The derivation of an Arctic sea ice normal through temporal mixture analysis of satellite imagery

The use of temporal mixture analysis (TMA) for creating a long-term baseline, or environmental “normal” is described. TMA is a promising analysis method derived from the hyperspectral image processing technique of spectral mixture analysis (SMA). TMA is algebraically identical to SMA, except that it is applied to temporal spectra rather than to electromagnetic spectra. TMA has particular potential to extract climate signals from long image sequences.     

城市土地利用变化的不透水面覆盖度检测方法

通过分析城市中不透水面数量和分布的变化与城市土地利用变化之间的对应关系, 综合中、高分辨率遥感数据各自的优势, 运用CART算法进行城市不透水面覆盖度(ISP)遥感估算, 基于ISP制图结果对城市土地利用变化进行检测。以山东省泰安市为例开展实验研究, 结果表明, 与传统的变化检测方法相比, 基于ISP的变化检测方法, 不仅能够反映土地利用类型转换的潜在信息, 而且可以灵活地量化定义和解释城市用地变化情况。这种方法为城市土地利用变化信息的提取和分析提供了一种新的思路, 可以作为现有变化检测方法的有益补充。     

Detection and mapping of maize streak virus using RapidEye satellite imagery

The aim of this study was to detect and map MSV using RapidEye multispectral sensor in Ofcolaco farm. To achieve this objective, the acquired RapidEye sensor was classified using the robust Random Forest algorithm. Furthermore, the variable importance technique was used to determine the influence of each spectral band and indices on the mapping accuracy. For better performance of image data, the value of the commonly used vegetation indices in improving the classification accuracy was tested. The results revealed that the use of RapidEye spectral bands in detection and mapping of MSV yielded good classification results with an overall accuracy of 82.75%. The inclusion of vegetation indices computed from RapidEye sensor improved the classification accuracies by 3.4%. The most important RapidEye spectral bands in classifying MSV were near infrared, blue and red-edge. On the other hand, the most important vegetation indices were the Soil adjusted vegetation index, Enhanced vegetation index, Red index and Normalized Vegetation Index. The current study recommends future studies to assess the importance of multi-temporal remote sensing applications in detecting and monitoring the spread of MSV.     

Stratified spectral mixture analysis of medium resolution imagery for impervious surface mapping

Linear spectral mixture analysis (LSMA) is widely employed in impervious surface estimation, especially for estimating impervious surface abundance in medium spatial resolution images. However, it suffers from a difficulty in endmember selection due to within-class spectral variability and the variation in the number and the type of endmember classes contained from pixel to pixel, which may lead to over or under estimation of impervious surface. Stratification is considered as a promising process to address the problem. This paper presents a stratified spectral mixture analysis in spectral domain (Sp_SSMA) for impervious surface mapping. It categorizes the entire data into three groups based on the Combinational Build-up Index (CBI), the intensity component in the color space and the Normalized Difference Vegetation Index (NDVI) values. A suitable endmember model is developed for each group to accommodate the spectral variation from group to group. The unmixing into the associated subset (or full set) of endmembers in each group can make the unmixing adaptive to the types of endmember classes that each pixel actually contains. Results indicate that the Sp_SSMA method achieves a better performance than full-set-endmember SMA and prior-knowledge-based spectral mixture analysis (PKSMA) in terms of R, RMSE and SE.     

Pinpointing source of Mekong and measuring its length through analysis of satellite imagery and field investigations

For centuries, explorers and scientists from different countries had made their own conclusions on the source of the Mekong. However, the geographic source of the Mekong is still arguable because of the complexity of the Mekong source water system, inaccessible environment and the varied technologies used by those explorers and scientists. The satellite remote sensing technology has been used to pinpoint the source of the Mekong, associated with the on-the-spot investigations made by the authors in June 1999 and September 2002. The actual length of the Mekong has also been calculated.     

STAMP: spatial–temporal analysis of moving polygons

Research questions regarding temporal change in spatial patterns are increasingly common in geographical analysis. In this research, we explore and extend an approach to the spatial–temporal analysis of polygons that are spatially distinct and experience discrete changes though time. We present five new movement events for describing spatial processes: displacement, convergence, divergence, fragmentation and concentration. Spatial–temporal measures of events for size and direction are presented for two time periods, and multiple time periods. Size change metrics are based on area overlaps and a modified cone-based model is used for calculating polygon directional relationships. Quantitative directional measures are used to develop application specific metrics, such as an estimation of the concentration parameter for a von Mises distribution, and the directional rate of spread. The utility of the STAMP methods are demonstrated by a case study on the spread of a wildfire in northwestern Montana.      

Indicator kriging applied to absorption band analysis in hyperspectral imagery: A case study from the Rodalquilar epithermal gold mining area,SE Spain

A new approach to the analysis of hyperspectral data for the purpose of surface compositional mapping is presented in this paper. We use an interpolated value of the absorption band position and the absorption band depth for the diagnostic of mineral absorption features. Using thresholds for this depth and position, the data is transformed to indicator [0,1] values. By kriging these values, we obtain the probability of exceeding certain absorption depth and the probability of a pixel exhibiting absorption features within a specified wavelength region. Using Bayesian statistics, the indicator kriging derived probabilities are used to produce a hard classification result. By adapting the prior probabilities to the dominant mineralogy in the various alteration facies mapped, data stratification is achieved. The classification results are compared to results derived using the spectral angle mapper and maximum likelihood classification. In addition, the results are statistically compared to field spectral data classified into dominant mineralogy. The indicator approach and the spectral angle mapper produce favourable results relative to field data and in comparison to the maximum likelihood classifier. A data set from the Rodalquilar high-sulfidation epithermal gold system in SE Spain, consisting of HyMAP airborne imaging spectrometer data and ASD field spectra focusing on the key minerals alunite, kaolinite, illite and chlorite, is used to illustrate the methodology.     

Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery

Efficient forest fire management requires precise and up-to-date knowledge regarding the composition and spatial distribution of forest fuels at various spatial and temporal scales. Fuel-type maps are essential for effective fire prevention strategies planning, as well as the alleviation of the environmental impacts of potential wildfire events. The aim of this study was to assess and compare the potential of Disaster Monitoring Constellation and Landsat-8 OLI satellite images (Operational Land Imager), combined with Object-Based Image Analysis (GEOBIA), in operational mapping of the Mediterranean fuel types at a regional scale. The results showcase that although the images of both sensors can be used with GEOBIA analysis for the generation of accurate fuel-type maps, only the OLI images can be considered as applicable for regional mapping of the Mediterranean fuel types on an operational basis.     

The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques

Urban impervious surface information is essential for urban and environmental applications at the regional/national scales. As a popular image processing technique, spectral mixture analysis (SMA) has rarely been applied to coarse-resolution imagery due to the difficulty of deriving endmember spectra using traditional endmember selection methods, particularly within heterogeneous urban environments. To address this problem, we derived endmember signatures through a least squares solution (LSS) technique with known abundances of sample pixels, and integrated these endmember signatures into SMA for mapping large-scale impervious surface fraction. In addition, with the same sample set, we carried out objective comparative analyses among SMA (i.e. fully constrained and unconstrained SMA) and machine learning (i.e. Cubist regression tree and Random Forests) techniques. Analysis of results suggests three major conclusions. First, with the extrapolated endmember spectra from stratified random training samples, the SMA approaches performed relatively well, as indicated by small MAE values. Second, Random Forests yields more reliable results than Cubist regression tree, and its accuracy is improved with increased sample sizes. Finally, comparative analyses suggest a tentative guide for selecting an optimal approach for large-scale fractional imperviousness estimation: unconstrained SMA might be a favorable option with a small number of samples, while Random Forests might be preferred if a large number of samples are available.     

Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery

Accelerated soil erosion, high sediment yields, floods and debris flow are serious problems in many areas of Iran, and in particular in the Golestan dam watershed, which is the area that was investigated in this study. Accurate land use and land cover (LULC) maps can be effective tools to help soil erosion control efforts. The principal objective of this research was to propose a new protocol for LULC classification for large areas based on readily available ancillary information and analysis of three single date Landsat ETM+ images, and to demonstrate that successful mapping depends on more than just analysis of reflectance values. In this research, it was found that incorporating climatic and topographic conditions helped delineate what was otherwise overlapping information. This study determined that a late summer Landsat ETM+ image yields the best results with an overall accuracy of 95%, while a spring image yields the poorest accuracy (82%). A summer image yields an intermediate accuracy of 92%. In future studies where funding is limited to obtaining one image, late summer images would be most suitable for LULC mapping. The analysis as presented in this paper could also be done with satellite images taken at different times of the season. It may be, particularly for other climatic zones, that there is a better time of season for image acquisition that would present more information.     

Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and Quantitative mapping of salinity stress in sugarcane fields

This study evaluates the feasibility of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields located in the southwest of Iran. For this purpose a Hyperion image acquired on September 2, 2010 and a Landsat7 ETM+ image acquired on September 7, 2010 were used as hyperspectral and multispectral satellite imagery. Field data including soil salinity in the sugarcane root zone was collected at 191 locations in 25 fields during September 2010. In the first section of the paper, based on the yield potential of sugarcane as influenced by different soil salinity levels provided by FAO, soil salinity was classified into three classes, low salinity (1.7–3.4 dS/m), moderate salinity (3.5–5.9 dS/m) and high salinity (6–9.5) by applying different classification methods including Support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) on Hyperion and Landsat images. In the second part of the paper the performance of nine vegetation indices (eight indices from literature and a new developed index in this study) extracted from Hyperion and Landsat data was evaluated for quantitative mapping of salinity stress. The experimental results indicated that for categorical classification of salinity stress, Landsat data resulted in a higher overall accuracy (OA) and Kappa coefficient (KC) than Hyperion, of which the MD classifier using all bands or PCA (1–5) as an input performed best with an overall accuracy and kappa coefficient of 84.84% and 0.77 respectively. Vice versa for the quantitative estimation of salinity stress, Hyperion outperformed Landsat. In this case, the salinity and water stress index (SWSI) has the best prediction of salinity stress with an R² of 0.68 and RMSE of 1.15 dS/m for Hyperion followed by Landsat data with an R² and RMSE of 0.56 and 1.75 dS/m respectively. It was concluded that categorical mapping of salinity stress is the best option for monitoring agricultural fields and for this purpose Landsat data are most suitable.     

Synergy of airborne LiDAR and Worldview-2 satellite imagery for land cover and habitat mapping: A BIO_SOS-EODHaM case study for the Netherlands

A major challenge is to develop a biodiversity observation system that is cost effective and applicable in any geographic region. Measuring and reliable reporting of trends and changes in biodiversity requires amongst others detailed and accurate land cover and habitat maps in a standard and comparable way. The objective of this paper is to assess the EODHaM (EO Data for Habitat Mapping) classification results for a Dutch case study. The EODHaM system was developed within the BIO_SOS (The BIOdiversity multi-SOurce monitoring System: from Space TO Species) project and contains the decision rules for each land cover and habitat class based on spectral and height information. One of the main findings is that canopy height models, as derived from LiDAR, in combination with very high resolution satellite imagery provides a powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping for any location across the globe. The assessment of the EODHaM classification results based on field data showed an overall accuracy of 74% for the land cover classes as described according to the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while the overall accuracy was lower (69.0%) for the habitat map based on the General Habitat Category (GHC) system for habitat surveillance and monitoring. A GHC habitat class is determined for each mapping unit on the basis of the composition of the individual life forms and height measurements. The classification showed very good results for forest phanerophytes (FPH) when individual life forms were analyzed in terms of their percentage coverage estimates per mapping unit from the LCCS classification and validated with field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might also be due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification results encouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, in preparation for new habitat classifications.     

环渤海滨海湿地时空格局变化遥感监测与分析

基于多期Landsat TM 等遥感数据, 采用目视解译和实地样点采集相互支撑的方法, 完成了环渤海 地区2000 年、2005 年及2008 年湿地提取和分类; 运用单一类型变化率模型、区域动态度模型和动态转移矩 阵, 揭示了环渤海湿地的时空格局、变化特点和驱动机制。研究显示环渤海地区的8 类湿地中, 属于人工湿 地类型的盐场和水库坑塘面积比例大且近8 年增长速度也最快, 年均分别增加205.52 km2 和146.10 km2, 滩 地和海涂减少最明显; 环渤海地区的三大流域中, 黄河流域湿地变化最显     

利用DS-InSAR技术监测沛北矿区地表形变

针对采空区地表散射特性不稳定、高相干点目标少且分布不均匀的特点,本文基于相干矩阵特征值分解（T-EVD）的DS-InSAR技术,利用Sentinel-1A数据获取并分析了徐州沛北矿区2017年1月-2018年12月的地表沉降特征。与常规时序InSAR监测对比表明,融合分布式目标的DS-InSAR技术监测点数量显著增加且空间分布更加均匀,能够更好地反映矿区地表沉降的时空分布特征,为煤炭枯竭型矿区地表沉降规律分析和机理研究提供了先进的技术支撑,在采空区地表沉降监测方面具有良好的应用前景。     

An historical empirical line method for the retrieval of surface reflectance factor from multi-temporal SPOT HRV,HRVIR and HRG multispectral satellite imagery

SPOT satellites have been imaging Earth's surface since SPOT 1 was launched in 1986. It is argued that absolute atmospheric correction is a prerequisite for quantitative remote sensing. Areas where land cover changes are occurring rapidly are also often areas most lacking in situ data which would allow full use of radiative transfer models for reflectance factor retrieval (RFR). Consequently, this study details the proposed historical empirical line method (HELM) for RFR from multi-temporal SPOT imagery. HELM is designed for use in landscape level studies in circumstances where no detailed overpass concurrent atmospheric or meteorological data are available, but where there is field access to the research site(s) and a goniometer or spectrometer is available. SPOT data are complicated by the ±27° off-nadir cross track viewing. Calibration to nadir only surface reflectance factor (ρ_s) is denoted as HELM-1, whilst calibration to ρ_s modelling imagery illumination and view geometries is termed HELM-2. Comparisons of field measured ρ_s with those derived from HELM corrected SPOT imagery, covering Helsinki, Finland, and Taita Hills, Kenya, indicated HELM-1 RFR absolute accuracy was ±0.02ρ_s in the visible and near infrared (VIS/NIR) bands and ±0.03ρ_s in the shortwave infrared (SWIR), whilst HELM-2 performance was ±0.03ρ_s in the VIS/NIR and ±0.04ρ_s in the SWIR. This represented band specific relative errors of 10–15%. HELM-1 and HELM-2 RFR were significantly better than at-satellite reflectance (ρ_SAT), indicating HELM was effective in reducing atmospheric effects. However, neither HELM approach reduced variability in mean ρ_s between multi-temporal images, compared to ρ_SAT. HELM-1 calibration error is dependent on surface characteristics and scene illumination and view geometry. Based on multiangular ρ_s measurements of vegetation-free ground targets, calibration error was negligible in the forward scattering direction, even at maximum off-nadir view. However, error exceeds 0.02ρ_s where off-nadir viewing was ≥20° in the backscattering direction within ±55° azimuth of the principal plane. Overall, HELM-1 results were commensurate with an identified VIS/NIR 0.02ρ_s accuracy benchmark. HELM thus increases applicability of SPOT data to quantitative remote sensing studies.     

Evaluation and forecast of human impacts based on land use changes using multi-temporal satellite imagery and GIS: A case study on Zanjan,Iran

Land use and land cover changes due to human activities in a time sequence. Detection of such changes may help decision makers and planners to understand the factors in land use and land cover change in order to take effective and useful measures. Remote sensing and GIS techniques may be used as efficient tools to detect and assess land use change.     

Assessment of Nigeriasat-1 satellite data for urban land use/land cover analysis using object-based image analysis in Abuja,Nigeria

This study assesses the usefulness of Nigeriasat-1 satellite data for urban land cover analysis by comparing it with Landsat and SPOT data. The data-sets for Abuja were classified with pixel- and object-based methods. While the pixel-based method was classified with the spectral properties of the images, the object-based approach included an extra layer of land use cadastre data. The classification accuracy results for OBIA show that Landsat 7 ETM, Nigeriasat-1 SLIM and SPOT 5 HRG had overall accuracies of 92, 89 and 96%, respectively, while the classification accuracy for pixel-based classification were 88% for Landsat 7 ETM, 63% for Nigeriasat-1 SLIM and 89% for SPOT 5 HRG. The results indicate that given the right classification tools, the analysis of Nigeriasat-1 data can be compared with Landsat and SPOT data which are widely used for urban land use and land cover analysis.     

Detection and mapping the spatial distribution of bracken fern weeds using the Landsat 8 OLI new generation sensor

Bracken fern is an invasive plant that presents serious environmental, ecological and economic problems around the world. An understanding of the spatial distribution of bracken fern weeds is therefore essential for providing appropriate management strategies at both local and regional scales. The aim of this study was to assess the utility of the freely available medium resolution Landsat 8 OLI sensor in the detection and mapping of bracken fern at the Cathedral Peak, South Africa. To achieve this objective, the results obtained from Landsat 8 OLI were compared with those derived using the costly, high spatial resolution WorldView-2 imagery. Since previous studies have already successfully mapped bracken fern using high spatial resolution WorldView-2 image, the comparison was done to investigate the magnitude of difference in accuracy between the two sensors in relation to their acquisition costs. To evaluate the performance of Landsat 8 OLI in discriminating bracken fern compared to that of Worldview-2, we tested the utility of (i) spectral bands; (ii) derived vegetation indices as well as (iii) the combination of spectral bands and vegetation indices based on discriminant analysis classification algorithm. After resampling the training and testing data and reclassifying several times (n = 100) based on the combined data sets, the overall accuracies for both Landsat 8 and WorldView-2 were tested for significant differences based on Mann-Whitney U test. The results showed that the integration of the spectral bands and derived vegetation indices yielded the best overall classification accuracy (80.08% and 87.80% for Landsat 8 OLI and WorldView-2 respectively). Additionally, the use of derived vegetation indices as a standalone data set produced the weakest overall accuracy results of 62.14% and 82.11% for both the Landsat 8 OLI and WorldView-2 images. There were significant differences {U (100) = 569.5, z = −10.8242, p < 0.01} between the classification accuracies derived based on Landsat OLI 8 and those derived using WorldView-2 sensor. Although there were significant differences between Landsat and WorldView-2 accuracies, the magnitude of variation (9%) between the two sensors was within an acceptable range. Therefore, the findings of this study demonstrated that the recently launched Landsat 8 OLI multispectral sensor provides valuable information that could aid in the long term continuous monitoring and formulation of effective bracken fern management with acceptable accuracies that are comparable to those obtained from the high resolution WorldView-2 commercial sensor.     

Assessment of depth and turbidity with airborne Lidar bathymetry and multiband satellite imagery in shallow water bodies of the Alaskan North Slope

Airborne Lidar bathymetry (ALB) is an effective and a rapidly advancing technology for mapping and characterizing shallow coastal water zones as well as inland fresh-water basins such as rivers and lakes. The ability of light beams to detect and traverse shallow water columns has provided valuable information about unmapped and often poorly understood coastal and inland water bodies of the world.Estimating ALB survey results at varying water clarity and depth conditions is essential for realizing project expectations and preparing budgets accordingly. In remote locations of the world where in situ water clarity measurements are not feasible or possible, using multiband satellite imagery can be an effective tool for estimating and addressing such considerations.For this purpose, we studied and classified reflected electromagnetic energy from selected water bodies acquired by RapidEye sensor and then correlated findings with ALB survey results. This study was focused not on accurately measuring depth from optical bathymetry but rather on using multiband satellite imagery to quickly predict ALB survey results and identify potentially turbid water bodies with limited depth penetration. For this study, we constructed an in-house algorithm to confirm ALB survey findings using bathymetric waveform information.The study findings are expected to contribute to the ongoing understanding of forecasting ALB survey expectations in unknown and varying water conditions, especially in remote and inaccessible parts of the world.     

Dark Times: nighttime satellite imagery as a detector of regional disparity and the geography of conflict

Satellite observations of night-time emitted lights describe a geography of the spatial distribution of resource use. Measurements of nocturnal lights enable the calculation of the total light emitted from each country of the world, and the light emitted per capita. We consider different groups of countries that share a land or maritime border and whose light per capita can be more equally/unequally distributed. A sharp difference in light per capita among neighboring countries reflects marked differences in economic welfare and in the extent of built environments. We demonstrate how this geography of nocturnal lights informs our understanding of the dynamics of conflict at the national and regional scale. We propose an index of regional disparity and test its ability to detect conflict dynamics by relating the index score with the occurrence and intensity of conflicts as classified by the Heidelberg Institute for International Conflict Research’s Conflict Barometer 2012 for the countries of the world. This method can be used to produce a global available temporal sampling of “cold spots” of disparity where conflicts are likely to occur. This will help foresee the identification and monitoring of regions of the world,which are becoming particularly unstable, assisting in the definition and execution of timely and proactive policies.