Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping

An image dataset from the Landsat OLI spaceborne sensor is compared with the Landsat TM in order to evaluate the excellence of the new imagery in urban landcover classification. Widely known pixel-based and object-based image analysis methods have been implemented in this work like Maximum Likelihood, Support Vector Machine, k-Nearest Neighbor, Feature Analyst and Sub-pixel. Classification results from Landsat OLI provide more accurate results comparing to the Landsat TM. Object-based classifications produced a more uniform result, but suffer from the absorption of small rare classes into large homogenous areas, as a consequence of the segmentation, merging and the spatial parameters in the spatial resolution (30 m) of Landsat images. Based exclusively on the overall accuracy reports, the SVM pixel-based classification from Landsat 8 proved to be the most accurate for the purpose of mapping urban land cover, using medium spatial resolution imagery.     

Fusion of TerraSAR-x and Landsat ETM+ data for protected area mapping in Uganda

TerraSAR-X satellite acquires very high spatial resolution data with potential for detailed land cover mapping. A known problem with synthetic aperture radar (SAR) data is the lack of spectral information. Fusion of SAR and multispectral data provides opportunities for better image interpretation and information extraction. The aim of this study was to investigate the fusion between TerraSAR-X and Landsat ETM+ for protected area mapping using high pass filtering (HPF), principal component analysis with band substitution (PCA) and principal component with wavelet transform (WPCA). A total of thirteen land cover classes were identified for classification using a non-parametric C 4.5 decision tree classifier. Overall classification accuracies of 74.99%, 83.12% and 85.38% and kappa indices of 0.7220, 0.8100 and 0.8369 were obtained for HPF, PCA and WPCA fusion approaches respectively. These results indicate a high potential for a combined use of TerraSAR-X and Landsat ETM+ data for protected area mapping in Uganda.     

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Abstract

Extracting built-up areas from remote sensing data like Landsat 8 satellite is a challenge. We have investigated it by proposing a new index referred as built-up land features extraction index (BLFEI). The BLFEI index takes advantage of its simplicity and good separability between the four major component of urban system, namely built-up, barren, vegetation and water. The histogram overlap method and the spectral discrimination index (SDI) are used to study separability. BLFEI index uses the two bands of infrared shortwaves, the red and green bands of the visible spectrum. OLI imagery of Algiers, Algeria, was used to extract built-up areas through BLFEI and some new previously developed built-up indices used for comparison. The water areas are masked out leading to Otsu’s thresholding algorithm to automatically find the optimal value for extracting built-up land from waterless regions. BLFEI, the new index improved the separability by 25% and the accuracy by 5%.     

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.     

Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover

Developing techniques are required to generate agricultural land cover maps to monitor agricultural fields. Landsat 8 Operational Land Imager (OLI) offers reflectance data over the visible to shortwave-infrared range. OLI offers several advantages, such as adequate spatial and spectral resolution, and 16 day repeat coverage, furthermore, spectral indices derived from Landsat 8 OLI possess great potential for evaluating the status of vegetation. Additionally, classification algorithms are essential for generating accurate maps. Recently, multi-Grained Cascade Forest, which is also called deep forest, was proposed, and it was shown to give highly competitive performance for classification. However, the ability of this algorithm to generate crop maps with satellite data had not yet been evaluated. In this study, the reflectance at 7 bands and 57 spectral indices calculated from Landsat 8 OLI data were evaluated for its potential for crop type identification.     

Remote estimation of water quality parameters of Himalayan lake (Kashmir) using Landsat 8 OLI imagery

The overarching aim of this study was to derive simple and accurate algorithms for the retrieval of water quality parameters for the Wular Lake using Landsat 8 OLI satellite data. The water quality parameters include pH, COD, DO, alkalinity, hardness, chloride, TDS, total suspended solids (TSS), turbidity, electric conductivity and phosphate. Regression analysis was performed using atmospherically corrected true reflectance values of original OLI bands, images after applying enhancement techniques (NDVI, principal components) and the values of the water quality parameters at different sample locations to obtain the empirical relationship. Most of the parameters were well correlated with single OLI bands with R² greater than 0.5, whereas phosphate showed a good correlation with NDVI image. The parameters like pH and DO showed a good relation with the principal component I and IV, respectively. The high concentration of pH, COD, turbidity and TSS and low concentration of DO infers the anthropogenic impact on lake.     

Sentinel-2与Landsat 8数据组合下的多特征冬小麦面积提取

遥感卫星的波段设置、信噪比及传感器观测角度等因素都会影响作物提取精度。为充分挖掘与发挥Sentinel-2卫星多光谱成像仪(MSI)与Landsat 8陆地成像仪(OLI)在冬小麦信息提取方面的优势,本文以商河县为研究区,基于两数据源的光谱特征、纹理特征、植被指数特征组合数据,利用随机森林(RF)与支持向量机(SVM)对冬小麦进行提取。结果表明:基于单一影像的最优Kappa系数与最优OA分别为0.89和95.13%,基于组合数据源的最优Kappa系数为0.92,最优OA为95.28%,两数据源组合的精度优于单一数据源提取精度;数据组合效果与分类器的性能有关,RF的Kappa系数相对于SVM分别提升0.04、0.20和0.11,OA分别提升2.41%、11.31%和6%,RF对冬小麦提取精度优于SVM。本文研究结果对于构建中高分辨率影像组合的典型农作物分类提取体系具有重要意义。     

遥感图像不同融合方法的适应性评价——以ZY-3和Landsat8图像为例

目前对常用融合方法用于ZY-3和Landsat8卫星图像的适用性研究较少,为此,探讨了小波变换法(WT法)、相位恢复法(Gram_Schimdt,G-S法)、彩色标准化变换法(Brovey法)、主成分变换法(PCA法)、彩色空间变换法(IHS法)以及超分辨率贝叶斯法(Pansharp法)等6种融合方法对ZY-3和Landsat8图像的适用性,并从"光谱信息保真度"和"空间信息融入度"2个方面对融合图像进行了评价。结果表明:在空间信息融入方面,对于ZY-3图像,Pansharp法的信息融入度最差,IHS法的信息融入度最好,PCA法、Brovey法、G-S法和WT法次之;对于Landsat8图像,G-S法的信息融入度最好,WT法最差。在光谱信息保真方面,对于ZY-3图像,PCA法具有较高的光谱保真度,IHS法、G-S法和Brovey法次之,WT法最差;对于Landsat8图像,G-S法具有较高的光谱保真度,Pansharp法和Brovey法次之,IHS法、WT法和PCA法较差。     

Applicability of Landsat 8 data for characterizing glacier facies and supraglacial debris

The present work evaluates the applicability of operational land imager (OLI) and thermal infrared sensor (TIRS) on-board Landsat 8 satellite. We demonstrate an algorithm for automated mapping of glacier facies and supraglacial debris using data collected in blue, near infrared (NIR), short wave infrared (SWIR) and thermal infrared (TIR) bands. The reflectance properties in visible and NIR regions of OLI for various glacier facies are in contrast with those in SWIR region. Based on the premise that different surface types (snow, ice and debris) of a glacier should show distinct thermal regimes, the ‘at-satellite brightness temperature’ obtained using TIRS was used as a base layer for developing the algorithm. This base layer was enhanced and modified using contrasting reflectance properties of OLI bands. In addition to facies and debris cover characterization, another interesting outcome of this algorithm was extraction of crevasses on the glacier surface which were distinctly visible in output and classified images. The validity of this algorithm was checked using field data along a transect of the glacier acquired during the satellite pass over the study area. With slight scene-dependent threshold adjustments, this work can be replicated for mapping glacier facies and supraglacial debris in any alpine valley glacier.     

Use of landsat ETM+ SLC-off segment-based gap-filled imagery for crop type mapping

Failure of the Scan Line Corrector (SLC) on the Landsat ETM+ sensor has had a major impact on many applications that rely on continuous medium resolution imagery to meet their objectives. The United States Department of Agriculture (USDA) Cropland Data Layer (CDL) program uses Landsat imagery as the primary source of data to produce crop-specific maps for 20 states in the USA. A new method has been developed to fill the image gaps resulting from the SLC failure to support the needs of Landsat users who require coincident spectral data, such as for crop type mapping and monitoring. We tested the new gap-filled method for a CDL crop type mapping project in eastern Nebraska. Scan line gaps were simulated on two Landsat 5 images (spring and late summer 2003) and then gap-filled using landscape boundary models, or segment models, that were derived from 1992 and 2002 Landsat images (used in the gap-fill process). Various date combinations of original and gap-filled images were used to derive crop maps using a supervised classification process. Overall kappa values were slightly higher for crop maps derived from SLC-off gap-filled images compared to crop maps derived from the original imagery (0.3–1.3% higher). Although the age of the segment model used to derive the SLC-off gap-filled product did not negatively impact the overall agreement, differences in individual cover type agreement did increase (?0.8%–1.6% using the 2002 segment model to ?5.0–5.1% using the 1992 segment model). Classification agreement also decreased for most of the classes as the size of the segment used in the gap-fill process increased.     

Evaluation of different topographic correction methods for Landsat imagery

The recent free availability of Landsat historical data provides new potentials for land-cover change studies. Multi-temporal studies require a previous radiometric and geometric homogenization of input images, to better identify true changes. Topographic normalization is one of the key steps to create consistent and radiometricly stable multi-temporal time series, since terrain shadows change throughout time. This paper aims to evaluate different methods for topographic correction of Landsat TM-ETM+ data. They were assessed for 15 ETM+ images taken under different illumination conditions, using two criteria: (a) reduction of the standard deviation (SD) for different land-covers and (b) increase in temporal stability of a time series for individual pixels. We observed that results improve when land-cover classes where processed independently when applying the more advanced correction algorithms such as the C-correction and the Minnaert correction. Best results were obtaining for the C-correction and the empiric–statistic correction. Decreases of the SD for bare soil pixels were larger than 100% for the C-correction and the empiric–statistic correction method compared to the other correction methods in the visible spectrum and larger than 50% in the IR region. In almost all tests the empiric–statistic method provided better results than the C-correction. When analyzing the multi-temporal stability, pixels under bad illumination conditions (northern orientation) improved after correction, while a deterioration was observed for pixels under good illumination conditions (southern orientation). Taken this observation into account, a simple but robust method for topographic correction of Landsat imagery is proposed.     

Building population mapping with aerial imagery and GIS data

Geospatial distribution of population at a scale of individual buildings is needed for analysis of people's interaction with their local socio-economic and physical environments. High resolution aerial images are capable of capturing urban complexities and considered as a potential source for mapping urban features at this fine scale. This paper studies population mapping for individual buildings by using aerial imagery and other geographic data. Building footprints and heights are first determined from aerial images, digital terrain and surface models. City zoning maps allow the classification of the buildings as residential and non-residential. The use of additional ancillary geographic data further filters residential utility buildings out of the residential area and identifies houses and apartments. In the final step, census block population, which is publicly available from the U.S. Census, is disaggregated and mapped to individual residential buildings. This paper proposes a modified building population mapping model that takes into account the effects of different types of residential buildings. Detailed steps are described that lead to the identification of residential buildings from imagery and other GIS data layers. Estimated building populations are evaluated per census block with reference to the known census records. This paper presents and evaluates the results of building population mapping in areas of West Lafayette, Lafayette, and Wea Township, all in the state of Indiana, USA.     

Comprehensive study of the biophysical parameters of agricultural crops based on assessing Landsat 8 OLI and Landsat 7 ETM+ vegetation indices

For three agricultural crop types, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), we estimated biophysical parameters including fresh and dry biomass, leaf area index (LAI), and vegetation water content, for which we found the equivalent water thickness (EWT), fuel moisture content per fresh weight (FMC_FW), and fuel moisture content per dry weight (FMC_DW). We performed these estimations using data from the newly launched Landsat 8 Operational Land Imager (OLI) sensor, as well as its predecessor the Landsat 7 Enhanced Thematic Mapper Plus (ETM+). Progress in the design of the new sensor (i.e., Landsat 8), including narrower near-infrared (NIR) wavebands, higher signal-to-noise ratio (SNR), and greater radiometric resolution highlights the necessity to investigate the biophysical parameters of agricultural crops, especially compared to data from its predecessor. This study aims to evaluate vegetation indices (VIs) derived from the Landsat 8 OLI and the Landsat 7 ETM+. Both the Landsat 8 OLI and Landsat 7 ETM+ VIs agreed well with in-situ data measurements. However, the Landsat 8 OLI-derived VIs were generally more consistent with in situ data than the Landsat 7 ETM+ VIs. We also note that the Landsat 8 OLI is better able to capture the small variability of the VIs because of its higher SNR and wider radiometric range; in addition, the saturation phenomenon occurred earlier for the Landsat 7 ETM+ than for the Landsat 8 OLI. This indicates that the new sensor is better able to estimate the biophysical parameters of crops.     

Landsat 8多光谱数据辅助下的高分影像多尺度分割

为解决高分影像分割的边缘锯齿性明显等问题,本文以黑龙江省伊春市桦皮羌子林场为研究区开展了有无多光谱数据辅助的高分影像分割对比试验。首先,本文设计了多尺度分割算法的相同尺度参数下分割试验,确定了该算法分割GF-2影像时应采用的最佳同质性准则组合参数;然后,基于影像分割对象同质性局部方差变化率反映最优分割尺度的思想,利用ESP2工具找出固定尺度范围内的最优分割尺度范围;最后执行最佳同质性准则组合参数配合下的最优分割尺度范围内各个尺度下的多尺度分割,并采用矢量距离指数、紧密度指数、形状指数对2种分割试验结果进行了评价。结果表明,与GF-2影像独立分割相比,Landsat 8多光谱数据辅助下的GF-2影像分割在矢量距离指数、紧密度指数、形状指数的质量上均有提升,平均提升率分别为8.05%、28.40%、11.76%。     

An analysis of urban expansion and its associated thermal characteristics using Landsat imagery

There has been an increasing interest in mapping and monitoring urban land use/land cover using remote sensing techniques. However, there still exist quite a number of challenges in deriving urban extent and its expansion density from remote sensing data quantitatively. This study utilized Landsat TM/ETM+ remote sensing data to assess urban expansion and its thermal characteristics with a case study in the city of Changsha, China. We proposed a new approach for quantitatively determining built-up area, its expansion density and their respective relationship with land surface temperature (LST) patterns. An urban expansion metric was also developed using a moving window mechanism to identify urban built-up area and its expansion density based on selected threshold values. The study suggested that urban extent and its expansion density, as well as surface thermal characteristics and patterns could be identified through quantitatively derived remotely sensed indices and LST, which offer meaningful characteristics in quantifying urban expansion density and urban thermal pattern. Results from the case study demonstrated that: (1) the built-up area and urban expansion density have significantly increased in the city of Changsha from 1990 to 2001; and (2) the differences of urban expansion densities correspond to thermal effects, where a high percentage of imperviousness is usually associated with the area covered by high surface temperature.     

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.     

Data fusion and classifier ensemble techniques for vegetation mapping in the coastal Everglades

This study examined the applicability of data fusion and classifier ensemble techniques for vegetation mapping in the coastal Everglades. A framework was designed to combine these two techniques. In the framework, 20-m hyperspectral imagery collected from Airborne Visible/Infrared Imaging Spectrometer was first merged with 1-m Digital Orthophoto Quarter Quads using a proposed pixel/feature-level fusion strategy. The fused data set was then classified with an ensemble approach based on two contemporary machine learning algorithms: Random Forest and Support Vector Machine. The framework was applied to classify nine vegetation types in a portion of the coastal Everglades. An object-based vegetation map was produced with an overall accuracy of 90% and Kappa value of 0.86. Per-class classification accuracy varied from 61% for identifying buttonwood forest to 100% for identifying red mangrove scrub. The result shows that the framework is promising for automated vegetation mapping in the Everglades.     

植被信息的Landsat8卫星影像提取方法

传统植被信息提取的信息源多以TM/ETM+等影像为主,在如何应用Landsat8影像提取高精度的植被信息方面研究较匮乏。针对上述问题,该文应用大理市Landsat8影像开展了植被信息提取方法研究。研究过程中通过对原始波段组合法、主成分分量组合法、衍生波段组合法、直接解译法提取结果进行对比分析,确定最佳波段组合;之后将纹理信息作为最佳波段组合影像新的波段,与光谱信息一同分类,并对结果进行分析。研究结果表明,添加纹理能够在一定程度上提高植被信息提取的精度。     

Landsat系列卫星对地观测40年回顾及LDCM前瞻

Landsat系列卫星数据凭借其长期连续、全球覆盖、适中的时间空间分辨率和科学的数据存档与分发策略等优势,逐渐成为地表特征和地球系统科学研究中最有效的遥感数据之一,并广泛应用于生态环境、农林地矿、能源资源、教育科研和政府管理等领域。而第8代陆地卫星--陆地卫星数据连续任务卫星(LDCM)于2013年2月发射升空,该卫星携带了运行性陆地成像仪(OLI)和热红外传感器(TIRS)两种传感器。与Landsat 7/ETM+相比,OLI/TIRS在波段设置、辐射分辨性能和扫描方式上都得到很大改进,其中OLI共包括9个波段,新增海岸带(coastal)监测和卷云(cirrus)识别波段,TIRS则设置了两个热红外波段。如果LDCM能够成功升空运行,它将继续承担起长期连续对地观测的使命。     

综合多特征的Landsat 8时序遥感图像棉花分类方法

传统的多时相遥感图像分类大多拘泥于单一特征,本文基于多时相的Landsat 8遥感数据,开展了综合多特征的特征提取与特征选择方法研究。综合了NDVI时间序列、最佳时相反射率光谱特征以及纹理特征作为初始分类特征,并采用基于属性重要度的粗糙集特征选择算法对其进行特征约简。分类结果表明:(1)利用初始分类特征,分类的总体精度达到92.81%,棉花提取精度达87.4%,与仅利用NDVI时间序列相比,精度分别提高5.53%和5.05%;(2)利用粗糙集选择后的特征分类,分类总体精度可达93.66%,棉花分类精度达92.73%,与初始分类特征提取结果相比,棉花分类精度提高5.33%。基于属性重要度的粗糙集特征选择不仅提高了分类精度,同时有效降低了分类器的计算复杂度。