基于Landsat TM的地表温度分解算法对比

如何综合可见光波段信息提高地表温度的空间分辨率一直是热红外遥感应用研究的重要方向。以北京市Landsat TM图像为数据源,对比分析了SUTM和E-Dis Trad模型地表温度分解的空间特征差异性和适用范围。结果表明:在植被覆盖较低、地表温度较高的中心城区,SUTM模型的地表温度分解效果更佳,最小均方根误差和平均绝对误差分别为1.522 K和1.191 K;在植被覆盖较高、地表温度较低的郊区,E-Dis Trad模型的地表温度分解效果更好,最小均方根误差和平均绝对误差分别为1.768 K和1.173 K。2种模型都能有效地提高地表温度的空间分辨率,但是在植被覆盖不同的地区分解结果呈现一定的差异性。     

艾比湖流域典型绿洲热环境时空变化特征分析

针对目前绿洲地表温度时空变化的研究较少的现状,该文采用覃志豪单窗算法,利用1990年、1998年和2011年三期Landsat数据对精河县的地表温度进行反演,并查询实时陆面地表温度进行对比检验;分析了不同时期精河县地表温度分布格局,将反演的地表温度划分为低温区、次低温区、中温区、次高温区、高温区和极高温区。研究得出:各温度区间的分布范围发生了巨大变化;反演结果符合水体温度最低、绿洲次之、裸地最高的"冷岛"现象。结果表明利用Landsat数据反演地表温度是可行的。     

Inventory of Glaciers in Mongolia,Derived from Landsat Imagery from 1989 to 2011

Glaciers in the Altai Mountains of Mongolia provide an estimated 11% of the total water resources within the country. Yet, their number and area in inconsistent. Using satellite imagery acquired from Landsat 4, 5, and 7, and SRTM digital elevation model (DEM) data, we present here an intuitive, robust, and inexpensive methodology to map the exposed ice of glaciers in the Altai Mountains for the period 1989 to 2011. The total glacierized area was 515 km² in 1989/1991, 428 km² in 1998/2001, and 372 km² in 2010/2011; it decreased by 17% from 1989/1991 to 1998/2001, 13% from 1998/2001 to 2010/2011, and 28% for the entire period 1989/1991 to 2010/2011. In analyzing a sub‐sample of 260 glaciers, 6% advanced, 11% were stable, and 83% receded from 1989 to 2011. The glacier dataset is available to the public free of charge at the Global Land Ice Measurements from Space (GLIMS) website.     

Integrating optical satellite data and airborne laser scanning in habitat classification for wildlife management

Wildlife habitat selection is determined by a wide range of factors including food availability, shelter, security and landscape heterogeneity all of which are closely related to the more readily mapped landcover types and disturbance regimes. Regional wildlife habitat studies often used moderate resolution multispectral satellite imagery for wall to wall mapping, because it offers a favourable mix of availability, cost and resolution. However, certain habitat characteristics such as canopy structure and topographic factors are not well discriminated with these passive, optical datasets. Airborne laser scanning (ALS) provides highly accurate three dimensional data on canopy structure and the underlying terrain, thereby offers significant enhancements to wildlife habitat mapping. In this paper, we introduce an approach to integrate ALS data and multispectral images to develop a new heuristic wildlife habitat classifier for western Alberta. Our method combines ALS direct measures of canopy height, and cover with optical estimates of species (conifer vs. deciduous) composition into a decision tree classifier for habitat – or landcover types. We believe this new approach is highly versatile and transferable, because class rules can be easily adapted for other species or functional groups. We discuss the implications of increased ALS availability for habitat mapping and wildlife management and provide recommendations for integrating multispectral and ALS data into wildlife management.     

Comparison of the Landsat Surface Reflectance Climate Data Record (CDR) and manually atmospherically corrected data in a semi-arid European study area

This study contributes to the quality assessment of atmospherically corrected Landsat surface reflectance data that are routinely generated by the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). This dataset, named Landsat Surface Reflectance Climate Data Record (Landsat CDR), is available at global scale and offers unprecedented opportunities to land monitoring and management services that require atmospherically corrected Earth observation (EO) data. Our assessment is based on the comparison of the Landsat CDR data against a set of Landsat and DEIMOS-1 images processed to a high degree of accuracy using an industry-standard atmospheric correction algorithm (ATCOR-2). The software package has been used for many years and its correction procedures can be considered consolidated and well-established. The dataset of Landsat and DEIMOS-1 images was acquired over a semi-arid agricultural area located in Lower Austria and was independently corrected by using a manual fine-tuning of ATCOR-2 parameters to reach the highest possible accuracy. Results show a very good correspondence of the surface reflectance in each of the six reflective spectral channels as well as for the NDVI (Normalized Difference Vegetation Index). An additional comparison against a NDVI time series from MODIS revealed also a good correspondence. Coefficients of determination (R²) between the two multi-year and multi-seasonal Landsat/DEIMOS datasets range between 0.91 (blue band) and 0.98 (nIR, SWIR-1 and SWIR-2). The results obtained for our semi-arid test site in Austria confirm previous findings and suggest that automatic atmospheric procedures, such as the one implemented by LEDAPS are accurate enough to be used in land monitoring services that require consistent multi-temporal surface reflectance data.     

Resolution vs. image quality in pre-tsunami imagery used for tsunami impact models in Aceh,Indonesia

Land cover roughness coefficients (LCRs) have been used in multivariate spatial models to test the mitigation potential of coastal vegetation to reduce impacts of the 2004 tsunami in Aceh, Indonesia. Previously, a Landsat 2002 satellite imagery was employed to derive land cover maps, which were then combined with vegetation characteristics, i.e., stand height, stem diameter and planting density to obtain LCRs. The present study tested LCRs extracted from 2003 and 2004 Landsat (30 m) images as well as a combination of 2003 and 2004 higher spatial resolution SPOT (10 m) imagery, while keeping the previous vegetation characteristics. Transects along the coast were used to extract land cover, whenever availability and visibility allowed. These new LCRs applied in previously developed tsunami impact models on wave outreach, casualties and damages confirmed previous findings regarding distance to the shoreline as a main factor reducing tsunami impacts. Nevertheless, the models using the new LCRs did not perform better than the original one. Particularly casualties models using 2002 LCRs performed better (δAIC > 2) than the more recent Landsat and SPOT counterparts. Cloud cover at image acquisition for Landsat and low area coverage for SPOT images decreased statistical predictive power (fewer observations). Due to the large spatial heterogeneity of tsunami characteristics as well as topographic and land-use features, it was more important to cover a larger area. Nevertheless, if more land cover classes would be referenced and high resolution imagery with low cloud cover would be available, the full benefits of higher spatial resolution imagery used to extract more precise land use roughness coefficients could be exploited.     

Landsat 8 地表温度产品降尺度深度学习方法研究

以光谱指数为趋势面因子的降尺度方法被广泛用于遥感地表温度尺度转换中,但面临构建的光谱指数难以凸显地表温度分布规律、浅层的统计模型难以精准刻画趋势面因子与地表温度之间的复杂关系的不足。为此,本文以Landsat 8 ARD 地表温度产品为降尺度对象,以Landsat 8 OLI原始数据为潜在趋势面因子,构建地表温度降尺度残差网络（LSTDRN）的深度学习模型;探索适用于Landsat 8地表温度产品空间降尺度的趋势面波段或组合,并在不同季节、不同地表类型下与经典传统方法TsHARP进行定量比较。结果表明：LSTDRN方法利用Landsat 8 OLI原始单波段作为趋势面因子就能有较好的降尺度效果,增加潜在趋势面因子的组合数量并不能提高降尺度效果。不同地表覆盖类型实验中,LSTDRN方法降尺度效果整体优于经典传统方法,且以近红外波段、红光波段和归一化植被指数为趋势面因子时,近红外波段降尺度效果定量评价表现最佳;不同地表覆盖类型的LSTDRN降尺度效果排序为：植被>建筑>水体,而经典传统方法则没有表现出明显的差异。不同季节实验中,LSTDRN方法在春夏冬3季的降尺度效果的定量评价表现明显好于经典传统方法,两类方法的秋季降尺度结果相当。因此,提出的LSTDRN对Landsat 8遥感地表温度产品具有较好的降尺度效果,整体优于经典传统方法且稳定性更强。     

Performance of Landsat TM in ship detection in turbid waters

The visible and near infrared bands of Landsat have limitations for detecting ships in turbid water. The potential of TM middle infrared bands for ship detection has so far not been investigated. This study analyzed the performance of the six Landsat TM visible and infrared bands for detecting dredging ships in the turbid waters of the Poyang Lake, China. A colour composite of principal components analysis (PCA) components 3, 2 and 1 of a TM image was used to randomly select 81 dredging ships. The reflectance contrast between ships and adjacent water was calculated for each ship. A z-score and related p-value were used to assess the ship detection performance of the six Landsat TM bands. The reflectance contrast was related to water turbidity to analyze how water turbidity affected the capability of ship identification. The results revealed that the TM middle infrared bands 5 and 7 better discriminated vessels from surrounding waters than the visible and near infrared bands 1–4. A significant relation between reflectance contrast and water turbidity in bands 1–4 could explain the limitations of bands 1–4; while water turbidity has no a significant relation to the reflectance contrast of bands 5 and 7. This explains why bands 5 and 7 detect ships better than bands 1–4.     

Classifiers vs. input variables—The drivers in image classification for land cover mapping

The study investigates the performance of image classifiers for landscape-scale land cover mapping and the relevance of ancillary data for the classification success in order to assess and to quantify the importance of these components in image classification. Specifically tested are the performance of maximum likelihood classification (MLC), artificial neural networks (ANN) and discriminant analysis (DA) based on Landsat7 ETM+ spectral data in combination with topographic measures and NDVI. ANN produced high accuracies of more than 75% also with limited input information, while MLC and DA produced comparable results only by incorporating ancillary data into the classification process. The superiority of ANN classification was less pronounced on the level of the single land cover classes. The use of ancillary data generally increased classification accuracy and showed a similar potential for increasing classification accuracy than the selection of the classifier. Therefore, a stronger focus on the development of appropriate and optimised sets of input variables is suggested. Also the definition and selection of land cover classes has shown to be crucial and not to be simply adaptable from existing land cover class schemes. A stronger research focus towards discriminating land cover classes by their typical spectral, topographic or seasonal properties is therefore suggested to advance image classification.     

Integrating TM and ancillary geographical data with classification trees for land cover classification of marsh area

The main objective of this research is to determine the capacity of land cover classification combining spectral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM image texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS information (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to implement and should be applicable in other settings and over larger extents.