Spectral Angle Mapper Technique Research Articles

Mapping and discrimination of the mineralization potential in granitoids from Banyo area (Adamawa, Cameroon), using Landsat 9 OLI, ASTER images and field observations

The Banyo area is situated in the west Cameroon domain of the Central African Fold Belt. In this study, field data and remote sensing conventional mapping techniques including Red-Green-Blue (RGB) colour combination, principal component analysis (PCA), Crosta-PCA, Band Ratios (BR), Constrained Energy Minimization (CEM), Minimum Noise Fraction (MNF), Spectral Angle Mapper (SAM) classification and Linear Spectral Unmixing (LSU) algorithms were used to process Landsat 9 OLI and ASTER data in order to discriminate various rock units such as biotite-amphibole granites, hornblende-biotite orthogneiss and map associated mineral deposits (e.g., iron-oxide, hydroxyl and corundum minerals). In addition, it was found that RGB combination (743) of OLI bands, PCA of OLI bands (PC123), BR of OLI bands (4/2, 6/7, 6/5), BR of ASTER (4/2, 7/6, 8/9), Crosta-PCA of OLI bands, MNF (123) of OLI bands, SAM, and LSU techniques applied to ASTER bands ascertain potential locations of granitoids and gneisses bearing iron and hydroxyl minerals. On the other hand, this study also revealed that corundum deposits are detected by applying the CEM method to OLI band ratio 6/7 highlighting red tones and processing SAM classification algorithm both using laboratory reference spectra. Automatic (using PCI Geomatica line tool) and manual extractions of lineaments revealed three major trends (N-S, E-W, and WNW-ESE to NW-SE) and an accessory trend (NE-SW to ENE-WSW) that surround hydrothermally altered rocks recognized as containing iron oxide, hydroxyl, and corundum mineral deposits. In the field, these lineaments may group to the tree deformation phases. The D1 phase (NW-SE); the D2 phase (NE-SW to ENE-WSW) and the D3 phase (N-S). Maximum Likelihood Calssification (MLC) and SAM classification methods showed to be more effective in discriminating rocks bearing Iron-oxide, hydroxyl and corundum minerals in the study area.This result permits to confirm that, the combination of remote sensing (Landsat 9 OLI and ASTER data in this case) and field data constitute a useful tool for mineral exploration in the shear zone. However, the accuracy assessment was relied on comparing the different maps produced from Landsat 9 OLI and ASTER data with the ground truth sample points and also, with the earliest geological map of Banyo area. Thus, quantitatively the results were accurate at 99%.

A Study on Wetland Cover Map Formulation and Evaluation Using Unmanned Aerial Vehicle High-Resolution Images

Wetlands possess significant ecological value and play a crucial role in the environment. Recent advancements in remote exploration technology have enabled a quantitative analysis of wetlands through surveys on the type of cover present. However, the classification of complex cover types as land cover types in wetlands remains challenging, leading to ongoing studies aimed at addressing this issue. With the advent of high-resolution sensors in unmanned aerial vehicles (UAVs), researchers can now obtain detailed data and utilize them for their investigations. In this paper, we sought to establish an effective method for classifying centimeter-scale images using multispectral and hyperspectral techniques. Since there are numerous classes of land cover types, it is important to build and extract effective training data for each type. In addition, computer vision-based methods, especially those that combine deep learning and machine learning, are attracting considerable attention as high-accuracy methods. Collecting training data before classifying by cover type is an important factor that which requires effective data sampling. To obtain accurate detection results, a few data sampling techniques must be tested. In this study, we employed two data sampling methods (endmember and pixel sampling) to acquire data, after which their accuracy and detection outcomes were compared through classification using spectral angle mapper (SAM), support vector machine (SVM), and artificial neural network (ANN) approaches. Our findings confirmed the effectiveness of the pixel-based sampling method, demonstrating a notable difference of 38.62% compared to the endmember sampling method. Moreover, among the classification methods employed, the SAM technique exhibited the highest effectiveness, with approximately 10% disparity observed in multispectral data and 7.15% in hyperspectral data compared to the other models. Our findings provide insights into the accuracy and classification outcomes of different models based on the sampling method employed in spectral imagery.

Evaluating the Performance of PRISMA Shortwave Infrared Imaging Sensor for Mapping Hydrothermally Altered and Weathered Minerals Using the Machine Learning Paradigm

Satellite images provide consistent and frequent information that can be used to estimate mineral resources over a large spatial extent. Advances in spaceborne hyperspectral remote sensing (HRS) and machine learning can help to support various remote-sensing-based applications, including mineral exploration. Leveraging these advances, the present study evaluates recently launched PRISMA spaceborne satellite images to map hydrothermally altered and weathered minerals using various machine-learning-based classification algorithms. The study was performed for the town of Jahazpur in Rajasthan, India (75°06′23.17″E, 25°25′23.37″N). The distribution map for minerals such as kaolinite, talc, and montmorillonite was generated using the spectral angle mapper technique. The resultant mineral distribution map was verified through an intensive field validation survey on surface exposures of the minerals. Furthermore, the obtained pixels of the end-members were used to develop the machine-learning-based classification models. Measures such as accuracy, kappa coefficient, F1 score, precision, recall, and ROC curve were employed to evaluate the performance of developed models. The results show that the stochastic gradient descent and artificial-neural-network-based multilayer perceptron classifiers were more accurate than other algorithms. Results confirm that the PRISMA dataset has enormous potential for mineral mapping in mountainous regions utilizing a machine-learning-based classification framework.

Apple powdery mildew infestation detection and mapping using high-resolution visible and multispectral aerial imaging technique

Powdery mildew (PM) in apple orchards is a critical fungal disease that considerably reduces yield, harvested fruit quality, and orchard health. Rapid detection and mapping of resulting infestation at orchard scale is a challenge with existing laborious manual scouting approaches. Therefore, this study explored the feasibility of detecting and mapping PM infestation in an apple orchard block using high-resolution visible (red-green-blue [RGB]) and multispectral imaging technique. Imaging campaigns were conducted over an experimental orchard using small unmanned aerial systems (UAS) integrated with the above optical sensors. K-means classifier trained on individual snapshots of RGB imagery had mean disease detection accuracy of 77%. Eight multispectral vegetation indices also showed significant differences (p < 0.001) between healthy (Mean: 0.25–0.84) and infected (Mean: 0.01–0.25) leaves. Modified Simple Ratio-Red (MSRR), Modified Simple Ratio-Blue (MSRB), and Optimized Soil Adjusted Vegetation Index (OSAVI) showed the highest contrast (Mean: 0.46–0.79). Orchard block-scale mapping using RGB orthomosaic layer, classified with k-means and spectral angle mapper techniques had accuracies up to 73%. Overall, high resolution imagery in the visible and multispectral domain could sufficiently detect the infection and generate site-specific disease heat maps. These maps could be useful to apple growers in directing prescriptive resources ( pruning labor or fungicide application) for PM management.

Land Use Change Detection Between Tarsus - Karataş in Lower Seyhan Plain with Spectral Angle Mapper Technique

Yaşam alanlarını oluşturan kara parçalarının bilinçsiz ve kontrolsüz kullanımı nedeni ile ortaya çıkan olumsuzlukların, doğada neden olduğu tahribatı engellemeye yönelik doğayı ve doğal kaynakları koruma düşüncesi arazi kullanım planlamalarının önemini artırmaktadır. Bu çalışmada, Çukurova’nın güney kesiminde ekolojik olarak öneme sahip olan doğal koruma alanlarından biri olan Akyatan yaban hayatı geliştirme sahasını da içeren ve Ramsar koruma alanlarının da yer aldığı, bir kısmı ile Seyhan ovası içerisinde bulunan Tarsus (Mersin) ile Karataş (Adana) arasındaki bölgede, 1985, 2000 ve 2019 yılları arasındaki arazi kullanımında meydana gelen değişimler incelenmiştir. Analizlerde 30 m mekânsal çözünürlüğe sahip Landsat-5TM 1985, Landsat-5TM 2000 ve Landsat-8 OLI 2019 uydu görüntüleri kullanılmıştır. Uydu görüntüleriyle öncelikli olarak geometrik, radyometrik kalibrasyon ve atmosferik düzenlemelerden oluşan ön işleme çalışmaları gerçekleştirilmiştir. Arazi kullanım değişim tespitinde Spektral Açı haritalama yöntemi kullanılmıştır. Elde edilen sonuçlara göre 1985-2019 yılları arasında yerleşim, ekili olmayan tarım, orman ve yarı doğal ve lagün/göller alanlarında %192, %37, %7 ve %8’lik büyüme gelişirken, çıplak ve ekili tarım alanlarda ise %43 ve %21’lik oranlarda azalmalar meydana gelmiştir. Aynı zamanda ülkemizde bulunan aktif fay hatlarından biri olan Karataş fayı civarında 500, 1000 ve 2000 m’lik tampon bölgeler içerisinde yerleşim birimlerinde meydana gelen değişimler değerlendirilmiştir. Karataş fayı civarında ise yerleşimin 1985 ile 2019 yılları arasında yapılaşmaların %192 oranında arttığı belirlenmiştir. Yapılan kontrollü sınıflama çalışmalarının doğruluğu kappa istatistiği ile değerlendirilmiş olup 1985, 2000 ve 2019 yılları için sırasıyla 0.80, 0.84, 0.87 olarak hesaplanmıştır.

Lithological and alteration mineral mapping for alluvial gold exploration in the south east of Birao area, Central African Republic using Landsat-8 Operational Land Imager (OLI) data

Geological field mapping and mineral exploration still remain very laborious in many parts of the inter tropical regions of African plate due to remoteness, low security and high cost. Currently, a number of remote sensing satellite imagery are freely available and can be broadly used for lithological mapping and mineral exploration in inaccessible regions of African plate. The Birao region of the Central African Republic is well known for its alluvial deposits of gold and copper. The Birao region is located in subtropical belt of Africa plate. In this study, Landsat-8 Operational Land Imager (OLI) satellite imagery was used for lithological and alteration mapping in the south-eastern part (inaccessible zone) of Birao region for alluvial gold exploration. Image processing techniques such as False Color Composite (FCC), Band Ratio (BR), Principal component analysis (PCA), minimum noise fraction (MNF) and Spectral Angle Mapper (SAM) were executed to extract spectral information related to lithological units and alteration minerals. Clay and carbonate minerals, iron oxides, ferrous silicates and lithological units, including the Neo-Chadian Formation, the Lower Proterozoic Formations and the Recent Alluvial Formation were identified and discriminated in the study area. The band ratio of 6/7, 4/2 and 6/5 were typically used to map clay, iron oxides and ferrous silicates, respectively. The results of band ratio and PCA techniques were integrated with fuzzy logic modeling that considerably highlighted hydrothermal alteration minerals and high perspective zones for alluvial gold exploration. Additionally, an updated lithological-mineralogical map was generated for south-eastern part of the Birao region by fusing the FCC, MNF and SAM techniques. Comparison with previous lithological map of the study area, virtual verification, fieldwork and laboratory data confirmed good rate of accuracy for the result obtained from the Landsat-8 OLI satellite data. The approach presented in this study is a low-cost technique that highly applicable for lithological mapping and mineral exploration in remote subtropical regions of African plate.

Lithological mapping in Sangan region in Northeast Iran using ASTER satellite data and image processing methods

ABSTRACTLithological mapping using satellite images, particularly the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data help in effectively defining the best initial targets for regional exploration. ASTER data allow for the discrimination of rock units in the broader region. This research work is focused on the use of remote sensing techniques for geological mapping using ASTER satellite image and generating a geological map of Sangan region. The study area is located in southeast of Khorasan-e-Razavi province and at the eastern edge of the Khaf-Kashmar-Bardskan Volcano-Plutonic Metallogenic Belt in northeast Iran. Band ratio (BR), spectral angle mapper (SAM) and support vector machines (SVMs) methods were used for classifying the main lithologic units in Sangan region. The results of BR, SAM and SVM techniques were quantitatively compared with geological boundaries mapped in the field showing an accuracy of nearly 79 %. SVMs method, in comparison to conventional methods of classification, was known to provide superior results. As the final result of this research, integration of remote sensing and field investigations led to generating high accuracy geological map in the Sangan region. Application of the methods has invaluable implications for geological mapping and mineral exploration in inaccessible regions.

Mapping the distribution of iron ore minerals and spatial correlation with environmental variables in hilltop mining areas

The prime contribution of this assignment was to examine the hyperspectral remote sensing, based on iron ore minerals identification using spectral angle mapper (SAM) technique. Correlation analyses between field iron contents and environmental variables (soil, water, and vegetation) have been performed. Spectral feature fitting (SFF) and multi-range spectral feature fitting (MRSFF) methods were used for accuracy assessment in extracting iron ore minerals from Hyperion EO-1 data. Spectral inspections as a reference were used in SAM technique for image classification for iron ore minerals: Hematite (24.26%), Goethite (32.98%) and Desert (42.76). Iron ore minerals classification is justified by the United States Geological Survey (USGS) spectral library and field sample points. The regression analysis of USGS and Hyperion reflectance spectra has shown the moderate positive correlation. The regression analyses between iron ore contents and environmental parameters (soil, water, and vegetation) have shown the moderate negative correlation. The examination was significantly effectual in extracting iron ore minerals: Hematite (SFF RMSE ≤ 0.51 MRSFF RMSE ≤ 0.48), Goethite (SFF RMSE ≤ 0.047 MRSFF RMSE ≤ 0.438) and Desert (SFF RMSE ≤ 0.63 and MRSFF RMSE ≤ 0.50); and the MRSFF RMSE histograms indicate the above result likened to a conventional SFF RMSE. MRSFF RMS error result is best because multiple absorption features typically characterize spectral signatures. This analysis demonstrates the potential applicability of the methodology for iron minerals identification framework and iron minerals impact on environmental parameters.

Unsupervised change detection in a particular vegetation land cover type using spectral angle mapper

This paper presents a new unsupervised change detection methodology for multispectral images applied to specific land covers. The proposed method involves comparing each image against a reference spectrum, where the reference spectrum is obtained from the spectral signature of the type of coverage you want to detect. In this case the method has been tested using multispectral images (SPOT5) of the community of Madrid (Spain), and multispectral images (Quickbird) of an area over Indonesia that was impacted by the December 26, 2004 tsunami; here, the tests have focused on the detection of changes in vegetation. The image comparison is obtained by applying Spectral Angle Mapper between the reference spectrum and each multitemporal image. Then, a threshold to produce a single image of change is applied, which corresponds to the vegetation zones. The results for each multitemporal image are combined through an exclusive or (XOR) operation that selects vegetation zones that have changed over time. Finally, the derived results were compared against a supervised method based on classification with the Support Vector Machine. Furthermore, the NDVI-differencing and the Spectral Angle Mapper techniques were selected as unsupervised methods for comparison purposes. The main novelty of the method consists in the detection of changes in a specific land cover type (vegetation), therefore, for comparison purposes, the best scenario is to compare it with methods that aim to detect changes in a specific land cover type (vegetation). This is the main reason to select NDVI-based method and the post-classification method (SVM implemented in a standard software tool). To evaluate the improvements using a reference spectrum vector, the results are compared with the basic-SAM method. In SPOT5 image, the overall accuracy was 99.36% and the κ index was 90.11%; in Quickbird image, the overall accuracy was 97.5% and the κ index was 82.16%. Finally, the precision results of the method are comparable to those of a supervised method, supported by low detection of false positives and false negatives, along with a high overall accuracy and a high kappa index. On the other hand, the execution times were comparable to those of unsupervised methods of low computational load.

Detecting and mapping different types of iron mineralization in Sangan mining region, NE Iran, using satellite image and airborne geophysical data

The Sangan mining region is the largest Fe skarn in western Asia has emplaced into the Khaf-Kashmar-Bardaskan volcano-plutonic belt in the NE Iran. In this region, carbonate rocks of Jurassic skarnified and hosted different epigenetic types of iron minerals, including magnetite, hematite, goethite, and limonite. The combination of remote sensing and airborne geophysical data is a powerful tool for mapping and interpreting iron mineralization in some area with intensely rugged topography or a broad expanse area, where systematic sampling and conventional geological mapping has some limitation and time consuming. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data and airborne geophysical magnetometry data were used for evaluating and mapping different types of iron mineralization in the Sangan mining region. Preprocessing of the datasets involved band ratio (BR), principal component analysis (PCA), spectral angle mapper (SAM), and constrained energy minimization (CEM) of the visible-near infrared and short wave infrared ASTER data were used to map four types of iron minerals (magnetite, hematite, goethite, and limonite). For preparing a lithological mapp of this region, an RGB image produced by combination of BR and PCA, (R:(5+7)/6, G:PC3, B:PC 5). Implementing SAM and CEM technique were useful for mapping and detecting magnetite, hematite, goethite, and limonite. The Reduce To the Pole (RTP) map of the airborne geophysical magnetometry data is a practical tool for iron ore exploration that were used in this region for enhancing high anomalous signature of magnetite after extracting granitoid rocks. The integration of the extracted information from the ASTER image processing algorithms and geophysical magnetometry, mapped iron mineralization and identified new potential of high magnetite mineralization. The results verified by geological map and comprehensive fieldwork. This integration model can generalize to other arid and semi-arid regions with iron potential for both regional and district scales.

Ground-based hyperspectral remote sensing of hydrocarbon-induced rock alterations at cement, Oklahoma

Hydrocarbon seepages bring oil and gas from petroleum reservoirs to the surface, these hydrocarbons can generate various kinds of rock alterations, including bleaching of red beds and mineralogical changes. Satellite and airborne remote sensing techniques have been utilized in the detection of rock alterations and hydrocarbon seepages; however, they have limited resolutions and are unable to map vertical outcrops. Ground-based remote sensing techniques hold great potential to characterize rocks with great precisions and resolutions. This study used ground-based Specim hyperspectral data to detect hydrocarbon-induced rock alterations of the Rush Springs sandstone at the Cement field in southeast Anadarko basin, Oklahoma. Hyperspectral data of outcrops and hand specimens were examined, and spectral angle mapper technique was used to compare spectral similarity between image pixels and reference spectra. Laboratory spectroscopy was used to assist with mineral identification and image classification. Remote sensing data detected bleaching of red beds and carbonate cementation. Combining lithological, spectroscopic, remote sensing and geochemical data, this study built a model for petroleum seepage and related rock alterations, and provided a workflow for employing remote sensing techniques in resource exploration.

Spatial distribution of altered minerals in the Gadag Schist Belt (GSB) of Karnataka, Southern India using hyperspectral remote sensing data

Spatial distribution of altered minerals in rocks and soils in the Gadag Schist Belt (GSB) is carried out using Hyperion data of March 2013. The entire spectral range is processed with emphasis on VNIR (0.4–1.0 μm) and SWIR regions (2.0–2.4 μm). Processing methodology includes Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes correction, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) in conjunction with spectra collected, using an analytical spectral device spectroradiometer. A total of 155 bands were analysed to identify and map the major altered minerals by studying the absorption bands between the 0.4–1.0-μm and 2.0–2.3-μm wavelength regions. The most important and diagnostic spectral absorption features occur at 0.6–0.7 μm, 0.86 and at 0.9 μm in the VNIR region due to charge transfer of crystal field effect in the transition elements, whereas absorption near 2.1, 2.2, 2.25 and 2.33 μm in the SWIR region is related to the bending and stretching of the bonds in hydrous minerals (Al-OH, Fe-OH and Mg-OH), particularly in clay minerals. SAM and SFF techniques are implemented to identify the minerals present. A score of 0.33–1 was assigned for both SAM and SFF, where a value of 1 indicates the exact mineral type. However, endmember spectra were compared with United States Geological Survey and John Hopkins University spectral libraries for minerals and soils. Five minerals, i.e. kaolinite-5, kaolinite-2, muscovite, haematite, kaosmec and one soil, i.e. greyish brown loam have been identified. Greyish brown loam and kaosmec have been mapped as the major weathering/altered products present in soils and rocks of the GSB. This was followed by haematite and kaolinite. The SAM classifier was then applied on a Hyperion image to produce a mineral map. The dominant lithology of the area included greywacke, argillite and granite gneiss.

Object-oriented and pixel-based classification approach for land cover using airborne long-wave infrared hyperspectral data

Our primary objective was to explore a classification algorithm for thermal hyperspectral data. Minimum noise fraction is applied to thermal hyperspectral data and eight pixel-based classifiers, i.e., constrained energy minimization, matched filter, spectral angle mapper (SAM), adaptive coherence estimator, orthogonal subspace projection, mixture-tuned matched filter, target-constrained interference-minimized filter, and mixture-tuned target-constrained interference minimized filter are tested. The long-wave infrared (LWIR) has not yet been exploited for classification purposes. The LWIR data contain emissivity and temperature information about an object. A highest overall accuracy of 90.99% was obtained using the SAM algorithm for the combination of thermal data with a colored digital photograph. Similarly, an object-oriented approach is applied to thermal data. The image is segmented into meaningful objects based on properties such as geometry, length, etc., which are grouped into pixels using a watershed algorithm and an applied supervised classification algorithm, i.e., support vector machine (SVM). The best algorithm in the pixel-based category is the SAM technique. SVM is useful for thermal data, providing a high accuracy of 80.00% at a scale value of 83 and a merge value of 90, whereas for the combination of thermal data with a colored digital photograph, SVM gives the highest accuracy of 85.71% at a scale value of 82 and a merge value of 90.

Informational Clustering of Hyperspectral Data

Hyperspectral remote sensing is recognized as a powerful tool for mineralogical mapping of exposed surfaces on Earth and planets, as well. It allows for more rigorous discrimination among materials than multispectral imaging. Nevertheless, the huge data volume that comes with single observations results in severe limitations to successful data exploitation. Many techniques of feature reduction that have been developed so far do not allow for the complete exploitation of the informational content of the hyper-dimensional space. The present investigation aims at providing a feature reduction technique that preserves the spectral information and improves the classification results. We accomplished the feature reduction of synthetic and real hypercubes through exponential Gaussian optimization (EGO) and compared the results of k-means, spectral angle mapper (SAM), support vector machines (SVMs), and CLUES clustering techniques. The results show that the k-means clustering of hyper-dimensional spaces is the most efficient technique, but it does not automatically retrieve the optimal number of clusters. The SAM and SVM techniques give discrete results in terms of data partitioning, although the process of endmembers' selection is challenging and the definition of model parameters is not trivial. The combination of EGO modeling and CLUES algorithm allows for correctly estimating the number of clusters and deriving the accurate partitions when the cluster separability lies on two variables, at least. With real data, the CLUES clustering in the reduced space allows for higher overall performances than the more conventional techniques, although it underestimates the number of categories.

MULTISPECTRAL AND HYPERSPECTRAL REMOTE SENSING: TARGET AREA GENERATION FOR PORPHYRY COPPER EXPLORATION IN THE NAMAQUA METAMORPHIC PROVINCE, SOUTH AFRICA

This research focuses on the application of the Spectral Angle Mapper technique on hyperspectral HyMap airborne data and band ratios of ASTER satellite multispectral data together with aeromagnetic data for target area detection for porphyry copper exploration in the Namaqua Metamorphic Province. Improved spectral and spatial resolution of both aerial and satellite remote sensors made it possible to map the lithology and mineralogy in greater detail than in the past. This study pinpointed one location with a high potential for porphyry copper deposits, warranting detailed field verification. The field verification would include geochemical and laboratory analysis to study economic viability of the deposit.

Classifying Forest Species Using Hyperspectral Data in Balah Forest Reserve, Kelantan, Peninsular Malaysia

This study attempts to classify forest species using hyperspectral data for supporting resources management. The primary dataset used was AISA sensor. The sensor was mounted onboard the NOMAD GAF-27 aircraft at 2,000 m altitude creating a 2 m spatial resolution on the ground. Pre-processing was carried out with CALIGEO software, which automatically corrects for both geometric and radiometric distortions of the raw image data. The radiance data set was then converted to at-sensor reflectance derived from the FODIS sensor. Spectral Angle Mapper (SAM) technique was used for image classification. The spectra libraries for tree species were established after confirming the appropriate match between field spectra and pixel spectra. Results showed that the highest spectral signature in NIR range were Kembang Semangkok (Scaphium macropodum), followed by Meranti Sarang Punai (Shorea parvifolia) and Chengal (Neobalanocarpus hemii). Meanwhile, the lowest spectral response were Kasai (Pometia pinnata), Kelat (Eugenia spp.) and Merawan (Hopea beccariana), respectively. The overall accuracy obtained was 79%. Although the accuracy of SAM techniques is below the expectation level, SAM classifier was able to classify tropical tree species. In future it is believe that the most effective way of ground data collection is to use the ground object that has the strongest response to sensor for more significant tree signatures.

Urban Vegetation Mapping from Fused Hyperspectral Image and LiDAR Data with Application to Monitor Urban Tree Heights

Urban vegetations have infinite proven benefits for urban inhabitants including providing shade, improving air quality, and enhancing the look and feel of communities. But creating a complete inventory is a time consuming and resource intensive process. The extraction of urban vegetation is a challenging task, especially to monitor the urban tree heights. In this study we present an efficient extraction method for mapping and monitoring urban tree heights using fused hyperspectral image and LiDAR data. Endmember distribution mapping using the spectral angle mapper technique is employed in this study. High convenience results achieved using fused hyperspectral and LiDAR data from this semiautomatics technique. This method could enable urban community organizations or local governments to map and monitor urban’s tree height and its spatial distribution.

EVALUATION OF ASTER IMAGES FOR CHARACTERIZATION AND MAPPING OF AMETHYST MINING RESIDUES

Abstract. The objective of this work was to evaluate the potential of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), subsystems VNIR (Visible and Near Infrared) and SWIR (Short Wave Infrared) images, for discrimination and mapping of amethyst mining residues (basalt) in the Ametista do Sul Region, Rio Grande do Sul State, Brazil. This region provides the most part of amethyst mining of the World. The basalt is extracted during the mining process and deposited outside the mine. As a result, mounts of residues (basalt) rise up. These mounts are many times smaller than ASTER pixel size (VNIR – 15 meters and SWIR – 30 meters). Thus, the pixel composition becomes a mixing of various materials, hampering its identification and mapping. Trying to solve this problem, multispectral algorithm Maximum Likelihood (MaxVer) and the hyperspectral technique SAM (Spectral Angle Mapper) were used in this work. Images from ASTER subsystems VNIR and SWIR were used to perform the classifications. SAM technique produced better results than MaxVer algorithm. The main error found by the techniques was the mixing between "shadow" and "mining residues/basalt" classes. With the SAM technique the confusion decreased because it employed the basalt spectral curve as a reference, while the multispectral techniques employed pixels groups that could have spectral mixture with other targets. The results showed that in tropical terrains as the study area, ASTER data can be efficacious for the characterization of mining residues.

Utilization of Hyperion data over Dongargarh, India, for mapping altered/weathered and clay minerals along with field spectral measurements

Hyperion data acquired over Dongargarh area, Chattisgarh (India), in December 2006 have been analysed to identify dominant mineral types present in the area, with special emphasis on mapping the altered/weathered and clay minerals present in the rocks and soils. Various advanced spectral processes such as reflectance calibration of the Hyperion data, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) have been used for comparison/mapping in conjunction with spectra of rocks and soils that have been collected in the field using Analytical Spectral Devices's FieldSpec instrument. In this study, 40 shortwave infrared channels ranging from 2.0 to 2.4 μm were analysed mainly to identify and map the major altered/weathered and clay minerals by studying the absorption bands around the 2.2 and 2.3 μm wavelength regions. The absorption characteristics were the results of O–H stretching in the lattices of various hydrous minerals, in particular, clay minerals, constituting altered/weathered rocks and soils. SAM and SFF techniques implemented in Spectral Analyst were applied to identify the minerals present in the scene. A score of 0–1 was generated for both SAM and SFF, where a value of 1 indicated a perfect match showing the exact mineral type. Endmember spectra were matched with those of the minerals as available in the United States Geological Survey Spectral Library. Four minerals, oligoclase, rectorite, kaolinite and desert varnish, have been identified in the studied area. The SAM classifier was then applied to produce a mineral map over a subset of the Hyperion scene. The dominant lithology of the area included Dongargarh granite, Bijli rhyolite and Pitepani volcanics of Palaeo-Proterozoic age. Feldspar is one of the most dominant mineral constituents of all the above-mentioned rocks, which is highly susceptible to chemical weathering and produces various types of clay minerals. Oligoclase (a feldspar) was found in these areas where mostly rock outcrops were encountered. Kaolinite was also found mainly near exposed rocks, as it was formed due to the weathering of feldspar. Rectorite is the other clay mineral type that is observed mostly in the southern part of the studied area, where Bijli rhyolite dominates the lithology. However, the most predominant mineral type coating observed in this study is desert varnish, which is nothing but an assemblage of very fine clay minerals and forms a thin veneer on rock/soil surfaces, rendering a dark appearance to the latter. Thus, from this study, it could be inferred that Hyperion data can be well utilized to identify and map altered/weathered and clay minerals based on the study of the shape, size and position of spectral absorption features, which were otherwise absent in the signatures of the broadband sensors.

Monitoring aquatic weeds in a river system using SPOT 5 satellite imagery

Aquatic weeds have caused significant problems in many lakes and river systems worldwide. Weed outbreaks of water hyacinth (Eichhornia crassipes) and para-grass (Urochloa mutica) are common in Australia and their ecological and recreational impacts mostly negative and costly. Remote sensing offers the ability to map and monitor the distribution of aquatic weeds and their early detection. The objective of this project was to develop an efficient method, using remote sensing techniques, to map and monitor the change of dense water weeds in a river system and to identify a suitable spatial scale for this process. Two SPOT (Satellite Pour l'Observation de la Terre) 5 images from May 2006 and May 2007 were used in combination with two mapping approaches on a) multispectral image data with 10 m spatial resolution and b) pan-sharpened multispectral image data with 2.5 m spatial resolution. A scale dependent validation resulted in case a) an overall producer's classification accuracy of 81%. Small outbreaks (~2 m2) alone were 71% accurate with increasing accuracies of >95% for outbreaks larger than 6.25m2 (2.5m x 2.5m pixel). Case b) generally had lower accuracies, with accuracies of >95% for outbreaks in the order of 100m2 (10m x 10m pixel) and larger. The results suggest that the river infestation by aquatic weeds in a test area of the mid-Brisbane River has increased by a factor of 2 to 3 during the 12-month period. The infested area is estimated to be between 13.6% and 15.9 % of the waterbody in 2007, while 6.2% to 6.8% in 2006. The method applied in this study included geometric and radiometric corrections, along with linear spectral unmixing and spectral angle mapper techniques. This method is applicable to other waterways worldwide and offers the potential for the early detection of infestations of aquatic surface weeds.