Continuum-based classification of remotely sensed imagery to describe urban sprawl on a watershed scale

Remote sensed imagery can both describe urban sprawl on a watershed scale and provide essential information for modeling the impact of sprawl on watercourses. This paper looks at six watersheds in greater Cleveland, OH: two urban; two rural; and two undergoing rapid urbanization. Thematic Mapper imagery from 1984, 1988, 1994, to 1999 was classified into functional classes describing each watershed in terms of the position of each pixel along continua of [1] percentage permeability and [2] canopy cover. Because the functional classes represent positions along independent continua rather than thematic land-cover classes, they can easily be compared from image to image, and they provide quantitative estimates of parameters at 30-m resolution suitable for spatial simulation models. The imagery classified in this way makes it possible to observe the progress of urban sprawl both within these watersheds and over a study area which extends from the inner city to its rural surroundings.     

Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data

Urban areas concentrate people, economic activity, and the built environment. As such, urbanization is simultaneously a demographic, economic, and land-use change phenomenon. Historically, the remote sensing community has used optical remote sensing data to map urban areas and the expansion of urban land-cover for individual cities, with little research focused on regional and global scale patterns of urban change. However, recent research indicates that urbanization at regional scales is growing in importance for economics, policy, land use planning, and conservation. Therefore, there is an urgent need to understand and monitor urbanization dynamics at regional and global scales. Here, we illustrate the use of multi-temporal nighttime light (NTL) data from the U.S Air Force Defense Meteorological Satellites Program/Operational Linescan System (DMSP/OLS) to monitor urban change at regional and global scales. We use independently derived data on population, land use and land cover to test the ability of multi-temporal NTL data to measure regional and global urban growth over time. We apply an iterative unsupervised classification method on multi-temporal NTL data from 1992 to 2008 to map urbanization dynamics in India, China, Japan, and the United States. For two-year intervals between 1992 and 2000, India consistently experienced higher rates of urban growth than China, and both countries exceeded the urban growth rates of the United States and Japan. This is not surprising given that the populations of India and China were growing faster than those of the U.S. and Japan during those periods. For two-year intervals between 2000 and 2008, China experienced higher rates of urban growth than India. Results show that the multi-temporal NTL provides a regional and potentially global measure of the spatial and temporal changes in urbanization dynamics for countries at certain levels of GDP and population-driven growth.     

应用混合算法实现卫星图像分割的分析

文章分析了基于应用混合算法实现卫星图像的分割,主要探讨了目前比较成熟的分水岭图像分割方法、基于模糊理论的图像分割方法,和比较前沿的基于神经网络的图像分割方法、基于支持向量机的图像分割方法等算法。文章探讨了根据图像的不同特性,混合使用两种或者多种算法来增加图像分割的准确度。     

A method for detecting large-scale forest cover change using coarse spatial resolution imagery

Many large countries, including Canada, rely on earth observation as a practical and cost-effective means of monitoring their vast inland ecosystems. A potentially efficient approach is one that detects vegetation changes over a hierarchy of spatial scales ranging from coarse to fine. This paper presents a Change Screening Analysis Technique (Change-SAT) designed as a coarse filter to identify the location and timing of large (>5-10 km²) forest cover changes caused by anthropogenic and natural disturbances at an annual, continental scale. The method uses change metrics derived from 1-km multi-temporal SPOT VEGETATION and NOAA AVHRR imagery (reflectance, temperature, and texture information) and ancillary spatial variables (proximity to active fires, roads, and forest tenures) in combination with logistic regression and decision tree classifiers. Major forest changes of interest include wildfires, insect defoliation, forest harvesting, and flooding. Change-SAT was tested for 1998-2000 using an independent sample of change and no-change sites over Canada. Overall accuracy was 94% and commission error, especially critical for large-area change applications, was less than 1%. Regions identified as having major or widespread changes could be targeted for more detailed investigation and mapping using field visits, aerial survey, or fine resolution EO methods, such as those being applied under Canadian monitoring programs. This multi-resolution approach could be used as part of a forest monitoring system to report on carbon stocks and forest stewardship.     

Mapping of riparian zone attributes using discrete return LiDAR, QuickBird and SPOT-5 imagery: Assessing accuracy and costs

Riparian zones in Australia are exposed to increasing pressures because of disturbance from agricultural and urban expansion, weed invasion, and overgrazing. Accurate and cost-effective mapping of riparian environments is important for assessing riparian zone functions associated with water quality, biodiversity, and wildlife habitats. The objective of this research was to compare the accuracy and costs of mapping riparian zone attributes from image data acquired by three different sensor types, i.e. Light Detection and Ranging (LiDAR) (0.5-2.4 m pixels), and multi-spectral QuickBird (2.4 m pixels) and SPOT-5 (10 m pixels). These attributes included streambed width, riparian zone width, plant projective cover, longitudinal continuity, vegetation overhang, and bank stability. The riparian zone attributes were mapped for a study area along Mimosa Creek in the Fitzroy Catchment, Central Queensland, Australia. Object-based image and regression analyses were used for mapping the riparian zone attributes. The validation of the LiDAR, QuickBird, and SPOT-5 derived maps of streambed width (R = 0.99, 0.71, and 0.44 respectively) and riparian zone width (R = 0.91, 0.87, and 0.74 respectively) against field derived measurements produced the highest accuracies for the LiDAR data and the lowest using the SPOT-5 image data. Cross-validation estimates of misclassification produced a root mean square error of 1.06, 1.35 and 1.51 from an ordinal scale from 0 to 4 of the bank stability score for the LiDAR, QuickBird and SPOT-5 image data, respectively. The validation and empirical modelling showed high correlations for all datasets for mapping plant projective cover (R > 0.93). The SPOT-5 image data were unsuitable for assessment of riparian zone attributes at the spatial scale of Mimosa Creek and associated riparian zones. Cost estimates of image and field data acquisition and processing of the LiDAR, QuickBird, and SPOT-5 image data showed that discrete return LiDAR can be used for costs lower than those for QuickBird image data over large spatial extents (e.g. 26,000 km of streams). With the higher level of vegetation structural and landform information, mapping accuracies, geometric precision, and lower overall costs at large spatial extents, LiDAR data are a feasible means for assessment of riparian zone attributes.     

A study of moisture conditions in the Pantanal of Brazil using satellite imagery

The combined use of additive viewing and digital processing of LANDSAT-2 imagery of part of the Pantanal of Brazil has allowed detailed maps of the drainage network to be constructed. The distributions have been made of wet and dry areas, including differentiations of clear water, water containing suspended sediments, and categories of land with differing moisture conditions. Some unconventional use of color filters and MSS band combinations are suggested in order to extract maximum information from the imagery. Density slicing has allowed gray-scale values to be placed on the three categories of land identified. The distribution of the identified categories are verified by comparing the information from the visual classification with the classes isolated by density slicing.     

Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation

A recently proposed method for automatic radiometric normalization of multi- and hyperspectral imagery based on the invariance property of the Multivariate Alteration Detection (MAD) transformation and orthogonal linear regression is extended by using an iterative re-weighting scheme involving no-change probabilities. The procedure is first investigated with partly artificial data and then applied to multitemporal, multispectral satellite imagery. Substantial improvement over the previous method is obtained for scenes which exhibit a high proportion of change.     

Survival analysis of a neotropical rainforest using multitemporal satellite imagery

We present results of an analysis of deforestation at a UNESCO Biosphere Reserve, the Parque National Yasuní, located in the rainforests of eastern Ecuador using multitemporal Landsat TM and ETM+ satellite imagery. Using survival analysis, we assessed both current and future trends in deforestation rates, and investigated the impact of spatial, cultural, and economic factors on deforestation. These factors included the distance from roads, rivers, research facilities, oil facilities, markets and towns, and land ownership by colonists, native inhabitants, and an oil company. We found the annual rate of deforestation is currently only 0.11%, but that this rate is increasing with time and, assuming that the trend of increasing rate of forest loss continues, we would predict that by 2063, 50% of the forest within 2 km of an oil access road will be lost to unhindered colonization and anthropogenic conversion. The Quechua colonists are associated with areas of the highest rate of deforestation, followed by the native Huaorani and the lowest region of deforestation was in areas occupied by a local oil company. By far, the strongest predictor of where deforestation is predicted to occur was proximity to the road. Proximity to research sites, oil facilities, market, and rivers significantly decreases deforestation rates, and proximity to towns significantly increases deforestation rates.     

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

多相活动轮廓模型在卫星云图分割中的应用研究

应用基于水平集的多相活动轮廓模型对云图进行多类别分割,云图被自动分割成几个区域,不同区域就对应着不同的云顶高度,区域分割结果可以使对一幅云图中不同高度云的分布以及哪种类型的云占主体有总体的认识和了解,从而对天气系统的分析具有一定的辅助参考作用。     

A spatial-temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery

Sudden Oak Death is a new and virulent disease affecting hardwood forests in coastal California. The spatial-temporal dynamics of oak mortality at the landscape scale are crucial indicators of disease progression. Modeling disease spread requires accurate mapping of the dynamic pattern of oak mortality in time through multi-temporal image analysis. Traditional mapping approaches using per-pixel, single-date image classifications have not generated consistently satisfactory results. Incorporation of spatial-temporal contextual information can improve these results. In this paper, we propose a spatial-temporally explicit algorithm to classify individual images using the spectral and spatial-temporal information derived from multiple co-registered images. This algorithm is initialized by a spectral classification using Support Vector Machines (SVM) for each individual image. Then, a Markov Random Fields (MRF) model accounting for ecological compatibility is used to model the spatial-temporal contextual prior probabilities of images. Finally, an iterative algorithm, Iterative Conditional Mode (ICM), is used to update the classification based on the combination of the initial SVM spectral classifications and MRF spatial-temporal contextual model. The algorithm was applied to two-year (2000, 2001) ADAR (Airborne Data Acquisition and Registration) images, from which three classes (bare, dead, forest) are detected. The results showed that the proposed algorithm achieved significantly better results (Year 2000: Kappa = 0.92; Year 2001: Kappa = 0.91), compared to traditional pixel-based single-date approaches (Year 2000: Kappa = 0.67; Year 2001: Kappa = 0.66). The improvement from the contributions of spatial-temporal contextual information indicated the importance of spatial-temporal modeling in multi-temporal remote sensing in general and forest disease modeling in particular.     

Background-subtraction using contour-based fusion of thermal and visible imagery

We present a new background-subtraction technique fusing contours from thermal and visible imagery for persistent object detection in urban settings. Statistical background-subtraction in the thermal domain is used to identify the initial regions-of-interest. Color and intensity information are used within these areas to obtain the corresponding regions-of-interest in the visible domain. Within each region, input and background gradient information are combined to form a Contour Saliency Map. The binary contour fragments, obtained from corresponding Contour Saliency Maps, are then fused into a single image. An A^* path-constrained search along watershed boundaries of the regions-of-interest is used to complete and close any broken segments in the fused contour image. Lastly, the contour image is flood-filled to produce silhouettes. Results of our approach are evaluated quantitatively and compared with other low- and high-level fusion techniques using manually segmented data.     

A framework for the segmentation of high-resolution satellite imagery using modified seeded-region growing and region merging

Image segmentation is becoming increasingly important in areas such as object-oriented image classification in the field of remote-sensing image analysis. We present a new approach for the image segmentation of a high-resolution pan-sharpened satellite image based on modified seeded-region growing and region merging. First, we conduct some pre-processing prior to image segmentation to improve segmentation quality. The initial seeds are automatically selected using the proposed block-based seed-selection method. After automatic selection of significant seeds, initial segmentation is achieved by applying the modified seeded-region growing procedure. Finally, region merging, based on a region-adjacency graph, is carried out in post-processing to obtain the final segmentation result. Experimental results demonstrate that the proposed method shows better performance than other approaches, and has good potential for its application to the segmentation of high-resolution satellite imagery.     

A two-step nearest neighbors algorithm using satellite imagery for predicting forest structure within species composition classes

Nearest neighbors techniques have been shown to be useful for predicting multiple forest attributes from forest inventory and Landsat satellite image data. However, in regions lacking good digital land cover information, nearest neighbors selected to predict continuous variables such as tree volume must be selected without regard to relevant categorical variables such as forest/non-forest. The result is that non-zero volume predictions may be obtained for pixels predicted to be non-forest, and volume predictions for pixels predicted to be forest may be erroneously small due to non-forest nearest neighbors. For users who wish to circumvent this discrepancy, a two-step algorithm is proposed in which the class of a relevant categorical variable such as land cover is predicted in the first step, and continuous variables such as volume are predicted in the second step subject to the constraint that all nearest neighbors must come from the predicted class of the categorical variable. Nearest neighbors, multinomial logistic regression, and discriminant analysis techniques were investigated for use in the first step. The results were generally similar for the three techniques, although the multinomial logistic regression technique was slightly superior. The k-Nearest Neighbors technique was used in the second step because many continuous forest inventory variables do not satisfy the distributional assumptions necessary for parametric multivariate techniques. The results for six 15-km × 15-km areas of interest in northern Minnesota, USA, indicate that areal estimates of tree volume, basal area, and density obtained from pixel predictions are comparable to plot-based estimates and estimates by conifer and deciduous classes are also comparable to plot-based estimates. When a mixed conifer/deciduous class was included, predictions for the mixed and deciduous class were confused.     

Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery

Conservation tillage management has been advocated for carbon sequestration and soil quality preservation purposes. Past satellite image analyses have had difficulty in differentiating between no-till (NT) and minimal tillage (MT) conservation classes due to similarities in surface residues, and may have been restricted by the availability of cloud-free satellite imagery. This study hypothesized that the inclusion of high temporal data into the classification process would increase conservation tillage accuracy due to the added likelihood of capturing spectral changes in MT fields following a tillage disturbance. Classification accuracies were evaluated for Random Forest models based on 250-m and 500-m MODIS, 30-m Landsat, and 30-m synthetic reflectance values. Synthetic (30-m) data derived from the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) were evaluated because high frequency Landsat image sets are often unavailable within a cropping season due to cloud issues. Classification results from a five-date Landsat model were substantially better than those reported by previous classification tillage studies, with 94% total and ≥ 88% class producer's accuracies. Landsat-derived models based on individual image scenes (May through August) yielded poor MT classifications, but a monthly increase in accuracy illustrated the importance of temporal sampling for capturing regional tillage disturbance signatures. MODIS-based model accuracies (90% total; ≥ 82% class) were lower than in the five-date Landsat model, but were higher than previous image-based and survey-based tillage classification results. Almost all the STARFM prediction-based models had classification accuracies higher than, or comparable to, the MODIS-based results (> 90% total; ≥ 84% class) but the resulting model accuracies were dependent on the MODIS/Landsat base pairs used to generate the STARFM predictions. Also evident within the STARFM prediction-based models was the ability for high frequency data series to compensate for degraded synthetic spectral values when classifying field-based tillage. The decision to use MODIS or STARFM-based data within conservation tillage analysis is likely situation dependent. A MODIS-based approach requires little data processing and could be more efficient for large-area mapping; however a STARFM-based analysis might be more appropriate in mixed-pixel situations that could potentially compromise classification accuracy.     

Classification of Amazonian primary rain forest vegetation using Landsat ETM+ satellite imagery

A lack of spatially and thematically accurate vegetation maps complicates conservation and management planning, as well as ecological research, in tropical rain forests. Remote sensing has considerable potential to provide such maps, but classification accuracy within primary rain forests has generally been inadequate for practical applications. Here we test how accurately floristically defined forest types in lowland tropical rain forests in Peruvian Amazonia can be recognized using remote sensing data (Landsat ETM+ satellite image and STRM elevation model). Floristic data and a vegetation classification with four forest classes were available for eight line transects, each 8 km long, located in an area of ca 800 km². We compared two sampling unit sizes (line transect subunits of 200 and 500 m) and several image feature combinations to analyze their suitability for image classification. Mantel tests were used to quantify how well the patterns in elevation and in the digital numbers of the satellite image correlated with the floristic patterns observed in the field. Most Mantel correlations were positive and highly significant. Linear discriminant analysis was used first to build a function that discriminates between forest classes in the eight field-verified transects on the basis of remotely sensed data, and then to classify those parts of the line transects and the satellite image that had not been visited in the field. Classification accuracy was quantified by 8-fold crossvalidation. Two of the tierra firme (non-inundated) forest types were combined because they were too often misclassified. The remaining three forest types (inundated forest, terrace forest and Pebas formation/intermediate tierra firme forest) could be separated using the 500-m sampling units with an overall classification accuracy of 85% and a Kappa coefficient of 0.62. For the 200-m sampling units, the classification accuracy was clearly lower (71%, Kappa 0.35). The forest classification will be used as habitat data to study wildlife habitat use in the same area. Our results show that remotely sensed data and relatively simple classification methods can be used to produce reasonably accurate forest type classifications, even in structurally homogeneous primary rain forests.     

Estimating evapotranspiration of European forests from NOAA-imagery at satellite overpass time: Towards an operational processing chain for integrated optical and thermal sensor data products

Evapotranspiration (ET) using the Integral NOAA-imagery processing Chain (iNOAA-Chain) is quantified by implementing visible and thermal satellite information on a regional scale. ET is calculated based on the energy balance closure principle. The combination of evaporative fraction (EF), soil heat flux and instantaneous net radiation, results in an instantaneous spatial distribution of ET values. Surface broadband albedo and land surface temperature (LST) serve to determine EF. EF is derived using four methods based on NOAA/AVHRR satellite imagery. Instantaneous evapotranspiration, i.e. at time of satellite overpass, on European continental scale with emphasis on forest stands is estimated using the iNOAA-Chain. Finally, the estimated net radiation (R_n), soil heat fluxes (G₀) and evaporative fraction and evapotranspiration at time of satellite overpass are validated against EUROFLUX site data for the growing season of 1997 (March-October). The regression line for the pooled R_n (iNOAA-Chain versus EUROFLUX) has a slope, intercept, Pearson product moment correlation coefficient (R²) and relative root mean square error (RRMSE) of respectively 0.943, 17.120, 0.926 and 5.5%. The soil heat fluxes, calculated with two approaches are not-well modelled with slopes smaller than − 3.000 and a R² in the order of zero. We observe a slight underestimation of the iNOAA Chain estimated EF. The regression line for pooled EF data for the best performing method (SPLIT-method) has a slope of 0.935, an intercept of 0.041 and the R² is 0.847. A pooled RRMSE EF value of 12.3% is found. The pooled slope, intercept, R² and RRMSE for EF derived with SORT-method 1 are respectively 0.449, 0.251, 0.043 and 65.1%, with SORT-method 2, 0.567, 0.203, 0.174 and 39.1%, and with SORT-method 3, 0.568, 0.254, 0.288, and 32.8%. Also instantaneous evapotranspiration is underestimated with a pooled RRMSE on ET of 23.4%. The regression curve of pooled ET data for the best performing method has a slope of 0.889 an intercept of 15.880 and a Pearson product moment correlation coefficient of 0.771. The other method gives a slope of 0.781, an intercept of 17.541 and a R² of 0.776. Error propagation analysis reveals that the relative error on evapotranspiration at satellite overpass time is at least 27%.     

Orientation accuracy analysis of multiple satellite networks using epidemic model

Micro-navigation sensors provide position and orientation information for the network of satellites. Each micro-navigation sensor system consists of a global positioning system receiver, a solid-state inertial measurement unit and lasers. To maintain the orientation accuracy of the satellites in the network, an orientation information transfer (OIT) method was developed. Two laser links are established to align a satellite with low orientation accuracy from a satellite with high orientation accuracy. The OIT process is similar to the spread of an epidemic, which has been extensively studied in the epidemiology. Therefore, the information transfer process was analysed by applying a modified epidemic model. The relation between OIT process and the orientation accuracy of the satellites in the network was mathematically investigated. It is shown that the OIT using lasers greatly improves the orientation accuracy of all satellites in the network. Furthermore, the overall orientation accuracy of the satellite network can be improved by increasing the alignment rate or decreasing the decay rate, which are the parameters of the system. The simulation results verified the analysis of the system. The results showed the feasibility of using epidemic theory to analyse orientation accuracy of a satellite network. Finally, this OIT model will allow coordination of relative attitudes between satellites.     

基于FALKO的城市轨道交通共线交路列车运行图编制

列车交路是城轨交通开行方案的重要组成部分,本文概述了城轨交通典型的列车交路形式及其特点,并结合理论分析了深圳地铁一号线实行共线大小交路的决策过程。其后介绍了FALKO运行图编制系统及运用该系统编制运行图基本步骤、最后编写了深圳地铁一号线罗湖至西乡大小交路运行图实例,为城市轨道交通选择交路形式及计算机编制运行图提供参考。