with resolutions, and (c) establish the causes of differences in resolution-based irrigated areas. The study was conducted in the very large Krishna River basin (India), which has a high degree of formal contiguous, and informal fragmented irrigated areas. The irrigated areas were mapped using satellite sensor data at four distinct resolutions: (a) NOAA AVHRR Pathfinder 10,000 m, (b) Terra MODIS 500 m, (c) Terra MODIS 250 m, and (d) Landsat ETM+ 30 m. The proportion of irrigated areas relative to Landsat 30 m derived irrigated areas (9.36 million hectares for the Krishna basin) were (a) 95 percent using MODIS 250 m, (b) 93 percent using MODIS 500 m, and (c) 86 percent using AVHRR 10,000 m. In this study, it was found that the precise location of the irrigated areas were better established using finer spatial resolution data. A strong relationship (R 2 = 0.74 to 0.95) was observed between irrigated areas determined using various resolutions. This study proved the hypotheses that “the finer the spatial resolution of the sensor used, greater was the irrigated area derived,” since at finer spatial resolutions, fragmented areas are detected better. Accuracies and errors were established consistently for three classes (surface water irrigated, ground water/conjunctive use irrigated, and non-irrigated) across the four resolutions mentioned above. The results showed that the Landsat data provided significantly higher overall accuracies (84 percent) when compared to MODIS 500 m (77 percent), MODIS 250 m (79 percent), and AVHRR 10,000 m (63 percent).

Krishna River basin↗