Low Spatial Resolution Data Research Articles

Dictionary-based estimation of spectra for wide-gamut color imaging

With the widespread use of commercialized wide-gamut displays, the demand for wide-gamut image content is increasing. To acquire wide-gamut image content using camera systems, color information should be accurately reconstructed from recorded image signals for a wide range of colors. However, it is difficult to obtain color information accurately, especially for saturated colors, if conventional color cameras are used. Spectrum-based color image reproduction can solve this problem; however, bulky spectral imaging systems are required for this purpose. To acquire spectral images more conveniently, a new spectral imaging scheme has been proposed that uses two types of data: high spatial-resolution red, green, and blue (RGB) images and low spatial-resolution spectral data measured from the same scene. Although this method estimates spectral images with high overall accuracy, the error becomes relatively large when multiple different colors, especially those with high saturation, are arranged in a small region. The main reason for this error is that the spectral data are utilized as low-order spectral statistics of local spectra in this method. To solve this problem, in this study, a nonlinear estimation method based on sparse and redundant dictionaries was used for spectral image estimation—where the dictionary contains a number of spectra—without loss of information from the low spatial-resolution spectral data. The estimated spectra are represented by a mixture of a few spectra included in the dictionary. Therefore, the respective feature of every spectrum is expected to be preserved in the estimation, and the color saturation is also preserved for any region. Experiments performed using the simulated data showed that the dictionary-based estimation can be used to obtain saturated colors accurately, even when multiple colors are arranged in a small region. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2013

Surface Area and the Seabed Area, Volume, Depth, Slope, and Topographic Variation for the World’s Seas, Oceans, and Countries

Depth and topography directly and indirectly influence most ocean environmental conditions, including light penetration and photosynthesis, sedimentation, current movements and stratification, and thus temperature and oxygen gradients. These parameters are thus likely to influence species distribution patterns and productivity in the oceans. They may be considered the foundation for any standardized classification of ocean ecosystems and important correlates of metrics of biodiversity (e.g., species richness and composition, fisheries). While statistics on ocean depth and topography are often quoted, how they were derived is rarely cited, and unless calculated using the same spatial resolution the resulting statistics will not be strictly comparable. We provide such statistics using the best available resolution (1-min) global bathymetry, and open source digital maps of the world's seas and oceans and countries' Exclusive Economic Zones, using a standardized methodology. We created a terrain map and calculated sea surface and seabed area, volume, and mean, standard deviation, maximum, and minimum, of both depth and slope. All the source data and our database are freely available online. We found that although the ocean is flat, and up to 71% of the area has a < 1 degree slope. It had over 1 million approximately circular features that may be seamounts or sea-hills as well as prominent mountain ranges or ridges. However, currently available global data significantly underestimate seabed slopes. The 1-min data set used here predicts there are 68,669 seamounts compared to the 30,314 previously predicted using the same method but lower spatial resolution data. The ocean volume exceeds 1.3 billion km(3) (or 1.3 sextillion liters), and sea surface and seabed areas over 354 million km(2). We propose the coefficient of variation of slope as an index of topographic heterogeneity. Future studies may improve on this database, for example by using a more detailed bathymetry, and in situ measured data. The database could be used to classify ocean features, such as abyssal plains, ridges, and slopes, and thus provide the basis for a standards based classification of ocean topography.

Modelling of urban sensible heat flux at multiple spatial scales: A demonstration using airborne hyperspectral imagery of Shanghai and a temperature–emissivity separation approach

Urbanization related alterations to the surface energy balance impact urban warming (‘heat islands’), the growth of the boundary layer, and many other biophysical processes. Traditionally, in situ heat flux measures have been used to quantify such processes, but these typically represent only a small local-scale area within the heterogeneous urban environment. For this reason, remote sensing approaches are very attractive for elucidating more spatially representative information. Here we use hyperspectral imagery from a new airborne sensor, the Operative Modular Imaging Spectrometer (OMIS), along with a survey map and meteorological data, to derive the land cover information and surface parameters required to map spatial variations in turbulent sensible heat flux ( Q H). The results from two spatially-explicit flux retrieval methods which use contrasting approaches and, to a large degree, different input data are compared for a central urban area of Shanghai, China: (1) the Local-scale Urban Meteorological Parameterization Scheme (LUMPS) and (2) an Aerodynamic Resistance Method (ARM). Sensible heat fluxes are determined at the full 6 m spatial resolution of the OMIS sensor, and at lower resolutions via pixel aggregation and spatial averaging. At the 6 m spatial resolution, the sensible heat flux of rooftop dominated pixels exceeds that of roads, water and vegetated areas, with values peaking at ∼ 350 W m − 2 , whilst the storage heat flux is greatest for road dominated pixels (peaking at around 420 W m − 2 ). We investigate the use of both OMIS-derived land surface temperatures made using a Temperature–Emissivity Separation (TES) approach, and land surface temperatures estimated from air temperature measures. Sensible heat flux differences from the two approaches over the entire 2 × 2 km study area are less than 30 W m − 2 , suggesting that methods employing either strategy maybe practica1 when operated using low spatial resolution (e.g. 1 km) data. Due to the differing methodologies, direct comparisons between results obtained with the LUMPS and ARM methods are most sensibly made at reduced spatial scales. At 30 m spatial resolution, both approaches produce similar results, with the smallest difference being less than 15 W m − 2 in mean Q H averaged over the entire study area. This is encouraging given the differing architecture and data requirements of the LUMPS and ARM methods. Furthermore, in terms of mean study Q H, the results obtained by averaging the original 6 m spatial resolution LUMPS-derived Q H values to 30 and 90 m spatial resolution are within ∼ 5 W m − 2 of those derived from averaging the original surface parameter maps prior to input into LUMPS, suggesting that that use of much lower spatial resolution spaceborne imagery data, for example from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is likely to be a practical solution for heat flux determination in urban areas.

Assessing the multi-resolution information content of remotely sensed variables and elevation for evapotranspiration in a tall-grass prairie environment

Understanding the spatial scaling behavior of evapotranspiration and its relation to controlling factors on the land surface is necessary to accurately estimate regional water cycling. We propose a method for ascertaining this scaling behavior via a combination of wavelet multi-resolution analysis and information theory metrics. Using a physically-based modeling framework, we are able to compute spatially distributed latent heat fluxes over the tall-grass prairie in North-central Kansas for August 8, 2005. Comparison with three eddy-covariance stations and a large aperture scintillometer demonstrates good agreement, and thus give confidence in the modeled fluxes. Results indicate that the spatial variability in radiometric temperature (a proxy for soil moisture) most closely controls the spatial variability in evapotranspiration. Small scale variability in the water flux can be ascribed to the small scale spatial variance in the fractional vegetation. In addition, correlation analysis indicates general scale invariance and that low spatial resolution data may be adequate for accurately determining water cycling in prairie ecosystems.

Evaluating MODIS data for mapping wildlife habitat distribution

Habitat distribution models have a long history in ecological research. With the development of geospatial information technology, including remote sensing, these models are now applied to an ever-increasing number of species, particularly those located in areas in which it is logistically difficult to collect habitat data in the field. Many habitat studies have used data acquired by multi-spectral sensor systems such as the Landsat Thematic Mapper (TM), due mostly to their availability and relatively high spatial resolution (30 m/pixel). The use of data collected by other sensor systems with lower spatial resolutions but high frequency of acquisitions has largely been neglected, due to the perception that such low spatial resolution data are too coarse for habitat mapping. In this study we compare two models using data from different satellite sensor systems for mapping the spatial distribution of giant panda habitat in Wolong Nature Reserve, China. The first one is a four-category scheme model based on combining forest cover (derived from a digital land cover classification of Landsat TM imagery acquired in June, 2001) with information on elevation and slope (derived from a digital elevation model obtained from topographic maps of the study area). The second model is based on the Ecological Niche Factor Analysis (ENFA) of a time series of weekly composites of WDRVI (Wide Dynamic Range Vegetation Index) images derived from MODIS (Moderate Resolution Imaging Spectroradiometer – 250 m/pixel) for 2001. A series of field plots was established in the reserve during the summer–autumn months of 2001–2003. The locations of the plots with panda feces were used to calibrate the ENFA model and to validate the results of both models. Results showed that the model using the seasonal variability of MODIS-WDRVI had a similar prediction success to that using Landsat TM and digital elevation model data, albeit having a coarser spatial resolution. This suggests that the phenological characterization of the land surface provides an appropriate environmental predictor for giant panda habitat mapping. Therefore, the information contained in remotely sensed data acquired with low spatial resolution but high frequency of acquisitions has considerable potential for mapping the habitat distribution of wildlife species.

Corn and Soybean Mapping in the United States Using MODIS Time‐Series Data Sets

Monitoring and mapping of U.S. croplands has long been a primary goal of many users of earth observation satellite data. The advantages of using low spatial and high temporal resolution data are (i) increased ability to monitor the phenological change of crop plants, and (ii) the possibility of generating consistent large area crop cover maps. This study investigates the potential of 500‐m MODIS (MODerate Resolution Imaging Spectroradiometer) data in estimating corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] area for the dominant production areas of the USA. To avoid cloud cover, MODIS 32‐day composites for all land bands, normalized difference vegetation index (NDVI), and land surface temperature (LST), were used covering March 2002 to February 2003. These time‐sequential images were further composited to produce 279 annual time‐integrated metrics. Using USDA‐NASS Cropland Data Layers (CDL) as subpixel training data, percentage soybean and corn cover per 500‐m pixel was calculated and accuracy was assessed at national, state, and county scales using data from the 2002 NASS Census of Agriculture. When these estimates were compared with the NASS Census, r2 values for corn, soybean, and combined corn and soybean areas were 0.957, 0.949, and 0.984 at the state level, respectively. At the national scale, MODIS estimates of corn and soybean cover differed by 6 and 4%, respectively. Results indicate a robust potential for using MODIS in crop type monitoring applications.

Optimizing the High-Pass Filter Addition Technique for Image Fusion

Pixel-level image fusion combines complementary image data, most commonly low spectral-high spatial resolution data with high spectral-low spatial resolution optical data. The presented study aims at refining and improving the High-Pass Filter Additive (HPFA) fusion method towards a tunable and versatile, yet standardized image fusion tool. HPFA is an image fusion method in the spatial domain, which inserts structural and textural details of the higher resolution image into the lower resolution image, whose spectral properties are thereby largely retained. Using various input image pairs, workable sets of HPFA parameters have been derived with regard to high-pass filter properties and injection weights. Improvements are the standardization of the HPFA parameters over a wide range of image resolution ratios and the controlled trade-off between resulting image sharpness and spectral properties. The results are evaluated visually and by spectral and spatial metrics in comparison with wavelet-based image fusion results as a benchmark.

On quantifying agricultural and water management practices from low spatial resolution RS data using genetic algorithms: A numerical study for mixed-pixel environment

In this paper, we present a genetic algorithm-based methodology to quantify agricultural and water management practices from remote sensing (RS) data in a mixed-pixel environment. First, we formulated a linear mixture model for low spatial resolution RS data where we considered three agricultural land uses as dominant inside the pixel—rainfed, irrigated with two, and three croppings a year; the mixing parameters we considered were the sowing dates, area fractions of agricultural land uses in the pixel, and their corresponding water management practices. Then, we carried out numerical experiments to evaluate the feasibility of the proposed approach. In the process, the mixing parameters were parameterized by data assimilation using evapotranspiration and leaf area index as conditioning criteria. The soil–water–atmosphere–plant system model SWAP was used to simulate the dynamics of these two biophysical variables in the pixel. The results of our numerical experiments showed that it is possible to derive some sub-pixel information from low spatial resolution data e.g. the existing agricultural and water management practices in a region, which are relevant for regional agricultural monitoring programs.

EVALUATION OF THE SWAT MODEL ON A COASTAL PLAIN AGRICULTURAL WATERSHED

The Better Assessment Science Integrating point and Nonpoint Sources (BASINS) system was developed by the U.S. Environmental Protection Agency to facilitate developing total maximum daily loads (TMDLs). The Soil Water Assessment Tool (SWAT) is one of the watershed-scale simulation models within BASINS. Because of the critical nature of the TMDL process, it is imperative that BASINS and SWAT be adequately validated for regions on which they are being applied. BASINS and SWAT were tested using six years of hydrologic data from a 22 km2 subwatershed of the Little River in Georgia. Comparisons were made between water balance results obtained using high and low spatial resolution data as well as those obtained using default initial parameters versus those modified for existing groundwater conditions. In general, all scenarios simulated general trends in the observed flow data. However, for the years with lower precipitation, the total water yields simulated with the low spatial resolution data and the default initial conditions were overpredicted by up to 27% of the annual precipitation input. Total water yields simulated using the high spatial resolution input data were within 20% of the observed yields for each year of the assessment. Nash-Sutcliffe model efficiencies (E) for monthly total water yields were 0.80 using the high spatial resolution data with the modified initial conditions and 0.64 using the low spatial resolution data with the default initial conditions. While the model simulated general streamflow trends, discrepancies were observed between observed and simulated hydrograph peaks, time to peak, and hydrograph durations. A one-day time lag between the simulated and observed time to peak was the primary cause of large errors in daily flow simulations. Model modification and more extensive calibration may be necessary to increase the accuracy of the daily flow estimates for TMDL development.

Advantages and drawbacks of NOAA-AVHRR and SPOT-VGT for burnt area mapping in a tropical savanna ecosystem

In this paper we compare and contrast the performances of two low spatial resolution Earth observing systems, National Oceanographic and Atmospheric Administration ‐ advanced very high resolution radiometer (NOAA‐AVHRR) and Système pour l'Observation de la Terre ‐ VEGETATION (SPOT‐VGT), for burnt area mapping in a tropical savanna ecosystem of northern Australia. A multi-temporal composite image was derived from each of the two data sets acquired at the beginning of the 1999 fire season, and a set of rules for extracting burnt areas was compiled using a classification and regression trees (CART) methodology. In the case of the VGT data, the rules were used to segment the entire composite image; for the AVHRR data, a seed-growing approach was tested, with the aim of reducing overestimation of burnt areas. Since burnt areas are often confused with other land cover types, active fires were first identified (the seeds), and then the mapping of burnt areas was restricted to the neighbourhood of the pixels labelled as active fires. For the accuracy assessment, a reference map was derived by visual interpretation of two Landsat thematic mapper (TM) frames. First, the proportions of burnt areas were calculated for 14 × 14 km cells on a regular grid overlaid on AVHRR, VGT, and TM maps and the low-resolution areas were regressed against the TM estimates. Second, the spatial agreement between burnt area maps derived using high and low spatial resolution data was assessed using confusion matrices. We conclude that NOAA‐AVHRR and SPOT‐VGT show complementary characteristics. The better geometry of the VGT sensor, compared to the AVHRR, allows a higher accuracy in burnt area estimation, but the lack of a thermal channel on VGT is certainly a drawback for both the compositing and the burnt area mapping processes. Both systems tend to overestimate burnt areas when these areas are very fragmented but clearly underestimate when the burn scars are too small to be detected. Lastly, the seed-growing method is promising and could form the basis for a combined use of NOAA-AVHRR and SPOT-VGT systems.

Volcano detection and monitoring using AVHRR data: the Krafla eruption, 1984

Abstract Abstract. Many volcanic eruptions go essentially unmonitored. Potentially the Advanced Very High Resolution Radiometer (AVHRR), with its global coverage, frequent return period, and sensitivity in the thermal infrared, represents a data source capable of monitoring surface volcanic activity unrecorded by ground observations or other satellite sensors. In this study an attempt is made to demonstrate this potential by extracting information for the 1984 eruption at Krafla, Iceland. Seven cloud-free AVHRR images were available for the 14 day period of eruptive activity. The surface activity was detectable as a major thermal anomaly in all three of the longer wavelength channels and was vigorous enough during one night-time pass to be detectable in the near-infrared channel (0.725-1.1μm). Channel 2 and 4 radiance data were used to calculate the size and temperature of sub-pixel heat sources within the lava flow field, and a heat source at 1050° C was estimated as occupying an area of approximately 240000 m2, which was distributed across 20 pixels. Detection and measurement of volcanic heat sources at such short wavelengths using low spatial resolution data has rarely been reported before. Field reports and maps were used to guide and confirm the analysis. Digital number variations within the anomaly could be related to various known features of the eruption. To monitor the eruption a weighted average method was derived and used to sharpen up the images, and the density sliced sharpened images enabled the development of the eruption to be mapped. Results compared well with field reports, suggesting that AVHRR and similar systems could be a useful source of data for monitoring eruptions where contemporaneous field observations are unavailable or incomplete.

Characteristics of composited AVHRR data and problems in their classification

Abstract Unsupervised classification procedures were applied to a temporal sequence of fifteen bi-weekly composited NDVI images produced from AVHRR LAC data. Individual examination of the input images appeared to show substantial contamination due to clouds which persist through the compositing period. These apparent cloud features dominated the results of the clustering procedures. The composites also include large numbers of far off-nadir pixels. This causes severe spatial smoothing and produces a blurred image appearance. Further combining the data to monthly composites largely eliminated the cloud cover problem, but did not necessarily reduce the frequency of large view zenith angles. Preprocessing of high temporal frequency; low spatial resolution data such as that provided by AVHRR and the planned EOS MODIS instrument must more effectively remove the effects of clouds, correct for anisotropic scattering from the atmosphere and bi-directional reflectance from the surface, and should be biased towards the selection of near-nadir measurements. The design and processing procedures for MODIS will reduce problems associated with atmospheric effects and the geometric distortion of pixels, while enhancing the detection and screening of clouds.

Comparison of multi-temporal NOAA-AVHRR and SPOT-XS satellite data for mapping land-cover dynamics in the west African Sahel

Multi-resolution and multi-temporal remote sensing data (SPOT-XS and AVHRR) were evaluated for mapping local land-cover dynamics in the Sahel of West Africa. The aim of this research was to evaluate the agricultural information that could be derived from both high and low spatial resolution data in areas where there is very often limited ground information. A combination of raster-based image processing and vector-based geographical information system mapping was found to be effective for understanding both spatial and spectral land-cover dynamics. The SPOT data proved useful for mapping local land-cover classes in a dominantly recessive agricultural region. The AVHRR-LAC data could be used to map the dynamics of riparian vegetation, but not the changes associated with recession agriculture. In areas where there was a complex mixture of recession and irrigated agriculture, as well as riparian vegetation, the AVHRR data did not provide an accurate temporal assessment of vegetation dynamics.