Evaluation of MIVIS Hyperspectral Data for Mapping Soil Degradation in an Upland Ecosystem of Southern Italy

Abstract

This paper describes the first stage of an experiment aiming to evaluate the potential and limitations of MIVIS data for mapping the degradational state of soils in a sub‐scene of a southern Apennines study area (Italy). After radiometric rectification of the image data and the collection of a field/laboratory spectral library, linear spectral mixture modelling (SMA) was used to decompose image spectra into fractions of spectrally distinct mixing components. Spectral endmember selection was based upon a principal component analysis (PCA) applied to a set of soil spectra, collected from the spectral library. The resulting abundance estimates (fractions) trough SMA were then analysed to identify soil conditions and to obtain an improved measure of dry and green vegetation cover. A map of soil conditions and dry‐green vegetation abundance, based upon MIVIS data was then derived from normalised fractions of soil‐vegetation endmembers obtained from SMA.     

A geostatistical temporal mixture analysis approach to address endmember variability for estimating regional impervious surface distributions

Spectral mixture analysis (SMA) is a major approach for estimating fractional land covers through modeling the relationship between the spectral signatures of a mixed remote sensing pixel and those of the comprised pure land covers (also termed as endmembers). When SMA is implemented, endmember variability has proven to have significant impact on the accuracy of land cover fraction estimates. To address the endmember variability problem, this article developed a geostatistical temporal mixture analysis (GTMA) technique, with which spatially varying per-pixel endmember sets were estimated using an ordinary kriging interpolation technique. The method was applied to time-series moderate-resolution imaging spectroradiometer normalized difference vegetation index imagery in Wisconsin and North Carolina, United States to estimate regional impervious surface distributions. Analysis of results suggests that GTMA has achieved a promising accuracy. Detailed analysis indicates that a better performance has been achieved in less-developed areas than developed areas, and slight underestimation and slight overestimation have been detected in developed areas and less-developed areas, respectively. Moreover, while the performance of GTMA is comparable to those of phenology-based TMA and phenology-based multiple endmember TMA over the entire study area and in less-developed areas, a much better performance has been achieved in developed areas. Finally, this article argues that endmember variability may be more essential in developed areas when compared to less-developed areas.     

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.     

Spectral mixture analysis to assess post-fire vegetation regeneration using Landsat Thematic Mapper imagery: Accounting for soil brightness variation

Post-fire vegetation cover is a crucial parameter in rangeland management. This study aims to assess the post-fire vegetation recovery 3 years after the large 2007 Peloponnese (Greece) wildfires. Post-fire recovery landscapes typically are mixed vegetation-substrate environments which makes spectral mixture analysis (SMA) a very effective tool to derive fractional vegetation cover maps. Using a combination of field and simulation techniques this study aimed to account for the impact of background brightness variability on SMA model performance. The field data consisted out of a spectral library of in situ measured reflectance signals of vegetation and substrate and 78 line transect plots. In addition, a Landsat Thematic Mapper (TM) scene was employed in the study. A simple SMA, in which each constituting terrain feature is represented by its mean spectral signature, a multiple endmember SMA (MESMA) and a segmented SMA, which accounts for soil brightness variations by forcing the substrate endmember choice based on ancillary data (lithological map), were applied. In the study area two main spectrally different lithological units were present: relatively bright limestone and relatively dark flysch (sand-siltstone). Although the simple SMA model resulted in reasonable regression fits for the flysch and limestones subsets separately (coefficient of determination R² of respectively 0.67 and 0.72 between field and TM data), the performance of the regression model on the pooled dataset was considerably weaker (R² = 0.65). Moreover, the regression lines significantly diverged among the different subsets leading to systematic over-or underestimations of the vegetative fraction depending on the substrate type. MESMA did not solve the endmember variability issue. The MESMA model did not manage to select the proper substrate spectrum on a reliable basis due to the lack of shape differences between the flysch and limestone spectra,. The segmented SMA model which accounts for soil brightness variations minimized the variability problems. Compared to the simple SMA and MESMA models, the segmented SMA resulted in a higher overall correlation (R² = 0.70), its regression slope and intercept were more similar among the different substrate types and its resulting regression lines more closely resembled the expected one-one line. This paper demonstrates the improvement of a segmented approach in accounting for soil brightness variations in estimating vegetative cover using SMA. However, further research is required to evaluate the model's performance for other soil types, with other image data and at different post-fire timings.     

基于光谱混合分析的毛乌素沙地油蒿群落覆盖度提取

油蒿群落覆盖度是判断毛乌素沙地荒漠化程度严重与否的直接指标,其信息的准确获取有助于更为合理的荒漠化监测与评价。本研究以Landsat ETM 为数据源,深入探讨了光谱混合分析过程中影像预处理、端元选取、光谱混合模型选择及光谱混合分析结果分析等关键问题,给出了恰当的解决方案,进而进行了油蒿群落覆盖度的提取,并通过野外实测数据对提取结果进行了验证。验证结果表明:基于光谱混合分析技术提取的油蒿群落分量与实测油蒿群落覆盖度线性相关显著,相关系数为0.88,因而研究区的油蒿群落覆盖度可以通过油蒿群落分量的线性变换得到。因此,光谱混合分析是提取毛乌素沙地油蒿群落覆盖度的有效技术。     

利用混合光谱分解与SVM估算不透水面覆盖率

对比了线性混合光谱分解模型(SMA)与支持向量机(SVM)在TM影像上估算不透水面覆盖率(ISP)的精度,通过SVM模型拟合TM像元光谱特征与样本ISP间的关联而获得对未知像元ISP的估算能力。对于天津市主城区的TM影像,选择学校区、工矿区和住宅区的高分辨率影像分类结果作为训练样本(7020个)和验证样本(1500个),SVM模型的ISP估算均方差(15.4%)优于SMA估算结果(19.4%);在增加缨帽变化“绿度分量”及混合光谱分解“高反射率分量”作为SVM特征变量后,ISP估算精度提高为12%。研究结果表明：SVM模型能够拟合各像元光谱组分间非线性关系且具有较好小样本泛化的性能,适用于地面样本较少的大区域ISP制图;增加与ISP相关性大的光谱特征向量作为SVM输入能提供更多的区域地物空间分布信息,能够调整无样本的地表类型的ISP估算值,提高区域ISP估算的整体精度。     

A Hybrid Approach for Land Use/Land Cover Classification

Atlanta has continuously changed its physical landscape as well as its socioeconomic appearance over the past decades. A hybrid image processing approach, which integrated unsupervised, supervised, and spectral mixture analysis (SMA) classification methods, was used to identify urban land use/land cover changes over a decade (from 1990 to 2000) in the Atlanta metropolitan area. During this process, SMA was proven to be an effective analytical approach for characterizing mixed feature areas, such as a metropolitan area. According to accuracy assessment, the classification results were acceptable.     

Use of field reflectance data for crop mapping using airborne hyperspectral image

Recent developments in hyperspectral remote sensing technologies enable acquisition of image with high spectral resolution, which is typical to the laboratory or in situ reflectance measurements. There has been an increasing interest in the utilization of in situ reference reflectance spectra for rapid and repeated mapping of various surface features. Here we examined the prospect of classifying airborne hyperspectral image using field reflectance spectra as the training data for crop mapping. Canopy level field reflectance measurements of some important agricultural crops, i.e. alfalfa, winter barley, winter rape, winter rye, and winter wheat collected during four consecutive growing seasons are used for the classification of a HyMAP image acquired for a separate location by (1) mixture tuned matched filtering (MTMF), (2) spectral feature fitting (SFF), and (3) spectral angle mapper (SAM) methods. In order to answer a general research question “what is the prospect of using independent reference reflectance spectra for image classification”, while focussing on the crop classification, the results indicate distinct aspects. On the one hand, field reflectance spectra of winter rape and alfalfa demonstrate excellent crop discrimination and spectral matching with the image across the growing seasons. On the other hand, significant spectral confusion detected among the winter barley, winter rye, and winter wheat rule out the possibility of existence of a meaningful spectral matching between field reflectance spectra and image. While supporting the current notion of “non-existence of characteristic reflectance spectral signatures for vegetation”, results indicate that there exist some crops whose spectral signatures are similar to characteristic spectral signatures with possibility of using them in image classification.     

混合像元分解技术及其进展

混合像元分解模型是定量遥感研究的重要组成部分,为各种地学应用提供了更精细的亚像元级地物信息,这一领域受到国内外学者们广泛关注。本文围绕混合像元分解研究的4个核心问题——光谱混合模型、端元提取、模型反演方法以及解混精度评估,总结了近20年来混合像元分解的重要研究进展,分析和介绍了典型算法模型的原理和思路。进一步阐述了现有研究在一些关键问题上存在的不足,如目前仍缺乏公认的线性和非线性模型的选择判据、已有的混合像元分解模型无法抑制由端元光谱相关造成的共线性问题。最后总结了混合像元分解未来的发展趋势和值得探索的研究方向。如结合辐射传输模型和地面试验,定量分析多次散射的影响机制,以及结合克服共线性的统计回归模型。     

端元可变非线性混合像元分解模型

遥感影像中混合像元普遍存在。端元固定的情况下对混合像元进行分解,很难高精度地识别影像地物。本文基于支持向量机,提出了端元可变的非线性混合像元分解模型。首先,通过构建多个支持向量机获取每个像元的优化端元集,在优化端元集的基础上运用支持向量机与两两配对方法相结合的算法获取像元组分。试验结果表明,本文提出的方法效果优于传统的多端元光谱分解法。     

Mapping Wildfire Burn Severity in Southern California Forests and Shrublands Using Enhanced Thematic Mapper Imagery

Abstract

Wildfire is a major disturbance agent in Mediterranean Type Ecosystems (MTEs). Providing reliable, quantitative information on the area of burns and the level of damage caused is therefore important both for guiding resource management and global change monitoring. Previous studies have successfully mapped burn severity using remote sensing, but reliable accuracy has yet to be gained using standard methods over different vegetation types. The objective of this research was to classify burn severity across several vegetation types using Landsat ETM imagery in two areas affected by wildfire in southern California in June 1999. Spectral mixture analysis (SMA) using four reference endmembers (vegetation, soil, shade, non‐photosynthetic vegetation) and a single (charcoal‐ash) image endmember were used to enhance imagery prior to burn severity classification using decision trees. SMA provided a robust technique for enhancing fire‐affected areas due to its ability to extract sub‐pixel information and minimize the effects of topography on single date satellite data. Overall kappa classification accuracy results were high (0.71 and 0.85, respectively) for the burned areas, using five canopy consumption classes. Individual severity class accuracies ranged from 0.5 to 0.94.     

Spectral mixture analysis to monitor defoliation in mixed-aged Eucalyptus globulus Labill plantations in southern Australia using Landsat5-TM and EO-1Hyperion data

Defoliation is a key parameter of forest health and is associated with reduced productivity and tree mortality. Assessing the health of forests requires regular observations over large areas. Satellite remote sensing provides a cost-effective alternative to traditional ground-based assessment of forest health, but assessing defoliation can be difficult due to mixed pixels where vegetation cover is low or fragmented. In this study we apply a novel spectral unmixing technique, referred to as weighted Multiple Endmember Spectral Mixture Analysis (wMESMA), to Landsat 5-TM and EO-1 Hyperion data acquired over a Eucalyptus globulus (Labill.) plantation in southern Australia. This technique combines an iterative mixture analysis cycle allowing endmembers to vary on a per pixel basis (MESMA) and a weighting algorithm that prioritizes wavebands based on their robustness against endmember variability. Spectral mixture analysis provides an estimate of the physically interpretable canopy cover, which is not necessarily correlated with defoliation in mixed-aged plantations due to natural variation in canopy cover as stands age. There is considerable variability in the degree of defoliation as well as in stand age among sites and in this study we found that results were significantly improved by the inclusion of an age correction algorithm for both the multi-spectral (R²_{no age correction} = 0.55 vs R²_{age correction} = 0.73 for Landsat) and hyperspectral (R²_{no age correction} = 0.12 vs R²_{age correction} = 0.50 for Hyperion) image data. The improved accuracy obtained from Landsat compared to the Hyperion data illustrates the potential of applying SMA techniques for analysis of multi-spectral datasets such as MODIS and SPOT-VEGETATION.     

Effectiveness of SID as Spectral Similarity Measure to Develop Crop Spectra from Hyperspectral Image

The present study was undertaken with the objective to check effectiveness of spectral information divergence (SID) to develop spectra from image for crop classes based on spectral similarity with field spectra. In multispectral and hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to develop crop spectra from the image itself. Hence, in this study methodology suggested to develop spectra for crops based on SID. Absorption features are unique and distinct; hence, validation of the developed spectra is carried out using absorption features by comparing it with field spectra and finding average correlation coefficient r?=?0.982 and computed SID equivalent r?=?0.989. Effectiveness of developed spectra for image classification was computed by probability of spectral discrimination (PSD) and resulted in higher probability for the spectra developed based on SID. Image classification was carried out using field spectra and spectra assigned by SID. Overall classification accuracy of the image classified by field spectra is 78.30% and for the image classified by spectra assigned through SID-based approach is 91.82%. Z test shows that image classification carried out using spectra developed by SID is better than classification carried out using field spectra and significantly different. Validation by absorption features, effectiveness by PSD and higher classification accuracy show possibility of new approach for spectra development based on SID spectral similarity measure.     

Enhancing endmember selection in multiple endmember spectral mixture analysis (MESMA) for urban impervious surface area mapping using spectral angle and spectral distance parameters

Successful retrieval of urban impervious surface area is achieved with remote sensing data using the multiple endmember spectral mixture analysis (MESMA). MESMA is well suited for studying the urban impervious surface area because it allows the number and types of the endmembers to vary on a per-pixel basis, thereby, allowing the control of the large spectral variability. However, MESMA must calculate all potential endmember combinations of each pixel to determine the best-fit one. Therefore, it is a time-consuming and inefficient unmixing technology, especially for hyperspectral images because these images have more complicated endmember categories. Hence, in this paper, we design an improved MESMA (SASD-MESMA: spectral angle and spectral distance MESMA) to enhance the computational efficiency of conventional MESMA, and we validate this new method by analyzing the Hyperion image (Jan-2011) and the field-spectra data of Guangzhou (China). In SASD-MESMA, the parameters of spectral angle (SA) and spectral distance (SD) are used to evaluate the similarity degree between library spectra and image spectra in order to identify the most representative endmember combination for each pixel. Results demonstrate that the SA and SD parameters are useful to reduce misjudgment in selecting candidate endmembers and effective for determining the appropriate endmembers in one pixel. Meanwhile, this research indicates that the proposed SASD-MESMA performs very well in retrieving impervious surface area, forest, grass and soil distributions on the sub-pixel level (the overall root mean square error (RMSE) is 0.15 and the correlation coefficient of determination (R²) is 0.68).     

Evaluating the robustness of models developed from field spectral data in predicting African grass foliar nitrogen concentration using WorldView-2 image as an independent test dataset

In this paper, we evaluate the extent to which the resampled field spectra compare with the actual image spectra of the new generation multispectral WorldView-2 (WV-2) satellite. This was achieved by developing models from resampled field spectra data and testing them on an actual WV-2 image of the study area. We evaluated the performance of reflectance ratios (RI), normalized difference indices (NDI) and random forest (RF) regression model in predicting foliar nitrogen concentration in a grassland environment. The field measured spectra were used to calibrate the RF model using a randomly selected training (n = 70%) nitrogen data set. The model developed from the field spectra resampled to WV-2 wavebands was validated on an independent field spectral test dataset as well as on the actual WV-2 image of the same area (n = 30%, bootstrapped a 100 times). The results show that the model developed using RI could predict nitrogen with a mean R² of 0.74 and 0.65 on an independent field spectral test data set and on the actual WV-2 image, respectively. The root mean square error of prediction (RMSE %) was 0.17 and 0.22 for the field test data set and the WV-2 image, respectively. Results provide an insight on the magnitude of errors that are expected when up-scaling field spectral models to airborne or satellite image data. The prediction also indicates the unceasing relevance of field spectroscopy studies to better understand the spectral models critical for vegetation quality assessment.     

Prior-knowledge-based spectral mixture analysis for impervious surface mapping

In this study, we developed a prior-knowledge-based spectral mixture analysis (PKSMA) to map impervious surfaces by using endmembers derived separately for high- and low-density urban regions. First, an urban area was categorized into high- and low-density urban areas, using a multi-step classification method. Next, in high-density urban areas that were assumed to have only vegetation and impervious surfaces (ISs), the vegetation–impervious model (V–I) was used in a spectral mixture analysis (SMA) with three endmembers: vegetation, high albedo, and low albedo. In low-density urban areas, the vegetation–impervious–soil model (V–I–S) was used in an SMA analysis with four endmembers: high albedo, low albedo, soil, and vegetation. The fraction of IS with high and low albedo in each pixel was combined to produce the final IS map. The root mean-square error (RMSE) of the IS map produced using PKSMA was about 11.0%, compared to 14.52% only using four-endmember SMA. Particularly in high-density urban areas, PKSMA (RMSE = 6.47%) showed better performance than four-endmember (15.91%). The results indicate that PKSMA can improve IS mapping compared to traditional SMA by using appropriately selected endmembers and is particularly strong in high-density urban areas.     

基于权重光谱解混方法的高光谱矿物填图

提出了一种基于Fisher权重分析的迭代光谱解混方法(WLSMA),该方法首先对高光谱图像进行区域分割,在分割后的各子块中自动提取端元;再次对提取的端元进行聚类,从光谱的整体特征上将不同类别的端元区分开,针对聚类结果中的每一类别各选取几个具有代表性的端元光谱,并对最优光谱进行窗口卷积处理,结合In_CoB指标构建端元光谱样本库;最后对图像进行迭代光谱解混处理,在丰度反演过程中引入基于Fisher准则的补偿权值矩阵以提高反演精度。AVIRIS高光谱数据实验证明,WLSMA不需要大量先验信息,利用Fisher准则和迭代光谱分析理论增强了相似性矿物的可分性,为加强对矿区地表岩性的认识和模拟提供了更大的灵活性和可能性,对高光谱矿物填图有一定的借鉴意义。     

一种基于多源遥感影像的亚端元光谱提取方法

提出一种利用多源影像来提取低分辨影像亚端元光谱的方法,该方法利用高分辨分类得到的组分与低分辨的光谱之间的线性混合关系,建立了反演出低分辨影像的亚端元光谱的线性模型,并提出一种快速鲁棒反演亚端元光谱的提取方法。利用IKONOS和ETM数据验证了本文提出方法,实验结果表明了该方法的有效性。     

Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area

Spectral feature fitting (SFF) is a commonly used strategy for hyperspectral imagery analysis to discriminate ground targets. Compared to other image analysis techniques, SFF does not secure higher accuracy in extracting image information in all circumstances. Multi range spectral feature fitting (MRSFF) from ENVI software allows user to focus on those interesting spectral features to yield better performance. Thus spectral wavelength ranges and their corresponding weights must be determined. The purpose of this article is to demonstrate the performance of MRSFF in oilseed rape planting area extraction. A practical method for defining the weighted values, the variance coefficient weight method, was proposed to set up criterion. Oilseed rape field canopy spectra from the whole growth stage were collected prior to investigating its phenological varieties; oilseed rape endmember spectra were extracted from the Hyperion image as identifying samples to be used in analyzing the oilseed rape field. Wavelength range divisions were determined by the difference between field-measured spectra and image spectra, and image spectral variance coefficient weights for each wavelength range were calculated corresponding to field-measured spectra from the closest date. By using MRSFF, wavelength ranges were classified to characterize the target's spectral features without compromising spectral profile's entirety. The analysis was substantially successful in extracting oilseed rape planting areas (RMSE ≤ 0.06), and the RMSE histogram indicated a superior result compared to a conventional SFF. Accuracy assessment was based on the mapping result compared with spectral angle mapping (SAM) and the normalized difference vegetation index (NDVI). The MRSFF yielded a robust, convincible result and, therefore, may further the use of hyperspectral imagery in precision agriculture.     

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.