Special Issue "Multi-Temporal Remote Sensing"

Guest Editor
Prof. Dr. Dave Verbyla
Department of Forest Sciences, School of Natural Resources and Agricultural Sciences, University of Alaska Fairbanks, Fairbanks, AK 99775-7200, USA
Website: http://nrm.salrm.uaf.edu/~dverbyla
E-Mail:
Interests: remote sensing; geographic information systems; spatial analysis; multi-temporal trends in boreal forests; boreal wildfire severity; bias in change detection estimates; GIS analysis techniques

Dear Colleagues,

The analysis of multi-temporal remotely sensed data is especially relevant with the increasing quantity and quality of historic and current multi-temporal data sets. Detecting and monitoring change with multi-temporal remote sensing has applications in many fields and scales.

This special issue is open to manuscripts focusing on multi-temporal remote sensing including image registration, calibration, and correction techniques, multi-temporal analyses, data fusion, and multi-temporal applications such as monitoring and change detection applications.

Prof. Dr. David Verbyla
Guest Editor

All manuscripts should be submitted to remotesensing@mdpi.org with a copy to the Guest Editor. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this Open Access journal is 300 CHF per accepted paper. English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

• multi-temporal
• change detection
• time series
• dynamic
• monitoring

Title: An Investigation of Change Detection Accuracy and Image Properties using Simulated Data
Authors: Abdullah Almutairi ¹ and Timothy A. Warner ²
Affiliations: ¹ Geography Department, Imam Mohammad bin Saud University, Riyadh, Saudi Arabia; E-Mail: alqutaimi@hotmail.com
² Department of Geology and Geography, West Virginia University, Morgantown WV 26508, USA; E-Mail: Tim.warner@mail.wvu.edu
Abstract: Simulated data was used to investigate the relationship between image properties and change detection accuracy in a systematic manner. The image properties examined were class separability, radiometric normalization and image spectral band-to-band correlation. The change detection methods used were post-classification comparison, direct classification of multidate imagery, image differencing, principal component analysis (PCA), and change vector analysis (CVA). The simulated data experiments showed that the relative accuracy of the change detection methods varied with changes in image properties, thus confirming the hypothesis that caution should be used in generalizing from relative classification determined through change detection studies that use only a single image pair.

Type: Article
Title: Analysis and Modeling of Urban Land Cover Change in Setúbal and Sesimbra, Portugal
Authors: Yikalo Araya ¹ and Pedro Cabral ²
Affiliation: ¹ Department of Geography, York University, Toronto, Canada; E-Mail: yikalo@yorku.ca
² Institute Superior de Estatistica e Gestão de Informação, ISEGI, Universidade Nova de Lisboa, Lisbon, Portugal; E-Mail: pcabral@isegi.unl.pt
Abstract: In this paper we study urban land cover change in the municipalities of Setúbal and Sesimbra, Portugal, between the years 1990 and 2006. Land cover classes are extracted from LISS-III, SPOT and Landsat satellite images using an object-oriented approach. The urban class dynamics, both for pattern and quantities, is studied using selected landscape metrics and Shannon’s Entropy index. Finally, urban land use change is modelled using Cellular Automata Markov chain analysis. Results indicate that the study area has undergone a tremendous change in urban areas during the studied period. Simulation results for year 2020 reveal that the rate of urban growth tends to continue and may threaten large areas that are currently reserved for forest cover, farming lands and natural parks. The combined use of remote sensing, landscape metrics and modelling techniques provided a consistent building block for successful urban planning, for exploring how and when urban growth is occurring, to test what-if scenarios and for helping subsequent research works.

Type of Paper: Article
Title: On the Exportability of Robust Satellite Techniques (RST) for Active Volcanoes Monitoring
Authors: F. Marchese ¹, M. Ciampa ², C. Filizzola 1, T. Lacava ¹, G. Mazzeo ², N. Pergola ^1,2, V. Tramutoli ^1,2
Affiliations: ¹ Institute of Methodologies for Environmental Analysis, CNR Contrada S.Loja 85050 Tito Scalo (Pz), Italy; E-Mail: pergola@imaa.cnr.it (N.P.)
² Department of Engineering and Physic of the Environment, University of Basilicata, Via dell’Ateneo Lucano 10, 85100, Potenza, Italy
Abstract: Satellite remote sensing has increasingly become a crucial tool for volcanic activity monitoring, thanks to continuous observations at global scale, provided with different spatial/spectral/temporal resolutions on the base of specific satellite platforms and a relatively low costs. Among the satellite techniques developed for volcanic activity monitoring the RST (Robust Satellite Techniques) approach has shown high performances in detecting hot spots as well as in automatically identifying ash plumes, well discriminating them from weather clouds. This method, based on an extensive multi-temporal analysis of long-term time series of homogeneous satellite records, has recently been implemented on EOS-MODIS and MSG-SEVIRI data for which further improvements of its performances are expected. In this paper, some preliminarily results of these analyses are presented, both regarding hot spot identification and ash cloud detection and tracking. The potential of RST, to be used within early warning systems devoted to volcanic hazard monitoring and mitigation, will also be discussed.

Type of Paper: Article
Title: Impact and Recovery Analysis of Cyclone Destructed Forest using Multi-Temporal Moderate Resolution Remote Sensing Image: A Case Study of Bangladesh
Authors: Mir Mustafizur Rahman ¹, Asif Iqbal ² and Rezwana Kaiser ²
Affiliations: ¹ Department of Geography, University of Calgary, Calgary, Alberta T2N1N4, Canada; E-Mail: mmrahm@ucalgary.ca
² Asian Institute of Technology, Thailand; E-mail: asifiqbal_es@yahoo.com (A.I.); st107939@ait.ac.th (R.K.)
Abstract: Bangladesh is a cyclone prone country because of its position in southern part of Asia bordered by Bay of Bengal. On November 15, 2007 a devastating cyclone (Cyclone Sydr) hit the southern coast of Bangladesh, which is the most damaging cyclone in the history of Bangladesh. The largest mangrove forest of the world, the Sundarbans was destroyed by the cyclone in great extent. This paper aims at analyzing the amount of destruction caused by cyclone Sydr at Sundarbans and estimating the time required for natural restoration. Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Differential Vegetation Index (NDVI) data is utilized for forest change detection. Image of the study area before the cyclone and immediately after the cyclone is collected and evaluated to estimate destruction caused by the storm. Restoration time is calculated using monthly time series data observed for the year 2008-2009. The paper reveals that the natural restoration of the damaged forest area is very quick and the forest is expected to recover to its original state by 2015. Thus the outcome of the paper is important for suitable management goal setting for such conditions in future.