Linear Spectral Unmixing Approach Research Articles

Using satellite image data to identify rice varieties through linear spectral unmixing method (case study: Karangjati Sub District, Ngawi Regency)

<p>Remote sensing technology has increasingly emerged as a potent tool for precision agriculture, particularly in facilitating the mapping and monitoring of crops on a large scale. An application of this technology is the identification of different types of rice by analyzing the pixels acquired in satellite images. Regrettably, the pixels in the image have been mixed from different recorded items. Therefore, they have the potential to influence the outcome of the identification. An effective approach to address this problem is to employ the linear spectral unmixing (LSU) technique. The LSU approach quantifies the ratio of pure objects in every pixel of an image by utilizing the spectral value associated with the endmember of the rice variety. The investigation was carried out in the Karangjati District during the generative stage (70 ± DAP) of the rice planting season. The data indicates that the dominant variety is Inpari 32 HDB. The data validation tests, which involved the use of a confusion matrix and Kappa analysis, resulted in an overall accuracy rate of 85.48% and a Kappa analysis score of 70.6%.<strong></strong></p>

Monitoring the Seasonal Hydrology of Alpine Wetlands in Response to Snow Cover Dynamics and Summer Climate: A Novel Approach with Sentinel-2

Climate change in the European Alps during recent years has led to decreased snow cover duration as well as increases in the frequency and intensity of summer heat waves. The risk of drought for alpine wetlands and temporary pools, which rely on water from snowmelt and provide habitat for specialist plant and amphibian biodiversity, is largely unknown and understudied in this context. Here, we test and validate a novel application of Sentinel-2 imagery aimed at quantifying seasonal variation in water surface area in the context of 95 small (median surface area <100 m2) and shallow (median depth of 20 cm) alpine wetlands in the French Alps, using a linear spectral unmixing approach. For three study years (2016–2018), we used path-analysis to correlate mid-summer water surface area to annual metrics of snowpack (depth and duration) and spring and summer climate (temperature and precipitation). We further sought to evaluate potential biotic responses to drought for study years by monitoring the survival of common frog (Rana temporaria) tadpoles and wetland plant biomass production quantified using peak Normalized Difference Vegetation Index (NDVI). We found strong agreement between citizen science-based observations of water surface area and Sentinel-2 based estimates (R2 = 0.8–0.9). Mid-summer watershed snow cover duration and summer temperatures emerged as the most important factors regulating alpine wetland hydrology, while the effects of summer precipitation, and local and watershed snow melt-out timing were not significant. We found that a lack of summer snowfields in 2017 combined with a summer heat wave resulted in a significant decrease in mid-summer water surface area, and led to the drying up of certain wetlands as well as the observed mortality of tadpoles. We did not observe a negative effect of the 2017 summer on the biomass production of wetland vegetation, suggesting that wetlands that maintain soil moisture may act as favorable microhabitats for above treeline vegetation during dry years. Our work introduces a remote sensing-based protocol for monitoring the surface hydrology of alpine wetland habitats at the regional scale. Given that climate models predict continued reduction of snow cover in the Alps during the coming years, as well as particularly intense warming during the summer months, our conclusions underscore the vulnerability of alpine wetlands in the face of ongoing climate change.

Characterization of urban materials in AVIRIS-NG data using a mixture tuned matched filtering (MTMF) approach

Assessment of energy fluxes and surface-atmosphere energy balance is considered an important study for an urban environment. The arrangement of manmade impervious surfaces (building and roads), their geometry and orientation, material composition, albedo/emissivity, topography, natural and delineated spaces of vegetation, and the concurrent atmospheric profiles together play a pivotal role in defining the urban morphological environment at micro-scale. Hyperspectral remote sensing images can prove to be instrumental in identifying the inherent material characteristics of the urban surfaces. The study focuses on identifying six man-made (asphalt, concrete, tin, iron, china mosaic, HDPE) and five natural features (lake water, river water, trees, soil, grass) in the city of Ahmedabad, India using ISRO-NASA airborne campaign hyperspectral data captured on February 11, 2016 under the Airborne Visible and InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG), of JPL (Jet Propulsion Laboratory), NASA bearing a spatial resolution of 8.1 m. The dimensionality reduction of the reflectance data was performed using a standard shift difference method in minimum noise fraction (MNF) technique by retaining the bands possessing coherent signals and dismissing the noise component within the data. The MNF data was further exposed to a partial linear spectral unmixing approach of mixture tuned matched filtering (MTMF) possessing the dual character of maximizing the target endmember response and suppressing the background redundant noisy information by a filtering and tuning process. It was observed that the overall accuracy of identification of various materials using random checkpoints (ranging from 50 to 200) varied from 82.3% to 87.7%. High accuracy was observed in the natural features than the man-made features/materials. The knowledge of materials in an urban purlieu shall aid in understanding the urban environment in terms of energy demand and energy-balance and further help in deciphering the cause of urban heating.

Linear spectral unmixing algorithm for modelling suspended sediment concentration of flash floods, upper Tekeze River, Ethiopia

Flash floods are the highest sediment transporting agent, but are inaccessible for in-situ sampling and have rarely been analyzed by remote sensing technology. Laboratory and field experiments were done to develop linear spectral unmixing (LSU) remote sensing model and evaluate its performance in simulating the suspended sediment concentration (SSC) in flash floods. The models were developed from continuous monitoring in the laboratory and the onsite spectral signature of river bed sediment deposits and flash floods in the Tekeze River and in its tributary, the Tsirare River. The Pearson correlation coefficient was used to determine the variability of correlations between reflectance and SSCs. The coefficient of determination (R2) and root mean square of error (RMSE) were used to evaluate the performance of the generated models. The results found that the Pearson correlation coefficient between SSCs and reflectance varied based on the level of the SSCs, geological colors, and grain sizes. The performance of the LSU model and empirical remote sensing approaches were computed to be R2 = 0.92, and RMSE = ±0.76 g/l in the Tsirare River and R2 = 0.91, and RMSE = ±0.73 g/l in the Tekeze River and R2 = 0.81, RMSE = ±2.65 g/l in the Tsirare river and R2 = 0.76, RMSE = ±10.87 g/l in the Tekeze River, respectively. Hence, the LSU approach of remote sensing was found to be relatively accurate in monitoring and modeling the variability of SSCs that could be applied to the upper Tekeze River basin.

大尺度不透水面遥感估算方法比较——以京津唐为例

PDF HTML阅读 XML下载 导出引用 引用提醒 大尺度不透水面遥感估算方法比较——以京津唐为例 DOI: 10.5846/stxb201309242350 作者: 作者单位: 中国科学院生态环境研究中心,中国科学院生态环境研究中心 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金面上项目(41371197);省部级项目(STSN-12-01) Remote sensing of impervious surface for the Beijing-Tianjin-Tangshan urban agglomeration: a comparison of different approaches Author: Affiliation: Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:城市不透水面既是常用的城市化程度指标,也是衡量环境质量的重要指标。采用遥感技术准确提取城市不透水面并分析其空间扩张过程,对生态城市建设具有重要意义。基于Landsat 5 TM影像,采用NDVI二元法和线性光谱分解法,分别提取北京、天津和唐山3个城市不透水面信息,并将不透水面估算结果与近同期的ALOS影像提取结果对比验证。结果表明,线性光谱分解法获取的不透水面结果较好,RMSE为20.6%,能有效提取大范围的不透水面信息。 Abstract:Impervious surfaces are mainly anthropogenic features such as paved roads, rooftops, driveways, sidewalks, and parking lots that are covered by impenetrable materials. With the urban expansion, vegetation and soils are replaced by impervious surfaces, which become a major ecological and environmental concern. This is because the increase of impervious surfaces generally leads to the decrease in vegetation, wetlands and agricultural lands, and consequently, to a series of environmental problems, such as the decease of groundwater recharge, the increase of surface runoffs and flood frequency and urban heat islands. The percent cover of impervious surfaces, as well as its spatial pattern, has been widely used as an indicator to quantify the urbanization level and urban environmental quality, and is essential to understand the interactions between human and the environment. Therefore, accurate mapping and estimating impervious surfaces is crucial for environmental and resources management. In this study, we compared and evaluated two methods: the Normalized Difference Vegetation Index (NDVI) based binary approach and the Linear Spectral Unmixing (LSU) method. These two approaches have been frequently used in mapping impervious surfaces. With the NDVI based binary approach, impervious surfaces are extracted based on information on vegetation distribution that can be well represented by NDVI,. Then vegetation fractional coverage was first estimated from a scaled NDVI, and then impervious surfaces were estimated as by subtracting the vegetation fraction from 1. This approach had the merit of simplicity. However, large errors may occur in impervious surface estimation. The LSU approach is based on the vegetation-impervious surface-soil (V-I-S) model proposed by Ridd in 1995, a novel conceptual model for remote sensing analysis of urban landscapes. The VIS model indicated that land cover in urban environment is a linear combination of these three components, that is, vegetation, impervious, and soils. The LSU approach has been widely used for remote sensing of impervious surfaces. This method provides a suitable technique to detect and map urban materials, and to address the mixed pixel problem in medium spatial resolution imagery. Taking the Beijing-Tianjin-Tangshan urban agglomeration as a case study, this research compared these two approaches on estimating impervious surfaces. The study area included Beijing City, Tianjin City, Tangshan City and Sanhe City, including a region with more than 40 000 square kilometers. Landsat 5 TM image data acquired in 2010 was used for mapping and estimating the impervious surfaces. A layer of impervious surfaces derived from ALOS images with spatial resolution of 2.5 m was used as a reference to evaluate the accuracies of the two methods. The results showed that the NDVI based binary approach had a root-mean-square error (RMSE) of 40.2%. The LSU approach was much better for impervious surfaces estimation than the NDVI based method, resulting in a RMSE of 20.0%. The residuals of the LSU approach ranged from -0.4 to 0.4. This accuracy was comparable to those from previous studies that were mostly conducted at a smaller geographical area, generally several thousands square kilometers. Our research expanded the knowledge of existing studies by proving that the LSU approach could be applied to a large study area for mapping impervious surfaces with acceptable accuracy. 参考文献 相似文献 引证文献

Determination of Carbonate Rock Chemistry Using Laboratory-Based Hyperspectral Imagery

The development of advanced laboratory-based imaging hyperspectral sensors, such as SisuCHEMA, has created an opportunity to extract compositional information of mineral mixtures from spectral images. Determining proportions of minerals on rock surfaces based on spectral signature is a challenging approach due to naturally-occurring minerals that exist in the form of intimate mixtures, and grain size variations. This study demonstrates the application of SisuCHEMA hyperspectral data to determine mineral components in hand specimens of carbonate rocks. Here, we applied wavelength position, spectral angle mapper (SAM) and linear spectral unmixing (LSU) approaches to estimate the chemical composition and the relative abundance of carbonate minerals on the rock surfaces. The accuracy of these classification methods and correlation between mineral chemistry and mineral spectral characteristics in determining mineral constituents of rocks are also analyzed. Results showed that chemical composition (Ca-Mg ratio) of carbonate minerals at a pixel (e.g., sub-grain) level can be extracted from the image pixel spectra using these spectral analysis methods. The results also indicated that the spatial distribution and the proportions of calcite-dolomite mixtures on the rock surfaces vary between the spectral methods. For the image shortwave infrared (SWIR) spectra, the wavelength position approach was found to be sensitive to all compositional variations of carbonate mineral mixtures when compared to the SAM and LSU approaches. The correlation between geochemical elements and spectroscopic parameters also revealed the presence of these carbonate mixtures with various chemical compositions in the rock samples. This study concludes that the wavelength position approach is a stable and reproducible technique for estimating carbonate mineral chemistry on the rock surfaces using laboratory-based hyperspectral data.

A MAXIMUM ENTROPY MODEL OF THE BEARDED CAPUCHIN MONKEY HABITAT INCORPORATING TOPOGRAPHY AND SPECTRAL UNMIXING ANALYSIS

Abstract. Movement patterns of bearded capuchin monkeys (Cebus (Sapajus) libidinosus) in northeastern Brazil are likely impacted by environmental features such as elevation, vegetation density, or vegetation type. Habitat preferences of these monkeys provide insights regarding the impact of environmental features on species ecology and the degree to which they incorporate these features in movement decisions. In order to evaluate environmental features influencing movement patterns and predict areas suitable for movement, we employed a maximum entropy modelling approach, using observation points along capuchin monkey daily routes as species presence points. We combined these presence points with spatial data on important environmental features from remotely sensed data on land cover and topography. A spectral mixing analysis procedure was used to generate fraction images that represent green vegetation, shade and soil of the study area. A Landsat Thematic Mapper scene of the area of study was geometrically and atmospherically corrected and used as input in a Minimum Noise Fraction (MNF) procedure and a linear spectral unmixing approach was used to generate the fraction images. These fraction images and elevation were the environmental layer inputs for our logistic MaxEnt model of capuchin movement. Our models' predictive power (test AUC) was 0.775. Areas of high elevation (>450 m) showed low probabilities of presence, and percent green vegetation was the greatest overall contributor to model AUC. This work has implications for predicting daily movement patterns of capuchins in our field site, as suitability values from our model may relate to habitat preference and facility of movement.

Spectral Unmixing Approach in Remotely Sensed Forest Cover Estimation: A Study of Subtropical Forest in Southeast China

Abstract Addressing the problem of spectral mixing in remotely sensed forest cover mapping, a linear spectral unmixing approach was employed in the study to assess if sub-pixel method would improve forest cover estimation accuracy in the context of complex subtropical forest ecosystem. After masking out water bodies using Modified Normalized Difference Water Index (MNDWI), the TM imagery of Pingnan County, Guangxi Zhuang Autonomous Region, China, was processed with Minimum Noise Fraction (MNF) Rotation transform and Pixel Purity Index (PPI), thus “pure” spectral endmembers of woody cover, herbaceous vegetation and bare ground were extracted as input into the spectral unmixing algorithm and produced forest map. The forest percentage is 55.7%, overestimated by 0.8% when compared with the National Continuous Forest Inventory 2004 statistics, reporting a fair agreement with ground truth. The approach also shows a better performance than Spectral Angle Mapper (SAM) classification (overall RMSE of 9.2 compared with 10.7 for latter).

Myosin‐actin interaction in Dictyostelium cells revealed by GFP‐based strain sensor and validated linear spectral unmixing

Myosin is an actin-based motor protein that is involved in a wide range of cellular motile processes. Although in vitro properties of the myosin-actin interaction have been extensively studied, the interaction in vivo remains poorly understood. Recently, we developed a GFP-based strain sensor termed PriSSM (PRIM-based strain sensor module), by using the proximity imaging (PRIM) technique, which detects spectral changes of two GFP molecules that are in direct contact. Using PriSSM-myosin II fusion proteins, the interaction between myosin II and F-actin can be detected in Dictyostelium cells. In the spectroscopic measurements of PriSSM, to decompose the measured spectra of the cells expressing the sensor proteins into the contributions from the sensor and the background autofluorescence, we applied the linear spectral unmixing approach, which was based on the assumption that the errors at each wavelength were independent. Cellular autofluorescence, however, often includes systematic errors, so that the unmixing procedures might lead to biased estimates. Here, to validate our spectral unmixing procedures, we estimate the possible maximum errors in the fluorescence ratio values that are obtained by unmixing spectra including such systematic errors. This estimation provided a general criterion to validate the results obtained by linear unmixing of spectra including serially correlated error terms. Using the proposed criterion and PriSSM-myosin II fusion proteins, we examined the interaction between myosin II and F-actin in Dictyostelium cells under several different conditions. The spectroscopic results, together with the microscopic observations of the cells expressing the proteins, suggest that the formation of myosin filaments through the tail region has only a slight effect on binding to F-actin but has significant effects on the cortical localization of myosin II.

A comparative study for unmixing based Landsat ETM+ and ASTER image fusion

The mineral environment of the Bouaouane–Jebel (Hill) Hallouf mine, in the north of Tunisia, is monitored and analyzed by making use of the laboratory analysis and remote sensing images, ETM+ as well as ASTER VNIR and SWIR data, acquired in the same period. The main contribution of this paper consists of a methodology using multispectral multi-sensor fusion for the refinement of the mine tailing cartography around the studied mine. The developed methodology is based on the linear spectral unmixing approach which is applied to a multispectral hybrid image. This image was generated from the fusion of Landsat ETM+ and ASTER SWIR data. A comparative study is made between the hybrid and ASTER (VNIR and SWIR) images classification with respect to laboratory analysis. The given results show that the fusion of Landsat ETM+ and ASTER SWIR multispectral image yields the best mineral detection.

Assessing plantation canopy condition from airborne imagery using spectral mixture analysis and fractional abundances

Pine plantations in Australia are subject to a range of abiotic and biotic damaging agents that affect tree health and productivity. In order to optimise management decisions, plantation managers require regular intelligence relating to the status and trends in the health and condition of trees within individual compartments. Remote sensing technology offers an alternative to traditional ground-based assessment of these plantations. Automated estimation of foliar crown health, especially in degraded crowns, can be difficult due to mixed pixels when there is low or fragmented vegetation cover. In this study we apply a linear spectral unmixing approach to high spatial resolution (50cm) multispectral imagery to quantify the fractional abundances of the key image endmembers: sunlit canopy, shadow, and soil. A number of Pinus radiata tree crown attributes were modelled using multiple linear regression and endmember fraction images. We found high levels of significance (r2=0.80) for the overall crown colour and colour of the crown leader (r2=0.79) in tree crowns affected by the fungal pathogen Sphaeropsis sapinea, which produces both needle necrosis and chlorosis. Results for stands associated with defoliation and chlorosis through infestation by the aphid Essigella californica were lower with an r2=0.33 for crown transparency and r2=0.31 for proportion of crown affected. Similar analysis of data from a nitrogen deficient site produced an outcome somewhat in between the other two damaging agents. Overall the sunlit canopy image fraction has been the most important variable used in the modelling of forest condition for all damaging agents.

ANALYSIS OF LAND SAT-5 TM IMAGERY FOR EXTRACTING AQUACULTURE FARMS INFORMATION IN THE KOREAN COASTAL WATERS

The objective of the present study is to compare certain conventional satellite image processing techniques with the recently evolved linear spectral unmixing method and to ascertain the best possible technique that can identify and position of aquaculture farms accurately in and around the Younggwang coastal region of Korea. Various conventional techniques existed to extract such information are spectral enhancement and classification. However, these techniques performed on the Landsat-TM imagery do not seem to yield accurate information about the aquaculture farms, and instead lead to misinterpretation and misclassification. A large discrepancy between the actual and extracted information results from spectral confusion and inadequate spatial resolution of the sensor, which leads to occurrence of mixture pixels or mixels, which are known to be the source of errors in the classified image. To over come this problem, more recently evolved methods such as step-by-step iterative partial spectral end-member extraction linear spectral unmixing methods are attempted. Large errors in extraction of aquaculture farms information through the conventional classification techniques are significantly minimized with the step-by-step iterative partial spectral end-member extraction approach and the accuracy of classification is further improved with linear spectral unmixing approach. The aquaculture fraction derived from unmxing of TM image data was validated using NDVI values in absence of field data. NDVI and aquaculture fraction are positively correlated (R2 = 0.91), indicating the reliability of the sub-pixel classification

Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands

The development of vegetation cover is one of the primary indicators for land degradation, stability, or regeneration in regions threatened by overgrazing. This paper addresses the problem how spatially explicit information about degradation processes in European Mediterranean rangelands can be derived from long time series of satellite data. The selected test site in central Crete, Greece, is considered to be representative for the highly heterogeneous character of such landscapes. The monitoring approach comprises the time period between 1977 and 1996, covered by nine Landsat TM and four Landsat MSS images. Special emphasis has hence been put on the evaluation of potentials and drawbacks when coupling Landsat TM and MSS based results. The data sets were geometrically and radiometrically pre-processed in a rigorous fashion, followed by a linear spectral unmixing approach and a time series analysis of vegetation fraction images. Based on the resulting map, the spatio-temporal patterns of vegetation cover changes are explained. Even a test site such as central Crete, with its limited spatial extend, exhibits heterogeneous patterns of change, supporting the hypothesis that long time series of EOS data from Landsat-like sensors are mandatory to identify the relevant changes at landscape level.

Applying linear spectral unmixing approach to the research of land cover change in Karst area: A case in Guanling County of Guizhou Province

Vegetation and bare soil are the main land cover types in Karst and non-Karst area.Bare rock another land cover type only appearing in Karst area,is distributed extensively. There are a variety of methods to detect land cover in Karst area with remotely sensed imageries. But most ordinary remotely sensed approaches can't get the three attributes at the same time. In fact,they almost can do nothing in detecting the quantitative result of rock desertification. The linear spectral unmixing approach,based on the linear mixture model can estimate vegetation cover, bare soil and bare rock abundance at the sub-pixel scale. It's fit for studying land cover in Karst area because of the obtained quantitative unmixing results of vegetation cover, bare soil, bare rock and other types. In the case study of this paper, four endmembers were identified from two TM images of six bands taken on 17th February 1987 and 27th November 1999 respectively. They represent four land cover types: vegetation, bare soil, rock and shadow. The abundant distribution and RMS (Root Mean Square) distribution of the four land types were derived by linear spectral unmixing. The land cover changes could be learned from the multi-time comparison. During the period from 1987 to 1999, the proportion of bare soil reduced evidently due to the increase of vegetation cover and the improved cultivation management. While, the proportion of rocky desertification enhanced owing to the little amount of soil in some place. Bare soil reduced and rock increased mostly in rainfed cropland, grassland and economic woodland. Over-cultivating and overgrazing are still the main driving forces of rocky desertification in Karst area. So people still need to pay attention to water and water soil conservation in economic woodland because economic woodland mainly came from dry land reforest. The linear spectral unmixing approach still needs to be improved, though it has been proved fitting for dealing with mixture pixel and detecting quantitative results of land cover by the case study and many other literature. If endmembers need to be identified from image, it's hard to detect those elements without extraordinary characters presenting in images of different bands. For example, urban region is just such an element difficult to detect. So the approach is not the most suitable method for urban region or rapid urbanized area. And the impact of shadow is still a problem, just as in other methods.