Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images

Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. However, the resulting super-resolution land-cover maps often have uncertainty as no information about sub-pixel land-cover patterns within the low-resolution pixels is used in the model. Accuracy can be improved by incorporating supplemental datasets to provide more land-cover information at the sub-pixel scale; but the effectiveness of this is limited by the availability and quality of these additional datasets. In this paper, a novel super-resolution land-cover mapping technology is proposed, which uses multiple sub-pixel shifted remotely sensed images taken by observation satellites. These satellites take images over the same area once every several days, but the images are not identical because of slight orbit translations. Low-resolution pixels in these remotely sensed images therefore contain different land-cover fractions that can provide useful information for super-resolution land-cover mapping. We have constructed a Hopfield Neural Network (HNN) model to solve it. Maximum spatial dependence is the goal of the proposed model, and the fraction maps of all images are constraints added to the energy function of HNN. The model was applied to synthetic artificial images as well as to a real degraded QuickBird image. The output maps derived from different numbers of images at different zoom factors were compared visually and quantitatively to the super-resolution map generated from a single image. The resulting land-cover maps with multiple remotely sensed images were more accurate than was the single image map. The use of multiple remotely sensed images is therefore a promising method for decreasing the uncertainty of super-resolution land-cover mapping. Moreover, remotely sensed images with similar spatial resolution from different satellite platforms can be used together, allowing a fusion of information obtained from remotely sensed imagery.     

基于各向异性Markov随机场的遥感影像亚像元尺度建筑物提取

基于遥感影像的建筑物自动提取方法容易受混合像元影响,目标提取精度不高。亚像元定位可以提取亚像元尺度地物分布信息,减轻混合像元对目标提取结果造成的影响。传统亚像元定位模型采用各向同性邻域描述地物的空间相关性,并没有考虑地物特有的形状信息,难以满足建筑物提取的需要。在考虑建筑物光谱特征的基础上,建立了平行与垂直于目标建筑物主方向的各向异性邻域,并采用基于各向异性Markov随机场的亚像元定位模型进行了亚像元尺度的建筑物提取。基于QuickBird多光谱数据与AVIRIS高光谱数据的实验结果表明,该模型提取的建筑物不仅具有更高的空间分辨率,而且能够较好地保持建筑物边缘与角点的形状信息,是一种有效的亚像元尺度建筑物提取方法。     

Application of remote sensing in estimating maize grain yield in heterogeneous African agricultural landscapes: a review

Maize (Zea mays L.) is the second most commonly grown crop worldwide and number one staple food in Africa where it accounts for more than 50% of the energy requirements. However, despite its widespread cultivation and the significance of maize information in Africa, maize crop maps and yield forecasts are hardly available. Yet, systematic area, spatial distribution, and maize yield estimates are important in understanding and addressing food security in Africa. Objective monitoring of maize yield statisics in a systematic way is possible with remotely sensed data. However, absence of maize yield forecasts using remote sensing in Africa has been attributed to the cost of acquiring satellite imagery and the heterogeneity of agricultural landscapes. The recent advances in sensors technology and availability of free high-resolution (spatial and temporal) multispectral satellite images afford an opportunity to forecast maize yield as well as mapping its spatial distribution in near real-time basis. This review gives an overview of maize yield estimation using remotely sensed information and its potential application in a fragmented and highly granular agricultural landscapes in Africa, including inherent challenges and research needs. The review was motivated by challenges faced by researchers and national agricultural statistical services agents when forecasting maize yield using conventional ground-based survey methods. These problems include, but are not limited to, restricted accuracy, and cost and time spent resulting in missed opportunities in food security early warning systems and proper developmental interventions. We conclude that by picking multispectral sensors with high spatial, temporal, and spectral resolution, as well as appropriate classification techniques and accurate ground-truthing data, remote sensing can be a practical option for estimating maize grain yield and its spatio-temporal dynamics in heterogeneous African agricultural landscapes for designing appropriate developmental interventions and technological out scaling.     

Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data

Accurate mapping of land-cover diversity within riparian areas at a regional scale is a major challenge for better understanding the influence of riparian landscapes and related natural and anthropogenic pressures on river ecological status. As the structure (composition and spatial organization) of riparian area land cover (RALC) is generally not accessible using moderate-scale satellite imagery, finer spatial resolution imagery and specific mapping techniques are needed. For this purpose, we developed a classification procedure based on a specific multiscale object-based image analysis (OBIA) scheme dedicated to producing fine-scale and reliable RALC maps in different geographical contexts (relief, climate and geology). This OBIA scheme combines information from very high spatial resolution multispectral imagery (satellite or airborne) and available spatial thematic data using fuzzy expert knowledge classification rules. It was tested over the Hérault River watershed (southern France), which presents contrasting landscapes and a total stream length of 1150 km, using the combination of SPOT (Système Probatoire d'Observation de la Terre) 5 XS imagery (10 m pixels), aerial photography (0.5 m pixels) and several national spatial thematic data. A RALC map was produced (22 classes) with an overall accuracy of 89% and a kappa index of 83%, according to a targeted land-cover pressures typology (six categories of pressures). The results of this experimentation demonstrate that the application of OBIA to multisource spatial data provides an efficient approach for the mapping and monitoring of RALC that can be implemented operationally at a regional or national scale. We further analysed the influence of map resolution on the quantification of riparian spatial indicators to highlight the importance of such data for studying the influence of landscapes on river ecological status at the riparian scale.     

An integrated approach to land cover classification: An example in the Island of Jersey

A land cover map of Jersey was created using remotely sensed images recorded by satellite. This map brought together a range of disparate techniques, developed in isolation and mostly applied experimentally, integrating multisensor, multitemporal, enhanced spatial resolution data within an object-oriented integrated Geographical Information System (GIS) for an applications-driven, operational programme. It was developed under the Classification of Environment with Vector- and Raster-Mapping (CLEVER-Mapping) project: this improved approach to operational land cover mapping used information on the subdivision of the landscape into land parcels to help classify remotely sensed images on a per-parcel basis. The object-oriented approach allowed the use of remotely sensed information which relates directly to ground features, and the application of improved knowledge-based corrections using a range of external data. Unlike a conventional map, the parcel-based approach produced a GIS database containing classified land parcels which could also be used as a storage framework and analysis tool for other datasets in later analyses. The GIS recorded 21 land cover types. Validation against reference land parcel data gave a correspondence of between 85% and 95% depending on the level of class aggregation.     

Regional mapping of human settlements in southeastern China with multisensor remotely sensed data

Mapping human settlements from remotely sensed data at regional and global scales has attracted increasingly attention but remains a challenge. The thresholding technique is a common approach for settlement mapping based on the DMSP-OLS data. However, this approach often omits the areas with small proportional settlements such as towns and villages and overestimates urban extents, resulting in information loss of spatial patterns. This paper explored an integrated approach based on a combined use of multiple remotely sensed data to map settlements in southeastern China. Human settlements for selected sites were mapped from Landsat ETM+ images with a hybrid approach and they were used as reference data. The DMSP-OLS and Terra MODIS NDVI data were combined to develop a settlement index image. This index image was used to map a pixel-based settlement image with expert rules. A regression model was established to estimate fractional settlements at the regional scale, which the DMSP-OLS and MODIS NDVI data were used as independent variables and the settlement data derived from ETM+ images were used as a dependent variable. This research indicated that a combination of DMSP-OLS and NDVI variables provided a better estimation performance than single DMSP-OLS or NDVI variable, and the integrated approach for settlement mapping at the regional scale was promising. Compared to the results from the traditional thresholding technique, the estimated fractional settlement image in this paper greatly improved the spatial patterns of settlement distribution and accuracy of settlement areas. This paper provided a rapid and accurate approach to estimate fractional settlements from coarse spatial resolution images at the regional scale by combining a limited number of medium spatial resolution images. This research is especially valuable for timely updating settlement databases at regional and global scales with limited time, labor, and cost.     

Parallel spatiotemporal autocorrelation and visualization system for large-scale remotely sensed images

Current studies on large-scale remotely sensed images are of great national importance for monitoring and evaluating global climate and ecological changes. In particular, real time distributed high-performance visualization and computation have become indispensable research components in facilitating the extraction of remotely sensed image textures to enable mining spatiotemporal patterns and dynamics of landscapes from massive geo-digital information collected from satellites. Remotely sensed images are usually highly correlated with rich landscape features. By exploiting the structures of these images and extracting their textures, fundamental insights of the landscape can be derived. Furthermore, the interdisciplinary collaboration on the remotely sensed image analysis demands multifarious expertise in a wide spectrum of fields including geography, computer science, and engineering.     

Selecting the spatial resolution of airborne MSS imagery for small-scale agricultural mapping

The spatial resolution determines the number of data and amount of information in a remotely sensed image of a given scene. The 'optimal' spatial resolution may be defined as that which maximizes the information per pixel, and this maximum is realized when the semivariance at a lag of one pixel (the average squared difference between neighbouring pixels) is maximized. For mapping, a spatial resolution should be chosen that is much finer than the 'optimal' spatial resolution as defined above. Airborne MSS images in both red and near-infrared wavelengths for three different dates and two sites were investigated to determine a spatial resolution suitable for mapping spatial variation in agricultural fields in the U.K. The spatial resolution most appropriate for mapping the spatial variation in the images was between 0.5 m and 3 m.     

Waterline mapping at the subpixel scale from remote sensing imagery with high‐resolution digital elevation models

Subpixel mapping technology is a promising method of increasing the spatial resolution of the classification results derived from remote sensing imagery. However, for waterline mapping problems, the traditional spatial dependence principle of subpixel mapping is not suitable as the water flow is always controlled by the topography. This letter presents a novel algorithm based on a high spatial resolution digital elevation model (DEM) to address the subpixel waterline mapping problem. The waterline was mapped at the subpixel scale with a proposed rule according to the physical features of the water flow and additional information provided by the DEM. The method was evaluated with degraded real remotely sensed imagery at different spatial resolutions. The results show that the proposed method can provide more accurate classifications than the traditional subpixel mapping method. Moreover, the fine spatial resolution DEM can be used as feasible supplementary data for subpixel waterline mapping from coarser spatial resolution imagery.     

Spatially adaptive smoothing parameter selection for Markov random field based sub-pixel mapping of remotely sensed images

Sub-pixel mapping is a process to provide the spatial distributions of land cover classes with finer spatial resolution than the size of a remotely sensed image pixel. Traditional Markov random field-based sub-pixel mapping (MRF_SPM) adopts a fixed smoothing parameter estimated based on the entire image to balance the spatial and spectral energies. However, the spectra of the remotely sensed pixels are always spatially variable. Adopting a fixed smoothing parameter disregards the local properties provided by each pixel spectrum, and may probably lead to insufficient smoothing in the homogeneous region and over-smoothing between class boundaries simultaneously. This article proposes a spatially adaptive parameter selection method for the MRF_SPM model to overcome the limitation of the fixed parameter. As pixel class proportions are indicators of the type and proportion of land cover classes within each coarse pixel, in the proposed method, fraction images providing pixel class proportions as local properties of each pixel spectrum are employed to constrain the smoothing parameter. Consequently, the smoothing parameter is spatially adaptive to each pixel spectrum of the remotely sensed image. Synthetic images and IKONOS multi-spectral images were employed. Results showed that compared with the hard classification method and the non-spatially adaptive MRF_SPM adopting a fixed smoothing parameter, the spatially adaptive MRF_SPM with the smoothing parameter constrained to each pixel spectrum yielded sub-pixel maps not only with higher accuracy but also with shapes and boundaries visually reconstructed more closely to the reference map.     

Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors

There is considerable interest in using remote sensing to characterize the hydrologic behavior of the land surface on a routine basis. Information on moisture fluxes between the surface and lower atmosphere reveals linkages and land-atmosphere feedback mechanisms, aiding our understanding of energy and water balance cycles. Techniques that combine information on land and atmospheric properties with remotely sensed variables would allow improved prediction for a number of hydrological variables. Over the last few decades, there has been a focus on better determining evapotranspiration and its spatial variability, but for many regions routine prediction is not generally available at a spatial resolution appropriate to the underlying surface heterogeneity. Over agricultural regions, this is particularly critical, since the spatial extent of typical field scales is not regularly resolved within the pixel resolution of satellite sensors. Understanding the role of landscape heterogeneity and its influence on the scaling behavior of surface fluxes as observed by satellite sensors with different spatial resolutions is a critical research need. To attend this task, data from Landsat-ETM (60 m), ASTER (90 m), and MODIS (1020 m) satellite platforms are employed to independently estimate evapotranspiration. The range of the satellite sensor resolutions allows analyses that span scales from (point-scale) in-situ tower measurements to the MODIS kilometer-scale. Evapotranspiration estimates derived at these multiple resolutions were assessed against eddy covariance flux measurements collected during the 2002 Soil Moisture Atmospheric Coupling Experiment (SMACEX) over the Walnut Creek watershed in Iowa. Together, these data allow a comprehensive scale intercomparison of remotely sensed predictions, which include intercomparisons of the evapotranspiration products from the various sensors as well as a statistical analysis for the retrievals at the watershed scale. A high degree of consistency was observed between the retrievals from the higher-resolution satellite platforms (Landsat-ETM and ASTER). The MODIS-based estimates, while unable to discriminate the influence of land surface heterogeneity at the field scale, effectively reproduced the watershed average response, illustrating the utility of this sensor for regional-scale evapotranspiration estimation.     

Sharpened Mapping of Tropical Forest Biophysical Properties from Coarse Spatial Resolution Satellite Sensor Data

Forest biophysical properties are typically estimated and mapped from remotely sensed data through the application of a vegetation index. This generally does not make full use of the information content of the remotely sensed data, using only the data acquired in a limited number of spectral channels, and may provide a relatively crude spatial representation of the biophysical variable of interest. Using imagery acquired by the NOAA AVHRR, it is shown that a standard neural network may use all the spectral channels available in a remotely sensed data set to derive more accurate estimates of the biophysical properties of tropical forests in Ghana than a series of vegetation indices. Additionally, the spatial representation derived can be refined by fusion with finer spatial resolution imagery, achieved with the application of a further neural network.     

基于小波变换的MODIS与ETM数据融合研究

遥感影像融合方法多样，但针对空间分辨率相差十倍、甚至十几倍的不同数据源影像进行融合的研究很有限。有效算法也较少。MODIS影像高光谱数据具有36个相互配准的光谱波段信息，然而其0．25km～1km的低空间分辨率，却限制了其应用潜力。本文基于小波变换的算法思想提出了一种MODIS与Landsat ETM(空间分辨率30m)数据融合的方法，能够有效的将MODIS的光谱信息和ETM的空间几何信息结合起来，并在此基础上分析了地形阴影对融合的影响，为MODIS数据用于制作较大比例尺的土地利用现状图等提供了可能。     

Super‐resolution mapping of the waterline from remotely sensed data

Methods for mapping the waterline at a subpixel scale from a soft image classification of remotely sensed data are evaluated. Unlike approaches based on hard classification, these methods allow the waterline to run through rather than between image pixels and so have the potential to derive accurate and realistic representations of the waterline from imagery with relatively large pixels. The most accurate predictions of waterline location were made from a geostatistical approach applied to the output of a soft classification (RMSE = 2.25 m) which satisfied the standards for mapping at 1 : 5000 scale from imagery with a 20 m spatial resolution.     

Blending MODIS and Landsat images for urban flood mapping

Satellite images provide important data sources for monitoring flood disasters. However, the trade-off between spatial and temporal resolutions of current satellite sensors limits their uses in urban flooding studies. This study applied and compared two data fusion models, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), in generating synthetic flooding images with improved temporal and spatial resolution for flood mapping. The synthetic images are produced in two scenarios: (1) for real-time prediction based on Landsat and MODIS images acquired before the investigated flooding; and (2) for post-disaster prediction based on images acquired after the flooding. The 2005 Hurricane Katrina in New Orleans was selected as a case study. The result shows that the Landsat-like images generated can be successfully applied in flood mapping. Particularly, ESTARFM surpasses STARFM in predicting surface reflectance in both real-time and post-flooding predictions. However, the flood mapping results from the Landsat-like images produced by both STARFM and ESTARFM are similar with overall accuracy around 0.9. Only for the flooding maps of real-time predictions does ESTARFM get a slightly higher overall accuracy than STARFM, indicating that the lower quality of the Landsat-like image generated by STARFM may not affect flood mapping accuracy, due to the marked contrast between land and water. This study suggests great potential of both STARFM and ESTARFM in urban flooding research. Blending multi-sources images could also support other disaster studies that require remotely sensed data with both high spatial and temporal resolution.     

Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America

While mapping vegetation and land cover using remotely sensed data has a rich history of application at local scales, it is only recently that the capability has evolved to allow the application of classification models at regional, continental and global scales. The development of a comprehensive training, testing and validation site network for the globe to support supervised and unsupervised classification models is fraught with problems imposed by scale, bioclimatic representativeness of the sites, availability of ancillary map and high spatial resolution remote sensing data, landscape heterogeneity, and vegetation variability. The System for Terrestrial Ecosystem Parameterization (STEP) - a model for characterizing site biophysical, vegetation and landscape parameters to be used for algorithm training and testing and validation - has been developed to support supervised land cover mapping. This system was applied in Central America using two classification systems based on 428 sites. The results indicate that: (1) it is possible to generate site data efficiently at the regional scale; (2) implementation of a supervised model using artificial neural network and decision tree classification algorithms is feasible at the regional level with classification accuracies of 75-88%; and (3) the STEP site parameter model is effective for generating multiple classification systems and thus supporting the development of global surface biophysical parameters.     

Mapping vegetation community types in a highly disturbed landscape: integrating hierarchical object-based image analysis with lidar-derived canopy height data

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island (SCI), California, we test the effectiveness of incorporating a hierarchical object-based image analysis (OBIA) approach with high-spatial resolution imagery and canopy height surfaces derived from light detection and ranging (lidar) data for mapping vegetation communities. The hierarchical approach entailed segmentation and classification of fine-scale patches of vegetation growth forms and bare ground, with shrub species identified, and a coarser-scale segmentation and classification to generate vegetation community maps. Such maps were generated for two areas of interest on SCI, with and without vegetation canopy height data as input, primarily to determine the effectiveness of such structural data on mapping accuracy. Overall accuracy is highest for the vegetation community map derived by integrating airborne visible and near-infrared imagery having very high spatial resolution with the lidar-derived canopy height data. The results demonstrate the utility of the hierarchical OBIA approach for mapping vegetation with very high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accurately mapping vegetation communities within highly disturbed landscapes.     

一种单时相高分辨率遥感影像时空融合算法

受制于传感器本身材料及卫星轨道参数,空间分辨率和时间分辨率是卫星遥感传感器固有的性能指标且难以兼备,使得高空间分辨率卫星的多时相数据合成问题至今仍是制约其广泛应用的关键问题之一。由于可有效综合空间-光谱-时间维的影像信息,多源遥感影像时空融合技术在近十年间得到迅速发展并已成为解决多时相数据合成问题的有力手段,其中基于学习的时空融合策略在合成精度上具有显著优势且应用潜力较高,但因其对字典训练过程的依赖程度较高而在融合过程中存在一定的不确定性。为提高基于学习的时空融合策略的预测精度、运算效率及鲁棒性,通过综合基于辐射归化的大气校正方法、基于误差约束的数据标准化转换机制、自适应多层递进融合策略以及高效的稀疏求解函数库,设计了一种适用于单时相高分辨率遥感影像的时空融合框架,并以国产高分二号卫星与Landsat-8卫星遥感影像为实验数据对该方法进行充分的对比性分析。实验结果表明,该融合框架不仅提升了运算效率,还在影像保真度、纹理特征描述以及光谱一致性等方面比当前的单数据对融合方法具有更好的重构质量。     

Temporal scale and spatial resolution effects on Amazon forest fragmentation assessment in Rondônia

The Amazon has been under an intense deforestation process for the last 30 years, causing landscape fragmentation in many different regions and at distinct stages. The fragmentation process is commonly assessed by land‐use maps derived from satellite sensor data and analysed at a landscape context. The analysis of fragmentation depends on an adequate choice of spatial resolution of land‐use maps, and temporal scale in landscape dynamics studies. In this study, spatial–temporal resolution variation effects on fragmentation assessment were analysed in the Quatro Cachoeiras watershed, located at central Rondônia, Brazilian Amazon. Land‐use maps derived from 1984 to 2002 satellite sensor data at 2‐year intervals were used for landscape structure analysis on 12 samples randomly distributed along the watershed. In the spatial resolution variation analysis, landscape metrics obtained at 30 m resolution were compared with those obtained at coarser spatial resolutions. Effects of temporal scale variation were tested by comparison of landscape metrics calculated at 2‐, 4‐ and 6‐year intervals in the studied period. Results show that fragmentation stage influences sensitivity of landscape metrics for spatial resolution and at initial stages of fragmentation finer spatial resolution is required. Also, coarser resolutions up to 100 m could be used to assess landscape fragmentation at regions and the adequate time interval for landscape dynamics studies should be between 3 and 4 years.