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Multi-Sensor and Multi-Data Integration in Remote Sensing
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Multi-Sensor and Multi-Data Integration in Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 August 2016) | Viewed by 159915

Special Issue Editors

Prof. Dr. Naser El-Sheimy

E-Mail Website
Guest Editor
Department of Geomatics Engineering, The University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: intelligent and autonomous systems; navigation & positioning technologies; satellite technologies; multi-sensor systems; wireless positioning; vehicles & transportation systems; driverless cars; technology development; applications
Special Issues, Collections and Topics in MDPI journals
Dr. Zahra Lari

E-Mail Website
Guest Editor
Mobile Multi-Sensor Systems Research Group, Department of Geomatics Engineering, The University of Calgary, 2500 University Drive NW, Calgary, AB, Canada
Interests: photogrammetry; laser scanning; unmanned aerial vehicles; 3D modelling and reconstruction
Dr. Adel Moussa

E-Mail Website
Guest Editor
Mobile Multi-Sensor Systems Research Group, Department of Geomatics Engineering, The University of Calgary 2500 University Drive NW, Calgary, AB, Canada
Interests: photogrammetry; remote sensing; computer vision; laser scanning; sensor integration; unmanned aerial vehicles

Special Issue Information

Dear Colleagues,
With the advances in sensor technology and the increasing quantity of multi-sensor, multi-temporal, and multi-resolution data from different sources, data integration has become as a valuable tool in remote sensing applications. The main objective of multi-sensor data integration is to synergistically combine sensory data from disparate sources—with different characteristics, resolution, and quality—in order to provide more reliable, accurate, and useful information required for diverse mapping, modelling and monitoring applications. Therefore, the integration of these disparate data contributes to the robust interpretation of the observed objects/scenes and provides the basis of effective planning and decision-making. Over the past few decades, multi-sensor data integration has received tremendous attention and different approaches and techniques have been presented with the aim of improving integration quality and exploring more application areas. However, with the emergence of new sensors and broader diversity of the intended applications, new methodologies and best practices for multi-sensor data integration need to be developed and their advantages and limitations actively to be shared by scientific research community.

This Special Issue invites submissions on latest advances in remote sensing multi-sensor data integration. The focus of the contributions to the Special Issue will be on reviewing current progress, on highlighting the latest methodologies that have been proposed to respond to the needs of multi-sensor data processing, and on pointing out the strategies to be thought to meet the requirements of potential applications.

The topics of interest include (but are not limited to):

  • Multi-sensor, multi-temporal, multi-resolution data integration
  • Fusion of heterogeneous sensor information
  • Heterogeneous data processing
  • Performance: measures and evaluation
  • Applications to urban studies, 3D reconstruction and modelling, environmental monitoring, etc.

Prof. Naser El-sheimy
Dr. Zahra Lari
Dr. Adel Moussa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Multi-sensor systems
  • multi-temporal data
  • computer vision
  • laser scanning
  • sensor integration
  • unmanned aerial vehicles
  • navigation and imaging technologies
  • photogrammetry and remote sensing

Published Papers (21 papers)

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Research

14018 KiB  
Article
A New Approach for Realistic 3D Reconstruction of Planar Surfaces from Laser Scanning Data and Imagery Collected Onboard Modern Low-Cost Aerial Mapping Systems
by Zahra Lari, Naser El-Sheimy and Ayman Habib
Remote Sens. 2017, 9(3), 212; https://doi.org/10.3390/rs9030212 - 25 Feb 2017
Cited by 4 | Viewed by 7374
Abstract
Over the past few years, accurate 3D surface reconstruction using remotely-sensed data has been recognized as a prerequisite for different mapping, modelling, and monitoring applications. To fulfill the needs of these applications, necessary data are generally collected using various digital imaging systems. Among [...] Read more.
Over the past few years, accurate 3D surface reconstruction using remotely-sensed data has been recognized as a prerequisite for different mapping, modelling, and monitoring applications. To fulfill the needs of these applications, necessary data are generally collected using various digital imaging systems. Among them, laser scanners have been acknowledged as a fast, accurate, and flexible technology for the acquisition of high density 3D spatial data. Despite their quick accessibility, the acquired 3D data using these systems does not provide semantic information about the nature of scanned surfaces. Hence, reliable processing techniques are employed to extract the required information for 3D surface reconstruction. Moreover, the extracted information from laser scanning data cannot be effectively utilized due to the lack of descriptive details. In order to provide a more realistic and accurate perception of the scanned scenes using laser scanning systems, a new approach for 3D reconstruction of planar surfaces is introduced in this paper. This approach aims to improve the interpretability of the extracted planar surfaces from laser scanning data using spectral information from overlapping imagery collected onboard modern low-cost aerial mapping systems, which are widely adopted nowadays. In this approach, the scanned planar surfaces using laser scanning systems are initially extracted through a novel segmentation procedure, and then textured using the acquired overlapping imagery. The implemented texturing technique, which intends to overcome the computational inefficiency of the previously-developed 3D reconstruction techniques, is performed in three steps. In the first step, the visibility of the extracted planar surfaces from laser scanning data within the collected images is investigated and a list of appropriate images for texturing each surface is established. Successively, an occlusion detection procedure is carried out to identify the occluded parts of these surfaces in the field of view of captured images. In the second step, visible/non-occluded parts of the planar surfaces are decomposed into segments that will be textured using individual images. Finally, a rendering procedure is accomplished to texture these parts using available images. Experimental results from overlapping laser scanning data and imagery collected onboard aerial mapping systems verify the feasibility of the proposed approach for efficient realistic 3D surface reconstruction. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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3520 KiB  
Article
Multivariate Spatial Data Fusion for Very Large Remote Sensing Datasets
by Hai Nguyen, Noel Cressie and Amy Braverman
Remote Sens. 2017, 9(2), 142; https://doi.org/10.3390/rs9020142 - 09 Feb 2017
Cited by 21 | Viewed by 6228
Abstract
Global maps of total-column carbon dioxide (CO2) mole fraction (in units of parts per million) are important tools for climate research since they provide insights into the spatial distribution of carbon intake and emissions as well as their seasonal and annual [...] Read more.
Global maps of total-column carbon dioxide (CO2) mole fraction (in units of parts per million) are important tools for climate research since they provide insights into the spatial distribution of carbon intake and emissions as well as their seasonal and annual evolutions. Currently, two main remote sensing instruments for total-column CO2 are the Orbiting Carbon Observatory-2 (OCO-2) and the Greenhouse gases Observing SATellite (GOSAT), both of which produce estimates of CO2 concentration, called profiles, at 20 different pressure levels. Operationally, each profile estimate is then convolved into a single estimate of column-averaged CO2 using a linear pressure weighting function. This total-column CO2 is then used for subsequent analyses such as Level 3 map generation and colocation for validation. In principle, total-column CO2 in these applications may be more efficiently estimated by making optimal estimates of the vector-valued CO2 profiles and applying the pressure weighting function afterwards. These estimates will be more efficient if there is multivariate dependence between CO2 values in the profile. In this article, we describe a methodology that uses a modified Spatial Random Effects model to account for the multivariate nature of the data fusion of OCO-2 and GOSAT. We show that multivariate fusion of the profiles has improved mean squared error relative to scalar fusion of the column-averaged CO2 values from OCO-2 and GOSAT. The computations scale linearly with the number of data points, making it suitable for the typically massive remote sensing datasets. Furthermore, the methodology properly accounts for differences in instrument footprint, measurement-error characteristics, and data coverages. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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6899 KiB  
Article
Modeling the Effects of the Urban Built-Up Environment on Plant Phenology Using Fused Satellite Data
by Norman Gervais, Alexander Buyantuev and Feng Gao
Remote Sens. 2017, 9(1), 99; https://doi.org/10.3390/rs9010099 - 23 Jan 2017
Cited by 9 | Viewed by 8585
Abstract
Understanding the effects that the Urban Heat Island (UHI) has on plant phenology is important in predicting ecological impacts of expanding cities and the impacts of the projected global warming. However, the underlying methods to monitor phenological events often limit this understanding. Generally, [...] Read more.
Understanding the effects that the Urban Heat Island (UHI) has on plant phenology is important in predicting ecological impacts of expanding cities and the impacts of the projected global warming. However, the underlying methods to monitor phenological events often limit this understanding. Generally, one can either have a small sample of in situ measurements or use satellite data to observe large areas of land surface phenology (LSP). In the latter, a tradeoff exists among platforms with some allowing better temporal resolution to pick up discrete events and others possessing the spatial resolution appropriate for observing heterogeneous landscapes, such as urban areas. To overcome these limitations, we applied the Spatial and Temporal Adaptive Reflectance Model (STARFM) to fuse Landsat surface reflectance and MODIS nadir BRDF-adjusted reflectance (NBAR) data with three separate selection conditions for input data across two versions of the software. From the fused images, we derived a time-series of high temporal and high spatial resolution synthetic Normalized Difference Vegetation Index (NDVI) imagery to identify the dates of the start of the growing season (SOS), end of the season (EOS), and the length of the season (LOS). The results were compared between the urban and exurban developed areas within the vicinity of Ogden, UT and across all three data scenarios. The results generally show an earlier urban SOS, later urban EOS, and longer urban LOS, with variation across the results suggesting that phenological parameters are sensitive to input changes. Although there was strong evidence that STARFM has the potential to produce images capable of capturing the UHI effect on phenology, we recommend that future work refine the proposed methods and compare the results against ground events. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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5507 KiB  
Article
An Integrated GNSS/INS/LiDAR-SLAM Positioning Method for Highly Accurate Forest Stem Mapping
by Chuang Qian, Hui Liu, Jian Tang, Yuwei Chen, Harri Kaartinen, Antero Kukko, Lingli Zhu, Xinlian Liang, Liang Chen and Juha Hyyppä
Remote Sens. 2017, 9(1), 3; https://doi.org/10.3390/rs9010003 - 23 Dec 2016
Cited by 106 | Viewed by 13567
Abstract
Forest mapping, one of the main components of performing a forest inventory, is an important driving force in the development of laser scanning. Mobile laser scanning (MLS), in which laser scanners are installed on moving platforms, has been studied as a convenient measurement [...] Read more.
Forest mapping, one of the main components of performing a forest inventory, is an important driving force in the development of laser scanning. Mobile laser scanning (MLS), in which laser scanners are installed on moving platforms, has been studied as a convenient measurement method for forest mapping in the past several years. Positioning and attitude accuracies are important for forest mapping using MLS systems. Inertial Navigation Systems (INSs) and Global Navigation Satellite Systems (GNSSs) are typical and popular positioning and attitude sensors used in MLS systems. In forest environments, because of the loss of signal due to occlusion and severe multipath effects, the positioning accuracy of GNSS is severely degraded, and even that of GNSS/INS decreases considerably. Light Detection and Ranging (LiDAR)-based Simultaneous Localization and Mapping (SLAM) can achieve higher positioning accuracy in environments containing many features and is commonly implemented in GNSS-denied indoor environments. Forests are different from an indoor environment in that the GNSS signal is available to some extent in a forest. Although the positioning accuracy of GNSS/INS is reduced, estimates of heading angle and velocity can maintain high accurate even with fewer satellites. GNSS/INS and the LiDAR-based SLAM technique can be effectively integrated to form a sustainable, highly accurate positioning and mapping solution for use in forests without additional hardware costs. In this study, information such as heading angles and velocities extracted from a GNSS/INS is utilized to improve the positioning accuracy of the SLAM solution, and two information-aided SLAM methods are proposed. First, a heading angle-aided SLAM (H-aided SLAM) method is proposed that supplies the heading angle from GNSS/INS to SLAM. Field test results show that the horizontal positioning accuracy of an entire trajectory of 800 m is 0.13 m and is significantly improved (by 70%) compared to that of a traditional GNSS/INS; second, a more complex information added SLAM solution that utilizes both heading angle and velocity information simultaneously (HV-aided SLAM) is investigated. Experimental results show that the horizontal positioning accuracy can reach a level of six centimetres with the HV-aided SLAM, which is a significant improvement (by 86%). Thus, a more accurate forest map is obtained by the proposed integrated method. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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22770 KiB  
Article
Exploiting TERRA-AQUA MODIS Relationship in the Reflective Solar Bands for Aerosol Retrieval
by Xingwang Fan and Yuanbo Liu
Remote Sens. 2016, 8(12), 996; https://doi.org/10.3390/rs8120996 - 03 Dec 2016
Cited by 1 | Viewed by 5095
Abstract
Satellite remote sensing has been providing aerosol data with ever-increasing accuracy, representative of the MODerate-resolution Imaging Spectroradiometer (MODIS) Dark Target (DT) and Deep Blue (DB) aerosol retrievals. These retrievals are generally performed over spectrally dark objects and therefore may struggle over bright surfaces. [...] Read more.
Satellite remote sensing has been providing aerosol data with ever-increasing accuracy, representative of the MODerate-resolution Imaging Spectroradiometer (MODIS) Dark Target (DT) and Deep Blue (DB) aerosol retrievals. These retrievals are generally performed over spectrally dark objects and therefore may struggle over bright surfaces. This study proposed an analytical TERRA-AQUA MODIS relationship in the reflective solar bands for aerosol retrieval. For the relationship development, the bidirectional reflectance distribution function (BRDF) effects were adjusted using reflectance ratios in the MODIS 2.13 μm band and the path radiance was approximated as an analytical function of aerosol optical thickness (AOT) and scattering phase function. Comparisons with MODIS observation data, MODIS AOT data, and sun photometer measurements demonstrate the validity of the proposed relationship for aerosol retrieval. The synergetic TERRA-AQUA MODIS retrievals are highly correlated with the ground measured AOT at TERRA MODIS overpass time (R2 = 0.617; RMSE = 0.043) and AQUA overpass time (R2 = 0.737; RMSE = 0.036). Compared to our retrievals, both the MODIS DT and DB retrievals are subject to severe underestimation. Sensitivity analyses reveal that the proposed method may perform better over non-vegetated than vegetated surfaces, which can offer a complement to MODIS operational algorithms. In an analytical form, the proposed method also has advantages in computational efficiency, and therefore can be employed for fine-scale (relative to operational 10 km MODIS product) MODIS aerosol retrieval. Overall, this study provides insight into aerosol retrievals and other applications regarding TERRA-AQUA MODIS data. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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7903 KiB  
Article
Interpolation of GPS and Geological Data Using InSAR Deformation Maps: Method and Application to Land Subsidence in the Alto Guadalentín Aquifer (SE Spain)
by Marta Béjar-Pizarro, Carolina Guardiola-Albert, Ramón P. García-Cárdenas, Gerardo Herrera, Anna Barra, Antonio López Molina, Serena Tessitore, Alejandra Staller, José A. Ortega-Becerril and Ramón P. García-García
Remote Sens. 2016, 8(11), 965; https://doi.org/10.3390/rs8110965 - 23 Nov 2016
Cited by 45 | Viewed by 7862
Abstract
Land subsidence resulting from groundwater extractions is a global phenomenon adversely affecting many regions worldwide. Understanding the governing processes and mitigating associated hazards require knowing the spatial distribution of the implicated factors (piezometric levels, lithology, ground deformation), usually only known at discrete locations. [...] Read more.
Land subsidence resulting from groundwater extractions is a global phenomenon adversely affecting many regions worldwide. Understanding the governing processes and mitigating associated hazards require knowing the spatial distribution of the implicated factors (piezometric levels, lithology, ground deformation), usually only known at discrete locations. Here, we propose a methodology based on the Kriging with External Drift (KED) approach to interpolate sparse point measurements of variables influencing land subsidence using high density InSAR measurements. In our study, located in the Alto Guadalentín basin, SE Spain, these variables are GPS vertical velocities and the thickness of compressible soils. First, we estimate InSAR and GPS rates of subsidence covering the periods 2003–2010 and 2004–2013, respectively. Then, we apply the KED method to the discrete variables. The resulting continuous GPS velocity map shows maximum subsidence rates of 13 cm/year in the center of the basin, in agreement with previous studies. The compressible deposits thickness map is significantly improved. We also test the coherence of Sentinel-1 data in the study region and evaluate the applicability of this methodology with the new satellite, which will improve the monitoring of aquifer-related subsidence and the mapping of variables governing this phenomenon. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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8288 KiB  
Article
Using Landsat, MODIS, and a Biophysical Model to Evaluate LST in Urban Centers
by Mohammad Z. Al-Hamdan, Dale A. Quattrochi, Lahouari Bounoua, Asia Lachir and Ping Zhang
Remote Sens. 2016, 8(11), 952; https://doi.org/10.3390/rs8110952 - 16 Nov 2016
Cited by 12 | Viewed by 7782
Abstract
In this paper, we assessed and compared land surface temperature (LST) in urban centers using data from Landsat, MODIS, and the Simple Biosphere model (SiB2). We also evaluated the sensitivity of the model’s LST to different land cover types, fractions (percentages), and emissivities [...] Read more.
In this paper, we assessed and compared land surface temperature (LST) in urban centers using data from Landsat, MODIS, and the Simple Biosphere model (SiB2). We also evaluated the sensitivity of the model’s LST to different land cover types, fractions (percentages), and emissivities compared to reference points derived from Landsat thermal data. This was demonstrated in three climatologically- and morphologically-different cities of Atlanta, GA, New York, NY, and Washington, DC. Our results showed that in these cities SiB2 was sensitive to both the emissivity and the land cover type and fraction, but much more sensitive to the latter. The practical implications of these results are rather significant since they imply that the SiB2 model can be used to run different scenarios for evaluating urban heat island (UHI) mitigation strategies. This study also showed that using detailed emissivities per land cover type and fractions from Landsat-derived data caused a convergence of the model results towards the Landsat-derived LST for most of the studied cases. This study also showed that SiB2 LSTs are closer in magnitude to Landsat-derived LSTs than MODIS-derived LSTs. It is important, however, to emphasize that both Landsat and MODIS LSTs are not direct observations and, as such, do not represent a ground truth. More studies will be needed to compare these results to in situ LST data and provide further validation. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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8339 KiB  
Article
The Use of C-/X-Band Time-Gapped SAR Data and Geotechnical Models for the Study of Shanghai’s Ocean-Reclaimed Lands through the SBAS-DInSAR Technique
by Antonio Pepe, Manuela Bonano, Qing Zhao, Tianliang Yang and Hanmei Wang
Remote Sens. 2016, 8(11), 911; https://doi.org/10.3390/rs8110911 - 02 Nov 2016
Cited by 63 | Viewed by 6221
Abstract
In this work, we investigate the temporal evolution of ground deformation affecting the ocean-reclaimed lands of the Shanghai (China) megacity, from 2007 to 2016, by applying the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique known as the Small BAseline Subset (SBAS) algorithm. For [...] Read more.
In this work, we investigate the temporal evolution of ground deformation affecting the ocean-reclaimed lands of the Shanghai (China) megacity, from 2007 to 2016, by applying the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique known as the Small BAseline Subset (SBAS) algorithm. For the analysis, we exploited two sets of non-time-overlapped synthetic aperture radar (SAR) data, acquired from 2007 to 2010, by the ASAR/ENVISAT (C-band) instrument, and from 2014 to 2016 by the X-band COSMO-SkyMed (CSK) sensors. The long time gap (of about three years) existing between the available C- and X-band datasets made the generation of unique displacement time-series more difficult. Nonetheless, this problem was successfully solved by benefiting from knowledge of time-dependent geotechnical models, which describe the temporal evolution of the expected deformation affecting Shanghai’s ocean-reclaimed platforms. The combined ENVISAT/CSK (vertical) deformation time-series were analyzed to gain insight into the future evolution of displacement signals within the investigated area. As an outcome, we find that ocean-reclaimed lands in Shanghai experienced, between 2007 and 2016, average cumulative (vertical) displacements extending down to 25 centimeters. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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19403 KiB  
Article
Towards Slow-Moving Landslide Monitoring by Integrating Multi-Sensor InSAR Time Series Datasets: The Zhouqu Case Study, China
by Qian Sun, Jun Hu, Lei Zhang and Xiaoli Ding
Remote Sens. 2016, 8(11), 908; https://doi.org/10.3390/rs8110908 - 02 Nov 2016
Cited by 55 | Viewed by 6713
Abstract
Although the past few decades have witnessed the great development of Synthetic Aperture Radar Interferometry (InSAR) technology in the monitoring of landslides, such applications are limited by geometric distortions and ambiguity of 1D Line-Of-Sight (LOS) measurements, both of which are the fundamental weakness [...] Read more.
Although the past few decades have witnessed the great development of Synthetic Aperture Radar Interferometry (InSAR) technology in the monitoring of landslides, such applications are limited by geometric distortions and ambiguity of 1D Line-Of-Sight (LOS) measurements, both of which are the fundamental weakness of InSAR. Integration of multi-sensor InSAR datasets has recently shown its great potential in breaking through the two limits. In this study, 16 ascending images from the Advanced Land Observing Satellite (ALOS) and 18 descending images from the Environmental Satellite (ENVISAT) have been integrated to characterize and to detect the slow-moving landslides in Zhouqu, China between 2008 and 2010. Geometric distortions are first mapped by using the imaging geometric parameters of the used SAR data and public Digital Elevation Model (DEM) data of Zhouqu, which allow the determination of the most appropriate data assembly for a particular slope. Subsequently, deformation rates along respective LOS directions of ALOS ascending and ENVISAT descending tracks are estimated by conducting InSAR time series analysis with a Temporarily Coherent Point (TCP)-InSAR algorithm. As indicated by the geometric distortion results, 3D deformation rates of the Xieliupo slope at the east bank of the Pai-lung River are finally reconstructed by joint exploiting of the LOS deformation rates from cross-heading datasets based on the surface–parallel flow assumption. It is revealed that the synergistic results of ALOS and ENVISAT datasets provide a more comprehensive understanding and monitoring of the slow-moving landslides in Zhouqu. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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14402 KiB  
Article
Integrating Data of ASTER and Landsat-8 OLI (AO) for Hydrothermal Alteration Mineral Mapping in Duolong Porphyry Cu-Au Deposit, Tibetan Plateau, China
by Tingbin Zhang, Guihua Yi, Hongmei Li, Ziyi Wang, Juxing Tang, Kanghui Zhong, Yubin Li, Qin Wang and Xiaojuan Bie
Remote Sens. 2016, 8(11), 890; https://doi.org/10.3390/rs8110890 - 28 Oct 2016
Cited by 74 | Viewed by 9470
Abstract
One of the most important characteristics of porphyry copper deposits (PCDs) is the type and distribution pattern of alteration zones which can be used for screening and recognizing these deposits. Hydrothermal alteration minerals with diagnostic spectral absorption properties in the visible and near-infrared [...] Read more.
One of the most important characteristics of porphyry copper deposits (PCDs) is the type and distribution pattern of alteration zones which can be used for screening and recognizing these deposits. Hydrothermal alteration minerals with diagnostic spectral absorption properties in the visible and near-infrared (VNIR) through the shortwave infrared (SWIR) regions can be identified by multispectral and hyperspectral remote sensing data. Six Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) bands in SWIR have been shown to be effective in the mapping of Al-OH, Fe-OH, Mg-OH group minerals. The five VNIR bands of Landsat-8 (L8) Operational Land Imager (OLI) are useful for discriminating ferric iron alteration minerals. In the absence of complete hyperspectral coverage area, an opportunity, however, exists to integrate ASTER and L8-OLI (AO) to compensate each other’s shortcomings in covering area for mineral mapping. This study examines the potential of AO data in mineral mapping in an arid area of the Duolong porphyry Cu-Au deposit(Tibetan Plateau in China) by using spectral analysis techniques. Results show the following conclusions: (1) Combination of ASTER and L8-OLI data (AO) has more mineral information content than either alone; (2) The Duolong PCD alteration zones of phyllic, argillic and propylitic zones are mapped using ASTER SWIR bands and the iron-bearing mineral information is best mapped using AO VNIR bands; (3) The multispectral integration data of AO can provide a compensatory data of ASTER VNIR bands for iron-bearing mineral mapping in the arid and semi-arid areas. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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2550 KiB  
Article
Integration of Aerial Thermal Imagery, LiDAR Data and Ground Surveys for Surface Temperature Mapping in Urban Environments
by Emanuele Mandanici, Paolo Conte and Valentina A. Girelli
Remote Sens. 2016, 8(10), 880; https://doi.org/10.3390/rs8100880 - 23 Oct 2016
Cited by 16 | Viewed by 7503
Abstract
A single-band surface temperature retrieval method is proposed, aiming at achieving a better accuracy by exploiting the integration of aerial thermal images with LiDAR data and ground surveys. LiDAR data allow the generation of a high resolution digital surface model and a detailed [...] Read more.
A single-band surface temperature retrieval method is proposed, aiming at achieving a better accuracy by exploiting the integration of aerial thermal images with LiDAR data and ground surveys. LiDAR data allow the generation of a high resolution digital surface model and a detailed modeling of the Sky-View Factor (SVF). Ground surveys of surface temperature and emissivity, instead, are used to estimate the atmospheric parameters involved in the model (through a bounded least square adjustment) and for a first assessment of the accuracy of the results. The RMS of the difference between the surface temperatures computed from the model and measured on the check sites ranges between 0.8 °C and 1.0 °C, depending on the algorithm used to calculate the SVF. Results are in general better than the ones obtained without considering SVF and prove the effectiveness of the integration of different data sources. The proposed approach has the advantage of avoiding the modeling of the atmosphere conditions, which is often difficult to achieve with the desired accuracy; on the other hand, it is highly dependent on the accuracy of the data measured on the ground. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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11073 KiB  
Article
Incorporating Diversity into Self-Learning for Synergetic Classification of Hyperspectral and Panchromatic Images
by Xiaochen Lu, Junping Zhang, Tong Li and Ye Zhang
Remote Sens. 2016, 8(10), 804; https://doi.org/10.3390/rs8100804 - 29 Sep 2016
Cited by 13 | Viewed by 4473
Abstract
Derived from semi-supervised learning and active learning approaches, self-learning (SL) was recently developed for the synergetic classification of hyperspectral (HS) and panchromatic (PAN) images. Combining the image segmentation and active learning techniques, SL aims at selecting and labeling the informative unlabeled samples automatically, [...] Read more.
Derived from semi-supervised learning and active learning approaches, self-learning (SL) was recently developed for the synergetic classification of hyperspectral (HS) and panchromatic (PAN) images. Combining the image segmentation and active learning techniques, SL aims at selecting and labeling the informative unlabeled samples automatically, thereby improving the classification accuracy under the condition of small samples. This paper presents an improved synergetic classification scheme based on the concept of self-learning for HS and PAN images. The investigated scheme considers three basic rules, namely the identity rule, the uncertainty rule, and the diversity rule. By integrating the diversity of samples into the SL scheme, a more stable classifier is trained by using fewer samples. Experiments on three synthetic and real HS and PAN images reveal that the diversity criterion can avoid the problem of bias sampling, and has a certain advantage over the primary self-learning approach. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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17654 KiB  
Article
Automated Ortho-Rectification of UAV-Based Hyperspectral Data over an Agricultural Field Using Frame RGB Imagery
by Ayman Habib, Youkyung Han, Weifeng Xiong, Fangning He, Zhou Zhang and Melba Crawford
Remote Sens. 2016, 8(10), 796; https://doi.org/10.3390/rs8100796 - 24 Sep 2016
Cited by 50 | Viewed by 9576
Abstract
Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from [...] Read more.
Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging is based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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1928 KiB  
Article
Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed
by Austin Stanforth, Max J. Moreno-Madriñán and Jeffrey Ashby
Remote Sens. 2016, 8(9), 770; https://doi.org/10.3390/rs8090770 - 19 Sep 2016
Cited by 12 | Viewed by 7023
Abstract
Previous research on Dengue Fever have involved laboratory tests or study areas with less diverse temperature and elevation ranges than is found in Colombia; therefore, preliminary research was needed to identify location specific attributes of Dengue Fever transmission. Environmental variables derived from the [...] Read more.
Previous research on Dengue Fever have involved laboratory tests or study areas with less diverse temperature and elevation ranges than is found in Colombia; therefore, preliminary research was needed to identify location specific attributes of Dengue Fever transmission. Environmental variables derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellites were combined with population variables to be statistically compared against reported cases of Dengue Fever in the Río Magdalena watershed, Colombia. Three-factor analysis models were investigated to analyze variable patterns, including a population, population density, and empirical Bayesian estimation model. Results identified varying levels of Dengue Fever transmission risk, and environmental characteristics which support, and advance, the research literature. Multiple temperature metrics, elevation, and vegetation composition were among the more contributory variables found to identify future potential outbreak locations. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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21023 KiB  
Article
Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis
by Meryl L. McDowell and Fred A. Kruse
Remote Sens. 2016, 8(9), 757; https://doi.org/10.3390/rs8090757 - 15 Sep 2016
Cited by 10 | Viewed by 6704
Abstract
We developed a method to enhance compositional mapping from spectral remote sensing through the integration of visible to near infrared (VNIR, ~0.4–1 µm), shortwave infrared (SWIR, ~1–2.5 µm), and longwave infrared (LWIR, ~8–13 µm) data. Spectral information from the individual ranges was first [...] Read more.
We developed a method to enhance compositional mapping from spectral remote sensing through the integration of visible to near infrared (VNIR, ~0.4–1 µm), shortwave infrared (SWIR, ~1–2.5 µm), and longwave infrared (LWIR, ~8–13 µm) data. Spectral information from the individual ranges was first analyzed independently and then the resulting compositional information in the form of image endmembers and apparent abundances was integrated using ISODATA cluster analysis. Independent VNIR, SWIR, and LWIR analyses of a study area near Mountain Pass, California identified image endmembers representing vegetation, manmade materials (e.g., metal, plastic), specific minerals (e.g., calcite, dolomite, hematite, muscovite, gypsum), and general lithology (e.g., sulfate-bearing, carbonate-bearing, and silica-rich units). Integration of these endmembers and their abundances produced a final full-range classification map incorporating much of the variation from all three spectral ranges. The integrated map and its 54 classes provide additional compositional information that is not evident in the VNIR, SWIR, or LWIR data alone, which allows for more complete and accurate compositional mapping. A supplemental examination of hyperspectral LWIR data and comparison with the multispectral LWIR data used in the integration illustrates its potential to further improve this approach. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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8605 KiB  
Article
An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery
by Xiao Ling, Yongjun Zhang, Jinxin Xiong, Xu Huang and Zhipeng Chen
Remote Sens. 2016, 8(8), 672; https://doi.org/10.3390/rs8080672 - 19 Aug 2016
Cited by 28 | Viewed by 7499
Abstract
This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM) data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by [...] Read more.
This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM) data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by global SRTM data, after which the seed points are selected and matched. To produce more reliable matching results, a region segmentation-based matching propagation is proposed in this paper, whereby the region segmentations are extracted by image segmentation and are considered to be a spatial constraint. Moreover, a similarity measure integrating Distance, Angle and Normalized Cross-Correlation (DANCC), which considers geometric similarity and radiometric similarity, is introduced to find the optimal correspondences. Experiments using typical satellite images acquired from Resources Satellite-3 (ZY-3), Mapping Satellite-1, SPOT-5 and Google Earth demonstrated that the proposed method is able to produce reliable and accurate matching results. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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3850 KiB  
Article
Application of Helmert Variance Component Based Adaptive Kalman Filter in Multi-GNSS PPP/INS Tightly Coupled Integration
by Zhouzheng Gao, Wenbin Shen, Hongping Zhang, Maorong Ge and Xiaoji Niu
Remote Sens. 2016, 8(7), 553; https://doi.org/10.3390/rs8070553 - 29 Jun 2016
Cited by 39 | Viewed by 6946
Abstract
The integration of the Global Positioning System (GPS) and the Inertial Navigation System (INS) based on Real-time Kinematic (RTK) and Single Point Positioning (SPP) technology have been applied as a powerful approach in kinematic positioning and attitude determination. However, the accuracy of RTK [...] Read more.
The integration of the Global Positioning System (GPS) and the Inertial Navigation System (INS) based on Real-time Kinematic (RTK) and Single Point Positioning (SPP) technology have been applied as a powerful approach in kinematic positioning and attitude determination. However, the accuracy of RTK and SPP based GPS/INS integration mode will degrade visibly along with the increasing user-base distance and the quality of pseudo-range. In order to overcome such weaknesses, the tightly coupled integration between GPS Precise Point Positioning (PPP) and INS was proposed recently. Because of the rapid development of the multi-constellation Global Navigation Satellite System (multi-GNSS), we introduce the multi-GNSS into the tightly coupled integration of PPP and INS in this paper. Meanwhile, in order to weaken the impacts of the GNSS observations with low quality and the inaccurate state model on the performance of the multi-GNSS PPP/INS tightly coupled integration, the Helmert variance component estimation based adaptive Kalman filter is employed in the algorithm implementation. Finally, a set of vehicle-borne GPS + BeiDou + GLONASS and Micro-Electro-Mechanical-Systems (MEMS) INS data is analyzed to evaluate the performance of such algorithm. The statistics indicate that the performance of the multi-GNSS PPP/INS tightly coupled integration can be enhanced significantly in terms of both position accuracy and convergence time. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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7479 KiB  
Article
Generation of Land Cover Maps through the Fusion of Aerial Images and Airborne LiDAR Data in Urban Areas
by Yongmin Kim
Remote Sens. 2016, 8(6), 521; https://doi.org/10.3390/rs8060521 - 22 Jun 2016
Cited by 7 | Viewed by 5983
Abstract
Satellite images and aerial images with high spatial resolution have improved visual interpretation capabilities. The use of high-resolution images has rapidly grown and has been extended to various fields, such as military surveillance, disaster monitoring, and cartography. However, many problems were encountered in [...] Read more.
Satellite images and aerial images with high spatial resolution have improved visual interpretation capabilities. The use of high-resolution images has rapidly grown and has been extended to various fields, such as military surveillance, disaster monitoring, and cartography. However, many problems were encountered in which one object has a variety of spectral properties and different objects have similar spectral characteristics in terms of land cover. The problems are quite noticeable, especially for building objects in urban environments. In the land cover classification process, these issues directly decrease the classification accuracy by causing misclassification of single objects as well as between objects. This study proposes a method of increasing the accuracy of land cover classification by addressing the problem of misclassifying building objects through the output-level fusion of aerial images and airborne Light Detection and Ranging (LiDAR) data. The new method consists of the following three steps: (1) generation of the segmented image via a process that performs adaptive dynamic range linear stretching and modified seeded region growth algorithms; (2) extraction of building information from airborne LiDAR data using a planar filter and binary supervised classification; and (3) generation of a land cover map using the output-level fusion of two results and object-based classification. The new method was tested at four experimental sites with the Min-Max method and the SSI-nDSM method followed by a visual assessment and a quantitative accuracy assessment through comparison with reference data. In the accuracy assessment, the new method exhibits various advantages, including reduced noise and more precise classification results. Additionally, the new method improved the overall accuracy by more than 5% over the comparative evaluation methods. The high and low patterns between the overall and building accuracies were similar. Thus, the new method is judged to have successfully solved the inaccuracy problem of classification that is often produced by high-resolution images of urban environments through an output-level fusion technique. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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5662 KiB  
Article
Merging Alternate Remotely-Sensed Soil Moisture Retrievals Using a Non-Static Model Combination Approach
by Seokhyeon Kim, Robert M. Parinussa, Yi Y. Liu, Fiona M. Johnson and Ashish Sharma
Remote Sens. 2016, 8(6), 518; https://doi.org/10.3390/rs8060518 - 21 Jun 2016
Cited by 13 | Viewed by 5655
Abstract
Soil moisture is an important variable in the coupled hydrologic and climate system. In recent years, microwave-based soil moisture products have been shown to be a viable alternative to in situ measurements. A popular way to measure the performance of soil moisture products [...] Read more.
Soil moisture is an important variable in the coupled hydrologic and climate system. In recent years, microwave-based soil moisture products have been shown to be a viable alternative to in situ measurements. A popular way to measure the performance of soil moisture products is to calculate the temporal correlation coefficient (R) against in situ measurements or other appropriate reference datasets. In this study, an existing linear combination method improving R was modified to allow for a non-static or nonstationary model combination as the basis for improving remotely-sensed surface soil moisture. Previous research had noted that two soil moisture products retrieved using the Japan Aerospace Exploration Agency (JAXA) and Land Parameter Retrieval Model (LPRM) algorithms from the same Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor are spatially complementary in terms of R against a suitable reference over a fixed period. Accordingly, a linear combination was proposed to maximize R using a set of spatially-varying, but temporally-fixed weights. Even though this approach showed promising results, there was room for further improvements, in particular using non-static or dynamic weights that take account of the time-varying nature of the combination algorithm being approximated. The dynamic weighting was achieved by using a moving window. A number of different window sizes was investigated. The optimal weighting factors were determined for the data lying within the moving window and then used to dynamically combine the two parent products. We show improved performance for the dynamically-combined product over the static linear combination. Generally, shorter time windows outperform the static approach, and a 60-day time window is suggested to be the optimum. Results were validated against in situ measurements collected from 124 stations over different continents. The mean R of the dynamically-combined products was found to be 0.57 and 0.62 for the cases using the European Centre for Medium-Range Weather Forecasts Reanalysis-Interim (ERA-Interim) and Modern-Era Retrospective Analysis for Research and Applications Land (MERRA-Land) reanalysis products as the reference, respectively, outperforming the statically-combined products (0.55 and 0.54). Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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14161 KiB  
Article
Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI
by Limin Liao, Jinling Song, Jindi Wang, Zhiqiang Xiao and Jian Wang
Remote Sens. 2016, 8(6), 452; https://doi.org/10.3390/rs8060452 - 27 May 2016
Cited by 67 | Viewed by 7706
Abstract
Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI) datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new [...] Read more.
Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI) datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model (NDVI-BSFM) for accurately and effectively building frequent high spatial resolution Landsat-like NDVI datasets by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat NDVI. Experimental comparisons with the results obtained using other popular methods (i.e., the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and the Flexible Spatiotemporal DAta Fusion (FSDAF) method) showed that our proposed method has the following advantages: (1) it can obtain more accurate estimates; (2) it can retain more spatial detail; (3) its prediction accuracy is less dependent on the quality of the MODIS NDVI on the specific prediction date; and (4) it produces smoother NDVI time series profiles. All of these advantages demonstrate the strengths and the robustness of the proposed NDVI-BSFM in providing reliable high spatial and temporal resolution NDVI datasets to support other land surface process studies. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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13126 KiB  
Article
A Comparative Study of Cross-Product NDVI Dynamics in the Kilimanjaro Region—A Matter of Sensor, Degradation Calibration, and Significance
by Florian Detsch, Insa Otte, Tim Appelhans and Thomas Nauss
Remote Sens. 2016, 8(2), 159; https://doi.org/10.3390/rs8020159 - 19 Feb 2016
Cited by 27 | Viewed by 8358
Abstract
While satellite-based monitoring of vegetation activity at the earth’s surface is of vital importance for many eco-climatological applications, the degree of agreement among certain sensors and products providing estimates of the Normalized Difference Vegetation Index (NDVI) has been found to vary considerably. In [...] Read more.
While satellite-based monitoring of vegetation activity at the earth’s surface is of vital importance for many eco-climatological applications, the degree of agreement among certain sensors and products providing estimates of the Normalized Difference Vegetation Index (NDVI) has been found to vary considerably. In order to assess the extent of such differences in highly heterogeneous terrain, we analyze and compare intra-annual seasonal fluctuations and long-term monotonic trends (2003–2012) in the Kilimanjaro region, Tanzania. The considered NDVI datasets include the Moderate Resolution Imaging Spectroradiometer (MODIS) products from Terra and Aqua, Collections 5 and 6, and the 3rd Generation Global Inventory Modeling and Mapping Studies (GIMMS) product. The degree of agreement in seasonal fluctuations is assessed by calculating a pairwise Index of Association (IOAs), whereas long-term trends are derived from the trend-free pre-whitened Mann–Kendall test. On the seasonal scale, the two Terra-MODIS products (and, accordingly, the two Aqua-MODIS products) are best associated with each other, indicating that the seasonal signal remained largely unaffected by the new Collection 6 calibration approach. On the long-term scale, we find that the negative impacts of band ageing on Terra-MODIS NDVI have been accounted for in Collection 6, which now distinctly outweighs Aqua-MODIS in terms of greening trends. GIMMS NDVI, by contrast, fails to capture small-scale seasonal and trend patterns that are characteristic for the highly fragmented landscape which is likely owing to the coarse spatial resolution. As a short digression, we also demonstrate that the amount of false discoveries in the determined trend fraction is distinctly higher for p < 0.05 ( 52.6 % ) than for p < 0.001 ( 2.2 % ) which should point the way for any future studies focusing on the reliable deduction of long-term monotonic trends. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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