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Remote Sens., Volume 4, Issue 8 (August 2012), Pages 2199-2491

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Open AccessArticle Four-Component Scattering Power Decomposition Algorithm with Rotation of Covariance Matrix Using ALOS-PALSAR Polarimetric Data
Remote Sens. 2012, 4(8), 2199-2209; doi:10.3390/rs4082199
Received: 5 June 2012 / Revised: 11 July 2012 / Accepted: 16 July 2012 / Published: 25 July 2012
Cited by 4 | PDF Full-text (1598 KB) | HTML Full-text | XML Full-text
Abstract
The present study introduces the four-component scattering power decomposition (4-CSPD) algorithm with rotation of covariance matrix, and presents an experimental proof of the equivalence between the 4-CSPD algorithms based on rotation of covariance matrix and coherency matrix. From a theoretical point of [...] Read more.
The present study introduces the four-component scattering power decomposition (4-CSPD) algorithm with rotation of covariance matrix, and presents an experimental proof of the equivalence between the 4-CSPD algorithms based on rotation of covariance matrix and coherency matrix. From a theoretical point of view, the 4-CSPD algorithms with rotation of the two matrices are identical. Although it seems obvious, no experimental evidence has yet been presented. In this paper, using polarimetric synthetic aperture radar (POLSAR) data acquired by Phased Array L-band SAR (PALSAR) on board of Advanced Land Observing Satellite (ALOS), an experimental proof is presented to show that both algorithms indeed produce identical results. Full article
(This article belongs to the Special Issue Remote Sensing by Synthetic Aperture Radar Technology)
Open AccessArticle Influence of Surface Topography on ICESat/GLAS Forest Height Estimation and Waveform Shape
Remote Sens. 2012, 4(8), 2210-2235; doi:10.3390/rs4082210
Received: 16 May 2012 / Revised: 12 July 2012 / Accepted: 18 July 2012 / Published: 26 July 2012
Cited by 30 | PDF Full-text (716 KB) | HTML Full-text | XML Full-text
Abstract
This study explores ICESat/GLAS waveform data in Thuringian Forest, a low mountain range located in central Germany. Lidar remote sensing has been proven to directly derive tree height as a key variable of forest structure. The GLAS signal is, however, very sensitive [...] Read more.
This study explores ICESat/GLAS waveform data in Thuringian Forest, a low mountain range located in central Germany. Lidar remote sensing has been proven to directly derive tree height as a key variable of forest structure. The GLAS signal is, however, very sensitive to surface topography because of the large footprint size. This study therefore focuses on forests in a mountainous area to assess the potential of GLAS data to derive terrain elevation and tree height. The work enhances the empirical knowledge about the interaction between GLAS waveform and landscape structure regarding a special temperate forest site with a complex terrain. An algorithm to retrieve tree height directly from GLA01 waveform data is proposed and compared to an approach using GLA14 Gaussian parameters. The results revealed that GLAS height estimates were accurate for areas with a slope up to 10° whereas waveforms of areas above 15° were problematic. Slopes between 10–15° have been found to be a critical crossover. Further, different waveform shape types and landscape structure classes were developed as a new possibility to explore the waveform in its whole structure. Based on the detailed analysis of some waveform examples, it could be demonstrated that the waveform shape can be regarded as a product of the complex interaction between surface and canopy structure. Consequently, there is a great variety of waveform shapes which in turn considerably hampers GLAS tree height extraction in areas with steep slopes and complex forest conditions. Full article
(This article belongs to the Special Issue Laser Scanning in Forests)
Open AccessArticle An Approach to Mapping Forest Growth Stages in Queensland, Australia through Integration of ALOS PALSAR and Landsat Sensor Data
Remote Sens. 2012, 4(8), 2236-2255; doi:10.3390/rs4082236
Received: 15 June 2012 / Revised: 24 July 2012 / Accepted: 24 July 2012 / Published: 2 August 2012
Cited by 10 | PDF Full-text (10504 KB) | HTML Full-text | XML Full-text
Abstract
Whilst extensive clearance of forests in the eastern Australian Brigalow Belt Bioregion (BBB) has occurred since European settlement, appropriate management of those that are regenerating can facilitate restoration of biomass (carbon) and biodiversity to levels typical of relatively undisturbed or remnant formations. [...] Read more.
Whilst extensive clearance of forests in the eastern Australian Brigalow Belt Bioregion (BBB) has occurred since European settlement, appropriate management of those that are regenerating can facilitate restoration of biomass (carbon) and biodiversity to levels typical of relatively undisturbed or remnant formations. However, maps of forests are different stages of regeneration are needed to facilitate restoration planning, including prevention of further re-clearing. Focusing on the Tara Downs subregion of the BBB and on forests with brigalow (Acacia harpophylla) as a component, this research establishes a method for differentiating and mapping early, intermediate and remnant growth stages from Japan Aerospace Exploration Agency (JAXA) Advanced Land Observing Satellite (ALOS) Phased-Array L-band Synthetic Aperture Radar (PALSAR) Fine Beam Dual (FBD) L-band HH- and HV-polarisation backscatter and Landsat-derived Foliage Projective Cover (FPC). Using inventory data collected from 74 plots, located in the Tara Downs subregion, forests were assigned to one of three regrowth stages based on their height and cover relative to that of undisturbed stands. The image data were then segmented into objects with each assigned to a growth stage by comparing the distributions of L-band HV and HH polarisation backscatter and FPC to that of reference distributions using a z-test. Comparison with independent assessments of growth stage, based on time-series analysis of aerial photography and SPOT images, established an overall accuracy of > 70%, with this increasing to 90% when intermediate regrowth was excluded and only early-stage regrowth and remnant classes were considered. The proposed method can be adapted to respond to amendments to user-definitions of growth stage and, as regional mosaics of ALOS PALSAR and Landsat FPC are available for Queensland, has application across the state. Full article
(This article belongs to the Special Issue Remote Sensing by Synthetic Aperture Radar Technology)
Open AccessArticle Object-Based Classification of Urban Areas Using VHR Imagery and Height Points Ancillary Data
Remote Sens. 2012, 4(8), 2256-2276; doi:10.3390/rs4082256
Received: 10 June 2012 / Revised: 20 July 2012 / Accepted: 26 July 2012 / Published: 3 August 2012
Cited by 23 | PDF Full-text (964 KB) | HTML Full-text | XML Full-text
Abstract
Land cover classification of very high resolution (VHR) imagery over urban areas is an extremely challenging task. Impervious land covers such as buildings, roads, and parking lots are spectrally too similar to be separated using only the spectral information of VHR imagery. [...] Read more.
Land cover classification of very high resolution (VHR) imagery over urban areas is an extremely challenging task. Impervious land covers such as buildings, roads, and parking lots are spectrally too similar to be separated using only the spectral information of VHR imagery. Additional information, therefore, is required for separating such land covers by the classifier. One source of additional information is the vector data, which are available in archives for many urban areas. Further, the object-based approach provides a more effective way to incorporate vector data into the classification process as the misregistration between different layers is less problematic in object-based compared to pixel-based image analysis. In this research, a hierarchical rule-based object-based classification framework was developed based on a small subset of QuickBird (QB) imagery coupled with a layer of height points called Spot Height (SH) to classify a complex urban environment. In the rule-set, different spectral, morphological, contextual, class-related, and thematic layer features were employed. To assess the general applicability of the rule-set, the same classification framework and a similar one using slightly different thresholds applied to larger subsets of QB and IKONOS (IK), respectively. Results show an overall accuracy of 92% and 86% and a Kappa coefficient of 0.88 and 0.80 for the QB and IK Test image, respectively. The average producers’ accuracies for impervious land cover types were also 82% and 74.5% for QB and IK. Full article
Open AccessArticle Advanced Spaceborne Thermal Emission and Reflection Radometer (ASTER) Enhanced Vegetation Index (EVI) Products from Global Earth Observation (GEO) Grid: An Assessment Using Moderate Resolution Imaging Spectroradiometer (MODIS) for Synergistic Applications
Remote Sens. 2012, 4(8), 2277-2293; doi:10.3390/rs4082277
Received: 10 June 2012 / Revised: 21 June 2012 / Accepted: 23 July 2012 / Published: 3 August 2012
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Abstract
We assessed the compatibility of three Advanced Spaceborne Thermal Emission and Reflection Radometer (ASTER) based Enhanced Vegetation Index (EVI) products generated in the GEO Grid system to Moderate Resolution Imaging Spectroradiometer (MODIS) EVI. The three products were two forms of the two-band [...] Read more.
We assessed the compatibility of three Advanced Spaceborne Thermal Emission and Reflection Radometer (ASTER) based Enhanced Vegetation Index (EVI) products generated in the GEO Grid system to Moderate Resolution Imaging Spectroradiometer (MODIS) EVI. The three products were two forms of the two-band EVI with ASTER red and NIR bands but without a blue band and the original, three-band EVI computed with ASTER red and NIR, and MODIS blue reflectances. Our assessment results showed good compatibilities of all the three ASTER EVI products with MODIS EVI, suggesting potential for synergistic applications of multi-resolution EVI. Full article
Open AccessArticle Spectral Difference in the Image Domain for Large Neighborhoods, a GEOBIA Pre-Processing Step for High Resolution Imagery
Remote Sens. 2012, 4(8), 2294-2313; doi:10.3390/rs4082294
Received: 19 June 2012 / Revised: 26 July 2012 / Accepted: 30 July 2012 / Published: 7 August 2012
Cited by 3 | PDF Full-text (2102 KB) | HTML Full-text | XML Full-text
Abstract
Contrast plays an important role in the visual interpretation of imagery. To mimic visual interpretation and using contrast in a Geographic Object Based Image Analysis (GEOBIA) environment, it is useful to consider an analysis for single pixel objects. This should be done [...] Read more.
Contrast plays an important role in the visual interpretation of imagery. To mimic visual interpretation and using contrast in a Geographic Object Based Image Analysis (GEOBIA) environment, it is useful to consider an analysis for single pixel objects. This should be done before applying homogeneity criteria in the aggregation of pixels for the construction of meaningful image objects. The habit or “best practice” to start GEOBIA with pixel aggregation into homogeneous objects should come with the awareness that feature attributes for single pixels are at risk of becoming less accessible for further analysis. Single pixel contrast with image convolution on close neighborhoods is a standard technique, also applied in edge detection. This study elaborates on the analysis of close as well as much larger neighborhoods inside the GEOBIA domain. The applied calculations are limited to the first segmentation step for single pixel objects in order to produce additional feature attributes for objects of interest to be generated in further aggregation processes. The equation presented functions at a level that is considered an intermediary product in the sequential processing of imagery. The procedure requires intensive processor and memory capacity. The resulting feature attributes highlight not only contrasting pixels (edges) but also contrasting areas of local pixel groups. The suggested approach can be extended and becomes useful in classifying artificial areas at national scales using high resolution satellite mosaics. Full article
(This article belongs to the Special Issue Object-Based Image Analysis)
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Open AccessArticle Polarimetric Decomposition Analysis of ALOS PALSAR Observation Data before and after a Landslide Event
Remote Sens. 2012, 4(8), 2314-2328; doi:10.3390/rs4082314
Received: 15 June 2012 / Revised: 30 July 2012 / Accepted: 30 July 2012 / Published: 7 August 2012
Cited by 18 | PDF Full-text (1239 KB) | HTML Full-text | XML Full-text
Abstract
Radar scattering mechanisms over landslide areas were studied using representative full polarimetric parameters: Freeman–Durden decomposition, and eigenvalue–eigenvector decomposition. Full polarimetric ALOS (Advanced Land Observation Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar) datasets were used to examine landslides caused by the 2008 [...] Read more.
Radar scattering mechanisms over landslide areas were studied using representative full polarimetric parameters: Freeman–Durden decomposition, and eigenvalue–eigenvector decomposition. Full polarimetric ALOS (Advanced Land Observation Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar) datasets were used to examine landslides caused by the 2008 Iwate-Miyagi Nairiku Earthquake in northern Japan. The Freeman–Durden decomposition indicates that areas affected by large-scale landslides show dominance of the surface scattering component in both ascending and descending orbit data. The polarimetric parameters of eigenvalue–eigenvector decomposition, such as entropy, anisotropy, and alpha angle, were also computed over the landslide areas. Unsupervised classification based on the H- plane explicitly distinguishes landslide areas from others such as forest, water, and snow-covered areas, but does not perform well for farmland. A landslide area is difficult to recognize from a single-polarization image, whereas it is clearly extracted on the full polarimetric data obtained after the earthquake. From these results, we conclude that 30-m resolution full polarimetric data are more useful than 10-m resolution single-polarization PALSAR data in classifying land coverage, and are better suited to detect landslide areas. Additional information, such as pre-landslide imagery, is needed to distinguish landslide areas from farmland or bare soil. Full article
(This article belongs to the Special Issue Remote Sensing by Synthetic Aperture Radar Technology)
Open AccessArticle Four Methods for LIDAR Retrieval of Microscale Wind Fields
Remote Sens. 2012, 4(8), 2329-2355; doi:10.3390/rs4082329
Received: 25 June 2012 / Revised: 25 July 2012 / Accepted: 27 July 2012 / Published: 8 August 2012
PDF Full-text (695 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper evaluates four wind retrieval methods for micro-scale meteorology applications with volume and time resolution in the order of 30m3 and 5 s. Wind field vectors are estimated using sequential time-lapse volume images of aerosol density fluctuations. Suitably designed mono-static [...] Read more.
This paper evaluates four wind retrieval methods for micro-scale meteorology applications with volume and time resolution in the order of 30m3 and 5 s. Wind field vectors are estimated using sequential time-lapse volume images of aerosol density fluctuations. Suitably designed mono-static scanning backscatter LIDAR systems, which are sensitive to atmospheric density aerosol fluctuations, are expected to be ideal for this purpose. An important application is wind farm siting and evaluation. In this case, it is necessary to look at the complicated region between the earth’s surface and the boundary layer, where wind can be turbulent and fractal scaling from millimeter to kilometer. The methods are demonstrated using first a simple randomized moving hard target, and then with a physics based stochastic space-time dynamic turbulence model. In the latter case the actual vector wind field is known, allowing complete space-time error analysis. Two of the methods, the semblance method and the spatio-temporal method, are found to be most suitable for wind field estimation. Full article
Open AccessArticle Global Navigation Satellite Systems Reflectometry as a Remote Sensing Tool for Agriculture
Remote Sens. 2012, 4(8), 2356-2372; doi:10.3390/rs4082356
Received: 16 June 2012 / Revised: 11 July 2012 / Accepted: 30 July 2012 / Published: 10 August 2012
Cited by 29 | PDF Full-text (5987 KB) | HTML Full-text | XML Full-text
Abstract
The use of Global Navigation Satellite Systems (GNSS) signals for remote sensing applications, generally referred to as GNSS-Reflectometry (GNSS-R), is gaining increasing interest among the scientific community as a remote sensing tool for land applications. This paper describes a long term experimental [...] Read more.
The use of Global Navigation Satellite Systems (GNSS) signals for remote sensing applications, generally referred to as GNSS-Reflectometry (GNSS-R), is gaining increasing interest among the scientific community as a remote sensing tool for land applications. This paper describes a long term experimental campaign in which an extensive dataset of GNSS-R polarimetric measurements was acquired over a crop field from a ground-based stationary platform. Ground truth ancillary data were also continuously recorded during the whole experimental campaign. The duration of the campaign allowed to cover a full crop growing season, and as a consequence of seasonal rains on the experimental area, data could be recorded over a wide variety of soil conditions. This enabled a study on the effects of different land bio-geophysical parameters on GNSS scattered signals. It is shown that significant power variations in the measured GNSS reflected signals can be detected for different soil moisture and vegetation development conditions. In this work we also propose a technique based on the combination of the reflected signal’s polarizations in order to improve the integrity of the observables with respect to nuisance parameters such as soil roughness. Full article
Open AccessArticle Comparative Analysis of Four Models to Estimate Chlorophyll-a Concentration in Case-2 Waters Using MODerate Resolution Imaging Spectroradiometer (MODIS) Imagery
Remote Sens. 2012, 4(8), 2373-2400; doi:10.3390/rs4082373
Received: 12 June 2012 / Revised: 27 July 2012 / Accepted: 2 August 2012 / Published: 13 August 2012
Cited by 15 | PDF Full-text (1777 KB) | HTML Full-text | XML Full-text
Abstract
The occurrence and extent of intense harmful algal blooms (HABs) have increased in inland waters during recent decades. Standard monitor networks, based on infrequent sampling from a few fixed observation stations, are not providing enough information on the extent and intensity of [...] Read more.
The occurrence and extent of intense harmful algal blooms (HABs) have increased in inland waters during recent decades. Standard monitor networks, based on infrequent sampling from a few fixed observation stations, are not providing enough information on the extent and intensity of the blooms. Remote sensing has great potential to provide the spatial and temporal coverage needed. Several sensors have been designed to study water properties (AVHRR, SeaBAM, and SeaWIFS), but most lack adequate spatial resolution for monitoring algal blooms in small and medium-sized lakes. Over the last decade, satellite data with 250-m spatial resolution have become available with MODIS. In the present study, three models inspired by published approaches (Kahru, Gitelson, and Floating Algae Index (FAI)) and a new approach named APPEL (APProach by ELimination) were adapted to the specific conditions of southern Quebec and used to estimate chlorophyll-a concentration (Chl-a) using MODIS data. Calibration and validation were provided from in situ Chl-a measured in four lakes over 9 years (2000–2008) and concurrent MODIS imagery. MODIS bands 3 to 7, originally at 500-m spatial resolution, were downscaled to 250 m. The APPEL, FAI, and Kahru models yielded satisfactory results and enabled estimation of Chl-a for heavy blooming conditions (Chl-a > 50 mg∙m−3), with coefficients of determination reaching 0.95, 0.94, and 0.93, respectively. The model inspired from Gitelson did not provide good estimations compared to the others (R2 = 0.77). However, the performance of all models decreased when Chl-a was below 50 mg∙m−3. Full article
Open AccessArticle Use of Landsat and SRTM Data to Detect Broad-Scale Biodiversity Patterns in Northwestern Amazonia
Remote Sens. 2012, 4(8), 2401-2418; doi:10.3390/rs4082401
Received: 20 June 2012 / Revised: 10 August 2012 / Accepted: 10 August 2012 / Published: 15 August 2012
Cited by 15 | PDF Full-text (682 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Vegetation maps are the starting point for the design of protected areas and regional conservation plans. Accurate vegetation maps are missing for much of Amazonia, preventing the development of effective and compelling conservation strategies. Here we used a network of 160 inventories [...] Read more.
Vegetation maps are the starting point for the design of protected areas and regional conservation plans. Accurate vegetation maps are missing for much of Amazonia, preventing the development of effective and compelling conservation strategies. Here we used a network of 160 inventories across northwestern Amazonia to evaluate the use of Landsat and Shuttle Radar Topography Mission (SRTM) data to identify floristic and edaphic patterns in Amazonian forests. We first calculated the strength of the relationship between these remotely-sensed data, and edaphic and floristic patterns in these forests, and asked how sensitive these results are to image processing and enhancement. We additionally asked if SRTM data can be used to model patterns in plant species composition in our study areas. We find that variations in Landsat and SRTM data are strongly correlated with variations in soils and plant species composition, and that these patterns can be mapped solely on the basis of SRTM data over limited areas. Using these data, we furthermore identified widespread patch-matrix floristic patterns across northwestern Amazonia, with implications for conservation planning and study. Our findings provide further evidence that Landsat and SRTM data can provide a cost-effective means for mapping these forests, and we recommend that maps generated from a combination of remotely-sensed and field data be used as the basis for conservation prioritization and planning in these vast and remote forests. Full article
(This article belongs to the Special Issue Remote Sensing of Biological Diversity)
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Open AccessArticle Evaluation of ASTER GDEM2 in Comparison with GDEM1, SRTM DEM and Topographic-Map-Derived DEM Using Inundation Area Analysis and RTK-dGPS Data
Remote Sens. 2012, 4(8), 2419-2431; doi:10.3390/rs4082419
Received: 20 June 2012 / Revised: 25 July 2012 / Accepted: 2 August 2012 / Published: 15 August 2012
Cited by 24 | PDF Full-text (1097 KB) | HTML Full-text | XML Full-text
Abstract
This study evaluates the quality of the Advanced Spaceborne Thermal Emission Radiometer-Global Digital Elevation Model version 2 (ASTER GDEM2) in comparison with the previous version (GDEM1) as well as the Shuttle Radar Topographic Mission (SRTM) DEM and topographic-map-derived DEM (Topo-DEM) using inundation [...] Read more.
This study evaluates the quality of the Advanced Spaceborne Thermal Emission Radiometer-Global Digital Elevation Model version 2 (ASTER GDEM2) in comparison with the previous version (GDEM1) as well as the Shuttle Radar Topographic Mission (SRTM) DEM and topographic-map-derived DEM (Topo-DEM) using inundation area analysis for the projected location of the Karian dam, Indonesia. In addition, the vertical accuracy of each DEM is evaluated using the Real-Time Kinematic differential Global Positioning Systems (RTK-dGPS) data obtained from an intensive geodetic survey. The results of the inundation area analysis show that GDEM2 produced a higher maximum contour level (MCL) (64 m) than did GDEM1 (55 m), and thus, GDME2 has a better quality. In addition, the GDEM2-derived MCL is similar to those produced by SRTM DEM (69 m) and Topo-DEM (62 m). The improvement in the contour level in GDEM2 is believed to be related to the successful removal of voids (artifacts) and anomalies present in GDEM1. However, our RTK-dGPS results show that the vertical accuracy of GDEM2 is much lower than that of GDEM1 and the other DEMs, which is contradictory to the accuracy stated in the GDEM2 validation document. The vertical profiles of all DEMs show that GDEM2 contains a comparatively large number of undulation effects, thereby resulting in higher root mean square error (RMSE) values. These undulation effects may have been introduced during the GDEM2 validation process. Although the results of this study may be site-specific, it is important that they be considered for the improvement of the next GDEM version. Full article
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Open AccessArticle European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products
Remote Sens. 2012, 4(8), 2432-2454; doi:10.3390/rs4082432
Received: 30 June 2012 / Revised: 7 August 2012 / Accepted: 9 August 2012 / Published: 15 August 2012
Cited by 14 | PDF Full-text (3251 KB) | HTML Full-text | XML Full-text
Abstract
Mean snow cover duration was derived for the entire continent of Europe based on the MODIS daily snow cover products MOD10A1 and MYD10A1 for the period from 2000 to 2011. Dates of snow cover start and snow cover melt were also estimated. [...] Read more.
Mean snow cover duration was derived for the entire continent of Europe based on the MODIS daily snow cover products MOD10A1 and MYD10A1 for the period from 2000 to 2011. Dates of snow cover start and snow cover melt were also estimated. Polar darkness north of ~62°N and extensive cloud coverage affected the daily snow cover, preventing a direct derivation of the desired parameters. Combining sensor data from both MODIS platforms and applying a temporal cloud filter, cloud coverage and polar darkness were removed from the input data and accuracy remained above 90% for 87% of the area. The typical snow cover characteristics of the whole continent are illustrated and constitute a unique dataset with respect to spatial and temporal resolution. Abnormal events, glacier inventories or studies on possible impacts of climate change on snow cover characteristics are only some examples for applications where the presented results may be utilized. Full article
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Open AccessArticle Semi-Supervised Methods to Identify Individual Crowns of Lowland Tropical Canopy Species Using Imaging Spectroscopy and LiDAR
Remote Sens. 2012, 4(8), 2457-2476; doi:10.3390/rs4082457
Received: 24 June 2012 / Revised: 3 August 2012 / Accepted: 13 August 2012 / Published: 20 August 2012
Cited by 22 | PDF Full-text (1212 KB) | HTML Full-text | XML Full-text
Abstract
Our objective is to identify and map individuals of nine tree species in a Hawaiian lowland tropical forest by comparing the performance of a variety of semi-supervised classifiers. A method was adapted to process hyperspectral imagery, LiDAR intensity variables, and LiDAR-derived canopy [...] Read more.
Our objective is to identify and map individuals of nine tree species in a Hawaiian lowland tropical forest by comparing the performance of a variety of semi-supervised classifiers. A method was adapted to process hyperspectral imagery, LiDAR intensity variables, and LiDAR-derived canopy height and use them to assess the identification accuracy. We found that semi-supervised Support Vector Machine classification using tensor summation kernel was superior to supervised classification, with demonstrable accuracy for at least eight out of nine species, and for all combinations of data types tested. We also found that the combination of hyperspectral imagery and LiDAR data usually improved species classification. Both LiDAR intensity and LiDAR canopy height proved useful for classification of certain species, but the improvements varied depending upon the species in question. Our results pave the way for target-species identification in tropical forests and other ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Biological Diversity)
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Open AccessArticle Simulation of Image Performance Characteristics of the Landsat Data Continuity Mission (LDCM) Thermal Infrared Sensor (TIRS)
Remote Sens. 2012, 4(8), 2477-2491; doi:10.3390/rs4082477
Received: 6 July 2012 / Revised: 15 August 2012 / Accepted: 16 August 2012 / Published: 22 August 2012
Cited by 9 | PDF Full-text (910 KB) | HTML Full-text | XML Full-text
Abstract
The next Landsat satellite, which is scheduled for launch in early 2013, will carry two instruments: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). Significant design changes over previous Landsat instruments have been made to these sensors to potentially [...] Read more.
The next Landsat satellite, which is scheduled for launch in early 2013, will carry two instruments: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). Significant design changes over previous Landsat instruments have been made to these sensors to potentially enhance the quality of Landsat image data. TIRS, which is the focus of this study, is a dual-band instrument that uses a push-broom style architecture to collect data. To help understand the impact of design trades during instrument build, an effort was initiated to model TIRS imagery. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool was used to produce synthetic “on-orbit” TIRS data with detailed radiometric, geometric, and digital image characteristics. This work presents several studies that used DIRSIG simulated TIRS data to test the impact of engineering performance data on image quality in an effort to determine if the image data meet specifications or, in the event that they do not, to determine if the resulting image data are still acceptable. Full article

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Open AccessCorrection Correction on “Towards Detecting Swath Events in TerraSAR-X Time Series to Establish NATURA 2000 Grassland Habitat Swath Management as Monitoring Parameter”
Remote Sens. 2012, 4(8), 2455-2456; doi:10.3390/rs4082455
Received: 28 June 2012 / Accepted: 28 June 2012 / Published: 15 August 2012
Cited by 1 | PDF Full-text (110 KB) | HTML Full-text | XML Full-text
Abstract
We found a mistake in the swath detection rule in Section 2.4 [1]. Specifically, the percent deviation calculation in the definition of the signal changes D1 and D2 and axiom A2 are altered. The correct version shall be: Consequently, the [...] Read more.
We found a mistake in the swath detection rule in Section 2.4 [1]. Specifically, the percent deviation calculation in the definition of the signal changes D1 and D2 and axiom A2 are altered. The correct version shall be: Consequently, the proposed rule for the detection of swath events consists of two axioms (A1 and A2) that need to be satisfied. For the signal backscatter (σ°) at a specific acquisition order number (k) of the acquired scene in the time series (N), the positive or negative signal changes in percent deviation for the first (D1) and second (D2) acquisition after a potential swath event are considered as: [...] Full article
(This article belongs to the Special Issue Remote Sensing in Support of Environmental Policy)

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