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Special Issue "Analysis of Remote Sensing Image Data"

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

Deadline for manuscript submissions: closed (15 January 2014)

Special Issue Editors

Guest Editor
Dr. James C. Tilton

Computational and Information Sciences and Technology Office (Mail Code 606.3), NASA Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, U. S. A.
Website | E-Mail
Phone: 301-286-9510
Fax: +1 301 286 1776
Interests: image analysis; image segmentation; pattern recognition and image analysis applications to remote sensing; massively parallel computation
Guest Editor
Prof. Dr. Jón Atli Benediktsson

Faculty of Electrical and Computer Engineering, University of Iceland, Sæmundargata 2, 101 Reykjavik, Iceland
Website | E-Mail
Fax: 354 525 4038
Interests: remote sensing; analysis of biomedical signals; pattern recognition; data fusion; image processing; and signal processing
Guest Editor
Dr. Yuliya Tarabalka

INRIA Sophia Antipolis , 2004, route des Lucioles, 06902 Sophia Antipolis Cedex, France
Website | E-Mail
Fax: +33 4 92 38 76 43
Interests: image analysis and signal processing; pattern recognition; remote sensing; imaging spectroscopy; and high-performance computing

Special Issue Information

Dear Colleagues,

Computer-based analysis of remote sensing image data is of ever increasing importance as increasing volumes and types of digital image data become available from various aircraft and satellite based sensors. Effective utilization of this remote sensing image data requires an accurate extraction of the information contained in this data into terms relevant to the particular applications. And these applications are ever expanding, ranging from various land use and land cover mapping applications (e.g., monitoring urbanization, croplands, desertification, deforestation and forest health, glaciers and sea ice) to detecting and tracking air pollution and oil spills, to mineralogy other earth surface and atmospheric measurements.
With this special issue we compile state-of-the-art analysis methods for converting remote sensing image data into information relevant to various earth sciences and monitoring applications. We assume that the remote sensing image data has undergone radiometric and geometric correction processing. Analysis methods for image data collected and formed from optical, lidar and/or radar sensors are solicited. Review contributions are welcome as well as papers describing new analysis methods.

Dr. James C. Tilton
Prof. Dr. Jón Atli Benediktsson
Dr. Yuliya Tarabalka
Guest editors

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. 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 1600 CHF (Swiss Francs).

Keywords

• feature extraction
• image analysis
• image classification
• image edge detection
• image segmentation
• image texture
• object based image analysis
• pattern recognition
• machine learning
• mathematical morphology
• data fusion
• change detection

Published Papers (14 papers)

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Research

Open AccessArticle Comparison of Medium Spatial Resolution ENVISAT-MERIS and Terra-MODIS Time Series for Vegetation Decline Analysis: A Case Study in Central Asia
Remote Sens. 2014, 6(6), 5238-5256; doi:10.3390/rs6065238
Received: 2 October 2013 / Revised: 30 May 2014 / Accepted: 30 May 2014 / Published: 6 June 2014
Cited by 11 | PDF Full-text (2383 KB) | HTML Full-text | XML Full-text
Abstract
Accurate monitoring of land surface dynamics using remote sensing is essential for the synoptic assessment of environmental change. We assessed a Medium Resolution Imaging Spectrometer (MERIS) full resolution dataset for vegetation monitoring as an alternative to the more commonly used Moderate-Resolution Imaging Spectroradiometer
[...] Read more.
Accurate monitoring of land surface dynamics using remote sensing is essential for the synoptic assessment of environmental change. We assessed a Medium Resolution Imaging Spectrometer (MERIS) full resolution dataset for vegetation monitoring as an alternative to the more commonly used Moderate-Resolution Imaging Spectroradiometer (MODIS) data. Time series of vegetation indices calculated from 300 m resolution MERIS and 250 m resolution MODIS datasets were analyzed to monitor vegetation productivity trends in the irrigated lowlands in Northern Uzbekistan for the period 2003–2011. Mann-Kendall trend analysis was conducted using the time series of Normalized Differenced Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and MERIS-based Terrestrial Chlorophyll Index (MTCI) to detect trends and examine the capabilities of each sensor and index. The methodology consisted of (1) preprocessing of the original imagery; (2) processing and statistical analysis of the corresponding time series datasets; and (3) comparison of the resulting trends. Results confirmed the occurrence of widespread vegetation productivity decline, ranging from 5.5% (MERIS-MTCI) to 21% (MODIS-NDVI) of the total irrigated cropland in the study area. All indices identified the same spatial patterns of decreasing vegetation. Average vegetation index values of NDVI and SAVI were slightly higher when measured by MERIS than by MODIS. These differences merit further investigation to allow a fusion of these datasets for consistent monitoring of cropland productivity decline at scales suitable for guiding operational land management practices. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
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Open AccessArticle Semi-Supervised Learning for Ill-Posed Polarimetric SAR Classification
Remote Sens. 2014, 6(6), 4801-4830; doi:10.3390/rs6064801
Received: 2 December 2013 / Revised: 28 April 2014 / Accepted: 29 April 2014 / Published: 27 May 2014
Cited by 3 | PDF Full-text (7119 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, the interest in semi-supervised learning has increased, combining supervised and unsupervised learning approaches. This is especially valid for classification applications in remote sensing, while the data acquisition rate in current systems has become fairly large considering high- and very-high resolution
[...] Read more.
In recent years, the interest in semi-supervised learning has increased, combining supervised and unsupervised learning approaches. This is especially valid for classification applications in remote sensing, while the data acquisition rate in current systems has become fairly large considering high- and very-high resolution data; yet on the other hand, the process of obtaining the ground truth data may be cumbersome for such large repositories. In this paper, we investigate the application of semi-supervised learning approaches and particularly focus on the small sample size problem. To that extend, we consider two basic unsupervised approaches by enlarging the initial labeled training set as well as an ensemble-based self-training method. We propose different strategies within self-training on how to select more reliable candidates from the pool of unlabeled samples to speed-up the learning process and to improve the classification performance of the underlying classifier ensemble. We evaluate the effectiveness of the proposed semi-supervised learning approach over polarimetric SAR data. Results show that the proposed self-training approach using an ensemble-based classifier that is initially trained over a small training set can achieve a similar performance level of a fully supervised learning approach where the training is performed over significantly larger labeled data. Considering the difficulties of the manual data labeling in such massive volumes of SAR repositories, this is indeed a promising accomplishment for semi-supervised SAR classification. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
Open AccessArticle Automated Training Sample Extraction for Global Land Cover Mapping
Remote Sens. 2014, 6(5), 3965-3987; doi:10.3390/rs6053965
Received: 16 January 2014 / Revised: 10 April 2014 / Accepted: 18 April 2014 / Published: 2 May 2014
Cited by 13 | PDF Full-text (2530 KB) | HTML Full-text | XML Full-text
Abstract
Land cover is one of the essential climate variables of the ESA Climate Change Initiative (CCI). In this context, the Land Cover CCI (LC CCI) project aims at building global land cover maps suitable for climate modeling based on Earth observation by satellite
[...] Read more.
Land cover is one of the essential climate variables of the ESA Climate Change Initiative (CCI). In this context, the Land Cover CCI (LC CCI) project aims at building global land cover maps suitable for climate modeling based on Earth observation by satellite sensors.  The  challenge  is  to  generate  a  set  of  successive  maps  that  are  both  accurate and consistent over time. To do so, operational methods for the automated classification of optical images are investigated. The proposed approach consists of a locally trained classification using an automated selection of training samples from existing, but outdated land cover information. Combinations of local extraction (based on spatial criteria) and self-cleaning of training samples (based on spectral criteria) are quantitatively assessed. Two large study areas, one in Eurasia and the other in South America, are considered. The proposed morphological cleaning of the training samples leads to higher accuracies than the statistical outlier removal in the spectral domain. An optimal neighborhood has been identified for the local sample extraction. The results are coherent for the two test areas, showing an improvement of the overall accuracy compared with the original reference datasets and a significant reduction of macroscopic errors. More importantly, the proposed method partly controls the reliability of existing land cover maps as sources of training samples for supervised classification. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
Open AccessArticle A Three-Dimensional Index for Characterizing Crop Water Stress
Remote Sens. 2014, 6(5), 4025-4042; doi:10.3390/rs6054025
Received: 14 October 2013 / Revised: 24 March 2014 / Accepted: 15 April 2014 / Published: 2 May 2014
Cited by 3 | PDF Full-text (1110 KB) | HTML Full-text | XML Full-text
Abstract
The application of remotely sensed estimates of canopy minus air temperature (Tc-Ta) for detecting crop water stress can be limited in semi-arid regions, because of the lack of full ground cover (GC) at water-critical crop stages. Thus, soil background may restrict
[...] Read more.
The application of remotely sensed estimates of canopy minus air temperature (Tc-Ta) for detecting crop water stress can be limited in semi-arid regions, because of the lack of full ground cover (GC) at water-critical crop stages. Thus, soil background may restrict water stress interpretation by thermal remote sensing. For partial GC, the combination of plant canopy temperature and surrounding soil temperature in an image pixel is expressed as surface temperature (Ts). Soil brightness (SB) for an image scene varies with surface soil moisture. This study evaluates SB, GC and Ts-Ta and determines a fusion approach to assess crop water stress. The study was conducted (2007 and 2008) on a commercial scale, center pivot irrigated research site in the Texas High Plains. High-resolution aircraft-based imagery (red, near-infrared and thermal) was acquired on clear days. The GC and SB were derived using the Perpendicular Vegetation Index approach. The Ts-Ta was derived using an array of ground Ts sensors, thermal imagery and weather station air temperature. The Ts-Ta, GC and SB were fused using the hue, saturation, intensity method, respectively. Results showed that this method can be used to assess water stress in reference to the differential irrigation plots and corresponding yield without the use of additional energy balance calculation for water stress in partial GC conditions. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
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Open AccessArticle Estimating Fractional Shrub Cover Using Simulated EnMAP Data: A Comparison of Three Machine Learning Regression Techniques
Remote Sens. 2014, 6(4), 3427-3445; doi:10.3390/rs6043427
Received: 16 January 2014 / Revised: 11 April 2014 / Accepted: 14 April 2014 / Published: 22 April 2014
Cited by 18 | PDF Full-text (2233 KB) | HTML Full-text | XML Full-text
Abstract
Anthropogenic interventions in natural and semi-natural ecosystems often lead to substantial changes in their functioning and may ultimately threaten ecosystem service provision. It is, therefore, necessary to monitor these changes in order to understand their impacts and to support management decisions that help
[...] Read more.
Anthropogenic interventions in natural and semi-natural ecosystems often lead to substantial changes in their functioning and may ultimately threaten ecosystem service provision. It is, therefore, necessary to monitor these changes in order to understand their impacts and to support management decisions that help ensuring sustainability. Remote sensing has proven to be a valuable tool for these purposes, and especially hyperspectral sensors are expected to provide valuable data for quantitative characterization of land change processes. In this study, simulated EnMAP data were used for mapping shrub cover fractions along a gradient of shrub encroachment, in a study region in southern Portugal. We compared three machine learning regression techniques: Support Vector Regression (SVR); Random Forest Regression (RF); and Partial Least Squares Regression (PLSR). Additionally, we compared the influence of training sample size on the prediction performance. All techniques showed reasonably good results when trained with large samples, while SVR always outperformed the other algorithms. The best model was applied to produce a fractional shrub cover map for the whole study area. The predicted patterns revealed a gradient of shrub cover between regions affected by special agricultural management schemes for nature protection and areas without land use incentives. Our results highlight the value of EnMAP data in combination with machine learning regression techniques for monitoring gradual land change processes. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
Open AccessArticle A Combination of TsHARP and Thin Plate Spline Interpolation for Spatial Sharpening of Thermal Imagery
Remote Sens. 2014, 6(4), 2845-2863; doi:10.3390/rs6042845
Received: 25 November 2013 / Revised: 20 February 2014 / Accepted: 6 March 2014 / Published: 28 March 2014
Cited by 7 | PDF Full-text (1700 KB) | HTML Full-text | XML Full-text
Abstract
There have been many studies and much attention paid to spatial sharpening for thermal imagery. Among them, TsHARP, based on the good correlation between vegetation index and land surface temperature (LST), is regarded as a standard technique because of its operational simplicity and
[...] Read more.
There have been many studies and much attention paid to spatial sharpening for thermal imagery. Among them, TsHARP, based on the good correlation between vegetation index and land surface temperature (LST), is regarded as a standard technique because of its operational simplicity and effectiveness. However, as LST is affected by other factors (e.g., soil moisture) in the areas with low vegetation cover, these areas cannot be well sharpened by TsHARP. Thin plate spline (TPS) is another popular downscaling technique for surface data. It has been shown to be accurate and robust for different datasets; however, it has not yet been attempted in thermal sharpening. This paper proposes to combine the TsHARP and TPS methods to enhance the advantages of each. The spatially explicit errors of these two methods were firstly estimated in theory, and then the results of TPS and TsHARP were combined with the estimation of their errors. The experiments performed across various landscapes and data showed that the proposed combined method performs more robustly and accurately than TsHARP. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
Open AccessArticle Multi-Sensor Imaging and Space-Ground Cross-Validation for 2010 Flood along Indus River, Pakistan
Remote Sens. 2014, 6(3), 2393-2407; doi:10.3390/rs6032393
Received: 18 December 2013 / Revised: 26 February 2014 / Accepted: 3 March 2014 / Published: 19 March 2014
Cited by 8 | PDF Full-text (1135 KB) | HTML Full-text | XML Full-text
Abstract
Flood monitoring was conducted using multi-sensor data from space-borne optical, and microwave sensors; with cross-validation by ground-based rain gauges and streamflow stations along the Indus River; Pakistan. First; the optical imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) was processed to delineate the
[...] Read more.
Flood monitoring was conducted using multi-sensor data from space-borne optical, and microwave sensors; with cross-validation by ground-based rain gauges and streamflow stations along the Indus River; Pakistan. First; the optical imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) was processed to delineate the extent of the 2010 flood along Indus River; Pakistan. Moreover; the all-weather all-time capability of higher resolution imagery from the Advanced Synthetic Aperture Radar (ASAR) is used to monitor flooding in the lower Indus river basin. Then a proxy for river discharge from the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA’s Aqua satellite and rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) are used to study streamflow time series and precipitation patterns. The AMSR-E detected water surface signal was cross-validated with ground-based river discharge observations at multiple streamflow stations along the main Indus River. A high correlation was found; as indicated by a Pearson correlation coefficient of above 0.8 for the discharge gauge stations located in the southwest of Indus River basin. It is concluded that remote-sensing data integrated from multispectral and microwave sensors could be used to supplement stream gauges in sparsely gauged large basins to monitor and detect floods. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
Open AccessArticle Early Analysis of Landsat-8 Thermal Infrared Sensor Imagery of Volcanic Activity
Remote Sens. 2014, 6(3), 2282-2295; doi:10.3390/rs6032282
Received: 18 October 2013 / Revised: 21 February 2014 / Accepted: 10 March 2014 / Published: 12 March 2014
Cited by 7 | PDF Full-text (561 KB) | HTML Full-text | XML Full-text
Abstract
The Landsat-8 satellite of the Landsat Data Continuity Mission was launched by the National Aeronautics and Space Administration (NASA) in April 2013. Just weeks after it entered active service, its sensors observed activity at Paluweh Volcano, Indonesia. Given that the image acquired was
[...] Read more.
The Landsat-8 satellite of the Landsat Data Continuity Mission was launched by the National Aeronautics and Space Administration (NASA) in April 2013. Just weeks after it entered active service, its sensors observed activity at Paluweh Volcano, Indonesia. Given that the image acquired was in the daytime, its shortwave infrared observations were contaminated with reflected solar radiation; however, those of the satellite’s Thermal Infrared Sensor (TIRS) show thermal emission from the volcano’s summit and flanks. These emissions detected in sensor’s band 10 (10.60–11.19 µm) have here been quantified in terms of radiant power, to confirm reports of the actual volcanic processes operating at the time of image acquisition, and to form an initial assessment of the TIRS in its volcanic observation capabilities. Data from band 11 have been neglected as its data have been shown to be unreliable at the time of writing. At the instant of image acquisition, the thermal emission of the volcano was found to be 345 MW. This value is shown to be on the same order of magnitude as similarly timed NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer thermal observations. Given its unique characteristics, the TIRS shows much potential for providing useful, detailed and accurate volcanic observations in the future. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
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Open AccessArticle Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data
Remote Sens. 2014, 6(3), 2069-2083; doi:10.3390/rs6032069
Received: 12 December 2013 / Revised: 5 February 2014 / Accepted: 10 February 2014 / Published: 7 March 2014
Cited by 2 | PDF Full-text (606 KB) | HTML Full-text | XML Full-text
Abstract
This study proposed a new approach to measure the similarity between spectra to discriminate materials and evaluate the performance of parameter-selection procedures. Many pure pixel vector-based similarity measurements have been developed in the past to calculate the distance between two pixel vectors. However,
[...] Read more.
This study proposed a new approach to measure the similarity between spectra to discriminate materials and evaluate the performance of parameter-selection procedures. Many pure pixel vector-based similarity measurements have been developed in the past to calculate the distance between two pixel vectors. However, those methods may not be effective since they do not take full advantage of the spectral correlation. In this study, we adopt Ensemble Empirical Mode Decomposition (EEMD) to decompose the spectrum into serial components and employ these components to improve the performance of spectral discrimination. Performance evaluation was conducted with several commonly used measurements, and the spectral samples used for experimentation were provided by the spectral library of United States Geological Survey (USGS). The experimental results have demonstrated that EEMD can extract the spectral features more effectively than common spectral similarity measurements, and it better characterizes spectral properties. Our experimental results also suggest general rules for selecting noise standard deviation, the number of iterations for EEMD and the collection of Intrinsic Mode Functions (IMFs) for classification. Finally, since EEMD is a time-consuming algorithm, we also implement parallel processing with a Graphics Processing Unit (GPU) to increase the processing speed. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
Open AccessArticle Evaluation of InSAR and TomoSAR for Monitoring Deformations Caused by Mining in a Mountainous Area with High Resolution Satellite-Based SAR
Remote Sens. 2014, 6(2), 1476-1495; doi:10.3390/rs6021476
Received: 20 December 2013 / Revised: 8 February 2014 / Accepted: 10 February 2014 / Published: 19 February 2014
Cited by 9 | PDF Full-text (2104 KB) | HTML Full-text | XML Full-text
Abstract
Interferometric Synthetic Aperture Radar (InSAR) and Differential Interferometric Synthetic Aperture Radar (DInSAR) have shown numerous applications for subsidence monitoring. In the past 10 years, the Persistent Scatterer InSAR (PSI) and Small BAseline Subset (SBAS) approaches were developed to overcome the problem of decorrelation
[...] Read more.
Interferometric Synthetic Aperture Radar (InSAR) and Differential Interferometric Synthetic Aperture Radar (DInSAR) have shown numerous applications for subsidence monitoring. In the past 10 years, the Persistent Scatterer InSAR (PSI) and Small BAseline Subset (SBAS) approaches were developed to overcome the problem of decorrelation and atmospheric effects, which are common in interferograms. However, DInSAR or PSI applications in rural areas, especially in mountainous regions, can be extremely challenging. In this study we have employed a combined technique, i.e., SBAS-DInSAR, to a mountainous area that is severely affected by mining activities. In addition, L-band (ALOS) and C-band (ENVISAT) data sets, 21 TerraSAR-X images provided by German Aerospace Center (DLR) with a high resolution have been used. In order to evaluate the ability of TerraSAR-X for mining monitoring, we present a case study of TerraSAR-X SAR images for Subsidence Hazard Boundary (SHB) extraction. The resulting data analysis gives an initial evaluation of InSAR applications within a mountainous region where fast movements and big phase gradients are common. Moreover, the experiment of four-dimension (4-D) Tomography SAR (TomoSAR) for structure monitoring inside the mining area indicates a potential near all-wave monitoring, which is an extension of conventional InSAR. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
Open AccessArticle Validation of the Two Standard MODIS Satellite Burned-Area Products and an Empirically-Derived Merged Product in South Africa
Remote Sens. 2014, 6(2), 1275-1293; doi:10.3390/rs6021275
Received: 20 October 2013 / Revised: 18 November 2013 / Accepted: 30 December 2013 / Published: 4 February 2014
Cited by 6 | PDF Full-text (1092 KB) | HTML Full-text | XML Full-text
Abstract
The 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) burned area products, MCD45A1, MCD64A1, and a merged product were validated across six study sites in South Africa using independently-derived Landsat burned-area reference data during the fire season of 2007. The objectives of this study were
[...] Read more.
The 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) burned area products, MCD45A1, MCD64A1, and a merged product were validated across six study sites in South Africa using independently-derived Landsat burned-area reference data during the fire season of 2007. The objectives of this study were to: (i) investigate the likelihood of the improved detection of small burns through an empirically-derived merged product; (ii) quantify the probability of detection by each product using sub-pixel burned area measures; and, (iii) compare the mean percent concurrence of burned pixels between the standard products over a ten-year time series in each site. Results show that MCD45A1 presented higher detection probabilities (i.e., 3.0%–37.9%) for small fractions ≤50%, whereas MCD64A1 appeared more reliable (i.e., 12.0%–89.2%) in detecting large fractions >50% of a burned MODIS pixel, respectively. Overall, the merged product demonstrated improved detection of the burned area in all fractions. This paper also demonstrates that, on average, >50% of MODIS burned pixels temporally concur between the MCD45A1 and MCD64A1 products in each site. These findings have significant implications for fire monitoring in southern Africa and contribute toward the understanding of the range and of the sources of errors present in the MODIS burned area products. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
Open AccessArticle Impact of Tree-Oriented Growth Order in Marker-Controlled Region Growing for Individual Tree Crown Delineation Using Airborne Laser Scanner (ALS) Data
Remote Sens. 2014, 6(1), 555-579; doi:10.3390/rs6010555
Received: 18 November 2013 / Revised: 16 December 2013 / Accepted: 30 December 2013 / Published: 6 January 2014
Cited by 6 | PDF Full-text (1214 KB) | HTML Full-text | XML Full-text
Abstract
Region growing is frequently applied in automated individual tree crown delineation (ITCD) studies. Researchers have developed various rules for initial seed selection and stop criteria when applying the algorithm. However, research has rarely focused on the impact of tree-oriented growth order. This study
[...] Read more.
Region growing is frequently applied in automated individual tree crown delineation (ITCD) studies. Researchers have developed various rules for initial seed selection and stop criteria when applying the algorithm. However, research has rarely focused on the impact of tree-oriented growth order. This study implemented a marker-controlled region growing (MCRG) algorithm that considers homogeneity, crown size, and shape using airborne laser scanning (ALS) data, and investigated the impact of three growth orders (i.e., sequential, independent, and simultaneous) on tree crown delineation. The study also investigated the benefit of combining ALS data and orthoimagery in treetop detection at both plot and individual tree levels. The results showed that complementary data from the orthoimagery reduced omission error associated with small trees in the treetop detection procedure and improved treetop detection percentage on a plot level by 2%–5% compared to ALS alone. For tree crown delineation, the growth order applied in the MCRG algorithm influenced accuracy. Simultaneous growth yielded slightly higher accuracy (about 2% improvement for producer’s and user’s accuracy) than sequential growth. Independent growth provided comparable accuracy to simultaneous growth in this study by dealing with overlapping pixels among trees according to crown shape. This study provides several recommendations for applying region growing in future ITCD research. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
Open AccessArticle Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS Images
Remote Sens. 2014, 6(1), 540-554; doi:10.3390/rs6010540
Received: 30 October 2013 / Revised: 25 December 2013 / Accepted: 27 December 2013 / Published: 3 January 2014
Cited by 5 | PDF Full-text (587 KB) | HTML Full-text | XML Full-text
Abstract
Forest fires are one of the most dangerous natural hazards, especially when they are recurrent. In areas such as Galicia (Spain), forest fires are frequent and devastating. The development of fire risk models becomes a very important prevention task for these regions. Vegetation
[...] Read more.
Forest fires are one of the most dangerous natural hazards, especially when they are recurrent. In areas such as Galicia (Spain), forest fires are frequent and devastating. The development of fire risk models becomes a very important prevention task for these regions. Vegetation and moisture indices can be used to monitor vegetation status; however, the different indices may perform differently depending on the vegetation species. Eight different spectral indices were selected to determine the most appropriate index in Galicia. This study was extended to the adjacent region of Asturias. Six years of MODIS (Moderate Resolution Imaging Spectroradiometer) images, together with ground fire data in a 10 × 10 km grid basis were used. The percentage of fire events met the variations suffered by some of the spectral indices, following a linear regression in both Galicia and Asturias. The Enhanced Vegetation Index (EVI) was the index leading to the best results. Based on these results, a simple fire danger model was established, using logistic regression, by combining the EVI variation with other variables, such as fire history in each cell and period of the year. A seventy percent overall concordance was obtained between estimated and observed fire frequency. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
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Open AccessArticle Detection of Forest Clear-Cuts with Shuttle Radar Topography Mission (SRTM) and Tandem-X InSAR Data
Remote Sens. 2013, 5(11), 5449-5462; doi:10.3390/rs5115449
Received: 3 September 2013 / Revised: 17 October 2013 / Accepted: 18 October 2013 / Published: 24 October 2013
Cited by 7 | PDF Full-text (440 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this study was to determine whether forest clear-cuts during 2000–2011 could be detected as a decrease in surface height by combining Digital Surface Models (DSMs) from the Shuttle Radar Topography Mission (SRTM) and Tandem-X, and to evaluate the performance of
[...] Read more.
The aim of this study was to determine whether forest clear-cuts during 2000–2011 could be detected as a decrease in surface height by combining Digital Surface Models (DSMs) from the Shuttle Radar Topography Mission (SRTM) and Tandem-X, and to evaluate the performance of this method using SRTM X- and C-band data as references representing the heights before logging. The study area was located in a Norway spruce-dominated forest estate in southeastern Norway. We interpolated 11-year DSM changes into a 10 m × 10 m raster, and averaged these changes per forest stand. Based on threshold values for DSM decreases we classified the pixels and stands into the categories “clear-cut” and “not clear-cut”, and compared this to a complete record of logged stands during 2000–2011. The classification accuracy was moderate or fairly good. A correct detection was achieved for 59%–67% of the clear-cut stands. Omission errors were most common, occurring in 33%–42% of the stands. Commission errors were found in 13%–21% of the clear-cut stands. The results obtained for X-band SRTM were only marginally better than for C-band. In conclusion, the combination of SRTM and Tandem-X has the potential of providing near global data sets for the recent 12 years’ logging, which should be particularly valuable for deforestation mapping. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
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