Special Issue "Multi-Temporal Remote Sensing"
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A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (28 February 2010)
Special Issue Editor
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: dlverbyla@alaska.edu
Phone: +1 907 474 5553
Fax: +1 907 474 6184
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
Special Issue Information
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
Submission
All manuscripts should be submitted to remotesensing@mdpi.com 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.
Keywords
- multi-temporal
- change detection
- time series
- dynamic
- monitoring
Published Papers (8 papers)
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Received: 8 February 2010; in revised form: 27 March 2010 / Accepted: 30 March 2010 / Published: 7 April 2010
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Abstract: In light of rapid global urbanisation, monitoring and mapping of urban and population growth is of great importance. Population growth in Sana’a was investigated for this reason. The capital of the Republic of Yemen is a rapidly growing middle sized city where the population doubles almost every ten years. Satellite data from four different sensors were used to explore urban growth in Sana’a between 1989 and 2007, assisted by topographic maps and cadastral vector data. The analysis was conducted by delineating the built-up areas from the various optical satellite data, applying a fuzzy-rule-based composition of anisotropic textural measures and interactive thresholding. The resulting datasets were used to analyse urban growth and changes in built-up density per district, qualitatively as well as quantitatively, using a geographic information system. The built-up area increased by 87 % between 1989 and 2007. Built-up density has increased in all areas, but particularly in the northern and southern suburban districts, also reflecting the natural barrier of surrounding mountain ranges. Based on long-term population figures, geometric population growth was assumed. This hypothesis was used together with census data for 1994 and 2004 to estimate population figures for 1989 and 2007, resulting in overall growth of about 240%. By joining population figures to district boundaries, the spatial patterns of population distribution and growth were examined. Further, urban built-up growth and population changes over time were brought into relation in order to investigate changes in population density per built-up area. Population densities increased in all districts, with the greatest density change in the peripheral areas towards the North. The results reflect the pressure on the city’s infrastructure and natural resources and could contribute to sustainable urban planning in the city of Sana’a.
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Received: 25 February 2010; in revised form: 28 April 2010 / Accepted: 6 May 2010 / Published: 11 May 2010
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Abstract: “Snow gone” (SGN) stage is one of the critical variables that describe the start of the official forest fire season in the Canadian Province of Alberta. In this paper, our objective is to evaluate the potential of MODIS-based indices for determining the SGN stage. Those included: (i) enhanced vegetation index (EVI), (ii) normalized difference water index (NDWI) using the shortwave infrared (SWIR) spectral bands centered at 1.64 µm (NDWI1.64µm) and at 2.13 µm (NDWI2.13µm), and (iii) normalized difference snow index (NDSI). These were calculated using the 500 m 8-day gridded MODIS-based composites of surface reflectance data (i.e., MOD09A1 v.005) for the period 2006–08. We performed a qualitative evaluation of these indices over two forest fire prone natural subregions in Alberta (i.e., central mixedwood and lower boreal highlands). In the process, we generated and compared the natural subregion-specific lookout tower sites average: (i) temporal trends for each of the indices, and (ii) SGN stage using the ground-based observations available from Alberta Sustainable Resource Development. The EVI-values were found to have large uncertainty at the onset of the spring and unable to predict the SGN stages precisely. In terms of NDSI, it showed earlier prediction capabilities. On the contrary, both of the NDWI’s showed distinct pattern (i.e., reached a minimum value before started to increase again during the spring) in relation to observed SGN stages. Thus further analysis was carried out to determine the best predictor by comparing the NDWI’s predicted SGN stages with the ground-based observations at all of the individual lookout tower sites (approximately 120 in total) across the study area. It revealed that NDWI2.13µm demonstrated better prediction capabilities (i.e., on an average approximately 90% of the observations fell within ±2 periods or ±16 days of deviation) in comparison to NDWI1.64µm (i.e., on an average approximately 73% of the observations fell within ±2 periods or ±16 days of deviation).
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Received: 8 April 2010; in revised form: 17 May 2010 / Accepted: 21 May 2010 / Published: 1 June 2010
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Abstract: Based on field data simultaneous with Landsat overpasses from six different dates, we developed a robust linear model to predict subpixel fractions of water cover. The model was applied to a time series of 174 Landsat TM and ETM+ images to reconstruct the flooding regime of a system of small temporary ponds and to study their spatio-temporal changes in a 23-year period. We tried to differentiate natural fluctuations from trends in hydrologic variables (i.e., hydroperiod shortening) that may threaten the preservation of the system. Although medium-resolution remote sensing data have rarely been applied to the monitoring of small-sized wetlands, this study evidences its utility to understand the hydrology of temporary ponds at a local scale. We show that the temporary ponds in Doñana National Park constitute a large and heterogeneous system with high intra and inter-annual variability. We also evidence that the conservation value of this ecosystem is threatened by the observed tendency to shorter annual hydroperiods in recent years, probably due to aquifer exploitation. This system of temporary ponds deserves special attention for the high density and heterogeneity of natural ponds, not common in Europe. For this reason, management decisions to avoid its destruction or degradation are critical.
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Received: 14 April 2010; in revised form: 27 May 2010 / Accepted: 2 June 2010 / Published: 3 June 2010
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Abstract: Simulated data were used to investigate the relationships 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 evaluated were post-classification comparison, direct classification of multidate imagery, image differencing, principal component analysis, and change vector analysis. 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 studies that use only a single image pair. In most cases, direct classification and post-classification comparison were the least sensitive to changes in the image properties of class separability, radiometric normalization error and band correlation. Furthermore, these methods generally produced the highest accuracy, or were amongst those with a high accuracy. PCA accuracy was highly variable; the use of four principal components consistently resulted in substantial decreased classification accuracy relative to using six components, or classification using the original six bands. The accuracy of image differencing also varied greatly in the experiments. Of the three methods that require radiometric normalization, image differencing was the method most affected by radiometric error, relative to change vector and classification methods, for classes that have moderate and low separability. For classes that are highly separable, image differencing was relatively unaffected by radiometric normalization error. CVA was found to be the most accurate method for classes with low separability and all but the largest radiometric errors. CVA accuracy tended to be the least affected by changes in the degree of band correlation in situations where the class means were moderately dispersed, or clustered near the diagonal. For all change detection methods, the classification accuracy increased as simulated band correlation increased, and direct classification methods consistently had the highest accuracy, while PCA generally had the lowest accuracy.
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Received: 24 March 2010; in revised form: 31 May 2010 / Accepted: 2 June 2010 / Published: 9 June 2010
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Abstract: The expansion of cities entails the abandonment of forest and agricultural lands, and these lands’ conversion into urban areas, which results in substantial impacts on ecosystems. Monitoring these changes and planning urban development can be successfully achieved using multitemporal remotely sensed data, spatial metrics, and modeling. In this paper, urban land use change analysis and modeling was carried out for the Concelhos of Setúbal and Sesimbra in Portugal. An existing land cover map for the year 1990, together with two derived land cover maps from multispectral satellite images for the years 2000 and 2006, were utilized using an object-oriented classification approach. Classification accuracy assessment revealed satisfactory results that fulfilled minimum standard accuracy levels. Urban land use dynamics, in terms of both patterns and quantities, were studied using selected landscape metrics and the Shannon Entropy index. Results show that urban areas increased by 91.11% between 1990 and 2006. In contrast, the change was only 6.34% between 2000 and 2006. The entropy value was 0.73 for both municipalities in 1990, indicating a high rate of urban sprawl in the area. In 2006, this value, for both Sesimbra and Setúbal, reached almost 0.90. This is demonstrative of a tendency toward intensive urban sprawl. Urban land use change for the year 2020 was modeled using a Cellular Automata based approach. The predictive power of the model was successfully validated using Kappa variations. Projected land cover changes show a growing tendency in urban land use, which might threaten areas that are currently reserved for natural parks and agricultural lands.
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Received: 27 April 2010; in revised form: 1 June 2010 / Accepted: 7 June 2010 / Published: 14 June 2010
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Abstract: Light availability inside a forest canopy is of key importance to many ecosystem processes, such as photosynthesis and transpiration. Assessment of light availability and within-canopy light variability enables a more detailed understanding of these biophysical processes. The changing light-vegetation interaction in a homogeneous oak (Quercus robur L.) stand was studied at different moments during the growth season using terrestrial laser scanning datasets and ray tracing technology. Three field campaigns were organized at regular time intervals (24 April 2008; 07 May 2008; 23 May 2008) to monitor the increase of foliage material. The laser scanning data was used to generate 3D representations of the forest stands, enabling structure feature extraction and light interception modeling, using the Voxel-Based Light Interception Model (VLIM). The VLIM is capable of estimating the relative light intensity or Percentage of Above Canopy Light (PACL) at any arbitrary point in the modeled crown space. This resulted in a detailed description of the dynamic light environments inside the canopy. Mean vertical light extinction profiles were calculated for the three time frames, showing significant differences in light attenuation by the canopy between April 24 on the one hand, and May 7 and May 23 on the other hand. The proposed methodology created the opportunity to link these within-canopy light distributions to the increasing amount of photosynthetically active leaf material and its distribution in the considered 3D space.
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Received: 10 April 2010; in revised form: 27 May 2010 / Accepted: 8 June 2010 / Published: 17 June 2010
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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 at 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, effectively 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 performance improvements are expected. These satellite systems, in fact, offer improved spectral and/or temporal resolutions. 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.
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Received: 30 June 2010; in revised form: 23 August 2010 / Accepted: 24 August 2010 / Published: 2 September 2010
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Abstract: Monthly night averaged land surface temperature (LST) MODIS imagery was analyzed throughout a year-period (2006), in an attempt to segment the terrain of Egypt into regions with different LST seasonal variability, and represent them parametrically. Regions with distinct spatial and temporal LST patterns were outlined using several clustering techniques capturing aspects of spatial, temporal and temperature homogeneity or differentiation. Segmentation was supplemented, taking into consideration elevation, morphological features and landcover information. The northern coastal region along the Mediterranean Sea occupied by lowland plain areas corresponds to the coolest clusters indicating a latitude/elevation dependency of seasonal LST variability. On the other hand, for the inland regions, elevation and terrain dissection plays a key role in LST seasonal variability, while an east to west variability of clusters’ spatial distribution is evident. Finally, elevation biased clustering revealed annual LST differences among the regions with the same physiographic/terrain characteristics. Thermal terrain segmentation outlined the temporal variation of LST during 2006, as well as the spatial distribution of LST zones.
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Last update: 14 January 2011
