Linear Spectral Mixture Analysis Method Research Articles

Assessing impact of urbanisation on surface runoff using vegetation-impervious surface-soil (V-I-S) fraction and NRCS curve number (CN) model

The progressive decrease in natural surfaces to create space for urban dwellers with the consequential changes caused to hydrological regime of an area is taking a toll on environmental resources. The Natural Resources Conservation Service (NRCS) Curve Number (CN) model (earlier known as Soil Conservation Service Curve Number, SCS-CN) is used in the present study for assessing temporal variations in surface runoff in upper catchment of Rispana River, Dehradun city, India. Also, the composite CN is calculated using vegetation-impervious surface-soil (V-I-S) fraction derived from spectral normalisation and linear spectral mixture analysis method while assigning initial CN for hydrologic soil-cover complex for five years during 1991 to 2018. The results indicated a steady rise in impervious surface fraction and consequent surface runoff (8.25%) from 1991 to 2018, while urban green space fraction has shown depleting trends in north and central part of the city. The level of imperviousness highlighted an over 60% of central part of the city as impervious, which gradually decreases outwards from the city. A positive correlation is observed between surface runoff and ISA (R2 varies between 0.62 and 0.89, p < 0.05), whereas inverse relationship is observed with vegetative cover in all sub-basins (R2 varies between 0.61 and 0.91, p < 0.05). The study demonstrates the potential of NRCS-CN model with inputs drawn from V-I-S fraction for simulating surface runoff and understanding the urban flood risk. The work would be useful for the urban dwellers, administrators and planners for better planning and sustainable development of urban areas.

Linear Spectral Mixture Analysis Land Cover for Assessment Level Subpixel: A Case Study of Tasikmalaya City Area Based on Landsat Imagery

Land cover in urban areas can be detected through surveys or using high-resolution imagery with a better accuracy. Especially if the need related to land cover information regionally in the period of the nineties that require the availability of data and unavailability of high resolution images. Therefore, images with intermediate spatial resolution are still required. However, the use of medium-resolution images such as Landsat is constrained by the presence of mixed pixels that cause land cover in urban areas to vary. The mixed pixel will be the source of error in the multispectral classification process, so it takes analysis up to the subpixel level. The need for information up to the subpixel level for ground cover detection can be obtained through the Linear Spectral Mixture Analysis method, where one pixel in the Landsat image in this study will be separated into four endmember, ie vegetation, impervious surface, bare soil, and water. These four endmembers are assumed to represent linear combinations of land coverings contained in urban areas in the form of proportions in each pixel. The results show that the endmember can be well separated, whereas the RMS error of 1994th is 0.013 with an accuracy of 94.44%.

Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China

Urban forests are an important component of the urban ecosystem. Urban forest types are a key piece of information required for monitoring the condition of an urban ecosystem. In this study, we propose an urban forest type discrimination method based on linear spectral mixture analysis (LSMA) and a support vector machine (SVM) in the case study of Xuzhou, east China. From 10-m Sentinel-2A imagery data, three different vegetation endmembers, namely broadleaved forest, coniferous forest, and low vegetation, and their abundances were extracted through LSMA. Using a combination of image spectra, topography, texture, and vegetation abundances, four SVM classification models were performed and compared to investigate the impact of these features on classification accuracy. With a particular interest in the role that vegetation abundances play in classification, we also compared SVM and other classifiers, i.e., random forest (RF), artificial neural network (ANN), and quick unbiased efficient statistical tree (QUEST). Results indicate that (1) the LSMA method can derive accurate vegetation abundances from Sentinel-2A image data, and the root-mean-square error (RMSE) was 0.019; (2) the classification accuracies of the four SVM models were improved after adding topographic features, textural features, and vegetation abundances one after the other; (3) the SVM produced higher classification accuracies than the other three classifiers when identical classification features were used; and (4) vegetation endmember abundances improved classification accuracy regardless of which classifier was used. It is concluded that Sentinel-2A image data has a strong capability to discriminate urban forest types in spectrally heterogeneous urban areas, and that vegetation abundances derived from LSMA can enhance such discrimination.

Exploration of spatial and temporal characteristics of PM2.5 concentration in Guangzhou, China using wavelet analysis and modified land use regression model

ABSTRACTThis article attempts to detail time series characteristics of PM2.5 concentration in Guangzhou (China) from 1 June 2012 to 31 May 2013 based on wavelet analysis tools, and discuss its spatial distribution using geographic information system software and a modified land use regression model. In this modified model, an important variable (land use data) is substituted for impervious surface area, which can be obtained conveniently from remote sensing imagery through the linear spectral mixture analysis method. Impervious surface has higher precision than land use data because of its sub-pixel level. Seasonal concentration pattern and day-by-day change feature of PM2.5 in Guangzhou with a micro-perspective are discussed and understood. Results include: (1) the highest concentration of PM2.5 occurs in October and the lowest in July, respectively; (2) average concentration of PM2.5 in winter is higher than in other seasons; and (3) there are two high concentration zones in winter and one zone in spring.

Accuracy Assessment of Land Cover Fraction Map Derived from Landsat 8 Imagery Using Linear Spectral Mixture Analysis Method

Accuracy assessment is conducted to assess how well a classification method is. One of the land cover classification method for mixed pixel problem is Linear Spectral Mixture Analysis (LSMA). Error matrix is commonly used accuracy assessment of categorical data. But accuracy assessment for fraction map as result of LSMA processing is not really examined yet. LSMA method produces map fraction which contains land cover proportion. This experiment aims to examine the effect of sampling unit on land cover fraction map accuracy assessment result. 70 samples were taken using stratified random sampling. Geoeye 1 image is used as reference data corresponding to ground truth data. Accuracy assessment was performed in two scenario sampling unit which is single pixel area and a cluster of pixels area. The accuracy of LSMA model was influenced by position error between classified map and the reference data. Selection the right sampling unit is a one way to minimize the deviation caused by positional error corresponding reference data.

Measuring spatio-temporal dynamics of impervious surface in Guangzhou, China, from 1988 to 2015, using time-series Landsat imagery

This study evaluated the spatio-temporal change characteristics of urban development at different scales with time-series impervious surface fractions. Landsat-5 Thematic Mapper (TM) and Landsat-8 Operational Land Imager (OLI) images were used to extract impervious surface fractions using a modified linear spectral mixture analysis method in Guangzhou from 1988 to 2015. The results indicated that the impervious surface area has substantially increased, from 70.3 km2 in 1988 to 580.5 km2 in 2015. In 2015, the impervious surfaces were distributed almost throughout the whole region of the study area, except in the forest region. Next, impervious surface weighted mean centre (ISWMC) and the standard deviational ellipse (SDE) methods were used to systematically analyse the principle orientation, direction, spatio-temporal expansion trends, and the distribution differences of impervious surfaces at the whole and local region scales from 1988 to 2015. The spatio-temporal dynamics of ISWMC exhibited different expansion directions and intensities of impervious surfaces at the whole and local region scales. On a whole region scale, the principle expansion direction of impervious surfaces was northward. However, the expansion trend of impervious surfaces in the different districts was significantly different from other trends at the local region scale. The parameters of SDE were used to investigate the orientation and the clustering or dispersion degree of impervious surface at different scales. The results from SDE analysis indicated that the impervious surfaces exhibited uncertainty in the expansion direction at the whole region scale; in contrast, they had a distinct preferred orientation and expansion direction at the local region scale. The analysis revealed that urban expansion exhibited different change characteristics in various directions at the local region scale. In summary, the results at the local region scale can better reflect the change trajectory of spatio-temporal dynamics of urban development and its fine spatial structure than at the whole region scale.

Multi-Temporal Remotely Sensed Data for Degradation Dynamics in Linxia Rangeland, Northwest China

The importance of accurately monitoring rangeland degradation dynamics over decades is increasing in Linxia rangeland, the birthplace of the Yellow River in China. Since 2000, the Chinese government has implemented the “Grain for Green” program and enforced a grazing ban in Gansu province, one of the most degraded provinces, to mitigate the problem of rangeland degradation. The effects of these policies are controversial and have become a topic of public concern. In this study, a grading system was established for the estimation of Linxia rangeland degradation. Degrees of rangeland degradation were interpreted and the spatio-temporal dynamics of the degraded rangeland through several study periods were mapped and monitored using the Linear Spectral Mixture Analysis method on Landsat Thematic Mapper (TM)/ETM+ (Enhanced Thematic Mapper Plus) images for the years of 1996, 2001, 2006, and 2011. The results demonstrated that the time around the year 2001 appeared to be a turning point of the rangeland degradation reversion course, as the rangeland degradation reversed significantly since 2001. From 1996 to 2001, the total degraded area in Linxia rangeland increased from 2922.01 km2 to 3048.48 km2 (+4.33%), and decreased by 4.54% to 2909.97 km2 in 2011; the non-degraded rangeland gradually increased from 602.74 km2 to 710.01 km2, an increase of 17.80%. Degraded rangeland vegetation was restored significantly during 2001–2011: the area of slightly degraded rangeland increased by 3.71% and 3.83% annually during 2001–2006 and 2006–2011 intervals, respectively, and the area of moderately and severely degraded rangeland decreased annually by 4.77% and 2.41% from 2001 to 2006, and 4.58% and 0.81% during 2006–2011, respectively. These results indicated that the “Grain for Green” program and grazing ban policy, together with other ecological impacting factors, helped reverse the rangeland degradation and promote the rehabilitation of rangeland vegetation.

Coupling Modified Linear Spectral Mixture Analysis and Soil Conservation Service Curve Number (SCS-CN) Models to Simulate Surface Runoff: Application to the Main Urban Area of Guangzhou, China

Land surface characteristics, including soil type, terrain slope, and antecedent soil moisture, have significant impacts on surface runoff during heavy precipitation in highly urbanized areas. In this study, a Linear Spectral Mixture Analysis (LSMA) method is modified to extract high-precision impervious surface, vegetation, and soil fractions. In the modified LSMA method, the representative endmembers are first selected by combining a high-resolution image from Google Earth; the unmixing results of the LSMA are then post-processed to reduce errors of misclassification with Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). The modified LSMA is applied to the Landsat 8 Operational Land Imager (OLI) image from 18 October 2015 of the main urban area of Guangzhou city. The experimental result indicates that the modified LSMA shows improved extraction performance compared with the conventional LSMA, as it can significantly reduce the bias and root-mean-square error (RMSE). The improved impervious surface, vegetation, and soil fractions are used to calculate the composite curve number (CN) for each pixel according to the Soil Conservation Service curve number (SCS-CN) model. The composite CN is then adjusted with regional data of the terrain slope and total 5-day antecedent precipitation. Finally, the surface runoff is simulated with the SCS-CN model by combining the adjusted CN and real precipitation data at 1 p.m., 4 May 2015.

Improving Urban Impervious Surface Mapping by Linear Spectral Mixture Analysis and Using Spectral Indices

As a key indicator of urban built-up areas, impervious surfaces have been frequently analyzed in the studies of urbanization and environmental impacts. For various science and policy applications, it is necessary to accurately estimate and map urban impervious surface areas. Although linear spectral mixture analysis (LSMA) can provide spatial distribution and quantitative fraction information on urban impervious surfaces, using conventional LSMA alone to accurately extract impervious surfaces remains a great challenge. Misclassifications often occur when urban impervious surfaces are estimated by addition of low-albedo and high-albedo fraction images. The low-albedo fraction may relate to features other than impervious surface, such as water bodies and shaded areas, whereas the high-albedo fraction may be spectrally confused with dry soil and other land features. These issues can be acute in mapping of fractional impervious surfaces when the intra-variability of urban impervious surfaces is also considered. To solve these issues, an improved LSMA method was explored in this study using aided spectral indices to estimate urban impervious surface based on a study of Guangzhou, southern China. Three spectral indices were employed, namely, normalized differencing built-up index (NDBI), normalized differencing bare-soil index (NDBaI), and albedo. The improved LSMA method included three analytical steps: (1) High-albedo and low-albedo fraction images were extracted from satellite imagery using conventional LSMA; (2) NDBI, NDBaI, and albedo indices were then employed to remove misclassification pixels in the high-albedo and low-albedo fraction images by establishing thresholds for each index; and (3) accuracy of the impervious surface maps was assessed by using root mean square error (RMSE), mean absolute error (MAE), and systematic error (SE). The results indicate that the overall RMSE (0.10) was achieved for the impervious surface map estimated using the improved LSMA compared to 0.15 when conventional LSMA was employed. The improved LSMA method provides a way to enhance urban impervious surface estimation when LSMA is applied, a common approach for median spatial-resolution image mapping.

Estimating woody above-ground biomass in an arid zone of central Australia using Landsat imagery

Woody cover was found to be an easily measurable and significant variable for estimating woody above-ground biomass (AGB) based on in situ measurements in an arid zone of central Australia. In addition, the potential of woody cover to estimate woody AGB based on Landsat imagery was further tested. Linear spectral mixture analysis (LSMA) using multitemporal endmembers was employed to estimate the woody cover for Landsat imagery and the LSMA method was tested for different Landsat images under various drought conditions. The results show that the accuracy of the woody cover estimation increased as the accumulated rainfall prior to the image acquisition date decreased. Woody AGB across the study area was finally retrieved using the combination of plot-level woody AGB model and woody cover derived from dry-period Landsat images closest to the acquisition time of the field data, with an root mean square error of 0.798 t/ha . It is concluded that woody cover is able to replace tree basal area to estimate woody AGB at plot-level scale, and it is much more suitable for estimating woody AGB than the normalized difference vegetation index-AGB model for this arid zone covered with low-cover shrubs; however, woody AGB estimated using the proposed method is underestimated for some areas.

Spatial distribution characteristics and dynamics of &lt;i&gt;Eichhornia crassipes&lt;/i&gt; in the Shuikou Reservoir, Fujian Province

为研究闽江水口水库凤眼莲空间分布特征及动态变化,根据2001-2009年水口库区无云覆盖的24幅卫星遥感影像,构建研究区域凤眼莲干流-支流分布的空间结构,并通过建立端元的混合象元线性分解模型,对福建省水口水库不同时间、不同区域的凤眼莲分布特征进行系统分析,结果表明,随着时间的推移,凤眼莲的暴发区逐渐向下游迁移,同时,支流对研究区域凤眼莲的面积有重要影响,特别是河流较长、周边有人口居住的支流是凤眼莲源头治理的重点;各种人为或自然原因造成的水体富营养化、河流流速变缓等是凤眼莲泛滥的重要原因.建议针对研究区域上游及其周边进行养殖污染、工业污染整治,强化对污染源的监督管理,推进生态环境建设与保护,建设生态功能保护区等.本研究可为了解水口水库的凤眼莲时空分布规律,进而制定相应的预防与控制管理措施提供必不可少的数据基础和理论依据.;In order to study the spatial distribution characteristics and dynamics of Eichhornia crassipes in the Shuikou Reservoir, this study, using 24 unclouded remote sensing satellite images from 2001 to 2009 which covered the Shuikou Reservoir region, reconstructed the spatial structure of Eichhornia crassipes distributed in the mainstream and tributaries, and analyzed the situation of Eichhornia crassipes in the Shuikou Reservoir at different times and different observation zones according to the Linear Spectral Mixture Analysis Method. The results indicate that the outbreak area of Eichhornia crassipes moves from the upper reaches to the lower reaches gradually as time goes on. And some long tributaries with high riparian populations played an important role in the contribution of Eichhornia crassipes in this survey region. The significant factors about the fast growth of Eichhornia crassipes included water eutrophication, low velocity, etc.. It is suggested that local government should take actions to fight against the pollution from the industry, aquaculture, and poultry farming in this area, enhance the supervision and protection of ecological environment, and establish nature reserves. This study can enhance the understanding of the temporal and spatial distributions of Eichhornia crassipes, and then provide fundamental data and theory for managing and controlling the nuisance plants.

Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery

Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to estimate abundance fractions of materials present in an image pixel. In order for an LSMA-based estimator to produce accurate amounts of material abundance, it generally requires two constraints imposed on the linear mixture model used in LSMA, which are the abundance sum-to-one constraint and the abundance nonnegativity constraint. The first constraint requires the sum of the abundance fractions of materials present in an image pixel to be one and the second imposes a constraint that these abundance fractions be nonnegative. While the first constraint is easy to deal with, the second constraint is difficult to implement since it results in a set of inequalities and can only be solved by numerical methods. Consequently, most LSMA-based methods are unconstrained and produce solutions that do not necessarily reflect the true abundance fractions of materials. In this case, they can only be used for the purposes of material detection, discrimination, and classification, but not for material quantification. The authors present a fully constrained least squares (FCLS) linear spectral mixture analysis method for material quantification. Since no closed form can be derived for this method, an efficient algorithm is developed to yield optimal solutions. In order to further apply the designed algorithm to unknown image scenes, an unsupervised least squares error (LSE)-based method is also proposed to extend the FCLS method in an unsupervised manner.