Fine Spatial Resolution Images Research Articles

Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps

Studies of land cover dynamics would benefit greatly from the generation of land cover maps at both fine spatial and temporal resolutions. Fine spatial resolution images are usually acquired relatively infrequently, whereas coarse spatial resolution images may be acquired with a high repetition rate but may not capture the spatial detail of the land cover mosaic of the region of interest. Traditional image spatial–temporal fusion methods focus on the blending of pixel spectra reflectance values and do not directly provide land cover maps or information on land cover dynamics. In this research, a novel Spatial–Temporal remotely sensed Images and land cover Maps Fusion Model (STIMFM) is proposed to produce land cover maps at both fine spatial and temporal resolutions using a series of coarse spatial resolution images together with a few fine spatial resolution land cover maps that pre- and post-date the series of coarse spatial resolution images. STIMFM integrates both the spatial and temporal dependences of fine spatial resolution pixels and outputs a series of fine spatial–temporal resolution land cover maps instead of reflectance images, which can be used directly for studies of land cover dynamics. Here, three experiments based on simulated and real remotely sensed images were undertaken to evaluate the STIMFM for studies of land cover change. These experiments included comparative assessment of methods based on single-date image such as the super-resolution approaches (e.g., pixel swapping-based super-resolution mapping) and the state-of-the-art spatial–temporal fusion approach that used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Flexible Spatiotemporal DAta Fusion model (FSDAF) to predict the fine-resolution images, in which the maximum likelihood classifier and the automated land cover updating approach based on integrated change detection and classification method were then applied to generate the fine-resolution land cover maps. Results show that the methods based on single-date image failed to predict the pixels of changed and unchanged land cover with high accuracy. The land cover maps that were obtained by classification of the reflectance images outputted from ESTARFM and FSDAF contained substantial misclassification, and the classification accuracy was lower for pixels of changed land cover than for pixels of unchanged land cover. In addition, STIMFM predicted fine spatial–temporal resolution land cover maps from a series of Landsat images and a few Google Earth images, to which ESTARFM and FSDAF that require correlation in reflectance bands in coarse and fine images cannot be applied. Notably, STIMFM generated higher accuracy for pixels of both changed and unchanged land cover in comparison with other methods.

Soft-then-hard super-resolution mapping based on a spatial attraction model with multiscale sub-pixel shifted images

ABSTRACTSuper-resolution mapping (SRM) is a technique for exploring spatial distribution information of the land-cover classes at finer spatial resolution. The soft-then-hard super-resolution mapping (STHSRM) algorithm is a type of SRM algorithm that first estimates the soft class values for sub-pixels at the target fine spatial resolution and then predicts the hard class labels for sub-pixels. The sub-pixel shifted images from the same area can be incorporated to improve the accuracy of STHSRM algorithm. In this article, multiscale sub-pixel shifted images (MSSI) based on the fine-scale model and the coarse-scale model are utilized to increase the accuracy of STHSRM. First, class fraction images are derived from multiple sub-pixel shifted coarse spatial resolution images by soft classification. Then using the sub-pixel/sub-pixel spatial attraction model as fine-scale and the sub-pixel/pixel spatial attraction model as coarse scale, all MSSI can be derived from fraction images. The MSSI for each class are then integrated to obtain the desired fine spatial resolution images. Finally, the integrated fine spatial resolution images are used to allocate classes for sub-pixel. Experiments on two synthetic remote sensing images and a real hyperspectral remote sensing imagery show that the proposed method produces higher mapping accuracy result.

Wetland mapping by fusing fine spatial and hyperspectral resolution images

Despite efforts and progress have been made in wetland mapping using multi-source remotely sensed data, a fine spatial and spectral resolution dynamic modeling of wetland coverage is limited. This research proposed a fusion model to generate fine-spatial-spectral-resolution images by blending multispectral images with fine spatial resolution and hyperspectral images with coarse spatial resolution. Applying the China Environment 1A series satellite (HJ-1A) CCD/HSI data, we showed that the proposed model produced reliable dataset that was not only able to capture spectral fidelity, but also could preserve spatial details. By integrating both fine spatial details and hyperspectral signatures, we further conducted a guided filtering based spectral-spatial mapping on the Poyang Lake wetland. Compared with the classification result of the CCD image, a significant higher classification accuracy of the synthetic fused image was achieved. Results also showed that the final guided-filtering based mapping result could remove potential misclassification biases and achieve higher accuracy than previous pixelwise classification methods Our study indicated a straightforward approach to blend multi-source remotely sensed data to generate reliable, high-quality dynamic dataset for wetland mapping and ecological modelling. The synthetic combination of spatial and hyperspectral details could improve our understanding of the significance of wetland ecosystem.

Spatiotemporal Data Fusion Using Temporal High-Pass Modulation and Edge Primitives

Most spaceborne sensors have a tradeoff between high spatial and high temporal resolutions. This tradeoff limits the use of remote sensing data in various applications that require images in both the high spatial and high temporal resolutions. In this paper, we propose a novel technique to create a fine spatial and high temporal resolution images at a ground-based data processing system. Resourcesat-2 is one of the Indian Space Research Organization missions, and it carries the Linear Imaging Self-Scanning sensors (LISS III and LISS IV) and an Advanced Wide-Field Sensor (AWiFS) onboard. The spatial resolution of LISS III is 23.5 m, and that of AWiFS is 56 m. The temporal resolution of LISS III is 24 days, and that of AWiFS is five days. The proposed method creates a synthetic LISS-III image at 23.5-m spatial and five-day temporal resolutions. It is based on the subpixel relationship between a single AWiFS–LISS-III image pair, which is acquired before or after the prediction date. In temporal data composition, spurious spatial discontinuities are inevitable for land-cover type changes. These discontinuities were identified with temporal edge primitives and were smoothed with a spatial-profile-averaging method. A synthetic LISS-III image for time $t_{k}$ is predicted from an AWiFS image at time $t_{k}$ and a single AWiFS–LISS-III image pair at time $t_{0}$ , where $t_{0}\ne t_{k}$ . Experimental results demonstrated that the proposed method is superior in terms of the computational efficiency and prediction accuracy with the other existing methods.

Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery

Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases emissions, food and water security, and human health. Rice paddy field maps were developed using optical images with high temporal resolution and coarse spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) or low temporal resolution and high spatial resolution (e.g., Landsat TM/ETM+). In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. In this paper, time series MODIS and Landsat ETM+/OLI images, and the pixel- and phenology-based algorithm are used to map paddy rice planting area. The unique physical features of rice paddy fields during the flooding/open-canopy period are captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. The algorithm is tested in the Sanjiang Plain (path/row 114/27) in China in 2013. The overall accuracy of the resulted map of paddy rice planting area generated by both Landsat ETM+ and OLI is 97.3%, when evaluated with areas of interest (AOIs) derived from geo-referenced field photos. The paddy rice planting area map also agrees reasonably well with the official statistics at the level of state farms (R2=0.94). These results demonstrate that the combination of fine spatial resolution images and the phenology-based algorithm can provide a simple, robust, and automated approach to map the distribution of paddy rice agriculture in a year.

Enhancing the spatial resolution of satellite-derived land surface temperature mapping for urban areas

Land surface temperature (LST) is an important environmental variable for urban studies such as those focused on the urban heat island (UHI). Though satellite-derived LST could be a useful complement to traditional LST data sources, the spatial resolution of the thermal sensors limits the utility of remotely sensed thermal data. Here, a thermal sharpening technique is proposed which could enhance the spatial resolution of satellite-derived LST based on super-resolution mapping (SRM) and super-resolution reconstruction (SRR). This method overcomes the limitation of traditional thermal image sharpeners that require fine spatial resolution images for resolution enhancement. Furthermore, environmental studies such as UHI modelling typically use statistical methods which require the input variables to be independent, which means the input LST and other indices should be uncorrelated. The proposed Super-Resolution Thermal Sharpener (SRTS) does not rely on any surface index, ensuring the independence of the derived LST to be as independent as possible from the other variables that UHI modelling often requires. To validate the SRTS, its performance is compared against that of four popular thermal sharpeners: the thermal sharpening algorithm (TsHARP), adjusted stratified stepwise regression method (Stepwise), pixel block intensity modulation (PBIM), and emissivity modulation (EM). The privilege of using the combination of SRR and SRM was also verified by comparing the accuracy of SRTS with sharpening process only based on SRM or SRR. The results show that the SRTS can enhance the spatial resolution of LST with a magnitude of accuracy that is equal or even superior to other thermal sharpeners, even without requiring fine spatial resolution input. This shows the potential of SRTS for application in conditions where only limited meteorological data sources are available yet where fine spatial resolution LST is desirable.

Comparison of spatial sampling strategies for ground sampling and validation of MODIS LAI products

The development of an efficient ground sampling strategy is critical to assess uncertainties associated with moderate- or coarse-resolution remote-sensing products. This work presents a comparison of estimating spatial means from fine spatial resolution images using spatial random sampling (SRS), Block Kriging (BK), and Means of Surface with Nonhomogeneity (MSN) at 1 km2 spatial scale. Towards this goal, we focus on the sampling strategies for ground data measurements and provide an assessment of the MODIS LAI product validated by the spatial means estimated by the above-mentioned three methods. The results of this study indicate that: (1) for its effective stratification strategies and its criteria of minimum mean square estimation error, MSN demonstrates the lowest mean squared estimation error for estimating the means of stratified nonhomogeneous surface; (2) BK is efficient in estimating the means of homogeneous surfaces without bias and with minimum mean squared estimation errors. The MODIS LAI product is assessed using the means estimated by SRS, BK, and MSN based on Landsat 8 OLI and SPOT HRV fine-resolution LAI maps. For heterogeneous surfaces, MSN results in low RMSE and high accuracy of MODIS LAI product compared with BK and SRS, whereas for homogeneous surfaces, the statistical parameters outputted by these three methods are similar. These results reveal that MSN is an effective method for estimating the spatial means for heterogeneous surfaces. There are differences in the accuracies of MODIS LAI product assessed by these three methods.

Spatio-temporal reflectance fusion via unmixing: accounting for both phenological and land-cover changes

Owing to technical limitations the acquisition of fine spatial resolution images (e.g. Landsat data) with frequent (e.g. daily) coverage remains a challenge. One approach is to generate frequent Landsat surface reflectances through blending with coarse spatial resolution images (e.g. Moderate Resolution Imaging Spectroradiometer, MODIS). Existing implementations for data blending, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced STARFM (ESTARFM), have their shortcomings, particularly in predicting the surface reflectance characterized by land-cover-type changes. This article proposes a novel blending model, namely the Unmixing-based Spatio-Temporal Reflectance Fusion Model (U-STFM), to estimate the reflectance change trend without reference to the change type, i.e. phenological change (e.g. seasonal change in vegetation) or land-cover change (e.g. conversion of a vegetated area to a built-up area). It is based on homogeneous change regions (HCRs) that are delineated by segmenting the Landsat reflectance difference images. The proposed model was tested on both simulated and actual data sets featuring phenological and land-cover changes. It proved more capable of capturing both types of change compared to STARFM and ESTARFM. The improvement was particularly observed on those areas characterized by land-cover-type changes. This improved fusion algorithm will thereby open new avenues for the application of spatio-temporal reflectance fusion.

A new source for high spatial resolution night time images — The EROS-B commercial satellite

City lights present one of humankind's most unique footprints on Earth as seen from space. Resulting light pollution from artificial lights obscures the night sky for astronomy and has negative impacts on biodiversity as well as on human health. However, remote sensing studies of night lights to date have been mostly limited to coarse spatial resolution sensors such as the DMSP-OLS. Here we present a new source for high spatial resolution mapping of night lights from space, derived from a commercial satellite. We tasked the Israeli EROS-B satellite to acquire two night-time light images (at a spatial resolution of 1m) of Brisbane, Australia, and analyzed their radiometric quality and content with respect to land cover and land use. The spatial distribution of night lights as imaged by EROS-B corresponded with night-time images acquired by an airborne camera, although EROS-B was not as sensitive to low light levels. Using land cover and land use data at the statistical local area level, we could statistically explain 89% of the variability in night-time lights. Arterial roads and commercial and service areas were found to be some of the brightest land use types. Overall, we found that EROS-B imagery provides fine spatial resolution images of night lights, opening new avenues for studying light pollution in cities worldwide.

Utilizing Multiple Subpixel Shifted Images in Subpixel Mapping With Image Interpolation

In this letter, multiple subpixel shifted images (MSIs) were utilized to increase the accuracy of subpixel mapping (SPM), based on the fast bilinear and bicubic interpolation. First, each coarse spatial resolution image of MSI is soft classified to obtain class fraction images. Using bilinear or bicubic interpolation, all fraction images of MSI are upsampled to the desired fine spatial resolution. The multiple fine spatial resolution images for each class are then integrated. Finally, the integrated fine spatial resolution images are used to allocate hard class labels to subpixels. Experiments on two remote sensing images showed that, with MSI, both bilinear and bicubic interpolation-based SPMs are more accurate. The new methods are fast and do not need any prior spatial structure information.

DSCOKRI: A library of computer programs for downscaling cokriging in support of remote sensing applications

The main purpose of this paper is to describe and provide a suite of computer programs for performing downscaling cokriging with satellite sensor images. The usual setting of downscaling cokriging is to increase the spatial resolution of multispectral images with coarse spatial resolution by fusing them with a fine spatial resolution image of a different band from the same or a different sensor. This problem has also been described as image sharpening and commonly the spatial resolution of the fused image is equal to the finest spatial resolution used in the fusion. Nevertheless, downscaling cokriging allows the spatial resolution of the predicted image to be increased beyond that of any of the input data sets, a procedure usually referred to as super-resolution sharpening. The programs provided here support all the required stages for image fusion and super-resolution sharpening by downscaling cokriging. These stages are: (i) compute the empirical variogram of each spectral band and empirical cross-variograms for each pair of spectral bands; (ii) estimate and fit a model to the point-support variogram of each spectral band and cross-variogram of each pair of spectral bands; (iii) set up the cokriging system and obtain the set of downscaling cokriging weights and (iv) obtain the sharpened image by application of the weights to the empirical remote sensing images. A case study with Landsat ETM+ images is provided to demonstrate the implementation of the method and to allow checking of the programs.

Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover

Global land cover is one of the essential terrestrial baseline datasets available for ecosystem modeling, however uncertainty remains an issue. Tools such as Google Earth offer enormous potential for land cover validation. With an ever increasing amount of very fine spatial resolution images (up to 50 cm × 50 cm) available on Google Earth, it is becoming possible for every Internet user (including non remote sensing experts) to distinguish land cover features with a high degree of reliability. Such an approach is inexpensive and allows Internet users from any region of the world to get involved in this global validation exercise. The Geo-Wiki Project is a global network of volunteers who wish to help improve the quality of global land cover maps. Since large differences occur between existing global land cover maps, current ecosystem and land-use science lacks crucial accurate data (e.g., to determine the potential of additional agricultural land available to grow crops in Africa), volunteers are asked to review hotspot maps of global land cover disagreement and determine, based on what they actually see in Google Earth and their local knowledge, if the land cover maps are correct or incorrect. Their input is recorded in a database, along with uploaded photos, to be used in the future for the creation of a new and improved hybrid global land cover map.

Modelling the semivariograms and cross-semivariograms required in downscaling cokriging by numerical convolution–deconvolution

A practical problem of interest in remote sensing is to increase the spatial resolution of a coarse spatial resolution image by fusing the information of that image with another fine spatial resolution image (from the same sensor or from sensors on different satellites). Thus, the problem is how to introduce spatial ‘detail’ into a coarse spatial resolution image (decrease the pixel size) such that it is coherent with the spectral information of the image. Cokriging provides a geostatistical solution to the problem and has several interesting advantages: it is a sound statistical method by being unbiased and minimizing a prediction variance (c.f. ad hoc procedures), it takes into account the effect of pixel size, and also autocorrelation in each image as well as the cross-correlation between images, it may be extended to incorporate extra information from other sources and it provides an estimation of the uncertainty of the final predictions. When formulating the cokriging system, semivariograms and cross-semivariograms (or covariances and cross-covariances) appear, some of which cannot be estimated from data directly. Cross-variograms between different variables as well as cross-semivariograms between different supports for the same variable are required. The problem is solved by using linear systems theory in which any variable for any pixel size is seen as the output of a linear system when the input is the same variable on a point support. In remote-sensing applications, the linear system is specified by the point-spread function (or impulse response) of the sensor. Linear systems theory provides the theoretical relations between the different semivariograms and cross-semivariograms. Overall, one must ensure that the whole set of covariances and cross-covariances is positive-definite and models must be estimated for non-observed semivariograms and cross-semivariograms. The models must also be realistic, taking into account, for example, the parabolic behaviour close to the origin presented in regularized semivariograms and cross-semivariograms. The solution proposed is to find by numerical deconvolution a positive-definite set of point covariances and cross-covariances and then any required model may be obtained by numerical convolution of the corresponding point model. The first step implies several numerical deconvolutions where some model parameters are fixed, while others are estimated using the available experimental semivariograms and cross-semivariograms, and some goodness-of-fit measure. The details of the proposed procedure are presented and illustrated with an example from remote sensing.

A technique for automatic concealment of confidential targets in fine spatial resolution imagery

With the commercial availability of very fine spatial resolution imagery, the detailed image information on various kinds of ground facilities has been revealed to the public. In Korea, distribution of very fine spatial resolution images of the national confidential facilities such as military bases and nuclear power plants to the public is regulated by the government. Image distributors should camouflage such national confidential facilities in the images before the distribution. Therefore, the concealment procedure has been one of the most important and costly procedures for the image distributors in Korea. Because the concealment procedure has involved the manual masking of the target objects, it was very difficult to ensure the quality of concealment effect. A spectral clustering based technique for automatic concealment of the confidential targets in fine spatial resolution imagery was proposed in this study. The proposed technique has been implemented as an executable software program and experimented with various kinds of spatial targets in fine spatial resolution imagery. The experimental results showed that the proposed technique can be used as a practical procedure for those who need to hide image information before public distribution.

Identifying the characteristic scale of scene variation in fine spatial resolution imagery with wavelet transform-based sub-image statistics

Understanding the spatial structure of fine spatial resolution images is instrumental for either pixel- or object-based image analysis. In this Letter, the characteristic scale of scene variation in images is evaluated using statistics of sub-images produced by a wavelet transform. Six statistics, namely mean, variance, standard deviation (SD), coefficient of variation, skewness and kurtosis, were calculated for three directional sub-images and their derivative energy signature image at a sequence of wavelet decomposition levels. A simulated image, an aerial AUSIMAGE™ image (spatial resolution 0.2 m) and a recent Système Probatoire de l'Observation de la Terre (SPOT)-5 High Resolution Geometric (HRG) panchromatic image (spatial resolution 2.5 m) were analysed. It was found that with energy signature images, the change rate of SD over spatial resolution ranges between two successive decomposition levels ( ( SD/ ( R) suggested a synoptic and approximate description for the characteristic scale of scene variation. However, by comparing the result with the ranges of geostatistical variograms, it is suggested that the geostatistical method can correctly identify the characteristic scales of scene variation; semivariances can be calculated at any lag and orientation, while standard wavelet transforms are decomposed at only limited spatial resolution and orientation levels.

Monitoring urban land use by Earth observation

This paper discuses the use of high resolution spaceborne Earth observation data in studies of the urban environment. Adopting a rigorous, mathematical approach, it introduces the Spatial Re-classification Kernel (SPARK) algorithm; this is appliedto a suburban area in the south-east of London. It also introduces a region-based, structural pattern recognition approach to the same problem through which urban land use might be inferred from an analysis of the morphological properties and structural relations of discrete land cover regions identified in fine spatial resolution images.

Unmixing multiple land-cover type reflectances from coarse spatial resolution satellite data

We present a technique for extracting subpixel cover type reflectances from the mixed pixels of a coarse spatial resolution image. A Gaussian filter is used to spatially degrade a set of fine spatial resolution images [based on, e.g., Landsat Thematic Mapper (TM)], each of which represent the spatial structure and extent of a land-cover type. From the degraded image data, a set of weights representing the proportions of cover types within the mixed pixels of a coarse spatial/fine temporal resolution image [e.g., NOAA Advanced Very High Resolution Radiometer (AVHRR)] is produced. These weights and the AVHRR spectral band reflectances are used in multiple linear regression analyses to extract mean cover type reflectances. A simulation study was conducted to define the operational limitations of this technique. Cover type reflectances are accurately estimated if the cover types are spatially well represented within the area of interest, are spectrally distinct, and have small within-class spectral variability. The accuracy of retrieved reflectances is most sensitive to errors in the coarse spatial resolution data and least sensitive to errors in the weights. By applying this technique to multiple images, temporal profiles of subpixel cover type reflectance may be obtained. This particular application is demonstrated by using TM and AVHRR data from the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) to create normalized difference vegetation index (NDVI) temporal profiles of the dominant land-cover types.