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Results for “Journal of Remote Sensing”

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At least 37 records · Page 2Linked to original sources
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↗
USGS2025

Assessing gap-filled Landsat land surface temperature time-series data using different observational datasets

Landsat Analysis Ready Data (ARD)-based time-series present challenges in monitoring surface urban heat islands (SUHI) due to rapid changes in land surface temperature (LST) compared to cloud-free satellite observations. This research investigates the use of a spatiotemporal gap-filling model as a feasible and cost-effective solution to produce Landsat time-series LST products with both high spatial resolution and temporal frequency. The study identified and filled Landsat ARD thermal times-series data gaps due to missing data, cloud and shadow effects, and data quality. The accuracy of Landsat gap-filled products was assessed using randomly selected clear observations of Landsat and uncertainty products from the gap-filling model and was evaluated using various existing temperature datasets, including climate data from NOAA Global Historical Climate Network station observations, Daily Surface Weather and Climatological Summaries (DAYMET), and LST including MODIS, VIIRS and ECOSTRESS. The result suggests that the gap-filled Landsat LST has significant correlations with existing datasets including field observation and remote sensing data derived from other sensors that have similar monthly and seasonal variation patterns. The uncertainty maps show spatial distributions of uncertainty for gap-filled pixels that have high or low uncertainties. The Landsat gap-filled time-series datasets can be used to measure annual, seasonal, or even monthly landscape thermal conditions, which are useful for SUHI and relevant research, and to perform multi-decade time-series LST change analysis under climate change conditions.

International Journal of Remote Sensing↗
USGS2007

Significance of non-isotropic scattering from vegetation for geobotanical remote sensing

Although it has been known for quite some time that individual leaves display non-Lambertian scattering at wavelengths of strong absorption in the visible part of the spectrum, while displaying near-Lambertian behaviour at wavelengths of little absorption in the near-infrared, this is widely ignored in geobotanical remote sensing investigations. Preliminary data show that non-Lambertian behaviour may have a significant effect on the ratio of infrared/red reflectance with changing illumination and observation angles, but little or no efTect on the location of the steep rise in reflectance from the red to the infrared (‘red edge’). These results indicate the need for a complete assessment of directional scattering from different vegetation and canopy types, and strongly suggest the need for caution in interpretation of observational data taken without regard for illumination and observation angles.

International Journal of Remote Sensing↗
USGS2014

Remote sensing education and Internet/World Wide Web technology

Remote sensing education is increasingly in demand across academic and professional disciplines. Meanwhile, Internet technology and the World Wide Web (WWW) are being more frequently employed as teaching tools in remote sensing and other disciplines. The current wealth of information on the Internet and World Wide Web must be distilled, nonetheless, to be useful in remote sensing education. An extensive literature base is developing on the WWW as a tool in education and in teaching remote sensing. This literature reveals benefits and limitations of the WWW, and can guide its implementation. Among the most beneficial aspects of the Web are increased access to remote sensing expertise regardless of geographic location, increased access to current material, and access to extensive archives of satellite imagery and aerial photography. As with other teaching innovations, using the WWW/Internet may well mean more work, not less, for teachers, at least at the stage of early adoption. Also, information posted on Web sites is not always accurate. Development stages of this technology range from on-line posting of syllabi and lecture notes to on-line laboratory exercises and animated landscape flyovers and on-line image processing. The advantages of WWW/Internet technology may likely outweigh the costs of implementing it as a teaching tool.

Canadian Journal of Remote Sensing↗
USGS2007

Analysis of impacts of urban land use and land cover on air quality in the Las Vegas region using remote sensing information and ground observations

Urban development in the Las Vegas Valley of Nevada (USA) has expanded rapidly over the past 50 years. The air quality in the valley has suffered owing to increases from anthropogenic emissions of carbon monoxide, ozone and criteria pollutants of particular matter. Air quality observations show that pollutant concentrations have apparent heterogeneous characteristics in the urban area. Quantified urban land use and land cover information derived from satellite remote sensing data indicate an apparent local influence of urban development density on air pollutant distributions. Multi‐year observational data collected by a network of local air monitoring stations specify that ozone maximums develop in the May and June timeframe, whereas minimum concentrations generally occur from November to February. The fine particulate matter maximum occurs in July. Ozone concentrations are highest on the west and northwest sides of the valley. Night‐time ozone reduction contributes to the heterogeneous features of the spatial distribution for average ozone levels in the Las Vegas metropolitan area. Decreased ozone levels associated with increased urban development density suggest that the highest ozone and lowest nitrogen oxides concentrations are associated with medium to low density urban development in Las Vegas.

International Journal of Remote Sensing↗
USGS2007

Remote sensing of ephemeral water bodies in western Niger

Research was undertaken to evaluate the feasibility of monitoring the small ephemeral water bodies of the Sahel with the 1.1 km resolution data of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). Twenty-one lakes of western Niger with good ground observation records were selected for examination. Thematic Mapper images from 1988 were first analysed to determine surface areas and temperature differences between water and adjacent land. Six AVHRR scenes from the 1988-89 dry season were then studied. It was found that a lake can be monitored until its surface area drops below 10 ha, in most cases. Furthermore, with prior knowledge of the location and shape of a water body, its surface area can be estimated from AVHRR band 5 data to within about 10 ha. These results are explained by the sharp temperature contrast between water and land, on the order of 13?? C.

International Journal of Remote Sensing↗
USGS2011

Testing a high-resolution satellite interpretation technique for crop area monitoring in developing countries

District-level crop area (CA) is a highly uncertain term in food production equations, which are used to allocate food aid and implement appropriate food security initiatives. Remote sensing studies typically overestimate CA and production, as subsistence plots are exaggerated at coarser resolution, which leads to overoptimistic food reports. In this study, medium-resolution (MR) Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images were manually classified for Niger and corrected using CA estimates derived from high-resolution (HR) sample image, topographic and socioeconomic data. A logistic model with smoothing splines was used to compute the block-average (0.1°) probability of an area being cropped. Livelihood zones and elevation explained 75% of the deviance in CA, while MR did not add explanatory power. The model overestimates CA when compared to the national inventory, possibly because of temporal changes in intercropping and the exclusion of some staple crops in the national inventory.

International Journal of Remote Sensing↗
USGS2010

Estimating suspended sediment concentrations in turbid coastal waters of the Santa Barbara Channel with SeaWiFS

A technique is presented for estimating suspended sediment concentrations of turbid coastal waters with remotely sensed multi-spectral data. The method improves upon many standard techniques, since it incorporates analyses of multiple wavelength bands (four for Sea-viewing Wide Field of view Sensor (SeaWiFS)) and a nonlinear calibration, which produce highly accurate results (expected errors are approximately ±10%). Further, potential errors produced by erroneous atmospheric calibration in excessively turbid waters and influences of dissolved organic materials, chlorophyll pigments and atmospheric aerosols are limited by a dark pixel subtraction and removal of the violet to blue wavelength bands. Results are presented for the Santa Barbara Channel, California where suspended sediment concentrations ranged from 0–200+ mg l −1 (±20 mg l −1 ) immediately after large river runoff events. The largest plumes were observed 10–30 km off the coast and occurred immediately following large El Niño winter floods.

California↗
USGS2007

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

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

International Journal of Remote Sensing↗
USGS2007

Terrestrial remote sensing science and algorithms planned for EOS/MODIS

The Moderate Resolution Imaging Spectroradiometer (MODIS) will be the primary daily global monitoring sensor on the NASA Earth Observing System (EOS) satellites, scheduled for launch on the EOS-AM platform in June 1998 and the EOS-PM platform in December 2000. MODIS is a 36 channel radiometer covering 0·415-14·235 μm wavelengths, with spatial resolution from 250 m to 1 km at nadir. MODIS will be the primary EOS sensor for providing data on terrestrial biospheric dynamics and process activity. This paper presents the suite of global land products currently planned for EOSDIS implementation, to be developed by the authors of this paper, the MODIS land team (MODLAND). These include spectral albedo, land cover, spectral vegetation indices, snow and ice cover, surface temperature and fire, and a number of biophysical variables that will allow computation of global carbon cycles, hydrologic balances and biogeochemistry of critical greenhouse gases. Additionally, the regular global coverage of these variables will allow accurate surface change detection, a fundamental determinant of global change.

International Journal of Remote Sensing↗
USGS2007

Mapping raster imagery to the interrupted Goode Homolosine projection

Because of the increasing emphasis on global monitoring, processing remotely-sensed raster image data onto global map projections has become an important issue. One class of map projections, interrupted equal-area projections, is especially useful for this purpose. The use of the Interrupted Goode Homolosine map projection for the Global Land Advanced Very High Resolution Radiometer (AVHRR) 1 km project and the AVHRR Pathfinder project has produced a map that is both attractive to the viewer and useful for data analysis. This interrupted, composite, equal-area map projection uses the Sinusoidal projection for low latitudes and the Mollweide projection for high latitudes and is broken into 12 regions, each with its own central meridian. This combination of projections helps to preserve the shape of the land masses and results in a map that has less distortion than an uninterrupted global map. The use of the interrupted projection also simplifies the processing and management of larger data sets, because the data can be processed either separately in the component projections that make up the interrupted projection or directly onto the interrupted projection.

International Journal of Remote Sensing↗
USGS2007

Remote sensing of tidal chlorophyll-a variations in estuaries

Simultaneous acquisition of surface chlorophyll-a concentrations for 39 samples from boats and Daedalus 1260 Multispectral Scanner data from a U-2 aircraft was conducted in the northern reaches of San Francisco Bay on 28 August 1980. These data were used to develop regression models for predicting surface chlorophyll-a concentrations over the study area for ebb-tide (8.40 a.m. P.D.T. (Pacific Daylight Time)) and flood-tide (3.10 p.m. P.D.T.) conditions. After selection of a single ‘best fitting’ model for both morning and afternoon data sets, the chlorophyll-a concentration was predicted for ebb and flood tide for the entire study area at approximately 40m × 40m resolution. The predicted spatial display of chlorophyll-a revealed a localized area of high phytoplankton biomass that has been inferred from field surveys and appears to be a common summer phenomenon. Knowledge of the distribution of phytoplankton and the location of this zone of high biomass is valuable in establishing management policies for this ecologically important estuary. Furthermore, the techniques used here may provide an alternative cost-effective method for assessing water-quality conditions and they may prove useful for studying spatial variations (patchiness) and seasonal variations in phytoplankton biomass in other estuaries and coastal waters.

California↗
USGS2013

Field calibration and validation of remote-sensing surveys

The Optical Collection Suite (OCS) is a ground-truth sampling system designed to perform in situ measurements that help calibrate and validate optical remote-sensing and swath-sonar surveys for mapping and monitoring coastal ecosystems and ocean planning. The OCS system enables researchers to collect underwater imagery with real-time feedback, measure the spectral response, and quantify the water clarity with simple and relatively inexpensive instruments that can be hand-deployed from a small vessel. This article reviews the design and performance of the system, based on operational and logistical considerations, as well as the data requirements to support a number of coastal science and management projects. The OCS system has been operational since 2009 and has been used in several ground-truth missions that overlapped with airborne lidar bathymetry (ALB), hyperspectral imagery (HSI), and swath-sonar bathymetric surveys in the Gulf of Maine, southwest Alaska, and the US Virgin Islands (USVI). Research projects that have used the system include a comparison of backscatter intensity derived from acoustic (multibeam/interferometric sonars) versus active optical (ALB) sensors, ALB bottom detection, and seafloor characterization using HSI and ALB.

Alaska↗
USGS2011

Assessing long-term variations in sagebrush habitat: characterization of spatial extents and distribution patterns using multi-temporal satellite remote-sensing data

An approach that can generate sagebrush habitat change estimates for monitoring large-area sagebrush ecosystems has been developed and tested in southwestern Wyoming, USA. This prototype method uses a satellite-based image change detection algorithm and regression models to estimate sub-pixel percentage cover for five sagebrush habitat components: bare ground, herbaceous, litter, sagebrush and shrub. Landsat images from three different months in 1988, 1996 and 2006 were selected to identify potential landscape change during these time periods using change vector (CV) analysis incorporated with an image normalization algorithm. Regression tree (RT) models were used to estimate percentage cover for five components on all change areas identified in 1988 and 1996, using unchanged 2006 baseline data as training for both estimates. Over the entire study area (24 950 km 2 ), a net increase of 98.83 km 2 , or 0.7%, for bare ground was measured between 1988 and 2006. Over the same period, the other four components had net losses of 20.17 km 2 , or 0.6%, for herbaceous vegetation; 30.16 km 2 , or 0.7%, for litter; 32.81 km 2 , or 1.5%, for sagebrush; and 33.34 km 2 , or 1.2%, for shrubs. The overall accuracy for shrub vegetation change between 1988 and 2006 was 89.56%. Change patterns within sagebrush habitat components differ spatially and quantitatively from each other, potentially indicating unique responses by these components to disturbances imposed upon them.

International Journal of Remote Sensing↗
USGS2017

Estimating carbon and showing impacts of drought using satellite data in regression-tree models

Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, allowing a better understanding of broad-scale ecosystem processes. The current study presents annual gross primary production (GPP) and annual ecosystem respiration (RE) for 2000–2013 in several short-statured vegetation types using carbon flux data from towers that are located strategically across the conterminous United States (CONUS). We calculate carbon fluxes (annual net ecosystem production [NEP]) for each year in our study period, which includes 2012 when drought and higher-than-normal temperatures influence vegetation productivity in large parts of the study area. We present and analyse carbon flux dynamics in the CONUS to better understand how drought affects GPP, RE, and NEP. Model accuracy metrics show strong correlation coefficients ( r ) ( r ≥ 94%) between training and estimated data for both GPP and RE. Overall, average annual GPP, RE, and NEP are relatively constant throughout the study period except during 2012 when almost 60% less carbon is sequestered than normal. These results allow us to conclude that this modelling method effectively estimates carbon dynamics through time and allows the exploration of impacts of meteorological anomalies and vegetation types on carbon dynamics.

International Journal of Remote Sensing↗
USGS2023

Crop water use dynamics over arid and semi-arid croplands in the lower Colorado River Basin

Numerous studies have evaluated the application of Remote Sensing (RS) techniques for mapping actual evapotranspiration (ETa) using Vegetation-Index-based (VI-based) and surface energy balance methods (SEB). SEB models computationally require a large effort for application. VI-based methods are fast and easy to apply and could therefore potentially be applied at high resolution; however, the accuracy of VI-based methods in comparison to SEB-based models remains unclear. We tested the ETa computed with the modified 2-band Enhanced Vegetation Index (METEVI2) implemented in the Google Earth Engine – for mapping croplands’ water use dynamics in the Lower Colorado River Basin. We compared METEVI2 with the well-established RS-based products of OpenET (Ensemble, eeMETRIC, SSEBop, SIMS, PT_JPL, DisALEXI and geeSEBAL). METEVI2 was then evaluated with measured ETa from four wheat fields (2017–2018). Results indicated that the monthly ETa variations for METEVI2 and OpenET models were comparable, though of varying magnitudes. On average, METEVI2 had the lowest difference rate from the average observed ETa with 17 mm underestimation, while SIMS had the highest difference rate (82 mm). Findings show that METEVI2 is a cost-effective ETa mapping tool in drylands to track crop water use. Future studies should test METEVI2’s applicability to croplands in more humid regions.

Lower Colorado River basin↗
USGS2019

Mapping a keystone shrub species, huckleberry (Vaccinium membranaceum), using seasonal colour change in the Rocky Mountains

Black huckleberries ( Vaccinium membranaceum ) provide a critical food resource to many wildlife species, including apex omnivores such as the grizzly bear ( Ursus arctos ), and play an important socioeconomic role for many communities in western North America, especially indigenous peoples. Remote sensing imagery offers the potential for accurate landscape-level mapping of huckleberries because the shrub changes colour seasonally. We developed two methods, for local and regional scales, to map a shrub species using leaf seasonal colour change from remote sensing imagery. We assessed accuracy with ground-based vegetation surveys. The high-resolution supervised random forest classification from one-meter resolution National Agricultural Imagery Program (NAIP) imagery achieved an overall accuracy of 75.31% (kappa = 0.26). The approach using multi-temporal 30-meter Landsat imagery similarly had an overall accuracy of 79.19% (kappa = .31). We found underprediction error was related to higher forest cover and a lack of visible colour change on the ground in some plots. Where forest cover was low, both models performed better. In areas with <10% forest cover, the high-resolution classification achieved an accuracy of 80.73% (kappa = 0.48), while the Landsat model had an accuracy of 82.55% (kappa = 0.47). Based on the fine-scale predictions, we found that 94% of huckleberry shrubs identified in our study area of Glacier National Park, Montana, USA are over 100 meters from human recreation trails. This information could be combined with productivity and phenology information to estimate the timing and availability of food resources for wildlife and to provide managers with a tool to identify and manage huckleberries. The development of the multi-temporal Landsat models sets the stage for assessment of impacts of disturbance at regional scales on this ecologically, culturally, and economically important shrub species. Our approach to map huckleberries is straightforward, efficient and accessible to wildlife and environmental managers and researchers in diverse fields.

Montana↗
USGS2014

An analysis of the global spatial variability of column-averaged CO 2 from SCIAMACHY and its implications for CO 2 sources and sinks

Satellite observations of carbon dioxide (CO 2 ) are important because of their potential for improving the scientific understanding of global carbon cycle processes and budgets. We present an analysis of the column-averaged dry air mole fractions of CO 2 (denoted XCO 2 ) of the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) retrievals, which were derived from a satellite instrument with relatively long-term records (2003–2009) and with measurements sensitive to the near surface. The spatial-temporal distributions of remotely sensed XCO 2 have significant spatial heterogeneity with about 6–8% variations (367–397 ppm) during 2003–2009, challenging the traditional view that the spatial heterogeneity of atmospheric CO 2 is not significant enough (<4%) to have any large effect on terrestrial ecosystem carbon cycles. By comparison with surface measurements from the NOAA Earth System Research Laboratory (ESRL) GLOBALVIEW network, significant statistical relationships between XCO 2 and surface CO 2 were found for major ecosystems, with the exception of tropical forest. In addition, when compared with a simulated terrestrial carbon uptake from the Integrated Biosphere Simulator (IBIS) and the Emissions Database for Global Atmospheric Research (EDGAR) carbon emission inventory, the latitudinal gradient of XCO 2 seasonal amplitude was influenced by the combined effect of terrestrial carbon uptake, carbon emission, and atmospheric transport, suggesting no direct implications for terrestrial carbon sinks. From the investigation of the growth rate of XCO 2 we found that the increase of CO 2 concentration was dominated by temperature in the northern hemisphere (20–90°N) and by precipitation in the southern hemisphere (20–90°S), with the major contribution to global average occurring in the northern hemisphere. These findings indicated that the satellite measurements of atmospheric CO 2 improve not only the estimations of atmospheric inversion, but also the understanding of the terrestrial ecosystem carbon dynamics and its feedback to atmospheric CO 2 .

International Journal of Remote Sensing↗