Linear Spectral Unmixing Method Research Articles

On-the-fly spectral unmixing based on Kalman filtering

This work introduces an on-the-fly (i.e., online) linear spectral unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis. After deriving a sequential counterpart of the conventional linear mixing model, the proposed approach recasts the linear unmixing problem into a linear state-space estimation framework. Under Gaussian noise and state models, the estimation of the pure spectra can be efficiently conducted by resorting to Kalman filtering. Interestingly, it is shown that this Kalman filter can operate in a lower-dimensional subspace to lighten the computational burden of the overall unmixing procedure. Experimental results obtained on synthetic and real Raman data sets show that this Kalman filter-based method offers a convenient trade-off between unmixing accuracy and computational efficiency, which is crucial for operating in an on-the-fly setting. The proposed method constitutes a valuable building block for benefiting from acquisition and processing frameworks recently proposed in the microscopy literature, which are motivated by practical issues such as reducing acquisition time and avoiding potential damages being inflicted to photosensitive samples. The code associated with the numerical illustrations reported in this paper is freely available online at https://github.com/HKouakou/KF-OSU.

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 addressing 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>

A phenology-based linear spectral unmixing method for rice varieties identification from Landsat 8 Satellite Image in Ngawi District, East Java, Indonesia

Abstract. Cipta IM, Jaelani LM, Sanjaya H. 2024. A phenology-based linear spectral unmixing method for rice varieties identification from Landsat 8 Satellite Image in Ngawi District, East Java, Indonesia. Biodiversitas 25: 1691-1702. Ngawi District is a prominent rice-producing district in East Java, Indonesia. Based on the BPS data from 2021, Ngawi District yielded a total of 786.49 thousand tons of paddy. Multiple strategies to enhance rice production include enhancing rice productivity, increasing paddy fields, and using effective land management techniques. Utilizing a superior breed of seeds can augment productivity. Remote sensing can be utilized to obtain such data, one of which is the Linear Spectral Unmixing (LSU) technique. The LSU approach is used to determine the proportion of a pure object's presence in a pixel. The Inpari 32 rice variety is the predominant type identified from the analysis of the processing outcomes utilizing satellite imagery. In addition, validation was performed by utilizing many sample locations distributed around Ploso Lor and Rejuno Villages, resulting in consistent findings between the data analysis and the actual conditions observed in the field. The RMSE calculation was conducted on both sets of processing results, and the most optimal RMSE value was achieved when processing was performed using Landsat 8 image data, yielding an RMSE value of roughly ±1.2%. The accuracy of the model was evaluated using a confusion matrix, which yielded an overall accuracy rate of 86.67%.

Timely monitoring of soil water-salt dynamics within cropland by hybrid spectral unmixing and machine learning models

Soil salinization and water scarcity are main restrictive factors for irrigated agriculture development in arid regions. Knowing dynamics of soil water and salt content is an important antecedent in remediating salinized soils and optimizing irrigation management. Previous studies mostly used remote sensing technologies to individually monitor water or salt content dynamics in agricultural areas. Their ability to asses different levels of crop water and salt management has been less explored. Therefore, how to extract effective diagnostic features from remote sensing images derived spectral information is crucial for accurately estimating soil water and salt content. In this study, Linear spectral unmixing method (LSU) was used to obtain the contribution of soil water and salt to each band spectrum (abundance), and endmember spectra from Sentinel-2 images. Calculating spectral indices and selecting optimal spectal combination were individually based on soil water and salt endmember spectra. The estimation models were constructed using six machine learning algorithms: BP Neural Network (BPNN), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Gradient Boost Regression Tree (GBRT), and eXtreme Gradient Boosting tree (XGBoost). The results showed that the spectral indices calculated from endmember spectra were able to effectively characterize the response of crop spectral properties to soil water and salt, which circumvent spectral ambiguity induced by water-salt mixing. NDRE spectral index was a reliable indicator for estimating water and salt content, with determination coefficients (R2) being 0.55 and 0.57, respectively. Compared to other models, LSU-XGBoost model achieved the best performance. This model properly reflected the process of soil water-salt dynamics in farmland during crop growth period. This study provided new methods and ideas for soil water-salt estimation in dry irrigated agricultural areas, and provided decision support for governance of salinized land and optimal management of irrigation.

Enhancing Solar-Induced Fluorescence Interpretation: Quantifying Fractional Sunlit Vegetation Cover Using Linear Spectral Unmixing

Solar-induced chlorophyll fluorescence (SIF) is closely related to plant photosynthetic activity and has been used in different studies as a proxy for vegetation health status. However, in order to use SIF as a relevant indicator of plant physiological stress, it is necessary to accurately quantify the amount of light absorbed by the photosynthetic plant pigments, called the absorbed photosynthetically active radiation (APAR). The ratio between fluorescence emission and light absorption (i.e., SIF and APAR) is known as the fluorescence quantum efficiency (FQE). In this work, simultaneous measurements of SIF and reflected radiance were performed both at the leaf and canopy levels for Salvia farinacea and Datura stramonium plants. With the aim of disentangling the proportion of sunlit and shaded absorbed PAR, an ad hoc experimental setup was designed to provide a wide range of fraction vegetation cover (FVC) canopy settings. A linear spectral unmixing method was proposed to estimate the contribution of soil, sunlit, and shaded vegetation from the total reflectance spectrum measured at the canopy level. Later, the retrieved sunlit FVC (FVCsunlit) was used to estimate the (dominant) green APAR flux, and this was combined with the integral of the spectrally resolved fluorescence to calculate the FQE. The results of this study demonstrated that under no-stress conditions and independently of the FVC, similar FQE values were observed when SIF was properly normalised by the green APAR flux. The results obtained showed that the reflectance spectra retrieved using a linear unmixing method had a maximum RMSE of less than 0.03 along the spectrum. The FVCsunlit evaluation showed an RMSE of 14% with an R2 of 0.84. Moreover, the FQE values obtained at the top of the canopy (TOC) were found statistically comparable to the reference values at the leaf level. These results support further efforts to improve the interpretation of fluorescence based on field spectroscopy and the further upscaling to imaging spectroscopy at airborne and satellite levels.

Chlorophyll-a concentrations in the Arabian Gulf waters of arid region: A case study from the northern coast of Qatar

Remote sensing of spectrally active Chlorophyll-a (Chl-a) in the gulf water of an arid region is important to understand the spatial and temporal variations of phytoplankton and biomass in the water. This study characterizes the spectral absorption of Chl-a and detects and maps the Chl-a of Al Arish–Al Ghariyah coastal region of northern Qatar using the data of Hyperion of EO-1, MultiSpectral Instrument (MSI) of Sentinel-2, and Operational Land Imager (OLI) of Landsat-8 satellites. The study of image spectra of Chl-a of coastal water of the Al Ghariyah region using the Hyperion image showed spectral band absorptions near 450 and 475 nm in the blue region and around 640, 730, 760, and 830 nm in the red region. The analysis of Hyperion data using image spectra by linear spectral unmixing (LSU) method showed the occurrence of Chl-a in very shallow and shallow water. The mapping of Chl-a of the Al Arish–Al​ Ghariyah coastal region was carried out using MSI and OLI data by NDCI, 2BDA, 3BDA, and FLH violet algorithms, which showed the concentrations and distributions of the Chl-a in the region. The performance of the algorithms was studied using WorldView-3 data, which provided the R2 values of 60% and the best suitability of the NDCI algorithm and MSI data to map the concentration of Chl-a. All the results were validated using field measurements, in-situ measurements, and laboratory analyses of field samples. The measurements of Chl-a showed the occurrence of poor occurrence of Chl-a and confirmed the imagery results. The remote sensing results with physical and chemical parameters of water samples allowed us to assess Chl-a concentrations in the region. This study evaluates the sensor’s capability and applications of satellite data and algorithms to map concentrations of Chl-a in the Gulf waters of the arid region.

Nonlinear Spectral Unmixing of Hyperspectral Imagery Based on Hapke Model and Relevance Vector Regression Algorithm

Abstract This paper put forward a nonlinear hyperspectral imagery unmixing method based on Hapke nonlinear spectral mixture model and the relevance vector regression. This paper presents a method of relevance vector regression algorithm for unmixing of hyperspectral images. The spectrum was mixed to train the relevance vector regression algorithm according to Hapke non-linear mixing model. This method made our training process coincident with the physical feature of non-linear spectral mixing process. The experiments of smiulated data and hyperspectral imagery are conducted to validate the methods. The smiulated data and hyperspectral image was used for accuracy assessment. Results show that relevance vector regression algorithm can provide better result as compared with linear spectral unmixing model and support vector regression method. And our method provides better result with relative less relevant vector. The proposed algorithm reduced computational complexity of the spectral unmixing problem.

Combined use of band shape algorithm, linear spectral un-mixing on Clementine & Moon Mineralogy Mapper data for identifying the imprints of magmatic differentiation – A study around Aristarchus Plateau

In this study, we have implemented a band shape algorithm (BSA) to identify the surface distribution of different minerals on parts of the Aristarchus Plateau using selected bands of Clementine and hyperspectral Moon Mineralogy Mapper (M3) data. BSA mineral maps derived using spectral bands of Clementine and M3 sensors could determine broadly similar surface distribution of minerals in the study area. Here we show that M3 based BSA mineral map could detect additional mineralogical details in selected places, especially around Vallis Schroteri, Aristarchus etc. areas. Therefore, we implemented linear spectral un-mixing (LSU) method on hyperspectral bands of M3 data using the reference spectra of Reflectance Experiment Laboratory (RELAB) to estimate the relative abundance of different minerals in above-mentioned areas. Based on the relative abundance of minerals in the LSU map, we could infer the presence of different rocks such as dunite/troctolite, norite, harzburgite, olivine-pyroxenite in those sites of the study area. The relative abundance of each mineral in a particular place was used as the basis for delineating specific rock in that place of the study area. These mineralogically diverse rocks with distinct proportions of mafic mineral and plagioclase content are exposed along a lunar structure that has been delineated using Gravity Recovery and Interior Laboratory (GRAIL) data. We hypothesize that some of these rocks might have formed by a magmatic differentiation process triggered by the structure by separating magma from the crystallized rocks and allowing the magma to compositionally evolve to produce different rocks under the influence of syn-magmatic structural activity.

Hypersharpening by an NMF-Unmixing-Based Method Addressing Spectral Variability

Hypersharpening consists in generating an unobservable high-spatial-resolution hyperspectral image by fusing an observed low-spatial-resolution hyperspectral image with an observed high-spatial-resolution panchromatic or multispectral one. The obtained image preserves the high spectral resolution of the first image and the high spatial resolution of the second one. Unlike standard hypersharpening methods that do not consider the spectral variability phenomenon, in this letter, a new approach, which addresses this phenomenon, is proposed for fusing hyperspectral and multispectral remote sensing images. This approach, linked to linear spectral unmixing methods, is based on an extension of nonnegative matrix factorization (NMF), namely the inertia-constrained pixel-by-pixel NMF (IP-NMF) algorithm. The developed fusion algorithm, called hyperspectral and multispectral data fusion based on IP-NMF (HMF-IPNMF), is applied to synthetic and real data sets. Experimental results clearly show that the developed fusion method yields sharpened hyperspectral images with higher spectral and spatial fidelities when compared to those provided by tested state-of-the-art methods that do not take spectral variability into account.

A novel spectral index for estimating fractional cover of non-photosynthetic vegetation using near-infrared bands of Sentinel satellite

Non-photosynthetic vegetation (NPV) is an essential component in various vegetation-soil ecosystems. Both phenology and disturbance lead to a transition from photosynthetic vegetation to NPV and vice versa. Due to the similar spectral reflectance of NPV and bare soil (BS) in the visible-near infrared region (400–1000 nm), NPV and BS separation is relying on the shortwave infrared (SWIR) bands in most cases. The lignin and cellulose absorption feature is around 2100 nm, which is the most distinctive feature of NPV. However, the water absorption feature is much stronger in the SWIR, increasing the difficulty for NPV-BS separation when wet. Recently, Sentinel-2/3 satellites add more bands in the near infrared (NIR), which provide an extra opportunity for index building and application. Based on the difference captured by derivative spectra, a spectral index, NPV-Soil Separation Index (NSSI), is proposed to realize the separation using two NIR bands within 750–900 nm range in this study. Using spectra of photosynthetic vegetation (PV), NPV, and BS acquired from world-recognized spectral libraries, NSSI is built and validated as effective for lab-collected data. With the triangle method, one of the linear spectral unmixing methods, the fractional cover of PV, NPV, and BS can be estimated. Over a woodland study area, the fractional cover retrieved by cellulose absorption index (CAI) and NDVI combination of ZY1-02D AHSI hyperspectral image is 26.41%, 37.56%, 36.03% for PV, NPV, and BS in order. With the proposed NSSI-NDVI combination, the corresponding estimated fractional cover is 23.31%, 38.44%, 38.25% using Sentinel-2 MSI and 24.58%, 36.74%, and 38.68% using Sentinel-3 OLCI image. The comparable validation result confirms that the proposed NSSI is effective for NPV-BS separation. Moreover, the triangle method of NSSI-NDVI combination is applied on both grassland and cropland images to examine its feasibility on varied types of typical vegetation-soil ecosystems, and the well-built triangular space supports its feasibility. Relying on NIR bands, NSSI can avoid strong water absorption in the SWIR. Also, the feasibility of NSSI being used on multiple multispectral satellite sensors, especially the Sentinel series, makes continuous mapping for NPV over a large spatial scale possible.

Learning Optical Scattering Through Symmetrical Orthogonality Enforced Independent Components for Unmixing Deep Tissue Photoacoustic Signals

Noninvasive mapping of chromophore distribution in deep tissue is highly desired in numerous biomedical applications. Multispectral photoacoustic (PA) imaging fused with linear spectral unmixing is employed to generate 2-D and 3-D maps of tissue chromophores. However, wavelength and depth dependent attenuation of optical fluence leads to nonuniform variations in the spectral behavior of biomolecules affecting the accuracy of conventional linear unmixing methods. To address this, a modified independent component analysis (ICA) model is proposed to construct adaptive and statistically independent components for each chromophore from the given spectral PA data. The model was trained with custom designed multispectral PA imaging simulations consisting of both endogenous (oxy- and deoxy-hemoglobin) and exogenous (indocyanine green - ICG) chromophores inside 30 mm deep tissue. End-to-end unsupervised nature of the proposed approach made the process independent of human labeling and outperformed the standard linear spectral unmixing method when tested on a set of simulated phantoms, experimental phantoms, and in vivo mouse imaging data.

Remote sensing of inland Sabkha and a study of the salinity and temporal stability for sustainable development: A case study from the West coast of Qatar

The inland sabkha of the Arabian Gulf is important to study for the occurrence of minerals, rocks, soil salinity, and stability of the sabkha due to the high demand for infrastructure and agriculture development region. This study describes the spectral absorptions of evaporite minerals, discriminates rocks, maps salt crusts, gypsiferous soil flats, and soil salinity, and studies the temporal stability of an inland sabkha of the Dukhan area, west coast of the State of Qatar. This was performed using satellite data of the Hyperion of EO1, ASTER of Terra, and multispectral instrument (MSI) of Sentinel-2. The occurrence of minerals in the area is detected using Hyperion data by the linear spectral unmixing (LSU) method and studied for their spatial distribution. The different geological formations of the sabkha were discriminated by using the VNIR (visible and near-infrared) and SWIR (shortwave infrared) spectral bands from ASTER by principal component analysis (PCA). The image developed by using the principal components (R:PC2, G:PC3, B:PC5) showed the formations in different tones. Salinity of the area was mapped using monthly data of MSI from 2018 to 2020 by normalized difference salinity index (NDSI) (band11-band12)/(band11 + band12). The results of the index displayed the distribution of salinity in the area. Besides, moisture of the area was studied by using the normalized difference moisture index (NDMI) (b8-b11)/(b8 + b11) and described the temporal stability of the sabkha. All the results of image analyses were validated through field and laboratory studies. The study of laboratory spectra of evaporite minerals namely gypsum, anhydrite, and halite present in the salt crusts and gypsiferous soil flats showed their unique spectral absorptions in between 1.4–1.5 μm and 1.9–2.0 μm whereas, the calcite and dolomite minerals of the carbonate formations exhibited deep absorptions near 2.345 and 2.495 μm respectively.

WorldView-3 mapping of Tarmat deposits of the Ras Rakan Island, Northern Coast of Qatar: Environmental perspective

This study characterizes the spectral behavior of tarmats and maps the tarmat deposits found along the coast of Ras Rakan Island off Qatar using WorldView-3 (WV-3) sensor data. The laboratory spectra of tar materials showed diagnostic absorptions features at 0.6 and 1.1 μm in the visible and near-infrared (VNIR) and 1.52, 1.73, 2.04, and 2.31 μm in the short wave infrared (SWIR) region. The panchromatic grayscale image and FCC showed the tarmat deposit as a linear warp feature between beach and water. The mapping of deposits using WV-3 data by decorrelation stretch and Linear Spectral Unmixing (LSU) methods discriminated the tarmats from the sandy soil, vegetation and sabkha features in a different tone. The capability of WV-3 sensor and the potential of image processing methods were verified by mapping the tar distribution of the Ras Ushayriq and NE of Al Ruwais.

Application of Fengyun-4 Satellite to Flood Disaster Monitoring through a Rapid Multi-Temporal Synthesis Approach

Fengyun-4A (FY-4A) belongs to the second generation of geostationary meteorological satellite series in China. Its observations with high frequency and resolution provide a better data basis for monitoring of extreme weather such as sudden flood disasters. In this study, the flood disasters occurred in Bangladesh, India, and some other areas of South Asia in August 2018 were investigated by using a rapid multi-temporal synthesis approach for the first time for removal of thick clouds in FY-4A images. The maximum between-class variance algorithm (OTSU; developed by Otsu in 2007) and linear spectral unmixing methods are used to extract the water area of flood disasters. The accuracy verification shows that the water area of flood disasters extracted from FY-4A is highly correlated with that from the high-resolution satellite datasets Gaofen-1 (GF-1) and Sentinel-1A, with the square correlation coefficient R2 reaching 0.9966. The average extraction accuracy of FY-4A is over 90%. With the rapid multi-temporal synthesis approach used in flood disaster monitoring with FY-4A satellite data, advantages of the wide coverage, fast acquisition, and strong timeliness with geostationary meteorological satellites are effectively combined. Through the synthesis of multi-temporal images of the flood water body, the influence of clouds is effectively eliminated, which is of great significance for the real-time flood monitoring. This also provides an important service guarantee for the disaster prevention and reduction as well as economic and social development in China and the Asia–Pacific region.

Extraction of Bohai Sea ice from MODIS data based on multi-constraint endmembers and linear spectral unmixing

ABSTRACT A Linear Spectral Unmixing method based on Multi-Constraint Endmembers (LSU-MCE) is proposed to extract the sea ice in the Bohai Sea using the Moderate Resolution Imaging Spectroradiometer (MODIS) images. The method first extracts the spectral features, texture features, shape features, and temperature features of the sea ice and sea water, and then constructs the rules using the features to purify the endmembers, and finally decomposes the pixels using the linear spectral unmixing method. The method can properly detect sea ice including thin ice, drift ice, and melt ponds. Besides, the turbid sea water is separated from sea water as a separate endmember. So the proposed method can eliminate the disturbance caused by suspended sediment and successfully detect sea ice in high sediment waters. The method has good robustness to MODIS images of different satellites (Terra and Aqua) and the stripe noise. The method has been successfully applied to time series data of MODIS and the results show that it has high accuracy for sea ice detection in the Bohai Sea, with better visual results, and higher overall accuracy and kappa coefficient ().

Anomaly Detection of Hyperspectral Images Based on Linear Unmixing and Low-Rank Representation

Anomaly detection is an important branch in hyperspectral detection. There are some challenging problems mainly due to the highly mixture nature and noise corruption. In this paper, we propose a hyperspectral anomaly detection method based on Linear Unmixing and Low-Rank Representation (LU-LRR) with construction of a more robust dictionary. Firstly, the Kernel-PCA (KPCA) is used to estimate the numbers of endmembers and mine high-order correlation among spectral bands of original hyperspectral data. Secondly, data are decomposed into a product form of endmember matrix and abundance matrix based on linear spectral unmixing method. Abundance matrix usually possesses more distinctive feature to distinguish anomaly from background compared with original data. Finally, LRR is exploited to decompose abundance matrix into low-rank component and sparse component indicating background and anomalies separately, and furtherly introduce a more robust subspace basis dictionary based on dictionary learning into low-rank component. Experiments in real hyperspectral images have demonstrated that LU-LRR is more effective than traditional anomaly detection methods

A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas

China has been facing serious land degradation and desertification in its north and northwest arid and semi-arid areas. Monitoring the dynamics of percentage vegetation cover (PVC) using remote sensing imagery in these areas has become critical. However, because these areas are large, remote, and sparsely populated, and also because of the existence of mixed pixels, there have been no accurate and cost-effective methods available for this purpose. Spectral unmixing methods are a good alternative as they do not need field data and are low cost. However, traditional linear spectral unmixing (LSU) methods lack the ability to capture the characteristics of spectral reflectance and scattering from endmembers and their interactions within mixed pixels. Moreover, existing nonlinear spectral unmixing methods, such as random forest (RF) and radial basis function neural network (RBFNN), are often costly because they require field measurements of PVC from a large number of training samples. In this study, a cost-effective approach to mapping PVC in arid and semi-arid areas was proposed. A method for selection and purification of endmembers mainly based on Landsat imagery was first presented. A probability-based spectral unmixing analysis (PBSUA) and a probability-based optimized k nearest-neighbors (PBOkNN) approach were then developed to improve the mapping of PVC in Duolun County in Inner Mongolia, China, using Landsat 8 images and field data from 920 sample plots. The proposed PBSUA and PBOkNN methods were further validated in terms of accuracy and cost-effectiveness by comparison with two LSU methods, with and without purification of endmembers, and two nonlinear approaches, RF and RBFNN. The cost-effectiveness was defined as the reciprocal of cost timing relative root mean square error (RRMSE). The results showed that (1) Probability-based spectral unmixing analysis (PBSUA) was most cost-effective and increased the cost-effectiveness by 29.3% 29.3%, 33.5%, 50.8%, and 53.0% compared with two LSU methods, PBOkNN, RF, and RBFNN, respectively; (2) PBSUA, RF, and RBFNN gave RRMSE values of 22.9%, 21.8%, and 22.8%, respectively, which were not significantly different from each other at the significance level of 0.05. Compatibly, PBOkNN and LSU methods with and without purification of endmembers resulted in significantly greater RRMSE values of 27.5%, 32.4%, and 43.3%, respectively; (3) the average estimates of the sample plots and predicted maps from PBSUA, PBOkNN, RF, and RBFNN fell in the confidence interval of the test plot data, but those from two LSU methods did not, although the LSU with purification of endmembers improved the PVC estimation accuracy by 25.2% compared with the LSU without purification of endmembers. Thus, this study indicated that the proposed PBSUA had great potential for cost-effectively mapping PVC in arid and semi-arid areas.

Directional and Zonal Analysis of Urban Thermal Environmental Change in Fuzhou as an Indicator of Urban Landscape Transformation

Urban expansion results in landscape pattern changes and associated changes in land surface temperature (LST) intensity. Spatial patterns of urban LST are affected by urban landscape pattern changes and seasonal variations. Instead of using LST change data, this study analysed the variation of LST aggregation which was evaluated by hotspot analysis to measure the spatial dependence for each LST pixel, indicating the relative magnitudes of the LST values in the neighbourhood of the LST pixel and the area proportion of the hotspot area to gain new insights into the thermal effects of increasing impervious surface area (ISA) caused by urbanization in Fuzhou, China. The spatio-temporal relationship between urban landscape patterns, hotspot locations reflecting urban land cover change in space and the thermal environment were analysed in different sectors. The linear spectral unmixing method of fully constrained least squares (FCLS) was used to unmix the bi-temporal Landsat TM/OLI imagery to derive subpixel ISA and the accuracy of the percent ISA was assessed. Then, a minimum change threshold was chosen to remove random noise, and the change of ISA between 2000 and 2016 was analysed. The urban area was divided into three circular consecutive urban zones in the cardinal directions from the city centre and each circular zone was further divided into eight segments; thus, a total of 24 spatial sectors were derived. The LST aggregation was analysed in different directions and urban segments and hotspot density was further calculated based on area proportion of hotspot areas in each sector. Finally, variations of mean normalized LST (NLST), area proportion of ISA, area proportion of ISA with high LST, and area proportion of hotspot area were quantified for all sectors for 2000 and 2016. The four levels of hotspot density were classified for all urban sectors by proportional ranges of 0%–25%, 25%–50%, 50%–75% and 75%–100% for low-, medium-, sub-high, and high density, and the spatial dynamics of hotspot density between the two dates showed that urbanization mainly dominated in sectors south–southeast 2 (SSE2), south–southwest 2 (SSW2), west–southwest 2 (WSW2), west–northwest 2 (WNW2), north–southwest 2 (NSW2), south–southeast 3 (SSE3) and south–southwest 3 (SSW3). This paper suggests a methodology for characterizing the urban thermal environment and a scientific basis for sustainable urban development.

Regional scale prospecting for non-sulphide zinc deposits using ASTER data and different spectral processing methods

ABSTRACT The Ravar-Kuhbanan-Bahabad belt (RKBB) in Central Iran contains several carbonate-hosted non-sulphide Zn (zinc)-Pb (lead) deposits. The Gujer Zn mine area located in the middle of the RKBB was selected as the case study. Due to its large extent, dolomitic envelope in carbonate host rocks can be considered as a more appropriate exploratory target than small Zn-rich gossans or blind karst filling ore. Based on previous studies, the occurrence of red sandstone as a candidate of supplying metal for mineralization and evaporate as sulphate source for mineralized liquids in the vicinity of carbonate rocks can be important exploratory key in the RKBB. Non-sulphide Zn deposits were formed through oxidation of primary Mississippi Valley-type (MVT) deposits in the study area. Remote sensing studies were undertaken using visible to near-infrared (VNIR) and shortwave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with the objective of lithological mapping. Five traverse lines containing a total of 81 samples were designed and followed with subsequent chemical analysis, thin section studies, and spectroscopy to verify the results. Two types of carbonates, namely, magnesian dolomite as host rock and surrounding calcitic limestone, were realized through using magnesium oxide (MgO) to calcium oxide (CaO) ratio. Based on spectroscopy studies, calcite and dolomite showed distinct absorption features at 2.35 µm and 2.32 µm, respectively, in ASTER band 8 while a shoulder at 2.25 µm was seen in ASTER band 7 for dolomite. Three image processing methods including spectral angle mapper (SAM), linear spectral unmixing (LSU), and mixture-tuned matched-filtering (MTMF) were applied to separate dolomite and limestone. The accuracy of image classification was numerically estimated using a confusion matrix. Limestone with the accuracy of 95.83% was more precisely enhanced using MTMF method compared to SAM and LSU methods. Highest accuracy of 75% for dolomite was obtained through using LSU method. Red sandstone and evaporate units were classified using MTMF and SAM/LSU methods, respectively. Rock units with the highest accuracy were selected and simply overlain on an image of ASTER in a GIS platform to create the potential map of the study area. Results showed that ASTER data can be successfully used to prepare a potential map for regional scale prospecting for carbonate-hosted non-sulphide Zn-Pb deposits in geological setting and climate condition similar to the RKBB.

Mapping of hydrothermal alteration in the upper mantle-lower crust transition zone of the Tayin Massif, Sultanate of Oman using remote sensing technique

This study maps hydrothermal alteration of Wadi Tayin Massif of the Sultanate of Oman using the spectral bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the image processing methods namely band ratios, false color composite (FCC), spectral angle mapper (SAM), mixture tuned matched filtering (MTMF) and linear spectral unmixing (LSU) to understand alteration of the upper mantle-lower crust transition zone. The study of FCC image of the spectral bands 8, 4, and 1 in Red (R), Green (G), and Blue (B), respectively, showed the presence of the hydrothermal alteration in shades of purple and brown over the mantle peridotites, dunites, and crustal gabbros. The ASTER band ratios R:9/8, G:4/3, B:2/1 used in this study able to show the occurrence and distribution of alteration in orange distinctly. The SAM, LSU and MTMF image processing methods applied over the region confirmed the presence of alteration and altered minerals in the rocks. The assessment of accuracy for the methods provided the high accuracy of 91.86% and Kappa Coefficient = 0.89 in the LSU method. The image interpretations are studied in the field and the occurrence of alterations are validated by presence of strong serpentinization in the peridotites and dunites, and secondary veins in the micro cracks of gabbros. Laboratory studies showed presence of the serpentinization, iddingsitization, chloritization and development of pargasite in the altered rocks. This study shows application of the ASTER sensor to map hydrothermal alteration found near the fast-spreading oceanic ridge.