Spectral transformation of ASTER and Landsat TM bands for lithological mapping of Soghan ophiolite complex,south Iran

Spectral transformation methods, including correlation coefficient (CC) and Optimum Index Factor (OIF), band ratio (BR) and principal component analysis (PCA) were applied to ASTER and Landsat TM bands for lithological mapping of Soghan ophiolitic complex in south of Iran. The results indicated that the methods used evidently showed superior outputs for detecting lithological units in ophiolitic complexes. CC and OIF methods were used to establish enhanced Red–Green–Blue (RGB) color combination bands for discriminating lithological units. A specialized band ratio (4/1, 4/5, 4/7 in RGB) was developed using ASTER bands to differentiate lithological units in ophiolitic complexes. The band ratio effectively detected serpentinite dunite as host rock of chromite ore deposits from surrounding lithological units in the study area. Principal component images derived from first three bands of ASTER and Landsat TM produced well results for lithological mapping applications. ASTER bands contain improved spectral characteristics and higher spatial resolution for detecting serpentinite dunite in ophiolitic complexes. The developed approach used in this study offers great potential for lithological mapping using ASTER and Landsat TM bands, which contributes in economic geology for prospecting chromite ore deposits associated with ophiolitic complexes.     

Mapping hydrothermal alteration zones and lineaments associated with orogenic gold mineralization using ASTER data: A case study from the Sanandaj-Sirjan Zone,Iran

The Sanandaj-Sirjan Zone (SSZ) is considered as an important region for gold exploration in the western sector of Iran. Its mountainous topography and unpaved routes make its study challenging for researchers and raise the costs for mining companies strating new exploration plans. Gold mineralization mainly occurs as irregular to lenticular sulfide-bearing quartz veins along shear zones in deformed mafic to intermediate metavolcanic and metasedimentary rocks. In this investigation, ASTER data are used for mapping hydrothermal alteration minerals and to better discriminate geological structural features associated with orogenic gold occurrences in the area. Image transformation techniques such as specialized band ratioing and Principal Component Analysis are used to delineate lithological units and alteration minerals. Supervised classification techniques, namely Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) are applied to detect subtle differences between indicator alteration minerals associated with ground-truth gold locations in the area. The directional filtering technique is applied to help in tracing along the strike the different linear structures. Results demonstrate that the integration of image transformation techniques and supervised classification of ASTER data with fieldwork and geochemical exploration studies has a great efficiency in targeting new prospects of gold mineralization in the SSZ. The approach used in this research provides a fast, cost-efficient means to start a comprehensive geological and geochemical exploration programs in the study area and elsewhere in similar regions.     

Built-up land mapping capabilities of the ASTER and Landsat ETM+ sensors in coastal areas of southeastern China

Worldwide urbanization has accelerated expansion of urban built-up lands and resulted in substantial negative impacts on the global environments. Precisely measuring the urban sprawl is becoming an increasing need. Among the satellite-based earth observation systems, the Landsat and ASTER data are most suitable for mesoscale measurements of urban changes. Nevertheless, to date the difference in the capability of mapping built-up land between the two sensors is not clear. Therefore, this study compared the performances of the Landsat-7 ETM+ and ASTER sensors for built-up land mapping in the coastal areas of southeastern China. The comparison was implemented on three date-coincident image pairs and achieved by using three approaches, including per-band-based, index-based, and classification-based comparisons. The index used is the Index-based Built-up Index (IBI), while the classification algorithm employed is the Support Vector Machine (SVM). Results show that in the study areas, ETM+ and ASTER have an overall similar performance in built-up land mapping but also differ in several aspects. The IBI values determined from ASTER were consistently higher than from ETM+ by up to 45.54% according to percentage difference. The ASTER also estimates more built-up land area than ETM+ by 5.9–6.3% estimated with the IBI-based approach or 3.9–6.1% with the SVM classification. The differences in the spectral response functions and spatial resolution between relative spectral bands of the two sensors are attributed to these different performances.     

Remote sensing detection of gold related alteration zones in Um Rus area, Central Eastern Desert of Egypt

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Phased Array L-band Synthetic Aperture Radar (PALSAR) images covering the Um Rus area in the Central Eastern Desert of Egypt were evaluated for mapping geologic structure, lithology, and gold-related alteration zones. The study area is covered by Pan-African basement rocks including gabbro and granodiorite intruded into a variable mixture of metavolcanics and metasediments. The first three principal component analyses (PCA1, PCA2, PCA3) in a Red-Green-Blue (RGB) of the visible through shortwave-infrared (VNIR + SWIR) ASTER bands enabled the discrimination between lithological units. The results show that ASTER band ratios ((2 + 4)/3, (5 + 7)/6, (7 + 9)/8) in RGB identifies the lithological units and discriminates the granodiorite very well from the adjacent rock units.The granodiorites are dissected by gold-bearing quartz veins surrounded by alteration zones. The microscopic examination of samples collected from the alteration zones shows sericitic and argillic alteration zones. The Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) supervised classification methods were applied using the reference spectra of the USGS spectral library. The results show that these classification methods are capable of mapping the alteration zones as indicated by field verification work. The PALSAR image was enhanced for fracture mapping using the second moment co-occurrence filter. Overlying extracted faults and alteration zone classification images show that the N30E and N-S fractures represent potential zones for gold exploration. It is concluded that the proposed methods can be used as a powerful tool for ore deposit exploration.     

Selection of classification techniques for land use/land cover change investigation

The concerns over land use/land cover (LULC) change have emerged on the global stage due to the realisation that changes occurring on the land surface also influence climate, ecosystem and its services. As a result, the importance of accurate mapping of LULC and its changes over time is on the increase. Landsat satellite is a major data source for regional to global LULC analysis. The main objective of this study focuses on the comparison of three classification tools for Landsat images, which are maximum likelihood classification (MLC), support vector machine and artificial neural network (ANN), in order to select the best method among them. The classifiers algorithms are well optimized for the gamma, penalty, degree of polynomial in case of SVM, while for ANN minimum output activation threshold and RMSE are taken into account. The overall analysis shows that the ANN is superior to the kernel based SVM (linear, radial based, sigmoid and polynomial) and MLC. The best tool (ANN) is then applied on detecting the LULC change over part of Walnut Creek, Iowa. The change analysis of the multi temporal images indicates an increase in urban areas and a major shift in the agricultural practices.     

Object-based rice mapping using time-series and phenological data

Remote sensing techniques are often used in mapping rice, but high quality time-series remote sensing data are difficult to obtain due to the cloudy weather of rice growing areas and long satellite revisit interval. As such, rice mapping is usually based on mono-temporal Landsat TM/ETM+ data, which have large uncertainties due to the spectral similarity of different vegetation types. Moreover, conventional pixel-based classification method is unable to meet the required accuracy for rice mapping. Therefore, this study proposes a new strategy for mapping rice in cloud-prone areas using fused data of Landsat-8 OLI time-series and phenological parameters, based on the object-based method. We determine the critical growth stages of paddy rice from observed phenological data and MODIS-NDVI time-series data. The spatial and temporal adaptive reflectance fusion model (STARFM) is used to blend the MODIS and Landsat data to obtain a multi-temporal Landsat-like dataset for classification. Finally, an object-oriented algorithm is used to extract rice paddies from the Landsat-like, time-series dataset. The validation experiments show that the proposed method can provide high accuracy rice mapping, with an overall accuracy of 92.38% and a kappa coefficient of 0.85.     

Integration of Landsat-8 and Sentinel-1 dataset to extract geological lineaments in complex formations of Tepal mountain area,Shahrood, north Iran

Recently, the detection and extraction of geological lineaments have become an essential analytical technique to find relationships between the characteristics and occurrence of hydrogeology, and tectonic studies. The use of remote sensing, with the progressive development of image enhancement techniques, provides an opportunity to produce more reliable and comprehensive lineament maps. In this paper, semi-automatic approach based on Landsat 8 and Sentinel 1 radar data is proposed for lineaments extraction and validation. The combined method of linear filtering and automatic line module ensures a high degree of accuracy resulting in a lineament map. Based on identified lineaments, Sentinel1 is more capable of detecting edges than Landsat8, but the primary orientation lineaments extracted from Landsat8 and Sentinel1 were different. So, by combining band6 of Landsat8, and VV and VH polarization of Sentinel1, the area lineaments were extracted with high accuracy. Rose diagram showed the extracted lineaments' orientation is in good compliance with the region's existing faults. Also, the formations' lineament length density has good consistent with the density of the faults in the geological map.     

Identifying areas of high economic-potential copper mineralization using ASTER data in the Urumieh–Dokhtar Volcanic Belt,Iran

This study investigates the application of spectral image processing methods to ASTER data for mapping hydrothermal alteration zones associated with porphyry copper mineralization and related host rock. The study area is located in the southeastern segment of the Urumieh–Dokhtar Volcanic Belt of Iran. This area has been selected because it is a potential zone for exploration of new porphyry copper deposits. Spectral transform approaches, namely principal component analysis, band ratio and minimum noise fraction were used for mapping hydrothermally altered rocks and lithological units at regional scale. Spectral mapping methods, including spectral angle mapper, linear spectral unmixing, matched filtering and mixture tuned matched filtering were applied to differentiate hydrothermal alteration zones associated with porphyry copper mineralization such as phyllic, argillic and propylitic mineral assemblages.     

Is the phenology-based algorithm for mapping deciduous rubber plantations applicable in an emerging region of northern Laos?

Updated information of rubber plantations is essential for assessing socioeconomic and environmental impacts, especially in the emerging region of northern tropics. Here, a phenological method was modified to detect rubber plantations using Landsat Operational Land Imager (OLI) imagery in Phongsaly Province of northern Laos, where it begun a rubber boom in the mid-2000s due to geo-economic cooperation. It highlighted the landscape and pixel differences of deciduous rubber plantations in the tri-temporal phases (i.e., pre-defoliation, defoliation, and foliation) during the dry season due to phenological changes. Six commonly used vegetation indices (VIs), including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), Atmospherically Resistant Vegetation Index (ARVI), Normalized Burn Ratio (NBR and NBR2) derived from OLI imagery during 2013–2016 were compared to determine the most suitable VI for discriminating the phenological differences of rubber plantations from natural forests. Then, the Differences of Normalized Burn Ratio (DNBR) was applied to generate the 30 m map of rubber plantations in 2016, by combining two masks of Landsat-derived forest and suitable elevation for rubber trees cultivation. The resultant map of rubber plantations had a classification accuracy of 93.7% and the Kappa coefficient of 0.848. Our study demonstrated the usefulness of the Landsat-derived tri-temporal phenological DNBR approach in an emerging region of northern Laos, despite requiring more scenes compared with single- and double temporal window methods.     

Mineral mapping in the Kap Simpson complex,central East Greenland,using HyMap and ASTER remote sensing data

This research focuses on the application of HyMap airborne hyperspectral data and ASTER satellite multispectral data to mineral exploration and lithologic mapping in the Arctic regions of central East Greenland. The study area is the Kap Simpson complex in central East Greenland. The Kap Simpson complex is one of the largest exposed Palaeogene felsic complexes of East Greenland. It has been the target of several mineral exploration projects. The analysis of the HyMap data produced a detailed picture of the spatial distribution of the alteration minerals in the Kap Simpson complex, unavailable from field-based studies in the area. The analysis of the ASTER data produced mineral maps which due to the moderate spatial and spectral resolution of the ASTER imagery can be useful for reconnaissance level mineral exploration. Colour composites of the mean normalized ASTER thermal bands display lithological information and detected a large felsic igneous intrusion that has not been shown on the recently compiled geological maps of the area. The results of this research have considerable potential to evaluate the use of hyperspectral and multispectral remote sensing for geological purposes in the Arctic regions of central East Greenland.     

Geological exploration and landuse using Landsat data,Sierra Leone,West Africa

Band 7 Landsat Imagery was successfully used to map broad outcrop belts in Sierra Leone because of the lithological control of topography and vegetation. Digital analysis in specific areas suggest the usefulness of certain techniques in mapping excavated areas and to indicate areas of future mineral exploration.     

Detection of hydrothermal alteration zones using ASTER data in Nimu porphyry copper deposit,south Tibet,China

Advanced space-borne thermal emission and reflection radiometer (ASTER) data were evaluated for the hydrothermal alteration mapping of the Nimu porphyry copper deposit, southern Tibet. According to the metallogenic model for porphyry copper, we chose hydrothermal alteration mineral association and then established a remote sensing model. Relative absorption-band depth (RBD) and simple band combination methods in visible near infrared (VNIR) and shortwave infrared (SWIR) bands were used to retrieve information about lithological distributions. Principal component analyses (PCA) were applied to extract the prospecting information based on the spectral information of argillization mineral association, propylitization mineral association, and iron stained (limonite) effects. Results of the study match up well with known copper occurrences and a circular structure in study area, while circular structures usually have close relationship with mineralization. Combined with field validation, ASTER data are proved able to characterise the alteration zone of these porphyry deposits. With VNIR and SWIR bands, argillic, propylitic and ferritization alterations in a single deposit can be effectively discriminated.     

Integrating aeromagnetic data and Landsat-8 imagery for detection of post-accretionary shear zones controlling hydrothermal alterations: The Allaqi-Heiani Suture zone,South Eastern Desert,Egypt

The Arabian-Nubian Shield (ANS) underwent a regional carbonation event with CO₂ fluxes derived from the mantle and circulated along post-accretionary shear zones, thus affecting the redistribution of gold mineralization. Formerly, the analyses of remotely-sensed data have suggested regions of gold mineralization linked to the post-accretionary structured framework, but the results were insufficient for detailed prospecting in the ANS. In this research, aeromagnetic data and Landsat-8 imagery were integrated for delineating new high potential zones of gold mineralization in the Allaqi-Heiani Suture (AHS) zone, the ANS, South Eastern Desert (SED) of Egypt. Aeromagnetic data were enhanced using the Center for exploration targeting (CET) grid enhancement technique to detect the main structures that control hydrothermal alterations in the study area. Principal Component Analysis (PCA) technique and ratios of spectral bands were applied to Landsat-8 data for mapping zones of hydrothermal alteration associated with gold mineralization. The traced structural elements from RTP, CET and Landsat-8 maps were statistically plotted and interpreted using rose diagrams. These diagrams indicated that the NW and NE trends are the most dominant in the RTP map and that the intersection zones obtained from the CET analysis are aligned along the NW-oriented trend. While the NNW, WNW and NE trends were the most dominant ones in Landsat maps. The integration of the results indicates several new high potential zones of gold and Cu/Ni mineralization types, which are mostly concentrated at the western part of the AHS zone. The hydrothermal alteration zones and associated gold mineralization are strongly linked to NW, NE, E-W and WNW trending lineaments. The approach used in this study can be applied to other parts of the ANS and other regions with similar geological conditions around the world.     

Glacier mapping based on rough set theory in the Manas River watershed

Precise glacier information is important for assessing climate change in remote mountain areas. To obtain more accurate glacier mapping, rough set theory, which can deal with vague and uncertainty information, was introduced to obtain optimal knowledge rules for glacier mapping. Optical images, thermal infrared band data, texture information and morphometric parameters were combined to build a decision table used in our proposed rough set theory method. After discretizing the real value attributes, decision rules were calculated through the decision rule generation algorithm for glacier mapping. A decision classifier based on the generated rules classified the multispectral image into glacier and non-glacier areas. The result of maximum likelihood classification (MLC) was used to compare with the result of the classification based on the rough set theory. Confusion matrix and visual interpretation were used to evaluate the overall accuracy of the results of the two methods. The accuracies of the rough set method and maximum likelihood classification were compared, yielding overall accuracies of 94.15% and 93.88%, respectively. It showed the area difference based on rough set was smaller by comparing the glacier areas of the rough set method and MLC with visual interpreter, respectively. The high accuracy for glacier mapping and the small area difference for glacier based on rough set theory demonstrated that this method was effective and promising for glacier mapping.     

Land use/land cover classification using time series Landsat 8 images in a heavily urbanized area

It is of great significance to timely, accurately, and effectively monitor land use/cover in city regions for the reasonable development and utilization of urban land resources. The remotely sensed dynamic monitoring of Land use/land cover (LULC) in rapidly developing city regions has increasingly depended on remote-sensing data at high temporal and spatial resolutions. However, due to the influence of revisiting periods and weather, it is difficult to acquire enough time-series images with high quality at both high temporal and spatial resolution from the same sensor. In this paper we used the temporal-spatial fusion model ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) to blend Landsat8 and MODIS data and obtain time-series Landsat8 images. Then, land cover information is extracted using an object-based classification method. In this study, the proposed method is validated by a case study of the Changsha City. The results show that the overall accuracy and Kappa coefficient were 94.38% and 0.88, respectively, and the user/producer accuracies of vegetation types were all over 85%. Our approach provides an accurate and efficient technical method for the effective extraction of land use/cover information in the highly heterogeneous regions.     

An ensemble deep learning based shoreline segmentation approach (WaterNet) from Landsat 8 OLI images

Shorelines constantly vary due to natural, urbanization and anthropogenic effects such as global warming, population growth, and environmental pollution. Sustainable monitoring of coastal changes is vital in terms of coastal resource management, environmental preservation and planning. Publicly available Landsat 8 OLI (Operational Land Manager) images provide accurate, reliable, temporal and up-to-date information about coastal areas. Recently, the use of machine learning and deep learning algorithms have become widespread. In this study, we used our public Landsat 8 OLI satellite image dataset to create a majority voting method which is an ensemble automatic shoreline segmentation system (WaterNet) to obtain shorelines automatically. For this purpose, different deep learning architectures have been utilized namely as Standard U-Net, Dilated U-Net, Fractal U-Net, FC-DenseNet, and Pix2Pix. Also, we have suggested a novel framework to create labeling data from OpenStreetMap service to create a unique dataset called YTU-WaterNet. According to the results, IoU and F1 scores have been calculated as 99.59% and 99.79% for the WaterNet. The results indicate that the WaterNet method outperforms other methods in terms of shoreline extraction from Landsat 8 OLI satellite images.     

Monitoring of rice cropping intensity in the upper Mekong Delta,Vietnam using time-series MODIS data

Information on rice growing areas is important for policymakers to devise agricultural plans. This research explores the monitoring of rice cropping intensity in the upper Mekong Delta, Vietnam (from 2001 to 2007) using time-series MODIS NDVI 250-m data. Data processing includes three steps: (1) noise is filtered from the time-series NDVI data using empirical mode decomposition (EMD); (2) endmembers are extracted from the filtered time-series data and trained in a linear mixture model (LMM) for classification of rice cropping systems; and (3) classification results are verified by comparing them with the ground-truth and statistical data. The results indicate that EMD is a good filter for noise removal from the time-series data. The classification results confirm the validity of LMM, giving an overall accuracy of 90.1% and a Kappa coefficient of 0.7. The lowest producer and user accuracies were associated with single crop rain-fed rice class due to the mixed pixel problems. A strong yearly correlation at the district level was revealed in the MODIS-derived areas (R² ? 0.9). Investigation of interannual changes in rice cropping intensity from 2001 to 2007 showed a remarkable conversion from double to triple crop irrigated rice from 2001 to 2003, especially in the Thoai Son and Phu Tan districts. A big conversion from triple crop rice back to double crop rice cultivation was also observed in Phu Tan from 2005 to 2006. These changes were verified by visual interpretation of Landsat images and examination of NDVI profiles.     

Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using night-time ASTER satellite data in highly urbanizing city,Delhi-India

The present study is an assessment and identification of urban heat island (UHI) in the environment of one of the fastest urbanizing city of India, Delhi Metropolis, employing satellite image of ASTER and Landsat 7 ETM+ in the thermal infrared region 3–14 μm. Temporal (2001 and 2005) ASTER datasets were used to analyze the spatial structure of the thermal urban environment subsequently urban heat island (UHI) in relation to the urban surface characteristics and land use/land cover (LULC). The study involves derivation of parameters governing the surface heat fluxes, constructing statistics of ASTER thermal infrared images along with validation through intensive in situ measurements. The average images reveal spatial and temporal variations of land surface temperature (LST) of night-time and distinct microclimatic patterns. Central Business District (CBD) of Delhi, (Connaught Place, a high density built up area), and commercial/industrial areas display heat islands condition with a temperature greater than 4 °C compared to the suburbs. The small increase in surface temperature at city level is mainly attributed to cumulative impact of human activities, changes in LULC pattern and vegetation density. In this study the methodology takes into account spatially-relative surface temperatures and impervious surface fraction value to measure surface UHI intensity between the urban land cover and rural surroundings. Both the spatial and temporal variation in surface temperature associated with impervious surface area (ISA) has been evaluated to assess the effect of urbanization on the local climate.     

Examining LiDAR – WorldView-3 data synergy to generate a detailed stand map in a mixed forest in the north-west of Turkey

Although stand delineation approach based on aerial photographs and field survey produces high accuracy maps, it is labour-intensive and time consuming. Furthermore, conventional forest stand maps may have some uncertainties that can hardly be verified due to the experiments and skills of photo-interpreters. Therefore, researchers have been seeking more objective and cost-effective methods for forest mapping. LiDAR (Light Detection and Ranging) data have a high potential to automatically delineate forest stands. Unlike optical sensors, LiDAR height data provides information about both the horizontal and vertical structural characteristics of forest stands. However, it deprives of spectral data that may be successfully used in separating tree species. In this study, we investigate the potential of LiDAR – WorldView-3 data synergy for the automatic generation of a detailed forest stand map which can be used for a tactical forest management plan. Firstly, image segmentation was applied to LiDAR data alone and LiDAR/WorldView-3 data set in order to obtain the most suitable image objects representing forest stands. Visual inspection of the segmentation results showed that image objects based on the LiDAR/WorldView-3 data set were more compatible with the reference forest stand boundaries. After the segmentation process, the LiDAR and LiDAR/WorldView-3 data sets were independently classified using object-based classification method. We tested two levels of classification. The first was a detailed classification with 14 classes considering reference stand types. The second was the rough classification with 9 classes where some stand types were combined. The mean, standard deviation and texture features of LiDAR metrics and spectral information were used in the classification. The accuracy assessment results of LiDAR data showed that the Overall Accuracy (OA) was calculated as 0.31 and 0.43, and the Kappa Index (KIA) was calculated as 0.26 and 0.32 for the detailed and rough classifications, respectively. For the LiDAR/WorldView-3 data set, the OA values were calculated as 0.50 and 0.61, and the KIA were calculated as 0.46 and 0.55 for the detailed and rough classifications, respectively. These results showed that the forest stand map derived from the LiDAR/WorldView-3 data synergy is more compatible with the reference forest stand map. In conclusion, it can be said that the forest stand maps produced in this study may provide strategic forest planning needs, but it is not sufficient for tactical forest management plans.     

Application of WorldView-3 imagery and ASTER TIR data to map alteration minerals associated with the Rodalquilar gold deposits,southeast Spain

The Rodalquilar epithermal quartz-alunite gold deposits that occur within the Rodalquilar caldera complex in southeast Spain, are associated with a pronounced hydrothermal alteration of the country rocks. The hydrothermal alteration zones that are exposed on the surface consist of the vuggy silica zone, the advanced argillic alteration zone, the intermediate argillic alteration zone, the propylitic alteration zone, and a second stage supergene acid sulfate alteration. High spatial resolution multispectral imagery recorded by the WorldView-3 satellite was used in this study to map the spatial distribution of the main alteration minerals in the Rodalquilar caldera complex. Thermal infrared (TIR) data of the ASTER satellite were used to detect the quartz-rich zones. The analysis of the Rodalquilar WorldView-3 data was based on the Adaptive Coherence Estimator (ACE), a partial unmixing algorithm. The ACE processing accurately mapped the spatial distribution of alunite, kaolinite, illite and goethite. Alunite is abundant in the vuggy silica and advanced argillic alteration zones, and in the second stage supergene acid sulfate alteration. Kaolinite is predominant in the intermediate argillic alteration zone. Illite is abundant in the outer parts of the intermediate argillic alteration zone. Goethite image maps gossans that mainly occur in the vuggy silica and advanced argillic alteration zones, and in the areas characterized by the second stage supergene acid sulfate alteration. The detection of quartz-rich zones from the ASTER TIR data complemented the WorldView-3 mapping results. The study shows the efficiency of high spatial resolution multispectral remote sensing imagery recorded by the WorldView-3 satellite for district-level mineral exploration studies.