A combined spectral and object-based approach to transparent cloud removal in an operational setting for Landsat ETM+

The automated cloud cover assessment (ACCA) algorithm has provided automated estimates of cloud cover for the Landsat ETM+ mission since 2001. However, due to the lack of a band around 1.375 μm, cloud edges and transparent clouds such as cirrus cannot be detected. Use of Landsat ETM+ imagery for terrestrial land analysis is further hampered by the relatively long revisit period due to a nadir only viewing sensor. In this study, the ACCA threshold parameters were altered to minimise omission errors in the cloud masks. Object-based analysis was used to reduce the commission errors from the extended cloud filters. The method resulted in the removal of optically thin cirrus cloud and cloud edges which are often missed by other methods in sub-tropical areas. Although not fully automated, the principles of the method developed here provide an opportunity for using otherwise sub-optimal or completely unusable Landsat ETM+ imagery for operational applications. Where specific images are required for particular research goals the method can be used to remove cloud and transparent cloud helping to reduce bias in subsequent land cover classifications.     

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.     

Converting local spectral and spatial information from a priori classifiers into contextual knowledge for impervious surface classification

A classification model was demonstrated that explored spectral and spatial contextual information from previously classified neighbors to improve classification of remaining unclassified pixels. The classification was composed by two major steps, the a priori and the a posteriori classifications. The a priori algorithm classified the less difficult image portion. The a posteriori classifier operated on the more challenging image parts and strived to enhance accuracy by converting classified information from the a priori process into specific knowledge. The novelty of this work relies on the substitution of image-wide information with local spectral representations and spatial correlations, in essence classifying each pixel using exclusively neighboring behavior. Furthermore, the a posteriori classifier is a simple and intuitive algorithm, adjusted to perform in a localized setting for the task requirements. A 2001 and a 2006 Landsat scene from Central New York were used to assess the performance on an impervious classification task. The proposed method was compared with a back propagation neural network. Kappa statistic values in the corresponding applicable datasets increased from 18.67 to 24.05 for the 2006 scene, and from 22.92 to 35.76 for the 2001 scene classification, mostly correcting misclassifications between impervious and soil pixels. This finding suggests that simple classifiers have the ability to surpass complex classifiers through incorporation of partial results and an elegant multi-process framework.     

Delineation of coalfield surface fires by thresholding Landsat TM-7 day-time image data

Surface fires are common in coalfields where coal is mined or exposed to sunlight for long durations of time. The heat energy emitted from these fires affects the signal recorded by sensors operating in the shortwave infrared regions of the electromagnetic spectrum. The Landsat TM/ETM+ band-7 is sensitive to solar reflected radiation as well as emitted radiation from a target. The ‘maximum solar reflection threshold’ method proposed in this study uses the highest spectral radiance that can be attributed to solar reflection as the conservative threshold to segregate the pixels with emitted component from those with reflected component of the EM energy. Investigations with Landsat TM/ETM+ data indicate a reflectance value of 0.23–0.25 as the most representative highest reflectance (threshold) in coal mining areas. The method apparently has the advantage that it is based on the reflectance characteristics of materials (sandstone-shale mixtures) typically found in coal mining areas.     

Land-use and vegetation-cover mapping of an indigenous land area in the state of Mato Grosso (Brazil) based on spectral linear mixing model,segmentation and region classification

A spectral linear-mixing model using Landsat ETM+ imagery was undertaken to estimate fraction images of green vegetation, soil and shade in an indigenous land area in the state of Mato Grosso in the central-western region of Brazil. The fraction images were used to classify different types of land use and vegetation cover. The fraction images were classified by the following two methods: (a) application of a segmentation based on the region-growing technique; and (b) grouping of the regions segmented using the per-region unsupervised classifier named ISOSEG. Adopting a 75% threshold, ISOSEG generated 44 clusters that were grouped into eight land-use and vegetation-cover classes. The mapping achieved an average accuracy of 83%, showing that the methodology is efficient in mapping areas of great land-use and vegetation-cover diversity, such as that found in the Brazilian cerrado (savanna).     

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.     

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.     

Geothermal area detection using Landsat ETM+ thermal infrared data and its mechanistic analysis—A case study in Tengchong,China

Thermal infrared (TIR) remote sensing is an important technique in the exploration of geothermal resources. In this study, a geothermal survey is conducted in Tengchong area of Yunnan province in China using TIR data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. Based on radiometric calibration, atmospheric correction and emissivity calculation, a simple but efficient single channel algorithm with acceptable precision is applied to retrieve the land surface temperature (LST) of study area. The LST anomalous areas with temperature about 4–10 K higher than background area are discovered. Four geothermal areas are identified with the discussion of geothermal mechanism and the further analysis of regional geologic structure. The research reveals that the distribution of geothermal areas is consistent with the fault development in study area. Magmatism contributes abundant thermal source to study area and the faults provide thermal channels for heat transfer from interior earth to land surface and facilitate the present of geothermal anomalies. Finally, we conclude that TIR remote sensing is a cost-effective technique to detect LST anomalies. Combining TIR remote sensing with geological analysis and the understanding of geothermal mechanism is an accurate and efficient approach to geothermal area detection.     

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.     

Developing detailed age-specific thematic maps for coffee (Coffea arabica L.) in heterogeneous agricultural landscapes using random forests applied on Landsat 8 multispectral sensor

Coffee is a commodity of international trade significance, and its value chain can benefit from age-specific thematic maps. This study aimed to assess the potential of Landsat 8 OLI to develop these maps. Using field-collected samples with the random forest classifier, splitting coffee into three age classes (Scheme A) was compared with running the classification with one compound coffee class (Scheme B). Higher overall classification accuracy was obtained in Scheme B (90.3% for OLI and 86.8% for ETM+) than in Scheme A (86.2% for OLI and 81.0% for ETM+). The NIR band of OLI was the most important band in intra-class discrimination of coffee. Landsat 8 OLI mapped area closely matched farm records (R^2?=?0.88) compared to that of Landsat 7 ETM+ (R^2?=?0.78). It was concluded that Landsat 8 OLI data can be used to produce age-specific thematic maps in coffee production areas although disaggregating coffee classes reduces overall accuracy.     

The potential of spectral mixture analysis to improve the estimation accuracy of tropical forest biomass

A main limitation of pixel-based vegetation indices or reflectance values for estimating above-ground biomass is that they do not consider the mixed spectral components on the earth's surface covered by a pixel. In this research, we decomposed mixed reflectance in each pixel before developing models to achieve higher accuracy in above-ground biomass estimation. Spectral mixture analysis was applied to decompose the mixed spectral components of Landsat-7 ETM+ imagery into fractional images. Afterwards, regression models were developed by integrating training data and fraction images. The results showed that the spectral mixture analysis improved the accuracy of biomass estimation of Dipterocarp forests. When applied to the independent validation data set, the model based on the vegetation fraction reduced 5–16% the root mean square error compared to the models using a single band 4 or 5, multiple bands 4, 5, 7 and all non-thermal bands of Landsat ETM+.     

基于ETM~+的遥感蚀变信息提取方法对比研究

为从微观到宏观了解、掌握和运用围岩蚀变的特征与原岩特征,寻找大型矿床或大型矿集区,在总结常用遥感蚀变信息提取模型的基础上,结合主成分分析(PCA)和支持向量机(SVM)算法,建立了基于“掩模处理-主成分分析-支持向量机( MASK - PCA - SVM,MPs)”的遥感蚀变信息提取模型.在新疆哈图地区进行的基于ETM+的不同蚀变信息提取模型应用对比研究表明,使用常用模型提取出的蚀变信息和矿点吻合效果较差,而基于MPS模型提取出的蚀变信息与已知矿点的吻合率可达86.51％;结合地质专家知识、地质图和一个已知异常点的分析结果表明,基于MPS模型的蚀变信息提取精度较高,在研究区地质应用中效果较好,为今后该区的遥感蚀变信息提取工作提供了一种新思路.     

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%.     

基于ETM+的植被覆盖信息提取及其动态变化分析——以广州市为例

以2000年和2005年ETM+影像为数据源,在水体掩膜和端元光谱优选对比基础上,利用线性光谱模型提取了广州市植被覆盖信息,并对植被覆盖信息的时空动态变化进行了定量分析,结果表明:应用线性光谱模型之前对所占面积不大的水体进行处理是非常必要的,选取植被、硬化地面、土壤三端元法比包含阴影的四端元法更有利于线性光谱分离,而且能达到很高的精度;此外,通过对植被覆盖信息空间结构和景观格局的研究,显示:2000-2005虽然广州市绿化取得了初步成效,但总体植被覆盖状况呈下滑趋势,植被景观破碎程度增加,各植被景观形状趋于复杂化,城市化的发展加之人类活动的干扰,低植被覆盖区不断扩大,而中、高植被覆盖区正处于不断退化时期。     

Analysis of ASTER data for mapping bauxite rich pockets within high altitude lateritic bauxite,Jharkhand, India

Bauxite deposits of Jharkhand in India are resulted from the lateritization process and therefore are often associated with the laterites. In the present study, ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer) image is processed to delineate bauxite rich pockets within the laterites. In this regard, spectral signatures of lateritic bauxite samples are analyzed in the laboratory with reference to the spectral features of gibbsite (main mineral constituent of bauxite) and goethite (main mineral constituent of laterite) in VNIR–SWIR (visible-near infrared and short wave infrared) electromagnetic domain. The analysis of spectral signatures of lateritic bauxite samples helps in understanding the differences in the spectral features of bauxites and laterites. Based on these differences; ASTER data based relative band depth and simple ratio images are derived for spatial mapping of the bauxites developed within the lateritic province. In order to integrate the complementary information of different index image, an index based principal component (IPC) image is derived to incorporate the correlative information of these indices to delineate bauxite rich pockets. The occurrences of bauxite rich pockets derived from density sliced IPC image are further delimited by the topographic controls as it has been observed that the major bauxite occurrences of the area are controlled by slope and altitude. In addition to above, IPC image is draped over the digital elevation model (DEM) to illustrate how bauxite rich pockets are distributed with reference to the topographic variability of the terrain. Bauxite rich pockets delineated in the IPC image are also validated based on the known mine occurrences and existing geological map of the bauxite. It is also conceptually validated based on the spectral similarity of the bauxite pixels delineated in the IPC image with the ASTER convolved laboratory spectra of bauxite samples.     

Estimation of impervious surfaces of Beijing,China, with spectral normalized images using linear spectral mixture analysis and artificial neural network

The objective of this article is to evaluate the effectiveness of various algorithms for estimating impervious surfaces. Linear spectral mixture analysis (LSMA) and multi-layer perceptron (MLP) network using original and spectral normalized images were applied to two ASTER images acquired on 31 August and 9 April 2004, respectively. Accuracy assessment was performed with a Quickbird image. Root-mean-square errors (RMSEs) were calculated and compared. Results indicated that LSMA with original images provided the poorest results. RMSE was 14.8% for the August image and 22.4% for the April image. Results from LSMA with normalized images improved significantly with RMSE of 12.6% for the August image and 18.9% for the April image. The MLP modelling with original images generated slightly better results with RMSE of 12.2% and 18.4% for each image. The MLP modelling of normalized images provided the best estimation, yielding a RMSE of 12.1% for the August image and 18.2% for the April image.     

基于混合像元分解的南方地区植被覆盖度遥感监测——以广州市为例

通过测量图像端元的地表反射率,对遥感图像进行精确大气校正;在对混合像元分解模型进行改进的基础上,建立了基于地表反射率的线性混合像元分解( Liner Spectral Unmixing,LSU)模型,有效地避免了因大气时间、空间差异所造成的多时相误差,实现了多时相对比;通过增加土壤湿度因子,消除了土壤湿度差异造成的误差...     

Comparative analysis on utilisation of linear spectral unmixing and band ratio methods for processing ASTER data to delineate bauxite over a part of Chotonagpur plateau,Jharkhand, India

We have attempted comparative analysis of the utility of linear spectral unmixing (LSU) method and band ratios for delineating bauxite from laterite within the lateritic bauxite provinces of Chotonagpur Plateau, Jharkhand of India. This was attempted based on processing of visible–near infrared (VNIR) and shortwave infrared (SWIR) spectral bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. In LSU method, spectral features of main constituent minerals of lateritic bauxite are used to decompose the pixel spectra to estimate the relative abundance of bauxite and laterite in each pixel to spatially delineate bauxite within laterite. We have also compared the bauxite map derived using LSU method with bauxite maps of two band ratios in terms of spatial disposition of bauxite. We also have attempted to relate the abundance values of pixels of LSU-based bauxite map with band ratio values of bauxite pixels of two selected bauxite indices.     

Detection of dryland degradation using Landsat spectral unmixing remote sensing with syndrome concept in Minqin County,China

This study was to detect dryland degradation coupling linear spectral unmixing model of Landsat images with syndrome concept in temperate dryland system, Minqin, China. The phenological contrast and complementation between green vegetation fraction in summer, sandland fraction and saline land fraction in spring, was firstly structured to quantify degradation characteristics by simple correlation analysis with ground data. The spatiotemporal patterns of the three degradation indicators were interpreted with the help “dust bowl” syndrome, qualitatively deciphered the degradation causal clusters, loops and important consequences in the study area. The results indicate water-using and distribution pattern was changed, agricultural intensity and productivity increased, salinization lessened in oasis, whereas sandification risk heightened. This approach developed in this study, has the potentially broad applicability, for dryland system monitoring and modelling.     

Landsat 8陆地成像仪影像的缨帽变换推导

针对Landsat 8陆地成像仪影像,该文提出了一种缨帽变换系数的推算方法。利用准同步的Landsat ETM+影像缨帽变换结果为目标值,采用普鲁克算法对不同地理分布下的陆地成像仪影像样本点的主成分轴分别进行旋转,得到相应的初始变换系数;通过广义普鲁克分析对所得初始变换系数进行平均化,并以格拉姆-施密特算法保持其正交性,最终得到基于陆地成像仪大气表观反射率数据的缨帽变换系数。验证结果表明,与现有陆地成像仪变换系数相比,该文提出的缨帽变换精度相对较高,误差较小,变换后的不同地物覆盖类型具有更好的可分性。