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Publisher: Elsevier   (Total: 3162 journals)

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Showing 1401 - 1600 of 3162 Journals sorted alphabetically
Intl. J. of Adhesion and Adhesives     Hybrid Journal   (Followers: 18, SJR: 0.926, CiteScore: 2)
Intl. J. of Africa Nursing Sciences     Open Access   (SJR: 0.396, CiteScore: 1)
Intl. J. of Antimicrobial Agents     Hybrid Journal   (Followers: 10, SJR: 1.699, CiteScore: 4)
Intl. J. of Applied Earth Observation and Geoinformation     Hybrid Journal   (Followers: 34, SJR: 1.591, CiteScore: 4)
Intl. J. of Approximate Reasoning     Hybrid Journal   (Followers: 1, SJR: 0.866, CiteScore: 3)
Intl. J. of Biochemistry & Cell Biology     Hybrid Journal   (Followers: 6, SJR: 1.492, CiteScore: 3)
Intl. J. of Biological Macromolecules     Hybrid Journal   (Followers: 2, SJR: 0.917, CiteScore: 4)
Intl. J. of Cardiology     Hybrid Journal   (Followers: 16, SJR: 1.2, CiteScore: 2)
Intl. J. of Chemical and Analytical Science     Full-text available via subscription   (Followers: 4)
Intl. J. of Child-Computer Interaction     Hybrid Journal   (Followers: 2, SJR: 0.479, CiteScore: 3)
Intl. J. of Clinical and Health Psychology     Open Access   (Followers: 20, SJR: 1.345, CiteScore: 4)
Intl. J. of Coal Geology     Hybrid Journal   (Followers: 4, SJR: 2.186, CiteScore: 5)
Intl. J. of Critical Infrastructure Protection     Hybrid Journal   (Followers: 8, SJR: 0.648, CiteScore: 2)
Intl. J. of Dental Science and Research     Full-text available via subscription   (Followers: 1)
Intl. J. of Developmental Neuroscience     Hybrid Journal   (Followers: 8, SJR: 0.986, CiteScore: 2)
Intl. J. of Diabetes Mellitus     Open Access   (Followers: 9)
Intl. J. of Disaster Risk Reduction     Hybrid Journal   (Followers: 18, SJR: 0.769, CiteScore: 2)
Intl. J. of Drug Policy     Hybrid Journal   (Followers: 456, SJR: 1.441, CiteScore: 3)
Intl. J. of e-Navigation and Maritime Economy     Open Access   (Followers: 3)
Intl. J. of Educational Development     Hybrid Journal   (Followers: 14, SJR: 0.822, CiteScore: 1)
Intl. J. of Educational Research     Hybrid Journal   (Followers: 27, SJR: 0.617, CiteScore: 1)
Intl. J. of Electrical Power & Energy Systems     Open Access   (Followers: 24, SJR: 1.276, CiteScore: 5)
Intl. J. of Engineering Science     Hybrid Journal   (Followers: 5, SJR: 2.82, CiteScore: 6)
Intl. J. of Epilepsy     Full-text available via subscription   (Followers: 2, SJR: 0.126, CiteScore: 0)
Intl. J. of Fatigue     Hybrid Journal   (Followers: 38, SJR: 1.402, CiteScore: 3)
Intl. J. of Food Microbiology     Hybrid Journal   (Followers: 14, SJR: 1.366, CiteScore: 4)
Intl. J. of Forecasting     Hybrid Journal   (Followers: 28, SJR: 1.879, CiteScore: 3)
Intl. J. of Gastronomy and Food Science     Open Access   (Followers: 4, SJR: 0.422, CiteScore: 1)
Intl. J. of Gerontology     Open Access   (Followers: 8, SJR: 0.215, CiteScore: 0)
Intl. J. of Greenhouse Gas Control     Partially Free   (Followers: 5, SJR: 1.458, CiteScore: 4)
Intl. J. of Heat and Fluid Flow     Hybrid Journal   (Followers: 36, SJR: 0.947, CiteScore: 3)
Intl. J. of Heat and Mass Transfer     Hybrid Journal   (Followers: 269, SJR: 1.498, CiteScore: 4)
Intl. J. of Hospitality Management     Hybrid Journal   (Followers: 20, SJR: 2.027, CiteScore: 4)
Intl. J. of Human-Computer Studies     Hybrid Journal   (Followers: 18, SJR: 0.605, CiteScore: 3)
Intl. J. of Hydrogen Energy     Partially Free   (Followers: 20, SJR: 1.116, CiteScore: 4)
Intl. J. of Hygiene and Environmental Health     Hybrid Journal   (Followers: 7, SJR: 1.334, CiteScore: 4)
Intl. J. of Impact Engineering     Hybrid Journal   (Followers: 9, SJR: 2.124, CiteScore: 4)
Intl. J. of Industrial Ergonomics     Hybrid Journal   (Followers: 15, SJR: 0.795, CiteScore: 2)
Intl. J. of Industrial Organization     Hybrid Journal   (Followers: 24, SJR: 0.873, CiteScore: 1)
Intl. J. of Infectious Diseases     Open Access   (Followers: 8, SJR: 1.514, CiteScore: 3)
Intl. J. of Information Management     Hybrid Journal   (Followers: 308, SJR: 1.373, CiteScore: 6)
Intl. J. of Intercultural Relations     Hybrid Journal   (Followers: 12, SJR: 0.732, CiteScore: 2)
Intl. J. of Law and Psychiatry     Hybrid Journal   (Followers: 8, SJR: 0.546, CiteScore: 1)
Intl. J. of Law, Crime and Justice     Hybrid Journal   (Followers: 56, SJR: 0.362, CiteScore: 1)
Intl. J. of Machine Tools and Manufacture     Hybrid Journal   (Followers: 7, SJR: 2.7, CiteScore: 6)
Intl. J. of Management Education     Hybrid Journal   (Followers: 8, SJR: 0.597, CiteScore: 2)
Intl. J. of Marine Energy     Full-text available via subscription   (Followers: 1, SJR: 0.92, CiteScore: 2)
Intl. J. of Mass Spectrometry     Hybrid Journal   (Followers: 17, SJR: 0.61, CiteScore: 2)
Intl. J. of Mechanical Sciences     Hybrid Journal   (Followers: 13, SJR: 1.595, CiteScore: 4)
Intl. J. of Medical Informatics     Hybrid Journal   (Followers: 9, SJR: 1.247, CiteScore: 4)
Intl. J. of Medical Microbiology     Hybrid Journal   (Followers: 8, SJR: 1.717, CiteScore: 4)
Intl. J. of Mineral Processing     Hybrid Journal   (Followers: 10, SJR: 0.782, CiteScore: 2)
Intl. J. of Mining Science and Technology     Open Access   (Followers: 3, SJR: 1.323, CiteScore: 2)
Intl. J. of Multiphase Flow     Hybrid Journal   (Followers: 8, SJR: 1.218, CiteScore: 3)
Intl. J. of Naval Architecture and Ocean Engineering     Open Access   (Followers: 3, SJR: 0.571, CiteScore: 1)
Intl. J. of Neuropharmacology     Full-text available via subscription   (Followers: 1)
Intl. J. of Non-Linear Mechanics     Hybrid Journal   (Followers: 8, SJR: 1.032, CiteScore: 2)
Intl. J. of Nursing Sciences     Open Access   (Followers: 1, SJR: 0.285, CiteScore: 1)
Intl. J. of Nursing Studies     Hybrid Journal   (Followers: 15, SJR: 1.646, CiteScore: 4)
Intl. J. of Obstetric Anesthesia     Full-text available via subscription   (Followers: 13, SJR: 0.717, CiteScore: 2)
Intl. J. of Oral and Maxillofacial Surgery     Hybrid Journal   (Followers: 8, SJR: 1.137, CiteScore: 2)
Intl. J. of Orthopaedic and Trauma Nursing     Hybrid Journal   (Followers: 10, SJR: 0.369, CiteScore: 1)
Intl. J. of Osteopathic Medicine     Hybrid Journal   (Followers: 2, SJR: 0.297, CiteScore: 1)
Intl. J. of Paleopathology     Partially Free   (Followers: 8, SJR: 0.618, CiteScore: 1)
Intl. J. of Pavement Research and Technology     Open Access   (Followers: 6, SJR: 0.311, CiteScore: 1)
Intl. J. of Pediatric Otorhinolaryngology     Full-text available via subscription   (Followers: 1, SJR: 0.783, CiteScore: 1)
Intl. J. of Pediatric Otorhinolaryngology Extra     Full-text available via subscription   (Followers: 1, SJR: 0.11, CiteScore: 0)
Intl. J. of Pediatrics and Adolescent Medicine     Open Access   (Followers: 1, SJR: 0.144, CiteScore: 1)
Intl. J. of Pharmaceutics     Hybrid Journal   (Followers: 36, SJR: 1.172, CiteScore: 4)
Intl. J. of Plasticity     Hybrid Journal   (Followers: 7, SJR: 3.395, CiteScore: 6)
Intl. J. of Pressure Vessels and Piping     Hybrid Journal   (Followers: 25, SJR: 0.981, CiteScore: 2)
Intl. J. of Production Economics     Hybrid Journal   (Followers: 15, SJR: 2.401, CiteScore: 5)
Intl. J. of Project Management     Hybrid Journal   (Followers: 49, SJR: 1.463, CiteScore: 5)
Intl. J. of Psychophysiology     Hybrid Journal   (Followers: 5, SJR: 1.157, CiteScore: 3)
Intl. J. of Radiation Oncology*Biology*Physics     Hybrid Journal   (Followers: 32, SJR: 2.485, CiteScore: 3)
Intl. J. of Refractory Metals and Hard Materials     Hybrid Journal   (Followers: 5)
Intl. J. of Refrigeration     Full-text available via subscription   (Followers: 5, SJR: 1.471, CiteScore: 3)
Intl. J. of Research in Marketing     Hybrid Journal   (Followers: 20, SJR: 2.528, CiteScore: 3)
Intl. J. of Rock Mechanics and Mining Sciences     Hybrid Journal   (Followers: 8, SJR: 2.259, CiteScore: 4)
Intl. J. of Sediment Research     Full-text available via subscription   (Followers: 3, SJR: 0.663, CiteScore: 2)
Intl. J. of Solids and Structures     Hybrid Journal   (Followers: 15, SJR: 1.295, CiteScore: 3)
Intl. J. of Spine Surgery     Hybrid Journal   (Followers: 3, SJR: 0.793, CiteScore: 2)
Intl. J. of Surgery     Hybrid Journal   (Followers: 8, SJR: 0.834, CiteScore: 3)
Intl. J. of Surgery Case Reports     Open Access   (Followers: 4, SJR: 0.26, CiteScore: 1)
Intl. J. of Surgery Open     Open Access   (SJR: 0.116, CiteScore: 0)
Intl. J. of Surgery Protocols     Open Access   (Followers: 1, SJR: 0.141, CiteScore: 1)
Intl. J. of Sustainable Built Environment     Open Access   (Followers: 5, SJR: 0.746, CiteScore: 3)
Intl. J. of the Sociology of Law     Hybrid Journal   (Followers: 18)
Intl. J. of Thermal Sciences     Hybrid Journal   (Followers: 18, SJR: 1.429, CiteScore: 4)
Intl. J. of Transportation Science and Technology     Open Access   (Followers: 10)
Intl. J. of Veterinary Science and Medicine     Open Access   (Followers: 4)
Intl. J. of Women's Dermatology     Open Access   (SJR: 0.213, CiteScore: 0)
Intl. Medical Review on Down Syndrome     Full-text available via subscription  
Intl. Orthodontics     Full-text available via subscription   (Followers: 3, SJR: 0.239, CiteScore: 0)
Intl. Perspectives on Child and Adolescent Mental Health     Full-text available via subscription   (Followers: 5)
Intl. Review of Cell and Molecular Biology     Full-text available via subscription   (Followers: 6, SJR: 1.973, CiteScore: 4)
Intl. Review of Cytology     Full-text available via subscription  
Intl. Review of Economics & Finance     Hybrid Journal   (Followers: 26, SJR: 0.841, CiteScore: 2)
Intl. Review of Economics Education     Hybrid Journal   (Followers: 1, SJR: 0.632, CiteScore: 1)
Intl. Review of Financial Analysis     Hybrid Journal   (Followers: 7, SJR: 0.755, CiteScore: 2)
Intl. Review of Law and Economics     Hybrid Journal   (Followers: 21, SJR: 0.572, CiteScore: 1)
Intl. Review of Neurobiology     Full-text available via subscription   (Followers: 2, SJR: 1.497, CiteScore: 3)
Intl. Review of Research in Mental Retardation     Full-text available via subscription   (Followers: 7)
Intl. Soil and Water Conservation Research     Open Access   (SJR: 0.667, CiteScore: 2)
Intl. Strategic Management Review     Open Access   (Followers: 4)
Investigación en Educación Médica     Open Access  
Investigaciones de Historia Económica     Full-text available via subscription   (SJR: 0.264, CiteScore: 0)
Investigaciones Europeas de Dirección y Economía de la Empresa     Open Access  
IRBM     Full-text available via subscription   (SJR: 0.298, CiteScore: 1)
IRBM News     Full-text available via subscription   (SJR: 0.139, CiteScore: 0)
ISA Transactions     Full-text available via subscription   (Followers: 1, SJR: 1.115, CiteScore: 4)
iScience     Open Access  
ISPRS J. of Photogrammetry and Remote Sensing     Hybrid Journal   (Followers: 70, SJR: 3.169, CiteScore: 8)
Italian Oral Surgery     Full-text available via subscription   (Followers: 1)
ITBM-RBM     Full-text available via subscription   (Followers: 1)
ITBM-RBM News     Full-text available via subscription   (Followers: 1)
J. de Chirurgie Viscerale     Full-text available via subscription   (Followers: 1, SJR: 0.264, CiteScore: 0)
J. de Gynécologie Obstétrique et Biologie de la Reproduction     Full-text available via subscription  
J. de Mathématiques Pures et Appliquées     Full-text available via subscription   (Followers: 4, SJR: 3.571, CiteScore: 2)
J. de Mycologie Médicale / J. of Medical Mycology     Full-text available via subscription   (Followers: 2, SJR: 0.495, CiteScore: 2)
J. de Pédiatrie et de Puériculture     Full-text available via subscription   (SJR: 0.116, CiteScore: 0)
J. de Radiologie     Full-text available via subscription  
J. de Radiologie Diagnostique et Interventionnelle     Full-text available via subscription   (Followers: 2)
J. de Thérapie Comportementale et Cognitive     Full-text available via subscription   (SJR: 0.111, CiteScore: 0)
J. de Traumatologie du Sport     Full-text available via subscription   (Followers: 2, SJR: 0.152, CiteScore: 0)
J. des Anti-infectieux     Full-text available via subscription   (Followers: 2, SJR: 0.107, CiteScore: 0)
J. des Maladies Vasculaires     Full-text available via subscription  
J. Européen des Urgences     Full-text available via subscription   (Followers: 1)
J. Européen des Urgences et de Réanimation     Hybrid Journal   (SJR: 0.108, CiteScore: 0)
J. for Nature Conservation     Hybrid Journal   (Followers: 28, SJR: 0.894, CiteScore: 2)
J. for Nurse Practitioners     Hybrid Journal   (Followers: 12, SJR: 0.179, CiteScore: 0)
J. Français d'Ophtalmologie     Full-text available via subscription   (Followers: 3, SJR: 0.292, CiteScore: 0)
J. of Academic Librarianship     Hybrid Journal   (Followers: 1031, SJR: 1.224, CiteScore: 2)
J. of Accounting and Economics     Hybrid Journal   (Followers: 37, SJR: 6.875, CiteScore: 4)
J. of Accounting and Public Policy     Hybrid Journal   (Followers: 7, SJR: 0.91, CiteScore: 2)
J. of Accounting Education     Hybrid Journal   (Followers: 6, SJR: 0.882, CiteScore: 1)
J. of Accounting Literature     Hybrid Journal   (Followers: 7, SJR: 0.986, CiteScore: 3)
J. of Acupuncture and Meridian Studies     Open Access   (Followers: 1, SJR: 0.347, CiteScore: 1)
J. of Acute Medicine     Open Access   (SJR: 0.196, CiteScore: 1)
J. of Adolescence     Hybrid Journal   (Followers: 14, SJR: 1.01, CiteScore: 2)
J. of Adolescent Health     Hybrid Journal   (Followers: 23, SJR: 1.851, CiteScore: 4)
J. of Advanced Research     Open Access   (Followers: 2, SJR: 0.741, CiteScore: 4)
J. of Aerosol Science     Hybrid Journal   (Followers: 5, SJR: 0.828, CiteScore: 3)
J. of Affective Disorders     Hybrid Journal   (Followers: 18, SJR: 2.053, CiteScore: 4)
J. of African Earth Sciences     Hybrid Journal   (Followers: 11, SJR: 0.681, CiteScore: 2)
J. of African Trade     Open Access  
J. of Aging Studies     Hybrid Journal   (Followers: 11, SJR: 0.8, CiteScore: 2)
J. of Air Transport Management     Hybrid Journal   (Followers: 9, SJR: 0.981, CiteScore: 2)
J. of Algebra     Full-text available via subscription   (Followers: 5, SJR: 1.187, CiteScore: 1)
J. of Algorithms     Full-text available via subscription   (Followers: 4)
J. of Allergy and Clinical Immunology     Hybrid Journal   (Followers: 30, SJR: 5.049, CiteScore: 7)
J. of Allergy and Clinical Immunology : In Practice     Full-text available via subscription   (Followers: 12, SJR: 1.461, CiteScore: 3)
J. of Alloys and Compounds     Hybrid Journal   (Followers: 12, SJR: 1.02, CiteScore: 4)
J. of American Association for Pediatric Ophthalmology and Strabismus     Hybrid Journal   (Followers: 7, SJR: 0.752, CiteScore: 1)
J. of Analytical and Applied Pyrolysis     Hybrid Journal   (Followers: 3, SJR: 1.129, CiteScore: 4)
J. of Anesthesia History     Full-text available via subscription   (Followers: 1, SJR: 0.19, CiteScore: 0)
J. of Anthropological Archaeology     Hybrid Journal   (Followers: 79, SJR: 1.24, CiteScore: 2)
J. of Anxiety Disorders     Hybrid Journal   (Followers: 16, SJR: 2.043, CiteScore: 4)
J. of Applied Biomedicine     Open Access   (Followers: 2, SJR: 0.348, CiteScore: 2)
J. of Applied Developmental Psychology     Hybrid Journal   (Followers: 13, SJR: 1.339, CiteScore: 3)
J. of Applied Economics     Full-text available via subscription   (Followers: 8, SJR: 0.235, CiteScore: 1)
J. of Applied Geophysics     Hybrid Journal   (Followers: 15, SJR: 0.636, CiteScore: 2)
J. of Applied Logic     Full-text available via subscription   (SJR: 0.277, CiteScore: 1)
J. of Applied Mathematics and Mechanics     Full-text available via subscription   (Followers: 9, SJR: 0.321, CiteScore: 0)
J. of Applied Research and Technology     Open Access   (SJR: 0.255, CiteScore: 1)
J. of Applied Research in Memory and Cognition     Partially Free   (Followers: 12, SJR: 1.303, CiteScore: 2)
J. of Applied Research on Medicinal and Aromatic Plants     Hybrid Journal   (SJR: 0.355, CiteScore: 2)
J. of Approximation Theory     Hybrid Journal   (Followers: 1, SJR: 0.907, CiteScore: 1)
J. of Archaeological Science     Hybrid Journal   (Followers: 66, SJR: 1.885, CiteScore: 3)
J. of Archaeological Science : Reports     Hybrid Journal   (Followers: 17, SJR: 0.659, CiteScore: 1)
J. of Arid Environments     Hybrid Journal   (Followers: 14, SJR: 0.763, CiteScore: 2)
J. of Arrhythmia     Open Access   (SJR: 0.398, CiteScore: 1)
J. of Arthroplasty     Hybrid Journal   (Followers: 48, SJR: 2.373, CiteScore: 3)
J. of Arthroscopy and Joint Surgery     Full-text available via subscription   (Followers: 2, SJR: 0.103, CiteScore: 0)
J. of Asia-Pacific Biodiversity     Open Access   (SJR: 0.361, CiteScore: 1)
J. of Asia-Pacific Entomology     Full-text available via subscription   (Followers: 6, SJR: 0.373, CiteScore: 1)
J. of Asian Ceramic Societies     Open Access   (Followers: 2, SJR: 0.509, CiteScore: 2)
J. of Asian Earth Sciences     Hybrid Journal   (Followers: 13, SJR: 1.488, CiteScore: 3)
J. of Asian Economics     Hybrid Journal   (Followers: 1, SJR: 0.419, CiteScore: 1)
J. of Atmospheric and Solar-Terrestrial Physics     Hybrid Journal   (Followers: 146, SJR: 0.696, CiteScore: 2)
J. of Autoimmunity     Hybrid Journal   (Followers: 11, SJR: 2.046, CiteScore: 7)
J. of Ayurveda and Integrative Medicine     Open Access   (Followers: 3, SJR: 0.338, CiteScore: 1)
J. of Banking & Finance     Hybrid Journal   (Followers: 174)
J. of Basic & Applied Zoology : Physiology     Open Access   (Followers: 3)
J. of Behavior Therapy and Experimental Psychiatry     Hybrid Journal   (Followers: 4, SJR: 1.42, CiteScore: 3)
J. of Behavior, Health & Social Issues     Open Access   (Followers: 7)
J. of Behavioral and Experimental Economics     Full-text available via subscription   (Followers: 8, SJR: 0.593, CiteScore: 1)
J. of Behavioral and Experimental Finance     Full-text available via subscription   (Followers: 3, SJR: 0.475, CiteScore: 1)
J. of Biochemical and Biophysical Methods     Hybrid Journal   (Followers: 4)
J. of Biomechanics     Hybrid Journal   (Followers: 37, SJR: 1.147, CiteScore: 3)
J. of Biomedical Informatics     Partially Free   (Followers: 15, SJR: 1.028, CiteScore: 4)
J. of Biomedical Research     Full-text available via subscription   (Followers: 3, SJR: 0.712, CiteScore: 2)
J. of Bionic Engineering     Full-text available via subscription   (SJR: 0.584, CiteScore: 3)
J. of Bioscience and Bioengineering     Full-text available via subscription   (Followers: 31, SJR: 0.675, CiteScore: 2)
J. of Biotechnology     Hybrid Journal   (Followers: 63, SJR: 0.929, CiteScore: 3)
J. of Bodywork and Movement Therapies     Hybrid Journal   (Followers: 17, SJR: 0.522, CiteScore: 1)
J. of Bone Oncology     Open Access   (Followers: 1, SJR: 0.941, CiteScore: 3)
J. of Building Engineering     Hybrid Journal   (Followers: 2, SJR: 0.753, CiteScore: 3)
J. of Business Research     Hybrid Journal   (Followers: 22, SJR: 1.26, CiteScore: 3)
J. of Business Venturing     Hybrid Journal   (Followers: 26, SJR: 5.212, CiteScore: 9)

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Journal Cover
ISPRS Journal of Photogrammetry and Remote Sensing
Journal Prestige (SJR): 3.169
Citation Impact (citeScore): 8
Number of Followers: 70  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0924-2716
Published by Elsevier Homepage  [3162 journals]
  • Joining multi-epoch archival aerial images in a single SfM block allows
           3-D change detection with almost exclusively image information
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): D. Feurer, F. Vinatier Archival aerial imagery is a worldwide resource for documenting past 3-D change at very high-resolution. However, external information is normally required so that accurate 3-D models can be computed from archival aerial imagery. In this research, we propose and test a new method which joins multi-epoch images in a single block in the first steps of the structure from motion (SfM) processing. It allows for computing coherent multi-temporal digital elevation models (DEMs) using just image information. This method is based on the invariance properties of the feature detection procedures that are at the root of the SfM algorithms.On a test site covering 170 km2, we applied SfM algorithms to a single image block consisting of all images captured at four different epochs and spanning a forty year period. We compared this approach to the more classical methods which imply a separation of epochs in different processing blocks. We tested different densities of ground control points derived simply and cheaply from a recent orthophoto and DEM, different ways of image preprocessing and different autocalibration procedures. By determining which choice most affected the final result through this extensive testing procedure, we evaluated the potential of the proposed method for detecting 3-D change.Our study showed that the proposed method resolves the problem of registration between epochs, so allowing the production of informative DEMs of difference using almost exclusively image information and limited photogrammetric expertise and human intervention. As the proposed method can be automatically applied using just image information, our results pave the way to more systematic processing of archival aerial imagery with very large spatio-temporal windows, which should greatly help document of past 3-D change.Graphical abstractGraphical abstract for this article
  • Estimating forest structural attributes using UAV-LiDAR data in Ginkgo
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Kun Liu, Xin Shen, Lin Cao, Guibin Wang, Fuliang Cao Estimating forest structural attributes in planted forests is crucial for sustainably management of forests and helps to understand the contributions of forests to global carbon storage. The Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) has become a promising technology and attempts to be used for forest management, due to its capacity to provide highly accurate estimations of three-dimensional (3D) forest structural information with a lower cost, higher flexibility and finer resolution than airborne LiDAR. In this study, the effectiveness of plot-level metrics (i.e., distributional, canopy volume and Weibull-fitted metrics) and individual-tree-summarized metrics (i.e., maximum, minimum and mean height of trees and the number of trees from the individual tree detection (ITD) results) derived from UAV-LiDAR point clouds were assessed, then these metrics were used to fit estimation models of six forest structural attributes by parametric (i.e., partial least squares (PLS)) and non-parametric (i.e., k-Nearest Neighbors (k-NN) and Random Forest (RF)) approaches, within a Ginkgo plantation in east China. In addition, we assessed the effects of UAV-LiDAR point cloud density on the derived metrics and individual tree segmentation results, and evaluated the correlations of these metrics with aboveground biomass (AGB) by a sensitivity analysis. The results showed that, in general, models based on both plot-level and individual-tree-summarized metrics (CV-R2 = 0.66–0.97, rRMSE = 2.83–23.35%) performed better than models based on the plot-level metrics only (CV-R2 = 0.62–0.97, rRMSE = 3.81–27.64%). PLS had a relatively high prediction accuracy for Lorey’s mean height (CV-R2 = 0.97, rRMSE = 2.83%), whereas k-NN performed well for predicting volume (CV-R2 = 0.94, rRMSE = 8.95%) and AGB (CV-R2 = 0.95, rRMSE = 8.81%). For the point cloud density sensitivity analysis, the canopy volume metrics showed a higher dependence on point cloud density than other metrics. ITD results showed a relatively high accuracy (F1-score > 74.93%) when the point cloud density was higher than 10% (16 pts·m−2). The correlations between AGB and the metrics of height percentiles, lower height level of canopy return densities and canopy cover appeared stable across different point cloud densities when the point cloud density was reduced from 50% (80 pts·m−2) to 5% (8 pts·m−2).
  • A multi-UAV cooperative route planning methodology for 3D fine-resolution
           building model reconstruction
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Xiaocui Zheng, Fei Wang, Zhanghua Li In order to provide a fast multi-UAV cooperative data acquisition approach for 3D building model reconstruction in emergency management domain, a route planning methodology is proposed. A minimum image set including camera shooting positions and attitudes can be firstly obtained, with the given parameters describing the target building, UAVs, cameras, and image overlap requirements. A specific flight route network is then determined, and the optimal solution for multi-UAV data capture route planning is computed on the basis of constraint conditions such as the time frame, UAV battery endurance, and take-off and landing positions. Furthermore, field experiments with manual operating UAV mode, single UAV mode, and multi-UAV mode were conducted to compare the data collection and processing runtimes, as well as the quality of created 3D building models. According to the five defined LoDs of OGC CityGML 2.0 standard, the fine 3D building models conform to the LoD3. Comparison results demonstrate that our method is able to greatly enhance the efficiency of 3D reconstruction by improving the data collection speed while minimizing redundant image datasets, as well as to provide a normalized approach to assign the single or multi-UAV data acquisition tasks. The quality analysis of 3D models shows that the metric difference is less than 20 cm mean error with a standard deviation of 11 cm, which is fairly acceptable in emergency management study field. A 3D GIS-based software demo was also implemented to enable route planning, flight simulation, and data collection visualization.
  • Successional stages and their evolution in tropical forests using
           multi-temporal photogrammetric surface models and superpixels
    • Abstract: Publication date: Available online 9 November 2018Source: ISPRS Journal of Photogrammetry and Remote SensingAuthor(s): Adilson Berveglieri, Nilton N. Imai, Antonio M.G. Tommaselli, Baltazar Casagrande, Eija Honkavaara Airborne photogrammetric image archives offer interesting possibilities for multi-temporal analyses of environmental evolution. The objective of this investigation was to develop a technique for classifying forest successional stages and performing multi-temporal analyses of the tree canopy based on tree height variances calculated from digital surface models (DSMs) created from photogrammetric imagery. Furthermore, our objective was to evaluate the usability of the technique in assessing the evolution of successional stages in a tropical forest. The local variance calculation in 3D space resulted in an image that was subdivided with a segmentation technique to generate small areas called superpixels. These superpixels, which use the local mean variance as an attribute, are assessed via cluster analysis to evaluate statistical similarity and define successional stage classes. The same superpixel shapes were located in georeferenced historical datasets to enable multi-temporal analysis. The cluster analysis of temporal superpixels enabled the spatiotemporal classification of forest canopy evolution. The technique was used to assess a tropical forest remnant in Brazil. Dense DSMs were generated with stereo-photogrammetric techniques using optical images (both film and digital images) from which height variances were computed. A cluster analysis of superpixels was performed to classify the forest canopy into four successional stages, which were consistent with Brazilian classification rules. The multi-temporal analysis identified six classes of forest cover evolution. Field data were collected in forest plots to validate the generated forest canopy classifications. The results showed that the proposed approach was feasible for forest cover classification and for identifying changes in the vertical forest structure and cover over time using only optical images.
  • Analyzing the role of pulse density and voxelization parameters on
           full-waveform LiDAR-derived metrics
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Pablo Crespo-Peremarch, Luis Ángel Ruiz, Ángel Balaguer-Beser, Javier Estornell LiDAR full-waveform (LFW) pulse density is not homogeneous along study areas due to overlap between contiguous flight stripes and, to a lesser extent, variations in height, velocity and altitude of the platform. As a result, LFW-derived metrics extracted at the same spot but at different pulse densities differ, which is called “side-lap effect”. Moreover, this effect is reflected in forest stand estimates, since they are predicted from LFW-derived metrics. This study was undertaken to analyze LFW-derived metric variations according to pulse density, voxel size and value assignation method in order to reduce the side-lap effect. Thirty LiDAR samples with a minimum density of 16 pulses.m−2 were selected from the testing area and randomly reduced to 2 pulses.m−2 with an interval of 1 pulse.m−2, then metrics were extracted and compared for each sample and pulse density at different voxel sizes and assignation values. Results show that LFW-derived metric variations as a function of pulse density follow a negative exponential model similar to the exponential semivariogram curve, increasing sharply until they reach a certain pulse density, where they become stable. This value represents the minimum pulse density (MPD) in the study area to optimally minimize the side-lap effect. This effect can also be reduced with pulse densities lower than the MPD modifying LFW parameters (i.e. voxel size and assignation value). Results show that LFW-derived metrics are not equally influenced by pulse density, such as number of peaks (NP) and ROUGHness of the outermost canopy (ROUGH) that may be discarded for further analyses at large voxel sizes, given that they are highly influenced by pulse density. In addition, side-lap effect can be reduced by either increasing pulse density or voxel size, or modifying the assignation value. In practice, this leads to a proper estimate of forest stand variables using LFW data.
  • Social functional mapping of urban green space using remote sensing and
           social sensing data
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Wei Chen, Huiping Huang, Jinwei Dong, Yuan Zhang, Yichen Tian, Zhiqi Yang Urban green space (UGS) is an indispensable component of urban environmental systems and is important to urban residents. Both physical features (e.g., shrubs, trees) and social functions (e.g., public parks, green buffers) are important in UGS mapping. Most UGS studies rely solely on remote sensing data to conduct UGS mapping of physical features, and few studies have focused on UGS mapping from a social function perspective. Due to the limitations of remote sensing in identifying social features; social sensing, which can reflect socioeconomic characteristics, is needed. As a result, a novel methodological framework for integrating these two different data sources to conduct the social functional mapping of UGS has been required. Consequently, we first extracted vegetation patches from an area in Beijing, via the Hyperplanes for Plant Extraction Methodology (HPEM) and considered the parcels segmented by the OpenStreetMap (OSM) road networks as the basic analytical units. Then, near-convex-hull analysis (NCHA) and text-concave-hull analysis (TCHA) were performed to integrate the multi-source data. The results show that the Level I and Level II (refer to Table 3) social function types of UGS had overall accuracies of 92.48% and 88.76%, respectively. Our study provides an improved understanding of UGS and can assist government departments in urban planning. It can also help researchers broaden their research scope by acting as a freely available data source for their work.
  • Towards operational marker-free registration of terrestrial lidar data in
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Jean-François Tremblay, Martin Béland Terrestrial laser scanning (TLS) often makes use of multiple scans in forests to allow for a complete view of a given area. Combining measurements from multiple locations requires accurate co-registration of the scans to a common reference coordinate system, which currently relies on markers, an often cumbersome process in forests. Existing algorithms for achieving marker-free registration of TLS scans in forests promise to significantly decrease field work time, but are not yet operational and their results have not been validated against traditional methods. Here we present a new implementation of an existing approach which runs in parallel mode and is able to process TLS data acquired over large forest areas. To validate our algorithm, point cloud registration matrices (translation and rotation) derived from our algorithm were compared to those obtained using reflective markers in multiple forest types. The results show that our approach can be used operationally in forests with relatively clear understory, and it provides accuracy similar to that obtained from using reflective markers. Furthermore, we identified factors that can lead to this approach falling short of providing acceptable results in terms of accuracy.
  • An improved progressive morphological filter for UAV-based photogrammetric
           point clouds in river bank monitoring
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Yumin Tan, Shuai Wang, Bo Xu, Jiabin Zhang With the advent of unmanned aerial vehicle (UAV)-based photogrammetry and structure from motion (SFM) software, it is possible to obtain high-density point clouds of which the accuracy can meet the requirements of river bank monitoring. Ground filtering, i.e., removing the points belonging to above-ground objects, is an important process of digital terrain model (DTM) generation which is essential to river bank monitoring. Progressive morphological filter (PM) is a widely-adopted ground filtering algorithm and performs well with LiDAR data. However, it may incorrectly classify vegetation points as ground points when used to filter UAV-based photogrammetric point clouds because ground points beneath vegetation cannot be captured with the digital camera on-board UAV. In this study, we propose the improved progressive morphological filter (IPM) algorithm to improve the accuracy of ground filtering on UAV-based photogrammetric point clouds by introducing visible-band difference vegetation index (VDVI) to PM. The proposed IPM is subsequently evaluated along with the original PM algorithm and four other widely-used ground filtering algorithms in four test sites along the Yangtze River. The results show that IPM improves the overall accuracy from PM in all the four test sites, and produces the best results among the six ground filtering algorithms in three out of the four sites. IPM proves to be an effective ground filtering algorithm for UAV-based photogrammetric point clouds in river bank monitoring.
  • Drainage ditch extraction from airborne LiDAR point clouds
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Jennifer Roelens, Bernhard Höfle, Stefaan Dondeyne, Jos Van Orshoven, Jan Diels Ditches are often absent in hydrographic geodatasets and their mapping would benefit from a cost and labor effective alternative to field surveys. We propose and evaluate an alternative that makes use of a high resolution LiDAR point cloud dataset. First the LiDAR points are classified as ditch and non-ditch points by means of a random forest classifier which considers subsets of the topographic and radiometric features provided by or derived from the LiDAR product. The LiDAR product includes for each georeferenced point, the elevation, the returned intensity value, and RGB values from simultaneously acquired aerial images. Next so-called ditch dropout points are reconstructed for the blind zones in the dataset using a new geometric approach. Finally, LiDAR ditch points and dropouts are assembled into ditch objects (2D-polygons and their derived centre lines). The procedure was evaluated for a grassland and a peri-urban agricultural area in Flanders, Belgium. A good point classification was obtained (Kappa = 0.77 for grassland and 0.73 for peri-urban area) by using all the features derived from the LiDAR product, whereby the geometric features had the greatest influence. However, even better results were obtained when the radiometric component of the LiDAR product was also taken into account. For the tested models for the extraction of ditch centre lines, the best resulted in an error of omission of 0.03 and an error of commission of 0.08 for the grassland study area and an error of omission of 0.14 and an error of commission of 0.07 for the peri-urban study area.
  • A framework for SAR-optical stereogrammetry over urban areas
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Hossein Bagheri, Michael Schmitt, Pablo d’Angelo, Xiao Xiang Zhu Currently, numerous remote sensing satellites provide a huge volume of diverse earth observation data. As these data show different features regarding resolution, accuracy, coverage, and spectral imaging ability, fusion techniques are required to integrate the different properties of each sensor and produce useful information. For example, synthetic aperture radar (SAR) data can be fused with optical imagery to produce 3D information using stereogrammetric methods. The main focus of this study is to investigate the possibility of applying a stereogrammetry pipeline to very-high-resolution (VHR) SAR-optical image pairs. For this purpose, the applicability of semi-global matching is investigated in this unconventional multi-sensor setting. To support the image matching by reducing the search space and accelerating the identification of correct, reliable matches, the possibility of establishing an epipolarity constraint for VHR SAR-optical image pairs is investigated as well. In addition, it is shown that the absolute geolocation accuracy of VHR optical imagery with respect to VHR SAR imagery such as provided by TerraSAR-X can be improved by a multi-sensor block adjustment formulation based on rational polynomial coefficients. Finally, the feasibility of generating point clouds with a median accuracy of about 2 m is demonstrated and confirms the potential of 3D reconstruction from SAR-optical image pairs over urban areas.
  • High-density stereo image matching using intrinsic curves
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Mozhdeh Shahbazi, Gunho Sohn, Jérome Théau One of the most active areas of research in photogrammetry and computer vision is dense three-dimensional (3D) reconstruction of the environment via high-density image matching. This research interest is mainly driven by the growing popularity of unconventional imaging solutions such as images captured from unmanned aerial vehicles. With such data, problems like large disparity search space, occlusion, noise, and matching ambiguity become more pronounced. In this paper, we present a dense matching method to deal with these issues partially. The proposed method uses the concepts of intrinsic curves (IC) and derives useful matching information from their geometric features. First, we propose sparse disparity hypotheses for each pixel based on the orientations of the curves. These hypotheses are propagated to the neighboring pixels based on the proximity in the IC space; a solution which adaptively considers the intensity-variations in the neighborhood of a pixel to enlarge the set of its possible disparities. Then, a global matching energy function is formed and minimized, in which occlusions are explicitly taken into account based on curvature similarities of the ICs. The proposed technique is extensively tested on close-range and unmanned aerial images. Its performance is also compared to the state-of-the-art of dense-matching, such as hierarchical semi-global matching. Evaluations by the Middlebury Computer Vision Stereo Benchmark also show that the proposed technique results in average 3% error (percentage of pixels which are matched with more than 1-pixel error). The proposed framework could achieve high levels of accuracy (averagely 92%) as well as high efficiency by reducing the disparity search space up to 98% with an average confidence of 92% that the correct match, if existing, is still in the reduced search space.Graphical abstractGraphical abstract for this article
  • Sparsity inspired pan-sharpening technique using multi-scale learned
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Rajesh Gogineni, Ashvini Chaturvedi The significant issues in remote sensing image fusion are enhancing the spatial details and preserving the essential spectral information. The classical pan-sharpening methods often incur spectral distortion and still striving to produce the fused images with prominent spatial and spectral attributes. Motivated by the desirable results of sparse representation (SR) theory, a novel pan-sharpening method is developed based on SR of high frequency (HF) components over a multi-scale learned dictionary (MSLD). MSLD technique acquires the capability of extracting the intrinsic characteristics of images, wherein, it possess the features of both multi-scale representation and learned dictionaries. In this paper, the dictionaries are adaptively learned from HF sub-images derived from the two versions of panchromatic image, realized at different spatial resolutions. A fast and computationally efficient algorithm is used for dictionary learning. The notion of SR together with patch recurrence over different scales is incorporated to estimate the high frequency details. The fused image is reconstructed by injecting the band specific spatial details into the up-sampled multi-spectral images. The performance of the proposed method is appraised with the datasets from different satellite sensors namely, QuickBird, IKONOS, WorldView-2 and Pléiades. The observations inferred from visual perception and quality indices analysis manifest the efficiency of proposed method over several well-known methods for the datasets considered at reduced-scale and full-scale resolutions. Further, the quantitative analysis of obtained performance measures confirms the efficacy of the proposed method for the reduced-scale and full-scale data sets. Especially, at a reduced-scale, proposed method yields an optimal value of Correlation coefficient, Structural similarity and Q4. In a comparative sense, usage of the proposed method at full-scale results in 4% and 2.56% improvement in the Spatial distortion index for QuickBird and WorldView-2 data respectively contrary to the best reported outcome obtained from Sparse Representation of injected details (SR-D) scheme. Invariably, for full-scale data, the QNR attains its optimal value.
  • Road safety evaluation through automatic extraction of road horizontal
           alignments from Mobile LiDAR System and inductive reasoning based on a
           decision tree
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): José Antonio Martín-Jiménez, Santiago Zazo, José Juan Arranz Justel, Pablo Rodríguez-Gonzálvez, Diego González-Aguilera Safe roads are a necessity for any society because of the high social costs of traffic accidents. This challenge is addressed by a novel methodology that allows us to evaluate road safety from Mobile LiDAR System data, taking advantage of the road alignment due to its influence on the accident rate. Automation is obtained through an inductive reasoning process based on a decision tree that provides a potential risk assessment. To achieve this, a 3D point cloud is classified by an iterative and incremental algorithm based on a 2.5D and 3D Delaunay triangulation, which apply different algorithms sequentially. Next, an automatic extraction process of road horizontal alignment parameters is developed to obtain geometric consistency indexes, based on a joint triple stability criterion. Likewise, this work aims to provide a powerful and effective preventive and/or predictive tool for road safety inspections. The proposed methodology was implemented on three stretches of Spanish roads, each with different traffic conditions that represent the most common road types. The developed methodology was successfully validated through as-built road projects, which were considered as “ground truth.”
  • HSW: Heuristic Shrink-wrapping for automatically repairing solid-based
           CityGML LOD2 building models
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Junqiao Zhao, Hugo Ledoux, Jantien Stoter, Tiantian Feng The Level-of-Detail (LOD) 2 building models defined in CityGML are used widely in three-dimensional (3D) city applications. Many of these applications demand valid solid-based geometry (closed 2-manifold), which is crucial for analytical and computational purposes. However, this condition is often violated in practice because of the way LOD2 models are constructed and exchanged. Examples of the resulting errors include missing surfaces, intersecting building parts, and superfluous interior geometry. In this study, we present a heuristic shrink-wrapping algorithm for reconstructing valid solid-based LOD2 buildings by repairing and generalizing invalid input models. A single building model is first decomposed as intersection-free and reassembled by constrained tetrahedralization. The bounding membrane is then shrunk by incrementally carving the selected boundary tetrahedra and wrapping the expected shape of the building. In the algorithm, combinations of heuristics are proposed to guide the carving process. Topological and geometrical constraints are proposed to ensure the validity and exactness of the output model. The semantics of the input geometry are preserved and missing semantics are deduced based on pragmatic rules. We evaluated the performance of the algorithm using 3D building models, including CityGML datasets. The results showed that our method achieved state-of-the-art performance at repairing 3D building models.
  • Determination of changes in leaf and canopy spectra of plants grown in
           soils contaminated with petroleum hydrocarbons
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): S. Gürtler, C.R. Souza Filho, I.D. Sanches, M.N. Alves, W.J. Oliveira Changes in vegetation near pipelines are symptomatic of petroleum hydrocarbon leakages, particularly diesel and gasoline, which are fuels regularly transported onshore. The investigation of such changes in leaf and canopy spectra of vegetation grown on contaminated soils is the goal of this work. A real scale experiment was installed in an area of 2000 m2. Two planting plots were firstly contaminated on a controlled style with diesel (6.25 L/m3 soil) and gasoline (8.33 L/m3 soil). A third plot was used as a control and preserved with no contaminants. Subsequently, maize, brachiaria and perennial soybean were planted on all plots. Visible and infrared spectral measurements of leaf and canopy targets were taken, respectively, between 28–184 days and 49–203 days after planting. The spectra of contaminated plants were compared to those of healthy plants. Significant differences were observed in the chlorophyll absorption feature and red edge position both at the canopy level. Narrowband spectral indices highlighted differences predominantly in plants affected by gasoline. The evident changes in the canopy spectra show that the hydrocarbons damaged the canopy structure extensively. The spectral patterns revealed here have important implications for detecting and monitoring areas likely contaminated with liquid HCs using hyperspectral remote sensing.
  • DEM refinement by low vegetation removal based on the combination of full
           waveform data and progressive TIN densification
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Hongchao Ma, Weiwei Zhou, Liang Zhang Filtering of low vegetation with height less than approximately 1.5 m is a challenging problem, especially in mountainous areas covered by heavy low foliage, bushes and sub-shrubberies, etc. The paper proposes an approach for obtaining a more accurate Digital Elevation Model (DEM) by removing low vegetation from point cloud. The approach combines point cloud with full waveform data, and begins by filtering point cloud by way of progressive TIN densification (PTD) method. Ground points are thus extracted, but mixed with false ground points, which are mainly from low vegetation and other manmade low objects. Gaussian decomposition by grouping Levenberg–Marquardt (LM) algorithm with F test is performed for the full waveforms corresponding to the extracted ground points. Echo widths and backscattering coefficients are calculated based on the parameters extracted from the decomposition, and used to discriminate points of low vegetation from points of other low objects, allowing the false ground points reflected from low vegetation to be labeled. New elevation values are calculated from the last echoes of the waveforms from low vegetation, and the DEM is updated by replacing the original elevations with the calculated ones. The resultants are assessed both quantitatively by check points and qualitatively by rendered DEM and contour lines generated from it. The accuracy of the refined DEM with low vegetation removal increases by 31% compared with the original DEM in the experiment, showing the effectiveness of the proposed approach.
  • Determining spectral groups to distinguish oil emulsions from Sargassum
           over the Gulf of Mexico using an airborne imaging spectrometer
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Jing Shi, Junnan Jiao, Yingcheng Lu, Minwei Zhang, Zhihua Mao, Yongxue Liu During the weathering of marine-spilled oils, various types of oil pollution are formed that can harm marine and coastal environments. Thus, the remote detection, classification and quantification of spilled oils is important in marine environmental monitoring. Although multispectral images can be used to observe various spilled oils, due to confusion between the multispectral backscattered signals, distinguishing spilled oils from floating algae in the same image is challenging. The spectral features of carbon-hydrogen (-C-H) and oxygen-hydrogen (-O-H) groups, and pigments, are diagnostic absorption features and are different from the backscattering signal, they have not been used to improve detection independently. In this study, all the spectral features of the groups were clearly interpreted using reflectance spectra collected from an airborne visible infrared imaging spectrometer (AVIRIS). A reflectance peak-trough detection method to characterize the different spectral groups was used to determine the spectral features of Deepwater Horizon (DWH) oil emulsions and floating Sargassum in the Gulf of Mexico (GOM). The results show that the spilled oils and floating Sargassum can be clearly identified, and the various spilled oils (i.e., different oil emulsions and oil slicks) could also be determined from the differences in the spectral features of the above groups. Finally, we discuss the spectral requirements for the identification of these groups and we conclude that optical remote sensing, including imaging spectrometers, will play an increasingly important role in assessing marine oil spills.Graphical abstractGraphical abstract for this article
  • Multiple instance hybrid estimator for hyperspectral target
           characterization and sub-pixel target detection
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Changzhe Jiao, Chao Chen, Ronald G. McGarvey, Stephanie Bohlman, Licheng Jiao, Alina Zare The Multiple Instance Hybrid Estimator for discriminative target characterization from imprecisely labeled hyperspectral data is presented. In many hyperspectral target detection problems, acquiring accurately labeled training data is difficult. Furthermore, each pixel containing target is likely to be a mixture of both target and non-target signatures (i.e., sub-pixel targets), making extracting a pure prototype signature for the target class from the data extremely difficult. The proposed approach addresses these problems by introducing a data mixing model and optimizing the response of the hybrid sub-pixel detector within a multiple instance learning framework. The proposed approach iterates between estimating a set of discriminative target and non-target signatures and solving a sparse unmixing problem. After learning target signatures, a signature based detector can then be applied on test data. Both simulated and real hyperspectral target detection experiments show the proposed algorithm is effective at learning discriminative target signatures and achieves superior performance over state-of-the-art comparison algorithms.
  • Color calibration of digital images for agriculture and other applications
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): S. Sunoj, C. Igathinathane, N. Saliendra, J. Hendrickson, D. Archer Image processing in agriculture relies primarily on correlating color changes within images’ region of interest to specific quality attributes (e.g., plant phenology, plant health, crop stress, maturity). Changes in lighting conditions during image acquisition affect image color, even though there is no change in quality, and produces misleading inference when used without calibration. The focus of this study was to develop a method for calibrating images to make them homogeneous to improve phenological comparisons. The method was developed with synthetic images and validated with actual plant images in laboratory and field conditions using a standard ColorChecker (X-Rite) chart. Six different color schemes were tested to determine the effect of patch order, and minimum number of patches required for efficient calibration. A user-coded ImageJ plugin named ‘ColorCal’ was developed in Fiji package for color calibration that derived and applied a [3×3] color calibration matrix, based on selected color patches and standard values. Modified total error and calibration performance index (CPI) were developed to evaluate calibration performance. Calibration using any 12 color patches taken in any order gave equal performance (0.14⩽CPI⩽0.26). Calibration performance using only commonly followed neutral color patches (e.g., white, gray) was poor (0.26⩽CPI⩽1.0). Using red (R), green (G), and blue (B) color patches was recommended as it produced visually similar images, the performance was comparable with 24 color patches (0.21⩽CPI⩽0.24), and was simple and practical. The developed plugin took ≈7 s for calibration (Windows laptop, Intel Core i5, and 8 GB RAM). Determining phenological and other applications using the plugin was more reliable than using the raw images.Graphical abstractGraphical abstract for this article
  • Mapping underrepresented land cover heterogeneity in arid regions: The
           Sahara-Sahel example
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): João Carlos Campos, José Carlos Brito Arid and semi-arid regions comprise high levels of habitat heterogeneity that usually remain undetected in available global land cover (GLC) maps. The Sahara-Sahel is a critical example of how GLC maps continue to oversimplify the complex landscape structure of deserts and arid environments. In this work, we aimed to overcome a generalized knowledge gap concerning the LC heterogeneity of arid regions, using the largest warm desert in the world as a study case. We intended to generate a 30x30m land cover map for the Wet Sahara-Sahel and compare the results with currently available GLC maps (ESA GlobCover and GlobeLand30). To do this, we collected an extensive series of GPS field control points (n = 48,857) and associated descriptive traits. We included the control points in a Hierarchical Cluster Analyses (HCA), and the resulting groups were used as LC classes in Landsat 8 image classification. Independent control points (n = 10,082) suggested a robust regional classification (83.3% correctly classified) of land cover. The Sahara was the most representative ecoregion (around 70% of the study area) and exhibited the highest classification accuracy (91%). The final map is composed by a total of 18 classes, providing a higher number of classes for arid regions than currently available GLC maps. Differences were evident amongst the arid regions of the Sahara, in which the derived map presented a more complex land cover in comparison to the analysed GLC maps. The map derived in this study constitutes framework data for mapping local land cover information and for improving the effectiveness of the assessment and management of natural resources for both local human populations and biodiversity. These results highlight the prevalent need for improving local and regional land cover categorization of arid and semi-arid regions, areas whose land cover heterogeneity is still underrepresented by most of the available GLC maps.
  • Detection of individual trees in urban alignment from airborne data and
           contextual information: A marked point process approach
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Josselin Aval, Jean Demuynck, Emmanuel Zenou, Sophie Fabre, David Sheeren, Mathieu Fauvel, Karine Adeline, Xavier Briottet With the current expansion of cities, urban trees have an important role for preserving the health of its inhabitants. With their evapotranspiration, they reduce the urban heat island phenomenon, by trapping CO2 emission, improve air quality. In particular, street trees or alignment trees, create shade on the road network, are structuring elements of the cities and decorate the roads. Street trees are also subject to specific conditions as they have little space for growth, are pruned and can be affected by the spread of diseases in single-species plantations. Thus, their detection, identification and monitoring are necessary. In this study, an approach is proposed for mapping these trees that are characteristic of the urban environment. Three areas of the city of Toulouse in the south of France are studied. Airborne hyperspectral data and a Digital Surface Model (DSM) for high vegetation detection are used. Then, contextual information is used to identify the street trees. Indeed, Geographic Information System (GIS) data are considered to detect the vegetation canopies close to the streets. Afterwards, individual street tree crown delineation is carried out by modeling the discriminative contextual features of individual street trees (hypotheses of small angle between the trees and similar heights) based on Marked Point Process (MPP). Compared to a baseline individual tree crown delineation method based on region growing, our method logically provides the best results with F-score values of 91%, 75% and 85% against 70%, 41% and 20% for the three studied areas respectively. Our approach mainly succeeds in identifying the street trees. In addition, the contribution of the angle, the height and the GIS data in the street tree mapping has been studied. The results encourage the use of the angle, the height and the GIS data together. However, with only the angle and the height, the results are similar to those obtained with the inclusion of the GIS data for the first and the second study cases with F-score values of 88%, 79% and 62% against 91%, 75% and 85% for the three study cases respectively. Finally, it is shown that the GIS data only is not sufficient.Graphical abstractGraphical abstract for this article
  • Super-resolution of Sentinel-2 images: Learning a globally applicable deep
           neural network
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Charis Lanaras, José Bioucas-Dias, Silvano Galliani, Emmanuel Baltsavias, Konrad Schindler The Sentinel-2 satellite mission delivers multi-spectral imagery with 13 spectral bands, acquired at three different spatial resolutions. The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance – GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution. We employ a state-of-the-art convolutional neural network (CNN) to perform end-to-end upsampling, which is trained with data at lower resolution, i.e., from 40 → 20 m, respectively 360 → 60 m GSD. In this way, one has access to a virtually infinite amount of training data, by downsampling real Sentinel-2 images. We use data sampled globally over a wide range of geographical locations, to obtain a network that generalises across different climate zones and land-cover types, and can super-resolve arbitrary Sentinel-2 images without the need of retraining. In quantitative evaluations (at lower scale, where ground truth is available), our network, which we call DSen2, outperforms the best competing approach by almost 50% in RMSE, while better preserving the spectral characteristics. It also delivers visually convincing results at the full 10 m GSD.
  • Structure from Motion for aerial thermal imagery at city scale:
           Pre-processing, camera calibration, accuracy assessment
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Paolo Conte, Valentina A. Girelli, Emanuele Mandanici Airborne thermal cameras are a valuable source of information for energy analyses at city scale. The generation of accurate high-resolution thermal orthomosaics is a necessary but still challenging task, especially when a thermal camera is the only imaging sensor on-board, because of the peculiar characteristics of thermal imagery (i.e. low dynamic range and poor detail definition), large geometric distortions induced by the optical system and weak acquisition geometry. This paper discusses potentials and limitations of Structure from Motion approach for the automated generation of thermal orthomosaics, with the aim to define the best practices and assess the achievable accuracy. After processing with different strategies two thermal flights over a 10 km2 area in Bologna city (Italy), it can be concluded that the absolute planimetric accuracy can be in the order of 3–4 pixels and the best results are obtained when computing camera calibration on a smaller subset of images, with a limited number of ground control points and an adaptive fitting algorithm. The analysis of generated point clouds (compared with reference LiDAR data) and calibration reports, in addition to check point residuals, proved to be crucial for a proper accuracy assessment.
  • Coping with environmental challenges in Latin America
    • Abstract: Publication date: Available online 12 October 2018Source: ISPRS Journal of Photogrammetry and Remote SensingAuthor(s): Cláudia M. de Almeida, Raul Q. Feitosa, Jaime Hernandez, Carlos M. Scavuzzo, Luiz E.O.C. de Aragão
  • Individual tree crown delineation in a highly diverse tropical forest
           using very high resolution satellite images
    • Abstract: Publication date: Available online 8 October 2018Source: ISPRS Journal of Photogrammetry and Remote SensingAuthor(s): Fabien Hubert Wagner, Matheus Pinheiro Ferreira, Alber Sanchez, Mayumi C.M. Hirye, Maciel Zortea, Emanuel Gloor, Oliver L. Phillips, Carlos Roberto de Souza Filho, Yosio Edemir Shimabukuro, Luiz E.O.C. Aragão Mapping tropical tree species at landscape scales to provide information for ecologists and forest managers is a new challenge for the remote sensing community. For this purpose, detection and delineation of individual tree crowns (ITCs) is a prerequisite. Here, we present a new method of automatic tree crown delineation based only on very high resolution images from WorldView-2 satellite and apply it to a region of the Atlantic rain forest with highly heterogeneous tropical canopy cover – the Santa Genebra forest reserve in Brazil. The method works in successive steps that involve pre-processing, selection of forested pixels, enhancement of borders, detection of pixels in the crown borders, correction of shade in large trees and, finally, segmentation of the tree crowns. Principally, the method uses four techniques: rolling ball algorithm and mathematical morphological operations to enhance the crown borders and ease the extraction of tree crowns; bimodal distribution parameters estimations to identify the shaded pixels in the gaps, borders, and crowns; and focal statistics for the analysis of neighbouring pixels. Crown detection is validated by comparing the delineated ITCs with a sample of ITCs delineated manually by visual interpretation. In addition, to test if the spectra of individual species are conserved in the automatic delineated crowns, we compare the accuracy of species prediction with automatic and manual delineated crowns with known species. We find that our method permits detection of up to 80% of ITCs. The seven species with over 10 crowns identified in the field were mapped with reasonable accuracy (30.5–96%) given that only WorldView-2 bands and texture features were used. Similar classification accuracies were obtained using both automatic and manual delineation, thereby confirming that species’ spectral responses are preserved in the automatic method and thus permitting the recognition of species at the landscape scale. Our method might support tropical forest applications, such as mapping species and canopy characteristics at the landscape scale.
  • Deep networks under scene-level supervision for multi-class geospatial
           object detection from remote sensing images
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Yansheng Li, Yongjun Zhang, Xin Huang, Alan L. Yuille Due to its many applications, multi-class geospatial object detection has attracted increasing research interest in recent years. In the literature, existing methods highly depend on costly bounding box annotations. Based on the observation that scene-level tags provide important cues for the presence of objects, this paper proposes a weakly supervised deep learning (WSDL) method for multi-class geospatial object detection using scene-level tags only. Compared to existing WSDL methods which take scenes as isolated ones and ignore the mutual cues between scene pairs when optimizing deep networks, this paper exploits both the separate scene category information and mutual cues between scene pairs to sufficiently train deep networks for pursuing the superior object detection performance. In the first stage of our training method, we leverage pair-wise scene-level similarity to learn discriminative convolutional weights by exploiting the mutual information between scene pairs. The second stage utilizes point-wise scene-level tags to learn class-specific activation weights. While considering that the testing remote sensing image generally covers a large region and may contain a large number of objects from multiple categories with large size variations, a multi-scale scene-sliding-voting strategy is developed to calculate the class-specific activation maps (CAM) based on the aforementioned weights. Finally, objects can be detected by segmenting the CAM. The deep networks are trained on a seemingly unrelated remote sensing image scene classification dataset. Additionally, the testing phase is conducted on a publicly open multi-class geospatial object detection dataset. The experimental results demonstrate that the proposed deep networks dramatically outperform the state-of-the-art methods.
  • Progressively Expanded Neural Network (PEN Net) for hyperspectral image
           classification: A new neural network paradigm for remote sensing image
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Paheding Sidike, Vijayan K. Asari, Vasit Sagan Hyperspectral image (HSI) has been used for a wide range of applications including forestry, urban planning, and precision agriculture. In recent years, machine learning based algorithms, such as support vector machines, decision trees, ensemble learning, and their variations have shown promising results in HSI analysis. Such methodologies, nevertheless, can lead to insufficient information abstraction in interpreting hyperspectral pixels. In this paper, we propose a novel neural network based classification algorithm, named Progressively Expanded Neural Network (PEN Net), that can effectively interpret hyperspectral pixels in nonlinear feature spaces and then determine their categories. Furthermore, a spectral-spatial HSI classification framework is also introduced to test the generality and robustness of the PEN Net. Experimental results on four standard hyperspectral datasets illustrate that: (1) PEN Net classifier yields better accuracy and competitive processing speed in HSI classification tasks compared to the state-of-the-art methods; (2) Multi-hidden layer based PEN Net generally provides better performance than single hidden layer one; (3) Combination of spectral and spatial features in the PEN Net classifier can significantly improve the classification accuracy by 6–15% compared to the spectral only based HSI classification. This study implies that the proposed neural network architecture opens a new window for future research and the potential for remote sensing image analysis.
  • Accuracy assessment of NLCD 2011 impervious cover data for the Chesapeake
           Bay region, USA
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): J. Wickham, N. Herold, S.V. Stehman, C.G. Homer, G. Xian, P. Claggett The National Land Cover Database (NLCD) contains three eras (2001, 2006, 2011) of percentage urban impervious cover (%IC) at the native pixel size (30 m-×-30 m) of the Landsat Thematic Mapper satellite. These data are potentially valuable to environmental managers and stakeholders because of the utility of %IC as an indicator of watershed and aquatic condition, but lack an accuracy assessment because of the absence of suitable reference data. Recently developed 1 m2 land cover data for the Chesapeake Bay region makes it possible to assess NLCD %IC accuracy for a 262,000 km2 region based on a census rather than a sample of reference data. We report agreement between the two %IC datasets for watersheds and the riparian zones within watersheds and four additional square units. The areas of the six assessment units were 40 ha cell, 433 ha (riparian mean), 2756 ha cell, 5626 ha cell, 8569 ha (watershed mean) and 22,500 ha cell. Mean Absolute Deviation (MAD) and Mean Deviation (MD) were about 1.5% and -1.5%, respectively, for each of the assessment units except for the riparian unit, for which MAD and MD were 0.88 and 0.62, respectively. NLCD reliably reproduced %IC from the 1 m2 data with a small, consistent tendency for underestimation. Results were sensitive to assessment unit choice. The results for the four largest assessment units had very similar regression parameters, R2 values, and bias patterns. Results for the riparian assessment were different from those for the watershed unit and the other three larger units. MAD was about 50% less for the riparian zones than it was for the watersheds, the direction of bias was less consistent, and NLCD %IC was uniformly higher than 1 m2 %IC in urbanized riparian zones. For the smallest unit, bias patterns were more similar to the riparian unit and regression results were more similar to the four larger units. MAD and MD were also sensitive to the amount of urbanization, increasing as NLCD %IC increased. The low overall bias and positive relationship between bias and urbanization suggest that the benefits of obtaining 1 m2 IC data outside of urban areas may not outweigh the costs of obtaining such data.
  • Hyperspectral anomalous change detection based on joint sparse
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Chen Wu, Bo Du, Liangpei Zhang Anomalous change detection aims at finding small but unusual changes from the unchanged or generally changed background in multi-temporal hyperspectral remote sensing images. It is important to model the spectral variations of background so as to highlight the anomalous changes. In this paper, we proposed a hyperspectral anomalous change detection method based on joint sparse representation. A background dictionary is constructed by the randomly selected pixels in the stacked multi-temporal images. The local neighborhood pixels surrounding the test pixel are presented by joint sparse representation with the background dictionary. Thus, the change tendencies in the local background are modeled by the active dictionary bases. The difference of separate reconstruction coefficients of the test pixel with the active bases will reflect the probability to be anomalously changed. Three detectors, which are coefficient difference, Mahalanobis distance of coefficient difference and multi-temporal residual analysis, are proposed to measure the change intensity. Two experiments with the datasets of “Viareggio 2013 Trial” and one Hyperion indicate that the proposed method obtains better performances than the comparative methods.
  • UAV-based multispectral remote sensing for precision agriculture: A
           comparison between different cameras
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Lei Deng, Zhihui Mao, Xiaojuan Li, Zhuowei Hu, Fuzhou Duan, Yanan Yan Unmanned aerial vehicle (UAV)-based multispectral remote sensing has shown great potential for precision agriculture. However, there are many problems in data acquisition, processing and application, which have stunted its development. In this study, a narrowband Mini-MCA6 multispectral camera and a sunshine-sensor-equipped broadband Sequoia multispectral camera were mounted on a multirotor micro-UAV. They were used to simultaneously collect multispectral imagery and soil–plant analysis development (SPAD) values of maize at multiple sampling points in the field, in addition to the spectral reflectances of six standard diffuse reflectance panels with different reflectance values (4.5%, 20%, 30%, 40%, 60% and 65%). The accuracies of the reflectance and vegetation indices (VIs) derived from the imagery were compared, and the effectiveness and accuracy of the SPAD prediction from the normalized difference vegetation index (NDVI) and red-edge NDVI (reNDVI) under different nitrogen treatments were examined at the plot level. The results show that the narrowband Mini-MCA6 camera could produce more accurate reflectance values than the broadband Sequoia camera, but only if the appropriate calibration method (the nonlinear subband empirical line method) was adopted, especially in visible (blue, green and red) bands. However, the accuracy of the VIs was not completely dependent on the accuracy of the reflectance, i.e., the NDVI from Mini-MCA6 was slightly better than that from Sequoia, whereas Sequoia produced more accurate reNDVI than did Mini-MCA6. At the plot level, reNDVI performed better than NDVI in SPAD prediction regardless of which camera was employed. Moreover, the reNDVI had relatively low sensitivity to the vegetation coverage and was insignificantly affected by environmental factors (e.g., exposed sandy soil). This study indicates that UAV multispectral remote sensing technology is instructive for precision agriculture, but more effort is needed regarding calibration methods for vegetation, postprocessing techniques and robust quantitative studies.
  • Radargrammetric approaches to the flat relief of the amazon coast using
           COSMO-SkyMed and TerraSAR-X datasets
    • Abstract: Publication date: Available online 13 September 2018Source: ISPRS Journal of Photogrammetry and Remote SensingAuthor(s): Ulisses Silva Guimarães, Igor da Silva Narvaes, Maria de Lourdes Bueno Trindade Galo, Arnaldo de Queiroz da Silva, Paulo de Oliveira Camargo The Amazonian coast consists of extensive flood plains and plateaus characterized by a high discharge of water and sediment from the Amazon River. This wide landscape occurs under a tropical climate with heavy rains and high cloud cover, making it unsuitable for conventional mapping based on optical images. Additionally, the flat relief and vegetation structure of the Brazilian Amazon coast define an incoherent to partially coherent behavior for the microwave signal, rendering radargrammetric models more suitable for the three-dimensional mapping of its surface. This study aimed to assess the digital surface models (DSMs) provided by Cosmo-SkyMed (CSK) and TerraSAR-X (TSX) Stripmap datasets throughout the radargrammetric models from SARscape and Toutin. The DSMs were generated from SAR (synthetic aperture radar) data with an acquisition geometry that addressed the need for a compromise between the intersection angles and low temporal decorrelation. The radargrammetric SARscape and Toutin’s models were developed from different amounts of stereo ground control points (SGCP). The generated DSMs were evaluated considering a set of 40 independent checkpoints (ICP) measured by GNSS in the field, in their entirety and disaggregated by coastal environment. The vertical accuracy was based on the estimation of the discrepancies, bias and precision (standard deviation and root mean square error – RMSE), and the Taylor and Target diagrams were used for a more comprehensive comparison. In the vertical accuracy analysis using all ICPs measured in situ, the DSM obtained by the SARscape’s model from the CSK SAR data resulted in the lowest RMSE (4.34 m) and mean discrepancy (0.05 m), but Toutin’s model had the lowest standard deviation (2.58 m) of the discrepancies. The Taylor and Target diagrams showed fluctuations in accuracy that alternated the DSMs generated from the two types of SAR data, indicating that TSX produced more stable models and CSK produced better vertical accuracy. The Amazon Coastal Plateau and Fluvial Marine Terrace environments defined three-dimensional representations with lower RMSEs (better than 7.8 and 8.9 m, respectively), regardless of the type of SAR data or the radargrammetric model used. The worst performance, which was for the Fluvial Marine Plain, was influenced by the specific characteristics of this coastal environment, such as the structure of the mangrove vegetation and the shoreline. In general, the high resolution and good ability to revisit the SAR data used, together with the radargrammetric models, allowed for the accurate mapping of the flat relief of the Amazon coastal environments, providing detailed spatial information that can be acquired in severe rainfall conditions in a region of intense morphological dynamics.Graphical abstractGraphical abstract for this article
  • Evaluation of the AVHRR DeepBlue aerosol optical depth dataset over
           mainland China
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Yahui Che, Yong Xue, Jie Guang, Lu She, Jianping Guo Advanced Very High Resolution Radiometer (AVHRR) on-board NOAA series satellites have been used to observe the Earth’s surface and clouds for almost 40 years. Limited by bands and problematic instrument calibration, aerosol studies using AVHRR data have focused on retrieving data over the ocean. However, continuous developments have made it possible to retrieve aerosol over land as well. The newly developed AVHRR Deep Blue (DB) technique has been applied to process global aerosol datasets over both land and the ocean during 1989–1990, 1995–1999 and 2006–2011. This paper aims to evaluate, in detail, the performance of the AVHRR DB aerosol optical depth (AOD) dataset over mainland China by comparison with both ground-based data and satellite aerosol products. The ground-based validation results show that DB AOD is close to ground-based AOD when AOD is moderate during winter, while DB underestimates AOD when AOD increases over 1.0 during summer over vegetated surfaces. AVHRR DB underestimates dry, urban and transitional surfaces in Western China due to the high uncertainty in low retrievals over bright surfaces. Cross-comparison with the Moderate-resolution imaging spectrometer (MODIS) DB aerosol dataset shows that the disadvantages of the single longer visible channel are greatly increased over bright surfaces. Together with problematic instrument calibration, the differences between the two datasets over most of mainland China are significant. Meanwhile, the differences show strong seasonal variation characteristics.
  • Variability in annual temperature cycle in the urban areas of the United
           States as revealed by MODIS imagery
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Peng Fu, Qihao Weng Due to its large spatial coverage and frequent revisit, satellite-derived land surface temperature (LST) has been recently used to explore annual temperature cycle (ATC) variations at regional and global scales. However, variability in seasonality of LSTs has not been examined in detail, particularly in urban areas where elevated temperatures are normally observed. By assuming repetitive temperature cycles, this study aims to reveal differences in ATC parameters between urban and rural areas and the impacts of surface urban heat island (UHI) on the ATC range over the continental United States. To this end, urban areas of larger than 10 km2 (a total of 1856 urban polygons) in the continental United States were identified from the map of urban extents produced by Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data from 2012. The corresponding rural polygons of the same size were generated by using a buffer method. The ATC parameters were optimized using a sinusoidal function fitted with the 8-day MODIS LST composite data. Results showed that urban and rural areas exhibited a significant difference, with a p-value
  • Iterative feature mapping network for detecting multiple changes in
           multi-source remote sensing images
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Tao Zhan, Maoguo Gong, Jia Liu, Puzhao Zhang Owing to the rapid development of remote sensing technology, various types of data can be easily acquired at present. However, it has become an important but more challenging task for effectively highlighting changes occurring on the land surface from these available data. In this paper, we propose an iterative feature mapping network learning framework for identifying multiple changes with focus on multi-source images, which are often obtained from sensors with different imaging modalities. Firstly, high-level and robust feature representations are extracted from multi-source images via unsupervised feature learning. Then, on this basis, an iterative feature mapping network is established to transform these features into a common high-dimensional feature space. It aims to learn more discriminative features by shrinking the difference between the paired features of unchanged positions while enlarging that of changed ones. Note that the network parameters are learned by optimizing a well-designed objective function, and the whole learning process is fully unsupervised. Finally, based on a hierarchical tree for clustering analysis, all possible change classes can be detected accurately. In addition, the proposed framework is found to be also suitable for change detection in homogeneous images. The impressive experimental results obtained over different types of remote sensing images demonstrate the effectiveness and robustness of the proposed model.
  • The impact of dataset selection on land degradation assessment
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Arden L. Burrell, Jason P. Evans, Yi Liu Accurate quantification of land degradation is a global need, particularly in the world’s dryland areas. However, there is a well-documented lack of field data and long-term observational studies for most of these regions. Remotely sensed data offers the only long-term vegetation record that can be used for land degradation assessment at a national, continental or global scale. Both the rainfall and vegetation datasets used for land degradation assessment contain errors and uncertainties, but little work has been done to understand how this may impact results. This study uses the recently developed Time Series Segmented RESidual TREND (TSS-RESTREND) method applied to six rainfall and two vegetation datasets to assess the impact of dataset selection on the estimates of dryland degradation over Australia. Large differences in the data and methods used to produce the precipitation datasets did not significantly impact results with the estimate of average change varying by 95% of regions. On the other hand, the vegetation dataset selection had a much greater impact. Calibration errors in the Global Inventory Monitoring and Modeling System Version 3 NDVI (GIMMSv3.0g) dataset caused significant errors in the trends over some of Australia’s dryland regions. Though identified over Australia, the problematic calibration in the GIMMSv3.0g dataset may have effected dryland NDVI values globally. These errors have been addressed in the updated GIMMSv3.1g which is strongly recommended for use in future studies. Our analysis suggests that using an ensemble composed of multiple runs performed using different datasets allows for the identification of errors that cannot be detected using only a single run or with the data quality flags of the input datasets. A multi-run ensemble made using different input datasets provides more comprehensive quantification of uncertainty and errors in space and time.
  • An automated mathematical morphology driven algorithm for water body
           extraction from remotely sensed images
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): C.A. Rishikeshan, H. Ramesh The detection and extraction of water bodies from satellite imagery is very important and useful for several planning and developmental activities such as shoreline identification, mapping riverbank erosion, watershed extraction and water resource management. Popular techniques for water body extraction like those based on the normalized difference water index (NDWI) require reflectance information in the green and near-infrared (NIR) bands of the light spectrum. Moreover, some commonly used approaches may perform differently according to the spatial resolution of the images. In this regard, mathematical morphological (MM) techniques for image processing have been employed for spatial feature extraction as they preserve edges and shapes. This study proposes a flexible MM driven approach which is very effective for the extraction of water bodies from several satellite images with different spatial resolution. MM provides effective tools for processing image objects based on size and shape and is particularly adapted for water bodies that have typically specific spatial characteristics. In greater details, the proposed extraction algorithm preserves the actual size and shape of the water bodies since it is based on morphological operators based on geodesic reconstruction. Moreover, the choice of the filter size (called structural element (SE) in MM) in the proposed algorithm is done dynamically allowing one to retain the most precise results from different set of inputs images of different spatial resolution and swath. The availability of more than one spectral band of satellite imagery is not necessary for the proposed algorithm as it utilizes only a single band for its computation. This makes it convenient to apply in single band imageries obtained from satellites such as Cartosat thereby making the proposed approach effective over commonly used methods. The accuracy assessment was carried out and compared with the maximum likelihood (ML) classifier and methods based on spectral indices. In all the five test datasets, extraction accuracy of the proposed MM approach was significantly higher than that of spectral indices and ML methods. The results drawn from visual and qualitative assessments indicated its capability and efficiency in water body extraction from different satellite images.
  • Tweets or nighttime lights: Comparison for preeminence in estimating
           socioeconomic factors
    • Abstract: Publication date: December 2018Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 146Author(s): Naizhuo Zhao, Guofeng Cao, Wei Zhang, Eric L. Samson Nighttime lights (NTL) imagery is one of the most commonly used tools to quantitatively study socioeconomic systems over large areas. In this study we aim to use location-based social media big data to challenge the primacy of NTL imagery on estimating socioeconomic factors. Geo-tagged tweets posted in the contiguous United States in 2013 were retrieved to produce a tweet image with the same spatial resolution of the NTL imagery (i.e., 0.00833° × 0.00833°). Sum tweet (the total number of tweets) and sum light (summed DN value of the NTL image) of each state or county were obtained from the tweets and the NTL images, respectively, to estimate three important socioeconomic factors: personal income, electric power consumption, and fossil fuel carbon dioxide emissions. Results show that sum tweet is a better measure of personal income and electric power consumption while carbon dioxide emissions can be more accurately estimated by sum light. We further exploited that African-Americans adults are more likely than White seniors to post geotagged tweets in the US, yet did not find any significant correlations between proportions of the subpopulations and the estimation accuracy of the socioeconomic factors. Existence of saturated pixels and blooming effects and failure to remove gas flaring reduce quality of NTL imagery in estimating socioeconomic factors, however, such problems are nonexistent in the tweet images. This study reveals that the number of geo-tagged tweets has great potential to be deemed as a substitute of brightness of NTL to assess socioeconomic factors over large geographic areas.
  • Improvements of the MODIS Gross Primary Productivity model based on a
           comprehensive uncertainty assessment over the Brazilian Amazonia
    • Abstract: Publication date: Available online 2 August 2018Source: ISPRS Journal of Photogrammetry and Remote SensingAuthor(s): Catherine Torres de Almeida, Rafael Coll Delgado, Lênio Sores Galvão, Luiz Eduardo de Oliveira Cruz e Aragão, María Concepción Ramos Tropical forests and savannas are responsible for the largest proportion of global Gross Primary Productivity (GPP), a major component of the global carbon cycle. However, there are still deficiencies in the spatial and temporal information of tropical photosynthesis and its relations with environmental controls. The MOD17 product, based on the Light Use Efficiency (LUE) concept, has been updated to provide GPP estimates around the globe. In this research, the MOD17 GPP collections 5.0, 5.5 and 6.0 and their sources of uncertainties were assessed by using measurements of meteorology and eddy covariance GPP from eight flux towers in Brazilian tropical ecosystems, from 2000 to 2006. Results showed that the MOD17 collections tend to overestimate GPP at low productivity sites (bias between 111% and 584%) and underestimate it at high productivity sites (bias between −2% and −18%). Overall, the MOD17 product was not able to capture the GPP seasonality, especially in the equatorial sites. Recalculations of MOD17 GPP using site-specific meteorological data, corrected land use/land cover (LULC) classification, and tower-based LUE parameter showed improvements for some sites. However, the improvements were not sufficient to estimate the GPP seasonality in the equatorial forest sites. The use of a new soil moisture constraint on the LUE, based on the Evaporative Fraction, just showed improvements in water-limited sites. Modifications in the algorithm to account for separate LUE for cloudy and clear sky days presented noticeably improved GPP estimates in the tropical ecosystems investigated, both in magnitude and in seasonality. The results suggest that the high cloudiness makes the diffuse radiation an important factor to be considered in the LUE control, especially over dense forests. Thus, the MOD17 GPP algorithm needs more updates to accurately estimate productivity in tropical ecosystems.
  • Spatial and temporal variation of human appropriation of net primary
           production in the Rio de la Plata grasslands
    • Abstract: Publication date: Available online 31 July 2018Source: ISPRS Journal of Photogrammetry and Remote SensingAuthor(s): Santiago Baeza, José M. Paruelo Latin America, and particularly, the Rio de la Plata Grasslands (RPG), are one of the regions with the highest rates of land use change worldwide. These changes drastically alter ecosystems energy flows, affecting biodiversity, atmospheric composition, and the ecosystem capacity to provide services. In this work we evaluated the impact of these changes on Net Primary Production (NPP), one of the most important and integrative ecosystem attributes, through the calculation of Human Appropriation of NPP (HANPP), a very complete indicator of human impact on ecosystems. Our results provide a comprehensive and fine grained description of HANPP patterns over an entire biogeographycal region for two periods that encompass a strong agricultural intensification process. We used medium resolution land use maps and NPP estimates from sub-national level agricultural statistics and remotely sensed data modeling. Results show that the human impact on the energy flow in RPG ecosystems reached very high levels compared to other regions of the world. The average appropriation of was 42% of the potential vegetation NPP in 2001/2002 and it increased 4.5% during the last years due to an intense land use changes. Most of the HANPP was explained by harvest rather than by land use changes, mainly in the last period due to crops yield increase and the expansion of double crop system as a common agronomic practice. High HANPP values found were associated to a set of environmental impacts that affect ecosystems sustainability and their ability to provide ecosystem services.
  • Monitoring Andean high altitude wetlands in central Chile with seasonal
           optical data: A comparison between Worldview-2 and Sentinel-2 imagery
    • Abstract: Publication date: Available online 13 April 2018Source: ISPRS Journal of Photogrammetry and Remote SensingAuthor(s): Rocío A. Araya-López, Javier Lopatin, Fabian E. Fassnacht, H. Jaime Hernández In the Maipo watershed, situated in central Chile, mining activities are impacting high altitude Andean wetlands through the consumption and exploitation of water and land. As wetlands are vulnerable and particularly susceptible to changes of water supply, alterations and modifications in the hydrological regime have direct effects on their ecophysiological condition and vegetation cover. The aim of this study was to evaluate the potential of Worldview-2 and Sentinel-2 sensors to identify and map Andean wetlands through the use of the one-class classifier Bias support vector machines (BSVM), and then to estimate soil moisture content of the identified wetlands during snow-free summer using partial least square regression.The results obtained in this research showed that the combination of remote sensing data and a small sample of ground reference measurements enables to map Andean high altitude wetlands with high accuracies. BSVM was capable to classify the meadow areas with an overall accuracy of over ∼78% for both sensors. Our results also indicate that it is feasible to map surface soil moisture with optical remote sensing data and simple regression approaches in the examined environment. Surface soil moisture estimates reached r2 values of up to 0.58, and normalized mean square errors of 19% using Sentinel-2 data, while Worldview-2 estimates resulted in non-satisfying results. The presented approach is particularly valuable for monitoring high-mountain wetland areas with limited accessibility such as in the Andes.
  • Early assessment of crop yield from remotely sensed water stress and solar
           radiation data
    • Abstract: Publication date: Available online 20 March 2018Source: ISPRS Journal of Photogrammetry and Remote SensingAuthor(s): Mauro E. Holzman, Facundo Carmona, Raúl Rivas, Raquel Niclòs Soil moisture (SM) available for evapotranspiration is crucial for food security, given the significant interannual yield variability of rainfed crops in large agricultural regions. Also, incoming solar radiation (Rs) influences the photosynthetic rate of vegetated surfaces and can affect productivity. The aim of this work is to evaluate the ability of crop water stress and Rs remotely sensed data to forecast yield at regional scale. Temperature Vegetation Dryness Index (TVDI) was computed as an indicator of crop water stress and soil moisture availability. TVDI during critical growth stage of crops was calculated from MODIS products: MODIS/AQUA 8-day composite LST at 1 km and 16-day composite vegetation index at 1 km. Rs data were obtained from Clouds and the Earth’s Radiant Energy System (CERES). The relationship between TVDI, Rs and yield of wheat, corn and soybean was analyzed. High R2 values (0.55–0.82, depending on crop and region) were found in different agro-climatic regions of Argentine Pampas. Validation results showed the suitability of the model RMSE = 330–1300 kg ha−1, Relative Error = 13–34%. However, results were significantly improved considering the most important factor affecting yield. Rs proved to be important for winter crops in humid areas, where incoming radiation can be a limiting factor. In semi-arid regions, soils with low water retention capacity and summer crops, crop water stress showed the best results. Overall, results reflected that the proposed approach is suitable for crop yield forecasting at regional scale several weeks previous to harvest.Graphical abstractGraphical abstract for this article
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