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Remote Sens., Volume 6, Issue 1 (January 2014) – 41 articles , Pages 1-906

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Open AccessEditorial
Remote Sensing Best Paper Award for the Year 2014
Remote Sens. 2014, 6(1), 905-906; https://doi.org/10.3390/rs6010905 - 22 Jan 2014
Viewed by 7318
Abstract
Remote Sensing has started to institute a “Best Paper” award to recognize the most outstanding papers in the area of remote sensing techniques, design and applications published in Remote Sensing. We are pleased to announce the first “Remote Sensing Best Paper [...] Read more.
Remote Sensing has started to institute a “Best Paper” award to recognize the most outstanding papers in the area of remote sensing techniques, design and applications published in Remote Sensing. We are pleased to announce the first “Remote Sensing Best Paper Award” for the year 2014. [...] Full article
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Open AccessArticle
Validation and Application of the Modified Satellite-Based Priestley-Taylor Algorithm for Mapping Terrestrial Evapotranspiration
Remote Sens. 2014, 6(1), 880-904; https://doi.org/10.3390/rs6010880 - 17 Jan 2014
Cited by 25 | Viewed by 5008
Abstract
Satellite-based vegetation indices (VIs) and Apparent Thermal Inertia (ATI) derived from temperature change provide valuable information for estimating evapotranspiration (LE) and detecting the onset and severity of drought. The modified satellite-based Priestley-Taylor (MS-PT) algorithm that we developed earlier, coupling both VI and ATI, [...] Read more.
Satellite-based vegetation indices (VIs) and Apparent Thermal Inertia (ATI) derived from temperature change provide valuable information for estimating evapotranspiration (LE) and detecting the onset and severity of drought. The modified satellite-based Priestley-Taylor (MS-PT) algorithm that we developed earlier, coupling both VI and ATI, is validated based on observed data from 40 flux towers distributed across the world on all continents. The validation results illustrate that the daily LE can be estimated with the Root Mean Square Error (RMSE) varying from 10.7 W/m2 to 87.6 W/m2, and with the square of correlation coefficient (R2) from 0.41 to 0.89 (p < 0.01). Compared with the Priestley-Taylor-based LE (PT-JPL) algorithm, the MS-PT algorithm improves the LE estimates at most flux tower sites. Importantly, the MS-PT algorithm is also satisfactory in reproducing the inter-annual variability at flux tower sites with at least five years of data. The R2 between measured and predicted annual LE anomalies is 0.42 (p = 0.02). The MS-PT algorithm is then applied to detect the variations of long-term terrestrial LE over Three-North Shelter Forest Region of China and to monitor global land surface drought. The MS-PT algorithm described here demonstrates the ability to map regional terrestrial LE and identify global soil moisture stress, without requiring precipitation information. Full article
Open AccessArticle
Continuous Extraction of Subway Tunnel Cross Sections Based on Terrestrial Point Clouds
Remote Sens. 2014, 6(1), 857-879; https://doi.org/10.3390/rs6010857 - 15 Jan 2014
Cited by 32 | Viewed by 3965
Abstract
An efficient method for the continuous extraction of subway tunnel cross sections using terrestrial point clouds is proposed. First, the continuous central axis of the tunnel is extracted using a 2D projection of the point cloud and curve fitting using the RANSAC (RANdom [...] Read more.
An efficient method for the continuous extraction of subway tunnel cross sections using terrestrial point clouds is proposed. First, the continuous central axis of the tunnel is extracted using a 2D projection of the point cloud and curve fitting using the RANSAC (RANdom SAmple Consensus) algorithm, and the axis is optimized using a global extraction strategy based on segment-wise fitting. The cross-sectional planes, which are orthogonal to the central axis, are then determined for every interval. The cross-sectional points are extracted by intersecting straight lines that rotate orthogonally around the central axis within the cross-sectional plane with the tunnel point cloud. An interpolation algorithm based on quadric parametric surface fitting, using the BaySAC (Bayesian SAmpling Consensus) algorithm, is proposed to compute the cross-sectional point when it cannot be acquired directly from the tunnel points along the extraction direction of interest. Because the standard shape of the tunnel cross section is a circle, circle fitting is implemented using RANSAC to reduce the noise. The proposed approach is tested on terrestrial point clouds that cover a 150-m-long segment of a Shanghai subway tunnel, which were acquired using a LMS VZ-400 laser scanner. The results indicate that the proposed quadric parametric surface fitting using the optimized BaySAC achieves a higher overall fitting accuracy (0.9 mm) than the accuracy (1.6 mm) obtained by the plain RANSAC. The results also show that the proposed cross section extraction algorithm can achieve high accuracy (millimeter level, which was assessed by comparing the fitted radii with the designed radius of the cross section and comparing corresponding chord lengths in different cross sections) and high efficiency (less than 3 s/section on average). Full article
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Open AccessArticle
The Inylchek Glacier in Kyrgyzstan, Central Asia: Insight on Surface Kinematics from Optical Remote Sensing Imagery
Remote Sens. 2014, 6(1), 841-856; https://doi.org/10.3390/rs6010841 - 14 Jan 2014
Cited by 13 | Viewed by 4098
Abstract
Mountain chains of Central Asia host a large number of glaciated areas that provide critical water supplies to the semi-arid populated foothills and lowlands of this region. Spatio-temporal variations of glacier flows are a key indicator of the impact of climate change on [...] Read more.
Mountain chains of Central Asia host a large number of glaciated areas that provide critical water supplies to the semi-arid populated foothills and lowlands of this region. Spatio-temporal variations of glacier flows are a key indicator of the impact of climate change on water resources as the glaciers react sensitively to climate. Satellite remote sensing using optical imagery is an efficient method for studying ice-velocity fields on mountain glaciers. In this study, temporal and spatial changes in surface velocity associated with the Inylchek glacier in Kyrgyzstan are investigated. We present a detailed map for the kinematics of the Inylchek glacier obtained by cross-correlation analysis of Landsat images, acquired between 2000 and 2011, and a set of ASTER images covering the time period between 2001 and 2007. Our results indicate a high-velocity region in the elevated part of the glacier, moving up to a rate of about 0.5 m/day. Time series analysis of optical data reveals some annual variations in the mean surface velocity of the Inylchek during 2000–2011. In particular, our findings suggest an opposite trend between periods of the northward glacial flow in Proletarskyi and Zvezdochka glacier, and the rate of westward motion observed for the main stream of the Inylchek. Full article
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Open AccessArticle
Burned Area Mapping in the North American Boreal Forest Using Terra-MODIS LTDR (2001–2011): A Comparison with the MCD45A1, MCD64A1 and BA GEOLAND-2 Products
Remote Sens. 2014, 6(1), 815-840; https://doi.org/10.3390/rs6010815 - 13 Jan 2014
Cited by 22 | Viewed by 3967
Abstract
An algorithm based on a Bayesian network classifier was adapted to produce 10-day burned area (BA) maps from the Long Term Data Record Version 3 (LTDR) at a spatial resolution of 0.05° (~5 km) for the North American boreal region from 2001 to [...] Read more.
An algorithm based on a Bayesian network classifier was adapted to produce 10-day burned area (BA) maps from the Long Term Data Record Version 3 (LTDR) at a spatial resolution of 0.05° (~5 km) for the North American boreal region from 2001 to 2011. The modified algorithm used the Brightness Temperature channel from the Moderate Resolution Imaging Spectroradiometer (MODIS) band 31 T31 (11.03 μm) instead of the Advanced Very High Resolution Radiometer (AVHRR) band T3 (3.75 μm). The accuracy of the BA-LTDR, the Collection 5.1 MODIS Burned Area (MCD45A1), the MODIS Collection 5.1 Direct Broadcast Monthly Burned Area (MCD64A1) and the Burned Area GEOLAND-2 (BA GEOLAND-2) products was assessed using reference data from the Alaska Fire Service (AFS) and the Canadian Forest Service National Fire Database (CFSNFD). The linear regression analysis of the burned area percentages of the MCD64A1 product using 40 km × 40 km grids versus the reference data for the years from 2001 to 2011 showed an agreement of R2 = 0.84 and a slope = 0.76, while the BA-LTDR showed an agreement of R2 = 0.75 and a slope = 0.69. These results represent an improvement over the MCD45A1 product, which showed an agreement of R2 = 0.67 and a slope = 0.42. The MCD64A1, BA-LTDR and MCD45A1 products underestimated the total burned area in the study region, whereas the BA GEOLAND-2 product overestimated it by approximately five-fold, with an agreement of R2 = 0.05. Despite MCD64A1 showing the best overall results, the BA-LTDR product proved to be an alternative for mapping burned areas in the North American boreal forest region compared with the other global BA products, even those with higher spatial/spectral resolution. Full article
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Open AccessArticle
A New Spaceborne Burst Synthetic Aperture Radar Imaging Mode for Wide Swath Coverage
Remote Sens. 2014, 6(1), 801-814; https://doi.org/10.3390/rs6010801 - 13 Jan 2014
Cited by 3 | Viewed by 3607
Abstract
This paper presents a new spaceborne synthetic aperture radar (SAR) burst mode named “Extended Terrain Observation by Progressive Scans (ETOPS)” for wide swath imaged coverage. This scheme extends the imaging performance of the conventional Terrain Observation by Progressive Scans (TOPS) mode with a [...] Read more.
This paper presents a new spaceborne synthetic aperture radar (SAR) burst mode named “Extended Terrain Observation by Progressive Scans (ETOPS)” for wide swath imaged coverage. This scheme extends the imaging performance of the conventional Terrain Observation by Progressive Scans (TOPS) mode with a very limited azimuth beam steering capability. Compared with the TOPS mode with the same azimuth beam steering range for the same swath width, a finer azimuth resolution could be obtained. With the same system parameters, examples of four burst SAR imaging modes named ScanSAR, TOPS, inverse TOPS (ITOPS) and ETOPS are given, and their corresponding system performances are analyzed and compared. Simulation results show that the proposed ETOPS mode could obtain a better high-resolution wide-swath imaging performance under the same conditions. Full article
Open AccessArticle
Cloud and Cloud-Shadow Detection in SPOT5 HRG Imagery with Automated Morphological Feature Extraction
Remote Sens. 2014, 6(1), 776-800; https://doi.org/10.3390/rs6010776 - 10 Jan 2014
Cited by 47 | Viewed by 4919
Abstract
Detecting clouds in satellite imagery is becoming more important with increasing data availability, however many earth observation sensors are not designed for this task. In Satellite pour l’Observation de la Terre 5 (SPOT5) High Resolution Geometrical (HRG) imagery, the reflectance properties of clouds [...] Read more.
Detecting clouds in satellite imagery is becoming more important with increasing data availability, however many earth observation sensors are not designed for this task. In Satellite pour l’Observation de la Terre 5 (SPOT5) High Resolution Geometrical (HRG) imagery, the reflectance properties of clouds are very similar to common features on the earth’s surface, in the four available bands (green, red, near-infrared and shortwave-infrared). The method presented here, called SPOTCASM (SPOT cloud and shadow masking), deals with this problem by using a series of novel image processing steps, and is the first cloud masking method to be developed specifically for SPOT5 HRG imagery. It firstly detects marker pixels using image specific threshold values, and secondly grows segments from these markers using the watershed-from-markers transform. The threshold values are defined as lines in a 2-dimensional histogram of the image surface reflectance values, calculated from two bands. Sun and satellite angles, and the similarity between the area of cloud and shadow objects are used to test their validity. SPOTCASM was tested on an archive of 313 cloudy images from across New South Wales (NSW), Australia, with 95% of images having an overall accuracy greater than 85%. Commission errors due to false clouds (such as highly reflective ground), and false shadows (such as a dark water body) can be high, as can omission errors due to thin cloud that is very similar to the underlying ground surface. These errors can be quickly reduced through manual editing, which is the current method being employed in the operational environment in which SPOTCASM is implemented. The method is being used to mask clouds and shadows from an expanding archive of imagery across NSW, facilitating environmental change detection. Full article
Open AccessArticle
Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa
Remote Sens. 2014, 6(1), 756-775; https://doi.org/10.3390/rs6010756 - 09 Jan 2014
Cited by 33 | Viewed by 5091
Abstract
Detecting and monitoring forest degradation in the tropics has implications for various fields of interest (biodiversity, emission calculations, self-sustenance of indigenous communities, timber exploitation). However, remote-sensing-based detection of forest degradation is difficult, as these subtle degradation signals are not easy to detect in [...] Read more.
Detecting and monitoring forest degradation in the tropics has implications for various fields of interest (biodiversity, emission calculations, self-sustenance of indigenous communities, timber exploitation). However, remote-sensing-based detection of forest degradation is difficult, as these subtle degradation signals are not easy to detect in the first place and quickly lost over time due to fast re-vegetation. To overcome these shortcomings, a time series analysis has been developed to map and monitor forest degradation over a longer period of time, with frequent updates based on Landsat data. This time series approach helps to reduce both the commission and the omission errors compared to, e.g., bi- or tri-temporal assessments. The approach involves a series of pre-processing steps, such as geometric and radiometric adjustments, followed by spectral mixture analysis and classification of spectral curves. The resulting pixel-based classification is then aggregated to degradation areas. The method was developed on a study site in Cameroon and applied to a second site in Central African Republic. For both areas, the results were finally evaluated against visual interpretation of very high-resolution optical imagery. Results show overall accuracies in both study sites above 85% for mapping degradation areas with the presented methods. Full article
Open AccessArticle
Radar-to-Radar Interference Suppression for Distributed Radar Sensor Networks
Remote Sens. 2014, 6(1), 740-755; https://doi.org/10.3390/rs6010740 - 09 Jan 2014
Cited by 7 | Viewed by 3151
Abstract
Radar sensor networks, including bi- and multi-static radars, provide several operational advantages, like reduced vulnerability, good system flexibility and an increased radar cross-section. However, radar-to-radar interference suppression is a major problem in distributed radar sensor networks. In this paper, we present a cross-matched [...] Read more.
Radar sensor networks, including bi- and multi-static radars, provide several operational advantages, like reduced vulnerability, good system flexibility and an increased radar cross-section. However, radar-to-radar interference suppression is a major problem in distributed radar sensor networks. In this paper, we present a cross-matched filtering-based radar-to-radar interference suppression algorithm. This algorithm first uses an iterative filtering algorithm to suppress the radar-to-radar interferences and, then, separately matched filtering for each radar. Besides the detailed algorithm derivation, extensive numerical simulation examples are performed with the down-chirp and up-chirp waveforms, partially overlapped or inverse chirp rate linearly frequency modulation (LFM) waveforms and orthogonal frequency division multiplexing (ODFM) chirp diverse waveforms. The effectiveness of the algorithm is verified by the simulation results. Full article
Open AccessArticle
Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring
Remote Sens. 2014, 6(1), 716-739; https://doi.org/10.3390/rs6010716 - 09 Jan 2014
Cited by 28 | Viewed by 4209
Abstract
Peatlands are important terrestrial carbon stores. Restoration of degraded peatlands to restore ecosystem services is a major area of conservation effort. Monitoring is crucial to judge the success of this restoration. Remote sensing is a potential tool to provide landscape-scale information on the [...] Read more.
Peatlands are important terrestrial carbon stores. Restoration of degraded peatlands to restore ecosystem services is a major area of conservation effort. Monitoring is crucial to judge the success of this restoration. Remote sensing is a potential tool to provide landscape-scale information on the habitat condition. Using an empirical modelling approach, this paper aims to use airborne hyperspectral image data with ground vegetation survey data to model vegetation abundance for a degraded upland blanket bog in the United Kingdom (UK), which is undergoing restoration. A predictive model for vegetation abundance of Plant Functional Types (PFT) was produced using a Partial Least Squares Regression (PLSR) and applied to the whole restoration site. A sensitivity test on the relationships between spectral data and vegetation abundance at PFT and single species level confirmed that PFT was the correct scale for analysis. The PLSR modelling allows selection of variables based upon the weighted regression coefficient of the individual spectral bands, showing which bands have the most influence on the model. These results suggest that the SWIR has less value for monitoring peatland vegetation from hyperspectral images than initially predicted. RMSE values for the validation data range between 10% and 16% cover, indicating that the models can be used as an operational tool, considering the subjective nature of existing vegetation survey results. These predicted coverage images are the first quantitative landscape scale monitoring results to be produced for the site. High resolution hyperspectral mapping of PFTs has the potential to assess recovery of peatland systems at landscape scale for the first time. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands I)
Open AccessArticle
Airborne Dual-Wavelength LiDAR Data for Classifying Land Cover
Remote Sens. 2014, 6(1), 700-715; https://doi.org/10.3390/rs6010700 - 08 Jan 2014
Cited by 31 | Viewed by 4322
Abstract
This study demonstrated the potential of using dual-wavelength airborne light detection and ranging (LiDAR) data to classify land cover. Dual-wavelength LiDAR data were acquired from two airborne LiDAR systems that emitted pulses of light in near-infrared (NIR) and middle-infrared (MIR) lasers. The major [...] Read more.
This study demonstrated the potential of using dual-wavelength airborne light detection and ranging (LiDAR) data to classify land cover. Dual-wavelength LiDAR data were acquired from two airborne LiDAR systems that emitted pulses of light in near-infrared (NIR) and middle-infrared (MIR) lasers. The major features of the LiDAR data, such as surface height, echo width, and dual-wavelength amplitude, were used to represent the characteristics of land cover. Based on the major features of land cover, a support vector machine was used to classify six types of suburban land cover: road and gravel, bare soil, low vegetation, high vegetation, roofs, and water bodies. Results show that using dual-wavelength LiDAR-derived information (e.g., amplitudes at NIR and MIR wavelengths) could compensate for the limitations of using single-wavelength LiDAR information (i.e., poor discrimination of low vegetation) when classifying land cover. Full article
Open AccessArticle
An Object Model for Integrating Diverse Remote Sensing Satellite Sensors: A Case Study of Union Operation
Remote Sens. 2014, 6(1), 677-699; https://doi.org/10.3390/rs6010677 - 07 Jan 2014
Cited by 8 | Viewed by 3973
Abstract
In the Earth Observation sensor web environment, the rapid, accurate, and unified discovery of diverse remote sensing satellite sensors, and their association to yield an integrated solution for a comprehensive response to specific emergency tasks pose considerable challenges. In this study, we propose [...] Read more.
In the Earth Observation sensor web environment, the rapid, accurate, and unified discovery of diverse remote sensing satellite sensors, and their association to yield an integrated solution for a comprehensive response to specific emergency tasks pose considerable challenges. In this study, we propose a remote sensing satellite sensor object model, based on the object-oriented paradigm and the Open Geospatial Consortium Sensor Model Language. The proposed model comprises a set of sensor resource objects. Each object consists of identification, state of resource attribute, and resource method. We implement the proposed attribute state description by applying it to different remote sensors. A real application, involving the observation of floods at the Yangtze River in China, is undertaken. Results indicate that the sensor inquirer can accurately discover qualified satellite sensors in an accurate and unified manner. By implementing the proposed union operation among the retrieved sensors, the inquirer can further determine how the selected sensors can collaboratively complete a specific observation requirement. Therefore, the proposed model provides a reliable foundation for sharing and integrating multiple remote sensing satellite sensors and their observations. Full article
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Open AccessArticle
Land Cover Change Monitoring Using Landsat MSS/TM Satellite Image Data over West Africa between 1975 and 1990
Remote Sens. 2014, 6(1), 658-676; https://doi.org/10.3390/rs6010658 - 07 Jan 2014
Cited by 64 | Viewed by 6144
Abstract
Monitoring land cover changes from the 1970s in West Africa is important for assessing the dynamics between land cover types and understanding the anthropogenic impact during this period. Given the lack of historical land cover maps over such a large area, Landsat data [...] Read more.
Monitoring land cover changes from the 1970s in West Africa is important for assessing the dynamics between land cover types and understanding the anthropogenic impact during this period. Given the lack of historical land cover maps over such a large area, Landsat data is a reliable and consistent source of information on land cover dynamics from the 1970s. This study examines land cover changes occurring between 1975 and 1990 in West Africa using a systematic sample of satellite imagery. The primary data sources for the land cover classification were Landsat Multispectral Scanner (MSS) for 1975 and Landsat Thematic Mapper (TM) for the 1990 period. Dedicated selection of the appropriate image data for land cover change monitoring was performed for the year 1975. Based on this selected dataset, the land cover analysis is based on a systematic sample of 220 suitable Landsat image extracts (out of 246) of 20 km × 20 km at each one degree latitude/longitude intersection. Object-based classification, originally dedicated for Landsat TM land cover change monitoring and adapted for MSS, was used to produce land cover change information for four different land cover classes: dense tree cover, tree cover mosaic, other wooded land and other vegetation cover. Our results reveal that in 1975 about 6% of West Africa was covered by dense tree cover complemented with 12% of tree cover mosaic. Almost half of the area was covered by other wooded land and the remaining 32% was represented by other vegetation cover. Over the 1975–1990 period, the net annual change rate of dense tree cover was estimated at −0.95%, at −0.37% for the other wooded land and very low for tree cover mosaic (−0.05%). On the other side, other vegetation cover increased annually by 0.70%, most probably due to the expansion of agricultural areas. This study demonstrates the potential of Landsat MSS and TM data for large scale land cover change assessment in West Africa and highlights the importance of consistent and systematic data processing methods with targeted image acquisition procedures for long-term monitoring. Full article
Open AccessArticle
Super-Resolution Reconstruction for Multi-Angle Remote Sensing Images Considering Resolution Differences
Remote Sens. 2014, 6(1), 637-657; https://doi.org/10.3390/rs6010637 - 06 Jan 2014
Cited by 47 | Viewed by 4496
Abstract
Multi-angle remote sensing images are acquired over the same imaging scene from different angles, and share similar but not identical information. It is therefore possible to enhance the spatial resolution of the multi-angle remote sensing images by the super-resolution reconstruction technique. However, different [...] Read more.
Multi-angle remote sensing images are acquired over the same imaging scene from different angles, and share similar but not identical information. It is therefore possible to enhance the spatial resolution of the multi-angle remote sensing images by the super-resolution reconstruction technique. However, different sensor shooting angles lead to different resolutions for each angle image, which affects the effectiveness of the super-resolution reconstruction of the multi-angle images. In view of this, we propose utilizing adaptive weighted super-resolution reconstruction to alleviate the limitations of the different resolutions. This paper employs two adaptive weighting themes. The first approach uses the angle between the imaging angle of the current image and that of the nadir image. The second is closely related to the residual error of each low-resolution angle image. The experimental results confirm the feasibility of the proposed method and demonstrate the effectiveness of the proposed adaptive weighted super-resolution approach. Full article
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Open AccessArticle
Temporal Behavior of Lake Size-Distribution in a Thawing Permafrost Landscape in Northwestern Siberia
Remote Sens. 2014, 6(1), 621-636; https://doi.org/10.3390/rs6010621 - 06 Jan 2014
Cited by 41 | Viewed by 4232
Abstract
Arctic warming alters regional hydrological systems, as permafrost thaw increases active layer thickness and in turn alters the pathways of water flow through the landscape. Further, permafrost thaw may change the connectivity between deeper and shallower groundwater and surface water altering the terrestrial [...] Read more.
Arctic warming alters regional hydrological systems, as permafrost thaw increases active layer thickness and in turn alters the pathways of water flow through the landscape. Further, permafrost thaw may change the connectivity between deeper and shallower groundwater and surface water altering the terrestrial water balance and distribution. Thermokarst lakes and wetlands in the Arctic offer a window into such changes as these landscape elements depend on permafrost and are some of the most dynamic and widespread features in Arctic lowland regions. In this study we used Landsat remotely sensed imagery to investigate potential shifts in thermokarst lake size-distributions, which may be brought about by permafrost thaw, over three distinct time periods (1973, 1987–1988, and 2007–2009) in three hydrological basins in northwestern Siberia. Results revealed fluctuations in total area and number of lakes over time, with both appearing and disappearing lakes alongside stable lakes. On the whole basin scales, there is no indication of any sustained long-term change in thermokarst lake area or lake size abundance over time. This statistical temporal consistency indicates that spatially variable change effects on local permafrost conditions have driven the individual lake changes that have indeed occurred over time. The results highlight the importance of using multi-temporal remote sensing data that can reveal complex spatiotemporal variations distinguishing fluctuations from sustained change trends, for accurate interpretation of thermokarst lake changes and their possible drivers in periods of climate and permafrost change. Full article
(This article belongs to the Special Issue Hydrological Remote Sensing)
Open AccessArticle
Melt Patterns and Dynamics in Alaska and Patagonia Derived from Passive Microwave Brightness Temperatures
Remote Sens. 2014, 6(1), 603-620; https://doi.org/10.3390/rs6010603 - 06 Jan 2014
Cited by 2 | Viewed by 3173
Abstract
Glaciers and icefields are critical components of Earth’s cryosphere to study and monitor for understanding the effects of a changing climate. To provide a regional perspective of glacier melt dynamics for the past several decades, brightness temperatures (Tb) from the passive [...] Read more.
Glaciers and icefields are critical components of Earth’s cryosphere to study and monitor for understanding the effects of a changing climate. To provide a regional perspective of glacier melt dynamics for the past several decades, brightness temperatures (Tb) from the passive microwave sensor Special Sensor Microwave Imager (SSM/I) were used to characterize melt regime patterns over large glacierized areas in Alaska and Patagonia. The distinctness of the melt signal at 37V-GHz and the ability to acquire daily data regardless of clouds or darkness make the dataset ideal for studying melt dynamics in both hemispheres. A 24-year (1988–2011) time series of annual Tb histograms was constructed to (1) characterize and assess temporal and spatial trends in melt patterns, (2) determine years of anomalous Tb distribution, and (3) investigate potential contributing factors. Distance from coast and temperature were key factors influencing melt. Years of high percentage of positive Tb anomalies were associated with relatively higher stream discharge (e.g., Copper and Mendenhall Rivers, Alaska, USA and Rio Baker, Chile). The characterization of melt over broad spatial domains and a multi-decadal time period offers a more comprehensive picture of the changing cryosphere and provides a baseline from which to assess future change. Full article
(This article belongs to the Special Issue Cryospheric Remote Sensing)
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Open AccessArticle
High Spatial Resolution WorldView-2 Imagery for Mapping NDVI and Its Relationship to Temporal Urban Landscape Evapotranspiration Factors
Remote Sens. 2014, 6(1), 580-602; https://doi.org/10.3390/rs6010580 - 06 Jan 2014
Cited by 62 | Viewed by 8994
Abstract
Evapotranspiration estimation has benefitted from recent advances in remote sensing and GIS techniques particularly in agricultural applications rather than urban environments. This paper explores the relationship between urban vegetation evapotranspiration (ET) and vegetation indices derived from newly-developed high spatial resolution WorldView-2 imagery. The [...] Read more.
Evapotranspiration estimation has benefitted from recent advances in remote sensing and GIS techniques particularly in agricultural applications rather than urban environments. This paper explores the relationship between urban vegetation evapotranspiration (ET) and vegetation indices derived from newly-developed high spatial resolution WorldView-2 imagery. The study site was Veale Gardens in Adelaide, Australia. Image processing was applied on five images captured from February 2012 to February 2013 using ERDAS Imagine. From 64 possible two band combinations of WorldView-2, the most reliable one (with the maximum median differences) was selected. Normalized Difference Vegetation Index (NDVI) values were derived for each category of landscape cover, namely trees, shrubs, turf grasses, impervious pavements, and water bodies. Urban landscape evapotranspiration rates for Veale Gardens were estimated through field monitoring using observational-based landscape coefficients. The relationships between remotely sensed NDVIs for the entire Veale Gardens and for individual NDVIs of different vegetation covers were compared with field measured urban landscape evapotranspiration rates. The water stress conditions experienced in January 2013 decreased the correlation between ET and NDVI with the highest relationship of ET-Landscape NDVI (Landscape Normalized Difference Vegetation Index) for shrubs (r2 = 0.66) and trees (r2 = 0.63). However, when the January data was excluded, there was a significant correlation between ET and NDVI. The highest correlation for ET-Landscape NDVI was found for the entire Veale Gardens regardless of vegetation type (r2 = 0.95, p > 0.05) and the lowest one was for turf (r2 = 0.88, p > 0.05). In support of the feasibility of ET estimation by WV2 over a longer period, an algorithm recently developed that estimates evapotranspiration rates based on the Enhanced Vegetation Index (EVI) from MODIS was employed. The results revealed a significant positive relationship between ETMODIS and ETWV2 (r2 = 0.9857, p > 0.05). This indicates that the relationship between NDVI using high resolution WorldView-2 imagery and ground-based validation approaches could provide an effective predictive tool for determining ET rates from unstressed mixed urban landscape plantings. Full article
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; https://doi.org/10.3390/rs6010555 - 06 Jan 2014
Cited by 25 | Viewed by 3333
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; https://doi.org/10.3390/rs6010540 - 03 Jan 2014
Cited by 16 | Viewed by 3996
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
Peat Mapping Associations of Airborne Radiometric Survey Data
Remote Sens. 2014, 6(1), 521-539; https://doi.org/10.3390/rs6010521 - 03 Jan 2014
Cited by 18 | Viewed by 3720
Abstract
This study considers recent airborne radiometric (gamma ray) survey data, obtained at high-resolution, across various regions of the UK. The datasets all display a very evident attenuation of signal in association with peat, and intra-peat variations are observed. The geophysical response variations are [...] Read more.
This study considers recent airborne radiometric (gamma ray) survey data, obtained at high-resolution, across various regions of the UK. The datasets all display a very evident attenuation of signal in association with peat, and intra-peat variations are observed. The geophysical response variations are examined in detail using example data sets across lowland areas (raised bogs, meres, fens and afforested peat) and upland areas of blanket bog, together with associated wetland zones. The radiometric data do not map soils per se. The bedrock (the radiogenic parent) provides a specific amplitude level. Attenuation of this signal level is then controlled by moisture content in conjunction with the density and porosity of the soil cover. Both soil and bedrock variations need to be jointly assessed. The attenuation theory, reviewed here, predicts that the behaviour of wet peat is distinct from most other soil types. Theory also predicts that the attenuation levels observed across wet peatlands cannot be generally used to map variations in peat thickness. Four survey areas at various scales, across England, Scotland, Wales and Ireland are used to demonstrate the ability of the airborne data to map peat zones. A 1:50 k national mapping of deep peat is used to provide control although variability in the definition of peat zones across existing databases is also demonstrated. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands I)
Open AccessReview
Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review
Remote Sens. 2014, 6(1), 470-520; https://doi.org/10.3390/rs6010470 - 31 Dec 2013
Cited by 85 | Viewed by 6103
Abstract
The frequency and severity of forest fires, coupled with changes in spatial and temporal precipitation and temperature patterns, are likely to severely affect the characteristics of forest and permafrost patterns in boreal eco-regions. Forest fires, however, are also an ecological factor in how [...] Read more.
The frequency and severity of forest fires, coupled with changes in spatial and temporal precipitation and temperature patterns, are likely to severely affect the characteristics of forest and permafrost patterns in boreal eco-regions. Forest fires, however, are also an ecological factor in how forest ecosystems form and function, as they affect the rate and characteristics of tree recruitment. A better understanding of fire regimes and forest recovery patterns in different environmental and climatic conditions will improve the management of sustainable forests by facilitating the process of forest resilience. Remote sensing has been identified as an effective tool for preventing and monitoring forest fires, as well as being a potential tool for understanding how forest ecosystems respond to them. However, a number of challenges remain before remote sensing practitioners will be able to better understand the effects of forest fires and how vegetation responds afterward. This article attempts to provide a comprehensive review of current research with respect to remotely sensed data and methods used to model post-fire effects and forest recovery patterns in boreal forest regions. The review reveals that remote sensing-based monitoring of post-fire effects and forest recovery patterns in boreal forest regions is not only limited by the gaps in both field data and remotely sensed data, but also the complexity of far-northern fire regimes, climatic conditions and environmental conditions. We expect that the integration of different remotely sensed data coupled with field campaigns can provide an important data source to support the monitoring of post-fire effects and forest recovery patterns. Additionally, the variation and stratification of pre- and post-fire vegetation and environmental conditions should be considered to achieve a reasonable, operational model for monitoring post-fire effects and forest patterns in boreal regions. Full article
Open AccessArticle
Airborne Measurements of CO2 Column Concentration and Range Using a Pulsed Direct-Detection IPDA Lidar
Remote Sens. 2014, 6(1), 443-469; https://doi.org/10.3390/rs6010443 - 30 Dec 2013
Cited by 53 | Viewed by 7688
Abstract
We have previously demonstrated a pulsed direct detection IPDA lidar to measure range and the column concentration of atmospheric CO2. The lidar measures the atmospheric backscatter profiles and samples the shape of the 1,572.33 nm CO2 absorption line. We participated [...] Read more.
We have previously demonstrated a pulsed direct detection IPDA lidar to measure range and the column concentration of atmospheric CO2. The lidar measures the atmospheric backscatter profiles and samples the shape of the 1,572.33 nm CO2 absorption line. We participated in the ASCENDS science flights on the NASA DC-8 aircraft during August 2011 and report here lidar measurements made on four flights over a variety of surface and cloud conditions near the US. These included over a stratus cloud deck over the Pacific Ocean, to a dry lake bed surrounded by mountains in Nevada, to a desert area with a coal-fired power plant, and from the Rocky Mountains to Iowa, with segments with both cumulus and cirrus clouds. Most flights were to altitudes >12 km and had 5–6 altitude steps. Analyses show the retrievals of lidar range, CO2 column absorption, and CO2 mixing ratio worked well when measuring over topography with rapidly changing height and reflectivity, through thin clouds, between cumulus clouds, and to stratus cloud tops. The retrievals shows the decrease in column CO2 due to growing vegetation when flying over Iowa cropland as well as a sudden increase in CO2 concentration near a coal-fired power plant. For regions where the CO2 concentration was relatively constant, the measured CO2 absorption lineshape (averaged for 50 s) matched the predicted shapes to better than 1% RMS error. For 10 s averaging, the scatter in the retrievals was typically 2–3 ppm and was limited by the received signal photon count. Retrievals were made using atmospheric parameters from both an atmospheric model and from in situ temperature and pressure from the aircraft. The retrievals had no free parameters and did not use empirical adjustments, and >70% of the measurements passed screening and were used in analysis. The differences between the lidar-measured retrievals and in situ measured average CO2 column concentrations were <1.4 ppm for flight measurement altitudes >6 km. Full article
(This article belongs to the Special Issue Optical Remote Sensing of the Atmosphere)
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Open AccessArticle
Improved Accuracy of Chlorophyll-a Concentration Estimates from MODIS Imagery Using a Two-Band Ratio Algorithm and Geostatistics: As Applied to the Monitoring of Eutrophication Processes over Tien Yen Bay (Northern Vietnam)
Remote Sens. 2014, 6(1), 421-442; https://doi.org/10.3390/rs6010421 - 30 Dec 2013
Cited by 30 | Viewed by 4338
Abstract
Sea eutrophication is a natural process of water enrichment caused by increased nutrient loading that severely affects coastal ecosystems by decreasing water quality. The degree of eutrophication can be assessed by chlorophyll-a concentration. This study aims to develop a remote sensing method suitable [...] Read more.
Sea eutrophication is a natural process of water enrichment caused by increased nutrient loading that severely affects coastal ecosystems by decreasing water quality. The degree of eutrophication can be assessed by chlorophyll-a concentration. This study aims to develop a remote sensing method suitable for estimating chlorophyll-a concentrations in tropical coastal waters with abundant phytoplankton using Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra imagery and to improve the spatial resolution of MODIS/Terra-based estimation from 1 km to 100 m by geostatistics. A model based on the ratio of green and blue band reflectance (rGBr) is proposed considering the bio-optical property of chlorophyll-a. Tien Yen Bay in northern Vietnam, a typical phytoplankton-rich coastal area, was selected as a case study site. The superiority of rGBr over two existing representative models, based on the blue-green band ratio and the red-near infrared band ratio, was demonstrated by a high correlation of the estimated chlorophyll-a concentrations at 40 sites with values measured in situ. Ordinary kriging was then shown to be highly capable of predicting the concentration for regions of the image covered by clouds and, thus, without sea surface data. Resultant space-time maps of concentrations over a year clarified that Tien Yen Bay is characterized by natural eutrophic waters, because the average of chlorophyll-a concentrations exceeded 10 mg/m3 in the summer. The temporal changes of chlorophyll-a concentrations were consistent with average monthly air temperatures and precipitation. Consequently, a combination of rGBr and ordinary kriging can effectively monitor water quality in tropical shallow waters. Full article
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Open AccessArticle
Remote Sensing-Derived Bathymetry of Lake Poopó
Remote Sens. 2014, 6(1), 407-420; https://doi.org/10.3390/rs6010407 - 27 Dec 2013
Cited by 35 | Viewed by 4889
Abstract
Located within the Altiplano at 3,686 m above sea level, Lake Poopó is remarkably shallow and very sensitive to hydrologic recharge. Progressive drying has been observed in the entire Titicaca-Poopó-Desaguadero-Salar de Coipasa (TPDS) system during the last decade, causing dramatic changes to Lake [...] Read more.
Located within the Altiplano at 3,686 m above sea level, Lake Poopó is remarkably shallow and very sensitive to hydrologic recharge. Progressive drying has been observed in the entire Titicaca-Poopó-Desaguadero-Salar de Coipasa (TPDS) system during the last decade, causing dramatic changes to Lake Poopó’s surface and its regional water supplies. Our research aims to improve understanding of Lake Poopó water storage capacity. Thus, we propose a new method based on freely available remote sensing data to reproduce Lake Poopó bathymetry. Laser ranging altimeter ICESat (Ice, Cloud, and land Elevation Satellite) is used during the lake’s lowest stages to measure vertical heights with high precision over dry land. These heights are used to estimate elevations of water contours obtained with Landsat imagery. Contour points with assigned elevation are filtered and grouped in a points cloud. Mesh gridding and interpolation function are then applied to construct 3D bathymetry. Complementary analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) surfaces from 2000 to 2012 combined with bathymetry gives water levels and storage evolution every 8 days. Full article
Open AccessArticle
Envisat/ASAR Images for the Calibration of Wind Drag Action in the Doñana Wetlands 2D Hydrodynamic Model
Remote Sens. 2014, 6(1), 379-406; https://doi.org/10.3390/rs6010379 - 27 Dec 2013
Cited by 9 | Viewed by 3623
Abstract
Doñana National Park wetlands are located in southwest Spain, on the right bank of the Guadalquivir River, near the Atlantic Ocean coast. The wetlands dry out completely every summer and progressively flood again throughout the fall and winter seasons. Given the flatness of [...] Read more.
Doñana National Park wetlands are located in southwest Spain, on the right bank of the Guadalquivir River, near the Atlantic Ocean coast. The wetlands dry out completely every summer and progressively flood again throughout the fall and winter seasons. Given the flatness of Doñana’s topography, the wind drag action can induce the flooding or emergence of extensive areas, detectable in remote sensing images. Envisat/ASAR scenes acquired before and during strong and persistent wind episodes enabled the spatial delineation of the wind-induced water displacement. A two-dimensional hydrodynamic model of Doñana wetlands was built in 2006 with the aim to predict the effect of proposed hydrologic restoration actions within Doñana’s basin. In this work, on-site wind records and concurrent ASAR scenes are used for the calibration of the wind-drag modeling by assessing different formulations. Results show a good adjustment between the modeled and observed wind drag effect. Displacements of up to 2 km in the wind direction are satisfactorily reproduced by the hydrodynamic model, while including an atmospheric stability parameter led to no significant improvement of the results. Such evidence will contribute to a more accurate simulation of hypothetic or design scenarios, when no information is available for the atmospheric stability assessment. Full article
(This article belongs to the Special Issue Hydrological Remote Sensing)
Open AccessArticle
Homogeneity Analysis of the CM SAF Surface Solar Irradiance Dataset Derived from Geostationary Satellite Observations
Remote Sens. 2014, 6(1), 352-378; https://doi.org/10.3390/rs6010352 - 27 Dec 2013
Cited by 10 | Viewed by 3771
Abstract
A satellite-based climate record of monthly mean surface solar irradiance (SIS) is investigated with regard to possible inhomogeneities in time. The data record is provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM [...] Read more.
A satellite-based climate record of monthly mean surface solar irradiance (SIS) is investigated with regard to possible inhomogeneities in time. The data record is provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF) for the period of 1983 to 2005, covering a disk area between ±70° in latitude and longitude. The Standard Normal Homogeneity Test (SNHT) and two other homogeneity tests are applied with and without the use of reference SIS data (from the Baseline Surface Radiation Network (BSRN) and from the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA -Interim reanalysis. The focus is on the detection of break-like inhomogeneities, which may occur due to satellite or SIS retrieval algorithm changes. In comparison with the few suitable BSRN SIS observation series with limited extension in time (no data before 1992), the CM SAF SIS time series do not show significant inhomogeneities, even though slight discrepancies in the surface measurements appear. The investigation of the full CM SAF SIS domain reveal inhomogeneities related to most of the documented satellite and retrieval changes, but only for relatively small domain fractions (especially in mountainous desert-like areas in Africa). In these regions the retrieval algorithm is not capable of adjusting for the changes of the satellite instruments. For other areas, e.g., Europe, no such breaks in the time series are found. We conclude that the CM SAF SIS data record has to be further assessed and regionally homogenized before climate trend investigations can be conducted. Full article
Open AccessArticle
Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil
Remote Sens. 2014, 6(1), 330-351; https://doi.org/10.3390/rs6010330 - 27 Dec 2013
Cited by 45 | Viewed by 6256
Abstract
Remote sensing has been used in karst studies to identify limestone terrain, describe exokarst features, analyze karst depressions, and detect geological structures important to karst development. The aim of this work is to investigate the use of ASTER-, SRTM- and ALOS/PRISM-derived digital elevation [...] Read more.
Remote sensing has been used in karst studies to identify limestone terrain, describe exokarst features, analyze karst depressions, and detect geological structures important to karst development. The aim of this work is to investigate the use of ASTER-, SRTM- and ALOS/PRISM-derived digital elevation models (DEMs) to detect and quantify natural karst depressions along the São Francisco River near Barreiras city, northeast Brazil. The study area is a karst landscape characterized by karst depressions (dolines), closed depressions in limestone, many of which contain standing water connected with the ground-water table. The base of dolines is typically sealed with an impermeable clay layer covered by standing water or herbaceous vegetation. We identify dolines by combining the extraction of sink depth from DEMs, morphometric analysis using GIS, and visual interpretation. Our methodology is a semi-automatic approach involving several steps: (a) DEM acquisition; (b) sink-depth calculation using the difference between the raw DEM and the corresponding DEM with sinks filled; and (c) elimination of falsely identified karst depressions using morphometric attributes. The advantages and limitations of the applied methodology using different DEMs are examined by comparison with a sinkhole map generated from traditional geomorphological investigations based on visual interpretation of the high-resolution remote sensing images and field surveys. The threshold values of the depth, area size and circularity index appropriate for distinguishing dolines were identified from the maximum overall accuracy obtained by comparison with a true doline map. Our results indicate that the best performance of the proposed methodology for meso-scale karst feature detection was using ALOS/PRISM data with a threshold depth > 2 m; areas > 13,125 m2 and circularity indexes > 0.3 (overall accuracy of 0.53). The overall correct identification of around half of the true dolines suggests the potential to substantially improve doline identification using higher-resolution LiDAR-generated DEMs. Full article
(This article belongs to the Special Issue Remote Sensing in Geomorphology)
Open AccessArticle
Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors
Remote Sens. 2014, 6(1), 310-329; https://doi.org/10.3390/rs6010310 - 27 Dec 2013
Cited by 166 | Viewed by 8612
Abstract
Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are currently operational for routine Earth observation. There are substantial differences between instruments onboard both satellites. The enhancements achieved with Landsat-8 refer to the scanning technology [...] Read more.
Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are currently operational for routine Earth observation. There are substantial differences between instruments onboard both satellites. The enhancements achieved with Landsat-8 refer to the scanning technology (replacing of whisk-broom scanners with two separate push-broom OLI and TIRS scanners), an extended number of spectral bands (two additional bands provided) and narrower bandwidths. Therefore, cross-comparative analysis is very necessary for the combined use of multi-decadal Landsat imagery. In this study, 3,311 independent sample points of four major land cover types (primary forest, unplanted cropland, swidden cultivation and water body) were used to compare the spectral bands of ETM+ and OLI. Eight sample plots with different land cover types were manually selected for comparison with the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), the Land Surface Water Index (LSWI) and the Normalized Burn Ratio (NBR). These indices were calculated with six pairs of ETM+ and OLI cloud-free images, which were acquired over the border area of Myanmar, Laos and Thailand just two days apart, when Landsat-8 achieved operational obit. Comparative results showed that: (1) the average surface reflectance of each band differed slightly, but with a high degree of similarities between both sensors. In comparison with ETM+, the OLI had higher values for the near-infrared band for vegetative land cover types, but lower values for non-vegetative types. The new sensor had lower values for the shortwave infrared (2.11–2.29 µm) band for all land cover types. In addition, it also basically had higher values for the shortwave infrared (1.57–1.65 µm) band for non-water land cover types. (2) The subtle differences of vegetation indices derived from both sensors and their high linear correlation coefficient (R2 > 0.96) demonstrated that ETM+ and OLI imagery can be used as complementary data. (3) LSWI and NBR performed better than NDVI and MNDWI for cross-comparison analysis of satellite sensors, due to the spectral band difference effects. Full article
Open AccessArticle
Training Area Concept in a Two-Phase Biomass Inventory Using Airborne Laser Scanning and RapidEye Satellite Data
Remote Sens. 2014, 6(1), 285-309; https://doi.org/10.3390/rs6010285 - 27 Dec 2013
Cited by 11 | Viewed by 3218 | Correction
Abstract
This study evaluated the accuracy of boreal forest above-ground biomass (AGB) and volume estimates obtained using airborne laser scanning (ALS) and RapidEye data in a two-phase sampling method. Linear regression-based estimation was employed using an independent validation dataset and the performance was evaluated [...] Read more.
This study evaluated the accuracy of boreal forest above-ground biomass (AGB) and volume estimates obtained using airborne laser scanning (ALS) and RapidEye data in a two-phase sampling method. Linear regression-based estimation was employed using an independent validation dataset and the performance was evaluated by assessing the bias and the root mean square error (RMSE). In the phase I, ALS data from 50 field plots were used to predict AGB and volume for the 200 surrogate plots. In the phase II, the ALS-simulated surrogate plots were used as a ground-truth to estimate AGB and volume from the RapidEye data for the study area. The resulting RapidEye models were validated against a separate set of 28 plots. The RapidEye models showed a promising accuracy with a relative RMSE of 19%–20% for both volume and AGB. The evaluated concept of biomass inventory would be useful to support future forest monitoring and decision making for sustainable use of forest resources. Full article
Open AccessArticle
Phenological Metrics Derived over the European Continent from NDVI3g Data and MODIS Time Series
Remote Sens. 2014, 6(1), 257-284; https://doi.org/10.3390/rs6010257 - 27 Dec 2013
Cited by 68 | Viewed by 4826
Abstract
Time series of normalized difference vegetation index (NDVI) are important data sources for environmental monitoring. Continuous efforts are put into their production and updating. The recently released Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data set is a consistent time series with [...] Read more.
Time series of normalized difference vegetation index (NDVI) are important data sources for environmental monitoring. Continuous efforts are put into their production and updating. The recently released Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data set is a consistent time series with 1/12° spatial and bi-monthly temporal resolution. It covers the time period from 1981 to 2011. However, it is unclear if vegetation density and phenology derived from GIMMS are comparable to those obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI with 250 m ground resolution. To check the consistency between GIMMS and MODIS data sets, a comparative analysis was performed. For a large European window (40 × 40°), data distribution, spatial and temporal agreement were analyzed, as well as the timing of important phenological events. Overall, only a moderately good agreement of NDVI values was found. Large differences occurred during winter. Large discrepancies were also observed for phenological metrics, in particular the start of season. Information regarding the maximum of season was more consistent. Hence, both data sets should be well inter-calibrated before being used concurrently. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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