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USGS2021

Can Landsat 7 preserve its science capability with a drifting orbit?

Since 2017, the orbit of Landsat 7 has drifted outside its nominal mission requirement toward an earlier acquisition time because of limited onboard fuel resources. This makes quantitative analyses from Landsat 7 data potentially unreliable for many scientific studies. To comprehensively understand the effect of ongoing (2018–2020) orbit drift on Landsat 7 data, we compared surface reflectance and Top-Of-Atmosphere (TOA) reflectance of growing season observations (July 1 ± 30 days) from Landsat 7 with orbit drift and Landsat 8 with nominal orbit using a total of 10,000 randomly selected Northern Hemisphere (0–75 0 N) terrestrial pixels. To evaluate the future (2021–2023) effect of Landsat 7's orbit drift, we analyzed the historical Northern Hemisphere terrestrial growing season Earth Observing-1 (EO-1) TOA reflectance images, which shared a similar orbit drift as Landsat 7 but occurred much earlier. Results suggest that Landsat 7's orbit drift has already led to a general decrease in surface reflectance and TOA reflectance in 2019 and 2020, with a limited impact (overall reflectance changes less than 0.007). The influence of orbit drift is more substantial for the two shortwave infrared (SWIR) bands and the near infrared (NIR) band, but less for the three visible bands (i.e., Red, Green, and Blue). The Normalized Difference Vegetation Index (NDVI), derived from either surface reflectance or TOA reflectance, increased less than 0.003 in 2020. According to the historical EO-1 TOA reflectance data, we estimate that the effect of Landsat 7's orbit drift will be much more dramatic in the future (e.g., the NIR and SWIR bands will decrease more than 0.015 since July 1, 2021), and for different land cover types, the effects of orbit drift are also quite different. To reduce this influence, we examined the c-factor Bidirectional Reflectance Distribution Function (BRDF) normalization approach to correct the orbit drift impact for Landsat 7 surface reflectance data collected between 2019 and 2020. We found that the c-factor BRDF can reduce the data difference substantially, but how this approach works after Landsat 7's orbit drifts further still requires more investigation. Therefore, we determined that Landsat 7 can preserve its science capability until 2020, but will be less reliable for remote sensing applications that need accurate absolute radiometric values after 2020. Correction methods such as c-factor BRDF could be a potential viable approach to maintain its science capability going forward.

Science of Remote Sensing↗

USGS1985

Detection of leaks in buried rural water pipelines using thermal infrared images

Leakage is a major problem in many pipelines. Minor leaks called 'seeper leaks', which generally range from 2 to 10 m 3 per day, are common and are difficult to detect using conventional ground surveys. The objective of this research was to determine whether airborne thermal-infrared remote sensing could be used in detecting leaks and monitoring rural water pipelines. This study indicates that such leaks can be detected using low-altitude 8.7- to 11.5. micrometer wavelength, thermal infrared images collected under proper conditions.

Photogrammetric Engineering and Remote Sensing↗

USGS2009

Synergistic use of optical and InSAR data for urban impervious surface mapping: A case study in Hong Kong

A wide range of urban ecosystem studies, including urban hydrology, urban climate, land use planning and watershed resource management, require accurate and up‐to‐date geospatial data of urban impervious surfaces. In this study, the potential of the synergistic use of optical and InSAR data in urban impervious surface mapping at the sub‐pixel level was investigated. A case study in Hong Kong was conducted for this purpose by applying a classification and regression tree (CART) algorithm to SPOT 5 multispectral imagery and ERS‐2 SAR data. Validated by reference data derived from high‐resolution colour‐infrared (CIR) aerial photographs, our results show that the addition of InSAR feature information can improve the estimation of impervious surface percentage (ISP) in comparison with using SPOT imagery alone. The improvement is especially notable in separating urban impervious surface from the vacant land/bare ground, which has been a difficult task in ISP modelling with optical remote sensing data. In addition, the results demonstrate the potential to map urban impervious surface by using InSAR data alone. This allows frequent monitoring of world's cities located in cloud‐prone and rainy areas.

International Journal of Remote Sensing↗

USGS1990

Lava flow surface textures: SIR-B radar image texture, field observations, and terrain measurements

Space Shuttle Imaging Radar (SIR-8) images, field observations, and small-scale (cm) terrain measurements are used to study lava flow surface textures related to emplacement processes of a single hawaiian lava flow. Although smooth pahoehoe textures are poorly characterized on the SIR-B data, rougher pahoehoe types and the a'a flow portion show image textures attributed to spatial variations in surface roughness. Field observations of six distinct lava flow textural units are described and used to interpret modes of emplacement. Terrain measurements of these units show that surface roughness differs across and along the flow, apparently due to shear during flow emplacement. The radar smooth/rough boundary between pahoehoe and a'a occurs at a vertical relief of -10 cm on this lava flow. While direct observation and measurement most readily yield information related to lava eruption and emplacement processes, analyses of remote sensing data such as those acquired by imaging radars and altimeters can provide a means of quantifying surface texture, identifying the size and distribution of flow components, and delineating textural unit boundaries.

Hawaii↗

USGS2010

Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data

Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission's Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised classification of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classification stratification was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover, U.S. Geological Survey Anderson System, Simple Biosphere Model, Simple Biosphere Model 2, Biosphere-Atmosphere Transfer Scheme, Olson Ecosystems and Running Global Remote Sensing Land Cover. The database also includes all digital sources that were used in the classification. The complete database can be sourced from the website: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html.

International Journal of Remote Sensing↗

USGS2005

Mapping variations in weight percent silica measured from multispectral thermal infrared imagery - Examples from the Hiller Mountains, Nevada, USA and Tres Virgenes-La Reforma, Baja California Sur, Mexico

Remotely sensed multispectral thermal infrared (8-13 ??m) images are increasingly being used to map variations in surface silicate mineralogy. These studies utilize the shift to longer wavelengths in the main spectral feature in minerals in this wavelength region (reststrahlen band) as the mineralogy changes from felsic to mafic. An approach is described for determining the amount of this shift and then using the shift with a reference curve, derived from laboratory data, to remotely determine the weight percent SiO2 of the surface. The approach has broad applicability to many study areas and can also be fine-tuned to give greater accuracy in a particular study area if field samples are available. The approach was assessed using airborne multispectral thermal infrared images from the Hiller Mountains, Nevada, USA and the Tres Virgenes-La Reforma, Baja California Sur, Mexico. Results indicate the general approach slightly overestimates the weight percent SiO2 of low silica rocks (e.g. basalt) and underestimates the weight percent SiO2 of high silica rocks (e.g. granite). Fine tuning the general approach with measurements from field samples provided good results for both areas with errors in the recovered weight percent SiO2 of a few percent. The map units identified by these techniques and traditional mapping at the Hiller Mountains demonstrate the continuity of the crystalline rocks from the Hiller Mountains southward to the White Hills supporting the idea that these ranges represent an essentially continuous footwall block below a regional detachment. Results from the Baja California data verify the most recent volcanism to be basaltic-andesite. ?? 2005 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗

USGS2018

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R 2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282 .

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2014

Terrestrial reference standard sites for postlaunch sensor calibration

In an era when the number of Earth observation satellites is rapidly growing and measurements from satellite sensors are used to address increasingly urgent global issues, often through synergistic and operational combinations of data from multiple sources, it is imperative that scientists and decision-makers are able to rely on the accuracy of Earth observation data products. The characterization and calibration of these sensors, particularly their relative biases, are vital to the success of the developing integrated Global Earth Observation System of Systems (GEOSS) for coordinated and sustained observations of the Earth. This can only reliably be achieved in the postlaunch environment through the careful use of observations by multiple sensor systems over common, well-characterized terrestrial targets (i.e., on or near the Earth's surface). Through greater access to and understanding of these vital reference standard sites and their use, the validity and utility of information gained from Earth remote sensing will continue to improve. This paper provides a brief overview of the use of reference standard sites for postlaunch sensor radiometric calibration from historical, current, and future perspectives. Emphasis is placed on optical sensors operating in the visible, near-infrared, and shortwave infrared spectral regions.

Canadian Journal of Remote Sensing↗

USGS2004

Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity

Our study compares data on burn severity collected from multi-temporal Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) with similar data from the Enhanced Thematic Mapper Plus (ETM+) using the differenced Normalized Burn Ratio (dNBR). Two AVIRIS and ETM+ data acquisitions recorded surface conditions immediately before the Hoover Fire began to spread rapidly and again the following year. Data were validated with 63 field plots using the Composite Burn Index (CBI). The relationship between spectral channels and burn severity was examined by comparing pre- and post-fire datasets. Based on the high burn severity comparison, AVIRIS channels 47 and 60 at wavelengths of 788 and 913 nm showed the greatest negative response to fire. Post-fire reflectance values decreased the most on average at those wavelengths, while channel 210 at 2370 nm showed the greatest positive response on average. Fire increased reflectance the most at that wavelength over the entire measured spectral range. Furthermore, channel 210 at 2370 nm exhibited the greatest variation in spectral response, suggesting potentially high information content for fire severity. Based on general remote sensing principles and the logic of variable spectral responses to fire, dNBR from both sensors should produce useful results in quantifying burn severity. The results verify the band–response relationships to burn severity as seen with ETM+ data and confirm the relationships by way of a distinctly different sensor system.

Remote Sensing of Environment↗

USGS2021

Coastal wetland shoreline change monitoring: A comparison of shorelines from high-resolution WorldView satellite imagery, aerial imagery, and field surveys

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into shoreline monitoring. Geospatial shoreline data created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020 were compared to contemporaneous field-surveyed Global Position System (GPS) data. WV-derived shorelines were found to have a mean difference of 2 ± 0.08 m of GPS data, but accuracy decreased at high-wave energy shorelines that were unvegetated, bordered by sandy beach or semi-submergent sand bars. Shoreline change rates calculated from WV imagery were comparable to those calculated from GPS surveys and geospatial data derived from aerial remote sensing but tended to overestimate shoreline erosion at highly erosive locations (greater than 2 m yr −1 ). High-resolution satellite imagery can increase the spatial scale-range of shoreline change monitoring, provide rapid response to estimate impacts of coastal erosion, and reduce cost of labor-intensive practices.

Remote Sensing↗

USGS2014

Establishing a baseline for regional scale monitoring of eelgrass ( Zostera marina ) habitat on the lower Alaska Peninsula

Seagrass meadows, one of the world’s most widespread and productive ecosystems, provide a wide range of services with real economic value. Worldwide declines in the distribution and abundance of seagrasses and increased threats to coastal ecosystems from climate change have prompted a need to acquire baseline data for monitoring and protecting these important habitats. We assessed the distribution and abundance of eelgrass ( Zostera marina ) along nearly 1200 km of shoreline on the lower Alaska Peninsula, a region of expansive eelgrass meadows whose status and trends are poorly understood. We demonstrate the effectiveness of a multi-scale approach by using Landsat satellite imagery to map the total areal extent of eelgrass while integrating field survey data to improve map accuracy and describe the physical and biological condition of the meadows. Innovative use of proven methods and processing tools was used to address challenges inherent to remote sensing in high latitude, coastal environments. Eelgrass was estimated to cover ~31,000 ha, 91% of submerged aquatic vegetation on the lower Alaska Peninsula, nearly doubling the known spatial extent of eelgrass in the region. Mapping accuracy was 80%–90% for eelgrass distribution at locations containing adequate field survey data for error analysis.

Alaska↗

USGS1991

Publication aspects of ethics in photogrammetry

According to the Code of Ethics of the American Society for Photogrammetry and Remote Sensing (ASPRS), the principles on which ethics are founded consist of honesty, justice, and courtesy, forming a moral philosophy associated with mutual interest among men. We will cover in particular the ethical problems of publication of photogrammetric material in the various media. There are many such problems, and we often face a dilemma in selecting a course which is the right thing to do.

Photogrammetric Engineering and Remote Sensing↗

USGS2023

Detection and monitoring of small-scale diamond and gold mining dredges using synthetic aperture radar on the Kadéï (Sangha) River, Central African Republic

Diamond and gold mining has been practiced by artisanal miners in the Central African Republic (CAR) for decades. The recent introduction of riverine dredges indicates a transition from artisanal/manual digging and sorting techniques to small-scale mining methods. This study implements a remote sensing analysis of Synthetic Aperture Radar (SAR) data to map gold and diamond dredges operating on the Kadéï (Sangha) river in the CAR. Riverine vessels are identified in Sentinel-1 SAR data between 2015 and 2019, and their activity levels are mapped over time. The number of active dredges identified on the river increased over the five years studied, with the largest increase occurring between 2016 and 2017. Detailing a method for mapping and monitoring riverine diamond and gold dredge mining is an important step in keeping up with evolving technologies and new areas of mineral exploitation and in helping address concerns over resource governance in remote and conflict-prone terrain. The use of SAR technology, with its weather-independence, broad coverage, and available wavelength combinations, allows for higher temporal resolution and improved vessel detection in the monitoring of small-scale mining (SSM) dredges.

Remote Sensing↗

USGS2005

InSAR studies of Alaska volcanoes

Interferometric synthetic aperture radar (InSAR) is a remote sensing technique capable of measuring ground surface deformation with sub-centimeter precision and spatial resolution in tens-ofmeters over a large region. This paper describes basics of InSAR and highlights our studies of Alaskan volcanoes with InSAR images acquired from European ERS-l and ERS-2, Canadian Radarsat-l, and Japanese JERS-l satellites.

Alaska↗

USGS2003

Estimating lava volume by precision combination of multiple baseline spaceborne and airborne interferometric synthetic aperture radar: The 1997 eruption of Okmok Volcano, Alaska

Interferometric synthetic aperture radar (InSAR) techniques are used to calculate the volume of extrusion at Okmok volcano, Alaska by constructing precise digital elevation models (DEMs) that represent volcano topography before and after the 1997 eruption. The posteruption DEM is generated using airborne topographic synthetic aperture radar (TOPSAR) data where a three-dimensional affine transformation is used to account for the misalignments between different DEM patches. The preeruption DEM is produced using repeat-pass European Remote Sensing satellite data; multiple interferograms are combined to reduce errors due to atmospheric variations, and deformation rates are estimated independently and removed from the interferograms used for DEM generation. The extrusive flow volume associated with the 1997 eruption of Okmok volcano is 0.154 ?? 0.025 km3. The thickest portion is approximately 50 m, although field measurements of the flow margin's height do not exceed 20 m. The in situ measurements at lava edges are not representative of the total thickness, and precise DEM data are absolutely essential to calculate eruption volume based on lava thickness estimations. This study is an example that demonstrates how InSAR will play a significant role in studying volcanoes in remote areas.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2012

Effects of land cover and regional climate variations on long-term spatiotemporal changes in sagebrush ecosystems

This research investigated the effects of climate and land cover change on variation in sagebrush ecosystems. We combined information of multi-year sagebrush distribution derived from multitemporal remote sensing imagery and climate data to study the variation patterns of sagebrush ecosystems under different potential disturbances. We found that less than 40% of sagebrush ecosystem changes involved abrupt changes directly caused by landscape transformations and over 60% of the variations involved gradual changes directly related to climatic perturbations. The primary increases in bare ground and declines in sagebrush vegetation abundance were significantly correlated with the 1996-2006 decreasing trend in annual precipitation.

GIScience and Remote Sensing↗

USGS2011

An approach to regional wetland digital elevation model development using a differential global positioning system and a custom-built helicopter-based surveying system

Accurate topographic data are critical to restoration science and planning for the Everglades region of South Florida, USA. They are needed to monitor and simulate water level, water depth and hydroperiod and are used in scientific research on hydrologic and biologic processes. Because large wetland environments and data acquisition challenge conventional ground-based and remotely sensed data collection methods, the United States Geological Survey (USGS) adapted a classical data collection instrument to global positioning system (GPS) and geographic information system (GIS) technologies. Data acquired with this instrument were processed using geostatistics to yield sub-water level elevation values with centimetre accuracy (±15 cm). The developed database framework, modelling philosophy and metadata protocol allow for continued, collaborative model revision and expansion, given additional elevation or other ancillary data.

Florida↗

USGS1999

The way forward

This paper focuses on the lessons hearned in the conduct of the lnternational Geosphere Biosphere Program's Data and Information System (rcnr-nts), global 1-km Land-Cover Mapping Project (n$cover). There is stiLL considerable fundamental research to be conducted dealing with the development and validation of thematic geospatial products derived from a combination of remotely sensed and ancillary data. Issues include database and data product development, classification legend definitions, processing and analysis techniques, and sampling strategies. A significant infrastructure is required to support an effort such as DISCover. The infrastructure put in place under the auspices of the IGBP-DIS serves as a model, and must be put in place to enable replication and development of projects such as Discover.

Photogrammetric Engineering and Remote Sensing↗