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Remote Sens., Volume 6, Issue 12 (December 2014) , Pages 11673-12908

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Open AccessArticle
An Improved Top-Hat Filter with Sloped Brim for Extracting Ground Points from Airborne Lidar Point Clouds
Remote Sens. 2014, 6(12), 12885-12908; https://doi.org/10.3390/rs61212885
Received: 14 September 2014 / Revised: 17 December 2014 / Accepted: 17 December 2014 / Published: 22 December 2014
Cited by 17 | Viewed by 3433 | PDF Full-text (7040 KB) | HTML Full-text | XML Full-text
Abstract
Airborne light detection and ranging (lidar) has become a powerful support for acquiring geospatial data in numerous geospatial applications and analyses. However, the process of extracting ground points accurately and effectively from raw point clouds remains a big challenge. This study presents an [...] Read more.
Airborne light detection and ranging (lidar) has become a powerful support for acquiring geospatial data in numerous geospatial applications and analyses. However, the process of extracting ground points accurately and effectively from raw point clouds remains a big challenge. This study presents an improved top-hat filter with a sloped brim to enhance the robustness of ground point extraction for complex objects and terrains. The top-hat transformation is executed and the elevation change intensity of the transitions between the obtained top-hats and outer brims is inspected to suppress the omission error caused by protruding terrain features. Finally, the nonground objects of complex structures, such as multilayer buildings, are identified by the brim filter that is extended outward. The performance of the proposed filter in various environments is evaluated using diverse datasets with difficult cases. The comparison of the proposed filter with the commercial software Terrasolid TerraScan and other popular filtering algorithms demonstrates the applicability and effectiveness of this filter. Experimental results show that the proposed filter has great promise in terms of its application in various types of landscapes. Abrupt terrain features with dramatic elevation changes are well preserved, and diverse objects with complicated shapes are effectively removed. This filter has minimal omission and commission error oscillation for different test areas and thus demonstrates a stable and reliable performance in diverse landscapes. In addition, the proposed algorithm has high computational efficiency because of its simple and efficient data structure and implementation. Full article
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Open AccessArticle
The Impact of Time Difference between Satellite Overpass and Ground Observation on Cloud Cover Performance Statistics
Remote Sens. 2014, 6(12), 12866-12884; https://doi.org/10.3390/rs61212866
Received: 20 October 2014 / Revised: 8 December 2014 / Accepted: 15 December 2014 / Published: 22 December 2014
Cited by 4 | Viewed by 3100 | PDF Full-text (3273 KB) | HTML Full-text | XML Full-text
Abstract
Cloud property data sets derived from passive sensors onboard the polar orbiting satellites (such as the NOAA’s Advanced Very High Resolution Radiometer) have global coverage and now span a climatological time period. Synoptic surface observations (SYNOP) are often used to characterize the accuracy [...] Read more.
Cloud property data sets derived from passive sensors onboard the polar orbiting satellites (such as the NOAA’s Advanced Very High Resolution Radiometer) have global coverage and now span a climatological time period. Synoptic surface observations (SYNOP) are often used to characterize the accuracy of satellite-based cloud cover. Infrequent overpasses of polar orbiting satellites combined with the 3- or 6-h SYNOP frequency lead to collocation time differences of up to 3 h. The associated collocation error degrades the cloud cover performance statistics such as the Hanssen-Kuiper’s discriminant (HK) by up to 45%. Limiting the time difference to 10 min, on the other hand, introduces a sampling error due to a lower number of corresponding satellite and SYNOP observations. This error depends on both the length of the validated time series and the SYNOP frequency. The trade-off between collocation and sampling error call for an optimum collocation time difference. It however depends on cloud cover characteristics and SYNOP frequency, and cannot be generalized. Instead, a method is presented to reconstruct the unbiased (true) HK from HK affected by the collocation differences, which significantly (t-test p < 0.01) improves the validation results. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
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Open AccessArticle
A Versatile, Production-Oriented Approach to High-Resolution Tree-Canopy Mapping in Urban and Suburban Landscapes Using GEOBIA and Data Fusion
Remote Sens. 2014, 6(12), 12837-12865; https://doi.org/10.3390/rs61212837
Received: 15 July 2014 / Revised: 8 December 2014 / Accepted: 16 December 2014 / Published: 22 December 2014
Cited by 26 | Viewed by 3894 | PDF Full-text (7247 KB) | HTML Full-text | XML Full-text
Abstract
The benefits of tree canopy in urban and suburban landscapes are increasingly well known: stormwater runoff control, air-pollution mitigation, temperature regulation, carbon storage, wildlife habitat, neighborhood cohesion, and other social indicators of quality of life. However, many urban areas lack high-resolution tree canopy [...] Read more.
The benefits of tree canopy in urban and suburban landscapes are increasingly well known: stormwater runoff control, air-pollution mitigation, temperature regulation, carbon storage, wildlife habitat, neighborhood cohesion, and other social indicators of quality of life. However, many urban areas lack high-resolution tree canopy maps that document baseline conditions or inform tree-planting programs, limiting effective study and management. This paper describes a GEOBIA approach to tree-canopy mapping that relies on existing public investments in LiDAR, multispectral imagery, and thematic GIS layers, thus eliminating or reducing data acquisition costs. This versatile approach accommodates datasets of varying content and quality, first using LiDAR derivatives to identify aboveground features and then a combination of LiDAR and imagery to differentiate trees from buildings and other anthropogenic structures. Initial tree canopy objects are then refined through contextual analysis, morphological smoothing, and small-gap filling. Case studies from locations in the United States and Canada show how a GEOBIA approach incorporating data fusion and enterprise processing can be used for producing high-accuracy, high-resolution maps for large geographic extents. These maps are designed specifically for practical application by planning and regulatory end users who expect not only high accuracy but also high realism and visual coherence. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle
Remote Sensing of Submerged Aquatic Vegetation in a Shallow Non-Turbid River Using an Unmanned Aerial Vehicle
Remote Sens. 2014, 6(12), 12815-12836; https://doi.org/10.3390/rs61212815
Received: 19 August 2014 / Revised: 9 December 2014 / Accepted: 15 December 2014 / Published: 22 December 2014
Cited by 43 | Viewed by 4608 | PDF Full-text (9712 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A passive method for remote sensing of the nuisance green algae Cladophora glomerata in rivers is presented using an unmanned aerial vehicle (UAV). Included are methods for UAV operation, lens distortion correction, image georeferencing, and spectral analysis to support algal cover mapping. Eighteen [...] Read more.
A passive method for remote sensing of the nuisance green algae Cladophora glomerata in rivers is presented using an unmanned aerial vehicle (UAV). Included are methods for UAV operation, lens distortion correction, image georeferencing, and spectral analysis to support algal cover mapping. Eighteen aerial photography missions were conducted over the summer of 2013 using an off-the-shelf UAV and three-band, wide-angle, red, green, and blue (RGB) digital camera sensor. Images were post-processed, mosaicked, and georeferenced so automated classification and mapping could be completed. An adaptive cosine estimator (ACE) and spectral angle mapper (SAM) algorithm were used to complete the algal identification. Digital analysis of optical imagery correctly identified filamentous algae and background coverage 90% and 92% of the time, and tau coefficients were 0.82 and 0.84 for ACE and SAM, respectively. Thereafter, algal cover was characterized for a one-kilometer channel segment during each of the 18 UAV flights. Percent cover ranged from <5% to >50%, and increased immediately after vernal freshet, peaked in midsummer, and declined in the fall. Results indicate that optical remote sensing with UAV holds promise for completing spatially precise, and multi-temporal measurements of algae or submerged aquatic vegetation in shallow rivers with low turbidity and good optical transmission. Full article
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Open AccessArticle
Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR
Remote Sens. 2014, 6(12), 12789-12814; https://doi.org/10.3390/rs61212789
Received: 30 August 2014 / Revised: 30 November 2014 / Accepted: 15 December 2014 / Published: 22 December 2014
Cited by 15 | Viewed by 3509 | PDF Full-text (6842 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Different rice crop information can be derived from different remote sensing sources to provide information for decision making and policies related to agricultural production and food security. The objective of this study is to generate complementary and comprehensive rice crop information from hypertemporal [...] Read more.
Different rice crop information can be derived from different remote sensing sources to provide information for decision making and policies related to agricultural production and food security. The objective of this study is to generate complementary and comprehensive rice crop information from hypertemporal optical and multitemporal high-resolution SAR imagery. We demonstrate the use of MODIS data for rice-based system characterization and X-band SAR data from TerraSAR-X and CosmoSkyMed for the identification and detailed mapping of rice areas and flooding/transplanting dates. MODIS was classified using ISODATA to generate cropping calendar, cropping intensity, cropping pattern and rice ecosystem information. Season and location specific thresholds from field observations were used to generate detailed maps of rice areas and flooding/transplanting dates from the SAR data. Error matrices were used for the accuracy assessment of the MODIS-derived rice characteristics map and the SAR-derived detailed rice area map, while Root Mean Square Error (RMSE) and linear correlation were used to assess the TSX-derived flooding/transplanting dates. Results showed that multitemporal high spatial resolution SAR data is effective for mapping rice areas and flooding/transplanting dates with an overall accuracy of 90% and a kappa of 0.72 and that hypertemporal moderate-resolution optical imagery is effective for the basic characterization of rice areas with an overall accuracy that ranged from 62% to 87% and a kappa of 0.52 to 0.72. This study has also provided the first assessment of the temporal variation in the backscatter of rice from CSK and TSX using large incidence angles covering all rice crop stages from pre-season until harvest. This complementarity in optical and SAR data can be further exploited in the near future with the increased availability of space-borne optical and SAR sensors. This new information can help improve the identification of rice areas. Full article
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Open AccessLetter
Evaluation of Radiometric Performance for the Thermal Infrared Sensor Onboard Landsat 8
Remote Sens. 2014, 6(12), 12776-12788; https://doi.org/10.3390/rs61212776
Received: 11 September 2014 / Revised: 16 November 2014 / Accepted: 15 December 2014 / Published: 19 December 2014
Cited by 6 | Viewed by 2492 | PDF Full-text (1995 KB) | HTML Full-text | XML Full-text
Abstract
The radiometric performance of remotely-sensed images is important for the applications of such data in monitoring land surface, ocean and atmospheric status. One requirement placed on the Thermal Infrared Sensor (TIRS) onboard Landsat 8 was that the noise-equivalent change in temperature (NEΔT) should [...] Read more.
The radiometric performance of remotely-sensed images is important for the applications of such data in monitoring land surface, ocean and atmospheric status. One requirement placed on the Thermal Infrared Sensor (TIRS) onboard Landsat 8 was that the noise-equivalent change in temperature (NEΔT) should be ≤0.4 K at 300 K for its two thermal infrared bands. In order to optimize the use of TIRS data, this study investigated the on-orbit NEΔT of the TIRS two bands from a scene-based method using clear-sky images over uniform ground surfaces, including lake, deep ocean, snow, desert and Gobi, as well as dense vegetation. Results showed that the NEΔTs of the two bands were 0.051 and 0.06 K at 300 K, which exceeded the design specification by an order of magnitude. The effect of NEΔT on the land surface temperature (LST) retrieval using a split window algorithm was discussed, and the estimated NEΔT could contribute only 3.5% to the final LST error in theory, whereas the required NEΔT could contribute up to 26.4%. Low NEΔT could improve the application of TIRS images. However, efforts are needed in the future to remove the effects of unwanted stray light that appears in the current TIRS images. Full article
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
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Open AccessArticle
Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data
Remote Sens. 2014, 6(12), 12752-12775; https://doi.org/10.3390/rs61212752
Received: 10 October 2014 / Revised: 10 December 2014 / Accepted: 15 December 2014 / Published: 19 December 2014
Cited by 22 | Viewed by 3487 | PDF Full-text (4721 KB) | HTML Full-text | XML Full-text
Abstract
Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of ~4 Mio km2. The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation [...] Read more.
Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of ~4 Mio km2. The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500–1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation. Full article
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Open AccessReview
Validating the Remotely Sensed Geography of Crime: A Review of Emerging Issues
Remote Sens. 2014, 6(12), 12723-12751; https://doi.org/10.3390/rs61212723
Received: 19 September 2014 / Revised: 18 November 2014 / Accepted: 19 November 2014 / Published: 18 December 2014
Cited by 2 | Viewed by 3132 | PDF Full-text (377 KB) | HTML Full-text | XML Full-text
Abstract
This paper explores the existing literature on the active detection of crimes using remote sensing technologies. The paper reviews sixty-one studies that use remote sensing to actively detect crime. Considering the serious consequences of misidentifying crimes or sites of crimes (e.g., opening that [...] Read more.
This paper explores the existing literature on the active detection of crimes using remote sensing technologies. The paper reviews sixty-one studies that use remote sensing to actively detect crime. Considering the serious consequences of misidentifying crimes or sites of crimes (e.g., opening that place and its residents up to potentially needless intrusion, intimidation, surveillance or violence), the authors were surprised to find a lack of rigorous validation of the remote sensing methods utilized in these studies. In some cases, validation was not mentioned, while in others, validation was severely hampered by security issues, rough terrain and weather conditions. The paper also considers the potential hazards of the use of Google Earth to identify crimes and criminals. The paper concludes by considering alternate, “second order” validation techniques that could add vital context and understanding to remotely sensed images in a law enforcement context. With this discussion, the authors seek to initiate a discussion on other potential “second order” validation techniques, as well as on the exponential growth of surveillance in our everyday lives. Full article
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Open AccessArticle
Aladdin’s Magic Lamp: Active Target Calibration of the DMSP OLS
Remote Sens. 2014, 6(12), 12708-12722; https://doi.org/10.3390/rs61212708
Received: 9 September 2014 / Revised: 24 November 2014 / Accepted: 24 November 2014 / Published: 17 December 2014
Cited by 3 | Viewed by 2717 | PDF Full-text (2153 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Nighttime satellite imagery from the Defense Meteorological Satellite Programs’ Operational Linescan System (DMSP OLS) is being used for myriad applications including population mapping, characterizing economic activity, disaggregate estimation of CO2 emissions, wildfire monitoring, and more. Here we present a method for in [...] Read more.
Nighttime satellite imagery from the Defense Meteorological Satellite Programs’ Operational Linescan System (DMSP OLS) is being used for myriad applications including population mapping, characterizing economic activity, disaggregate estimation of CO2 emissions, wildfire monitoring, and more. Here we present a method for in situ radiance calibration of the DMSP OLS using a ground based light source as an active target. We found that the wattage of light used by our active target strongly correlates with the signal measured by the DMSP OLS. This approach can be used to enhance our ability to make intertemporal and intersatellite comparisons of DMSP OLS imagery. We recommend exploring the possibility of establishing a permanent active target for the calibration of nocturnal imaging systems. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
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Open AccessArticle
Surface-Based Registration of Airborne and Terrestrial Mobile LiDAR Point Clouds
Remote Sens. 2014, 6(12), 12686-12707; https://doi.org/10.3390/rs61212686
Received: 9 October 2014 / Revised: 8 December 2014 / Accepted: 15 December 2014 / Published: 17 December 2014
Cited by 12 | Viewed by 2665 | PDF Full-text (4380 KB) | HTML Full-text | XML Full-text
Abstract
Light Detection and Ranging (LiDAR) is an active sensor that can effectively acquire a large number of three-dimensional (3-D) points. LiDAR systems can be equipped on different platforms for different applications, but to integrate the data, point cloud registration is needed to improve [...] Read more.
Light Detection and Ranging (LiDAR) is an active sensor that can effectively acquire a large number of three-dimensional (3-D) points. LiDAR systems can be equipped on different platforms for different applications, but to integrate the data, point cloud registration is needed to improve geometric consistency. The registration of airborne and terrestrial mobile LiDAR is a challenging task because the point densities and scanning directions differ. We proposed a scheme for the registration of airborne and terrestrial mobile LiDAR using the least squares 3-D surface registration technique to minimize the surfaces between two datasets. To analyze the effect of point density in registration, the simulation data simulated different conditions and estimated the theoretical errors. The test data were the point clouds of the airborne LiDAR system (ALS) and the mobile LiDAR system (MLS), which were acquired by Optech ALTM 3070 and Lynx, respectively. The resulting simulation analysis indicated that the accuracy of registration improved as the density increased. For the test dataset, the registration error of mobile LiDAR between different trajectories improved from 40 cm to 4 cm, and the registration error between ALS and MLS improved from 84 cm to 4 cm. These results indicate that the proposed methods can obtain 5 cm accuracy between ALS and MLS. Full article
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Open AccessArticle
Land Surface Temperature Retrieval Using Airborne Hyperspectral Scanner Daytime Mid-Infrared Data
Remote Sens. 2014, 6(12), 12667-12685; https://doi.org/10.3390/rs61212667
Received: 30 July 2014 / Revised: 15 October 2014 / Accepted: 1 December 2014 / Published: 16 December 2014
Cited by 7 | Viewed by 2446 | PDF Full-text (6107 KB) | HTML Full-text | XML Full-text
Abstract
Land surface temperature (LST) retrieval is a key issue in infrared quantitative remote sensing. In this paper, a split window algorithm is proposed to estimate LST with daytime data in two mid-infrared channels (channel 66 (3.746~4.084 μm) and channel 68 (4.418~4.785 μm)) from [...] Read more.
Land surface temperature (LST) retrieval is a key issue in infrared quantitative remote sensing. In this paper, a split window algorithm is proposed to estimate LST with daytime data in two mid-infrared channels (channel 66 (3.746~4.084 μm) and channel 68 (4.418~4.785 μm)) from Airborne Hyperspectral Scanner (AHS). The estimation is conducted after eliminating reflected direct solar radiance with the aid of water vapor content (WVC), the view zenith angle (VZA), and the solar zenith angle (SZA). The results demonstrate that the LST can be well estimated with a root mean square error (RMSE) less than 1.0 K. Furthermore, an error analysis for the proposed method is also performed in terms of the uncertainty of LSE and WVC, as well as the Noise Equivalent Difference Temperature (NEΔT). The results show that the LST errors caused by a LSE uncertainty of 0.01, a NEΔT of 0.33 K, and a WVC uncertainty of 10% are 0.4~2.8 K, 0.6 K, and 0.2 K, respectively. Finally, the proposed method is applied to the AHS data of 4 July 2008. The results show that the differences between the estimated and the ground measured LST for water, bare soil and vegetation areas are approximately 0.7 K, 0.9 K and 2.3K, respectively. Full article
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
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Open AccessCorrection
Correction: Scaioni M., et al. Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives. Remote Sens. 2014, 6, 9600-9652
Remote Sens. 2014, 6(12), 12666; https://doi.org/10.3390/rs61212666
Received: 9 December 2014 / Accepted: 15 December 2014 / Published: 16 December 2014
Viewed by 1809 | PDF Full-text (616 KB) | HTML Full-text | XML Full-text
Abstract
Due to an error on our part, the pagination and the doi number of this manuscript [1] were missing during updating. The correct pagination is 9600–9652, and the doi number is 10.3390/rs6109600. Here is the correct version:[...] Full article
Open AccessArticle
Characterization of Land Transitions Patterns from Multivariate Time Series Using Seasonal Trend Analysis and Principal Component Analysis
Remote Sens. 2014, 6(12), 12639-12665; https://doi.org/10.3390/rs61212639
Received: 5 October 2014 / Revised: 5 December 2014 / Accepted: 8 December 2014 / Published: 16 December 2014
Cited by 5 | Viewed by 2609 | PDF Full-text (1124 KB) | HTML Full-text | XML Full-text
Abstract
Characterizing biophysical changes in land change areas over large regions with short and noisy multivariate time series and multiple temporal parameters remains a challenging task. Most studies focus on detection rather than the characterization, i.e., the manner by which surface state variables [...] Read more.
Characterizing biophysical changes in land change areas over large regions with short and noisy multivariate time series and multiple temporal parameters remains a challenging task. Most studies focus on detection rather than the characterization, i.e., the manner by which surface state variables are altered by the process of changes. In this study, a procedure is presented to extract and characterize simultaneous temporal changes in MODIS multivariate times series from three surface state variables the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST) and albedo (ALB). The analysis involves conducting a seasonal trend analysis (STA) to extract three seasonal shape parameters (Amplitude 0, Amplitude 1 and Amplitude 2) and using principal component analysis (PCA) to contrast trends in change and no-change areas. We illustrate the method by characterizing trends in burned and unburned pixels in Alaska over the 2001–2009 time period. Findings show consistent and meaningful extraction of temporal patterns related to fire disturbances. The first principal component (PC1) is characterized by a decrease in mean NDVI (Amplitude 0) with a concurrent increase in albedo (the mean and the annual amplitude) and an increase in LST annual variability (Amplitude 1). These results provide systematic empirical evidence of surface changes associated with one type of land change, fire disturbances, and suggest that STA with PCA may be used to characterize many other types of land transitions over large landscape areas using multivariate Earth observation time series. Full article
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Open AccessArticle
Radiometric Cross Calibration of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+)
Remote Sens. 2014, 6(12), 12619-12638; https://doi.org/10.3390/rs61212619
Received: 1 August 2014 / Revised: 4 December 2014 / Accepted: 5 December 2014 / Published: 16 December 2014
Cited by 56 | Viewed by 3736 | PDF Full-text (4546 KB) | HTML Full-text | XML Full-text
Abstract
This study evaluates the radiometric consistency between Landsat-8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) using cross calibration techniques. Two approaches are used, one based on cross calibration between the two sensors using simultaneous image pairs, acquired during [...] Read more.
This study evaluates the radiometric consistency between Landsat-8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) using cross calibration techniques. Two approaches are used, one based on cross calibration between the two sensors using simultaneous image pairs, acquired during an underfly event on 29–30 March 2013. The other approach is based on using time series of image statistics acquired by these two sensors over the Libya 4 pseudo invariant calibration site (PICS) (+28.55°N, +23.39°E). Analyses from these approaches show that the reflectance calibration of OLI is generally within ±3% of the ETM+ radiance calibration for all the reflective bands from visible to short wave infrared regions when the ChKur solar spectrum is used to convert the ETM+ radiance to reflectance. Similar results are obtained comparing the OLI radiance calibration directly with the ETM+ radiance calibration and the results in these two different physical units (radiance and reflectance) agree to within ±2% for all the analogous bands. These results will also be useful to tie all the Landsat heritage sensors from Landsat 1 MultiSpectral Scanner (MSS) through Landsat-8 OLI to a consistent radiometric scale. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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Open AccessArticle
Persistent Scatterer Interferometry Processing of COSMO-SkyMed StripMap HIMAGE Time Series to Depict Deformation of the Historic Centre of Rome, Italy
Remote Sens. 2014, 6(12), 12593-12618; https://doi.org/10.3390/rs61212593
Received: 6 August 2014 / Revised: 25 November 2014 / Accepted: 27 November 2014 / Published: 15 December 2014
Cited by 35 | Viewed by 4645 | PDF Full-text (12167 KB) | HTML Full-text | XML Full-text
Abstract
We processed X-band COSMO-SkyMed 3-m resolution StripMap HIMAGE time series (March 2011–June 2013) with the Stanford Method for Persistent Scatterers (StaMPS), to retrieve an updated picture of the condition and structural health of the historic centre of Rome, Italy, and neighbouring quarters. Taking [...] Read more.
We processed X-band COSMO-SkyMed 3-m resolution StripMap HIMAGE time series (March 2011–June 2013) with the Stanford Method for Persistent Scatterers (StaMPS), to retrieve an updated picture of the condition and structural health of the historic centre of Rome, Italy, and neighbouring quarters. Taking advantage of an average target density of over 2800 PS/km2, we analysed the spatial distribution of more than 310,000 radar targets against: (1) land cover; (2) the location of archaeological ruins and restoration activities; and (3) the size, orientation and morphology of historical buildings. Radar interpretation was addressed from the perspective of conservators, and the deformation estimates were correlated to local geohazards and triggering factors of structural collapse. In the context of overall stability, deformation was identified at the single-monument scale, e.g., for the Roman cistern and exedra in the Oppian Hill. Comparative assessment against InSAR processing of C-band imagery (1992–2010) published in the literature confirms the persistence of ground motions affecting monuments and subsidence in southern residential quarters adjacent to the Tiber River, due to the consolidation of compressible deposits. Vertical velocity estimated from COSMO-SkyMed PS exceeds −7.0 mm/y in areas of recent urbanization. Full article
(This article belongs to the Special Issue New Perspectives of Remote Sensing for Archaeology)
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Open AccessArticle
The Influence of Polarimetric Parameters and an Object-Based Approach on Land Cover Classification in Coastal Wetlands
Remote Sens. 2014, 6(12), 12575-12592; https://doi.org/10.3390/rs61212575
Received: 19 July 2014 / Revised: 1 December 2014 / Accepted: 5 December 2014 / Published: 15 December 2014
Cited by 17 | Viewed by 2575 | PDF Full-text (6844 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The purpose of this study was to examine how different polarimetric parameters and an object-based approach influence the classification results of various land use/land cover types using fully polarimetric ALOS PALSAR data over coastal wetlands in Yancheng, China. To verify the efficiency of [...] Read more.
The purpose of this study was to examine how different polarimetric parameters and an object-based approach influence the classification results of various land use/land cover types using fully polarimetric ALOS PALSAR data over coastal wetlands in Yancheng, China. To verify the efficiency of the proposed method, five other classifications (the Wishart supervised classification, the proposed method without polarimetric parameters, the proposed method without an object-based analysis, the proposed method without textural and geometric information and the proposed method using the nearest-neighbor classifier) were applied for comparison. The results indicated that some polarimetric parameters, such as Shannon entropy, Krogager_Kd, Alpha, HAAlpha_T11, VanZyl3_Vol, Derd, Barnes2_T33, polarization fraction, Barnes1_T33, Neuman_delta_mod and entropy, greatly improved the classification results. The shape index was a useful feature in distinguishing fish ponds and rivers. The distance to the sea can be regarded as an important factor in reducing the confusion between herbaceous wetland vegetation and grasslands. Furthermore, the decision tree algorithm increased the overall accuracy by 6.8% compared with the nearest neighbor classifier. This research demonstrated that different polarimetric parameters and the object-based approach significantly improved the performance of land cover classification in coastal wetlands using ALOS PALSAR data. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle
Hydrological Impacts of Urbanization of Two Catchments in Harare, Zimbabwe
Remote Sens. 2014, 6(12), 12544-12574; https://doi.org/10.3390/rs61212544
Received: 1 April 2014 / Revised: 20 October 2014 / Accepted: 4 November 2014 / Published: 12 December 2014
Cited by 16 | Viewed by 3747 | PDF Full-text (9444 KB) | HTML Full-text | XML Full-text
Abstract
By increased rural-urban migration in many African countries, the assessment of changes in catchment hydrologic responses due to urbanization is critical for water resource planning and management. This paper assesses hydrological impacts of urbanization on two medium-sized Zimbabwean catchments (Mukuvisi and Marimba) for [...] Read more.
By increased rural-urban migration in many African countries, the assessment of changes in catchment hydrologic responses due to urbanization is critical for water resource planning and management. This paper assesses hydrological impacts of urbanization on two medium-sized Zimbabwean catchments (Mukuvisi and Marimba) for which changes in land cover by urbanization were determined through Landsat Thematic Mapper (TM) images for the years 1986, 1994 and 2008. Impact assessments were done through hydrological modeling by a topographically driven rainfall-runoff model (TOPMODEL). A satellite remote sensing based ASTER 30 metre Digital Elevation Model (DEM) was used to compute the Topographic Index distribution, which is a key input to the model. Results of land cover classification indicated that urban areas increased by more than 600 % in the Mukuvisi catchment and by more than 200 % in the Marimba catchment between 1986 and 2008. Woodlands decreased by more than 40% with a greater decrease in Marimba than Mukuvisi catchment. Simulations using TOPMODEL in Marimba and Mukuvisi catchments indicated streamflow increases of 84.8 % and 73.6 %, respectively, from 1980 to 2010. These increases coincided with decreases in woodlands and increases in urban areas for the same period. The use of satellite remote sensing data to observe urbanization trends in semi-arid catchments and to represent catchment land surface characteristics proved to be effective for rainfall-runoff modeling. Findings of this study are of relevance for many African cities, which are experiencing rapid urbanization but often lack planning and design. Full article
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Open AccessArticle
Investigating the Temporal and Spatial Variability of Total Ozone Column in the Yangtze River Delta Using Satellite Data: 1978–2013
Remote Sens. 2014, 6(12), 12527-12543; https://doi.org/10.3390/rs61212527
Received: 23 September 2014 / Revised: 24 November 2014 / Accepted: 5 December 2014 / Published: 12 December 2014
Cited by 4 | Viewed by 2483 | PDF Full-text (827 KB) | HTML Full-text | XML Full-text
Abstract
The objective of this work is to analyze the temporal and spatial variability of the total ozone column (TOC) trends over the Yangtze River Delta, the most populated region in China, during the last 35 years (1978–2013) using remote sensing-derived TOC data. Due [...] Read more.
The objective of this work is to analyze the temporal and spatial variability of the total ozone column (TOC) trends over the Yangtze River Delta, the most populated region in China, during the last 35 years (1978–2013) using remote sensing-derived TOC data. Due to the lack of continuous and well-covered ground-based TOC measurements, little is known about the Yangtze River Delta. TOC data derived from the Total Ozone Mapping Spectrometer (TOMS) for the period 1978–2005 and Ozone Monitoring Instrument (OMI) for the period 2004–2013 were used in this study. The spatial, long-term, seasonal, and short-term variations of TOC in this region were analyzed. For the spatial variability, the latitudinal variability has a large range between 3% and 13%, and also represents an annual cycle with maximum in February and minimum in August. In contrast, the longitudinal variability is not significant and just varies between 2% and 4%. The long-term variability represented a notable decline for the period 1978–2013. The ozone depletion was observed significantly during 1978–1999, with linear trend from (−3.2 ± 0.7) DU/decade to (−10.5 ± 0.9) DU/decade. As for seasonal variability, the trend of TOC shows a distinct seasonal pattern, with maximum in April or May and minimum in October or November. The short-term analysis demonstrates the day-to-day changes as well as the six-week system persistence of the TOC. The results can provide comprehensive descriptions of the TOC variations in the Yangtze River Delta and benefit climate change research in this region. Full article
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Open AccessTechnical Note
The Performance Analysis of the Tactical Inertial Navigator Aided by Non-GPS Derived References
Remote Sens. 2014, 6(12), 12511-12526; https://doi.org/10.3390/rs61212511
Received: 19 June 2014 / Revised: 3 December 2014 / Accepted: 5 December 2014 / Published: 11 December 2014
Cited by 4 | Viewed by 2000 | PDF Full-text (3025 KB) | HTML Full-text | XML Full-text
Abstract
The Inertial Navigation System (INS) is now widely applied in many navigation and mobile mapping applications due to its high sampling rates, high accuracy in short-term cases, and no limitations caused by interference or signal obstructions. In addition, the INS can continuously provide [...] Read more.
The Inertial Navigation System (INS) is now widely applied in many navigation and mobile mapping applications due to its high sampling rates, high accuracy in short-term cases, and no limitations caused by interference or signal obstructions. In addition, the INS can continuously provide the position, velocity and attitude of a vehicle. Conversely, the disadvantage of the stand-alone INS is that its accuracy degrades rapidly with time because of the accumulations of systematic errors and noises from accelerometers and gyroscopes. Therefore, this research aims to implement an integrated system with specific 3D position updates using non-GPS derived references to aid a tactical inertial navigator to provide seamless navigation solutions in the specific area without Global Positioning System (GPS) signals. An Extended Kalman Filter (EKF) is applied as the core estimator to provide superior performance and output the navigation solutions in real-time. The INS is updated by position from references such as the digital map, land mark, Digital Terrain Model (DTM) as well as waypoint to improve navigation accuracy in the long-term. In order to evaluate the performance of the proposed algorithm, field tests including land scenario in freeway and airborne scenario with an unmanned aerial test platform have been conducted. The preliminary results demonstrate that the proposed algorithm with non-GPS derived references aiding from digital map and waypoint for onboard aerial camera trigger to provide uninterrupted navigation solutions and better performance which can achieve the meter-level accuracy without GPS aiding for land and aerial scenarios, respectively. Full article
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Open AccessCorrection
Correction: Parks, S.A.; Dillon, G.K.; Miller, C. A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio. Remote Sens. 2014, 6, 1827–1844
Remote Sens. 2014, 6(12), 12509-12510; https://doi.org/10.3390/rs61212509
Received: 5 December 2014 / Accepted: 8 December 2014 / Published: 11 December 2014
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Abstract
Several columns in Table A2 (in the Appendix) [1] were mislabeled. [...] Full article
Open AccessArticle
Prevalence of Pure Versus Mixed Snow Cover Pixels across Spatial Resolutions in Alpine Environments
Remote Sens. 2014, 6(12), 12478-12508; https://doi.org/10.3390/rs61212478
Received: 2 October 2014 / Revised: 29 November 2014 / Accepted: 1 December 2014 / Published: 11 December 2014
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Abstract
Remote sensing of snow-covered area (SCA) can be binary (indicating the presence/absence of snow cover at each pixel) or fractional (indicating the fraction of each pixel covered by snow). Fractional SCA mapping provides more information than binary SCA, but is more difficult to [...] Read more.
Remote sensing of snow-covered area (SCA) can be binary (indicating the presence/absence of snow cover at each pixel) or fractional (indicating the fraction of each pixel covered by snow). Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed) pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013–August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%–93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow cover mapping, mixed pixels accounted for 67%–100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%–98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17–56 days and averaged 37 days. Our results suggest that mixed snow-covered snow-free pixels are common at the spatial resolutions imaged by both the Landsat and MODIS sensors. This highlights the additional information available from fractional SCA products and suggests fractional SCA can provide a major advantage for hydrological and climatological monitoring and modeling, particularly when accurate representation of the spatial distribution of snow cover is critical. Full article
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Open AccessArticle
Establishing a Baseline for Regional Scale Monitoring of Eelgrass (Zostera marina) Habitat on the Lower Alaska Peninsula
Remote Sens. 2014, 6(12), 12447-12477; https://doi.org/10.3390/rs61212447
Received: 20 May 2014 / Revised: 27 November 2014 / Accepted: 27 November 2014 / Published: 10 December 2014
Cited by 5 | Viewed by 2567 | PDF Full-text (5256 KB) | HTML Full-text | XML Full-text
Abstract
Seagrass meadows, one of the world’s most widespread and productive ecosystems, provide a wide range of services with real economic value. Worldwide declines in the distribution and abundance of seagrasses and increased threats to coastal ecosystems from climate change have prompted a need [...] Read more.
Seagrass meadows, one of the world’s most widespread and productive ecosystems, provide a wide range of services with real economic value. Worldwide declines in the distribution and abundance of seagrasses and increased threats to coastal ecosystems from climate change have prompted a need to acquire baseline data for monitoring and protecting these important habitats. We assessed the distribution and abundance of eelgrass (Zostera marina) along nearly 1200 km of shoreline on the lower Alaska Peninsula, a region of expansive eelgrass meadows whose status and trends are poorly understood. We demonstrate the effectiveness of a multi-scale approach by using Landsat satellite imagery to map the total areal extent of eelgrass while integrating field survey data to improve map accuracy and describe the physical and biological condition of the meadows. Innovative use of proven methods and processing tools was used to address challenges inherent to remote sensing in high latitude, coastal environments. Eelgrass was estimated to cover ~31,000 ha, 91% of submerged aquatic vegetation on the lower Alaska Peninsula, nearly doubling the known spatial extent of eelgrass in the region. Mapping accuracy was 80%–90% for eelgrass distribution at locations containing adequate field survey data for error analysis. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle
The S-NPP VIIRS Day-Night Band On-Orbit Calibration/Characterization and Current State of SDR Products
Remote Sens. 2014, 6(12), 12427-12446; https://doi.org/10.3390/rs61212427
Received: 1 October 2014 / Revised: 4 December 2014 / Accepted: 5 December 2014 / Published: 10 December 2014
Cited by 27 | Viewed by 3282 | PDF Full-text (3892 KB) | HTML Full-text | XML Full-text
Abstract
The launch of VIIRS on-board the Suomi-National Polar-orbiting Partnership (S-NPP) on 28 October 2011, marked the beginning of the next chapter on nighttime lights observation started by the Defense Meteorological Satellite Program’s (DMSP) OLS sensor more than two decades ago. The VIIRS observes [...] Read more.
The launch of VIIRS on-board the Suomi-National Polar-orbiting Partnership (S-NPP) on 28 October 2011, marked the beginning of the next chapter on nighttime lights observation started by the Defense Meteorological Satellite Program’s (DMSP) OLS sensor more than two decades ago. The VIIRS observes the nighttime lights on Earth through its day-night band (DNB), a panchromatic channel covering the wavelengths from 500 nm to 900 nm. Compared to its predecessors, the VIIRS DNB has a much improved spatial/temporal resolution, radiometric sensitivity and, more importantly, continuous calibration using on-board calibrators (OBCs). In this paper, we describe the current state of the NASA calibration and characterization methodology used in supporting mission data quality assurance and producing consistent mission-wide sensor data records (SDRs) through NASA’s Land Product Evaluation and Analysis Tool Element (Land PEATE). The NASA calibration method utilizes the OBCs to determine gains, offset drift and sign-to-noise ratio (SNR) over the entire mission. In gain determination, the time-dependent relative spectral response (RSR) is used to correct the optical throughput change over time. A deep space view acquired during an S-NPP pitch maneuver is used to compute the airglow free dark offset for DNB’s high gain stage. The DNB stray light is estimated each month from new-moon dark Earth surface observations to remove the excessive stray light over the day-night terminators. As the VIIRS DNB on-orbit calibration is the first of its kind, the evolution of the calibration methodology is evident when the S-NPP VIIRS’s official calibrations are compared with our latest mission-wide reprocessing. In the future, the DNB calibration methodology is likely to continue evolving, and the mission-wide reprocessing is a key to providing consistently calibrated DNB SDRs for the user community. In the meantime, the NASA Land PEATE provides an alternative source to obtain mission-wide DNB SDR products that are calibrated based on the latest NASA DNB calibration methodology. Full article
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
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Open AccessArticle
Mapping Forest Height in Alaska Using GLAS, Landsat Composites, and Airborne LiDAR
Remote Sens. 2014, 6(12), 12409-12426; https://doi.org/10.3390/rs61212409
Received: 27 June 2014 / Revised: 22 November 2014 / Accepted: 3 December 2014 / Published: 10 December 2014
Cited by 6 | Viewed by 3394 | PDF Full-text (4227 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation structure, including forest canopy height, is an important input variable to fire behavior modeling systems for simulating wildfire behavior. As such, forest canopy height is one of a nationwide suite of products generated by the LANDFIRE program. In the past, LANDFIRE has [...] Read more.
Vegetation structure, including forest canopy height, is an important input variable to fire behavior modeling systems for simulating wildfire behavior. As such, forest canopy height is one of a nationwide suite of products generated by the LANDFIRE program. In the past, LANDFIRE has relied on a combination of field observations and Landsat imagery to develop existing vegetation structure products. The paucity of field data in the remote Alaskan forests has led to a very simple forest canopy height classification for the original LANDFIRE forest height map. To better meet the needs of data users and refine the map legend, LANDFIRE incorporated ICESat Geoscience Laser Altimeter System (GLAS) data into the updating process when developing the LANDFIRE 2010 product. The high latitude of this region enabled dense coverage of discrete GLAS samples, from which forest height was calculated. Different methods for deriving height from the GLAS waveform data were applied, including an attempt to correct for slope. These methods were then evaluated and integrated into the final map according to predefined criteria. The resulting map of forest canopy height includes more height classes than the original map, thereby better depicting the heterogeneity of the landscape, and provides seamless data for fire behavior analysts and other users of LANDFIRE data. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle
A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images
Remote Sens. 2014, 6(12), 12381-12408; https://doi.org/10.3390/rs61212381
Received: 7 September 2014 / Revised: 25 November 2014 / Accepted: 27 November 2014 / Published: 10 December 2014
Cited by 16 | Viewed by 3388 | PDF Full-text (1591 KB) | HTML Full-text | XML Full-text
Abstract
A data assimilation method to produce complete temporal sequences of synthetic medium-resolution images is presented. The method implements a Kalman filter recursive algorithm that integrates medium and moderate resolution imagery. To demonstrate the approach, time series of 30-m spatial resolution NDVI images at [...] Read more.
A data assimilation method to produce complete temporal sequences of synthetic medium-resolution images is presented. The method implements a Kalman filter recursive algorithm that integrates medium and moderate resolution imagery. To demonstrate the approach, time series of 30-m spatial resolution NDVI images at 16-day time steps were generated using Landsat NDVI images and MODIS NDVI products at four sites with different ecosystems and land cover-land use dynamics. The results show that the time series of synthetic NDVI images captured seasonal land surface dynamics and maintained the spatial structure of the landscape at higher spatial resolution. The time series of synthetic medium-resolution NDVI images were validated within a Monte Carlo simulation framework. Normalized residuals decreased as the number of available observations increased, ranging from 0.2 to below 0.1. Residuals were also significantly lower for time series of synthetic NDVI images generated at combined recursion (smoothing) than individually at forward and backward recursions (filtering). Conversely, the uncertainties of the synthetic images also decreased when the number of available observations increased and combined recursions were implemented. Full article
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Open AccessArticle
BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data
Remote Sens. 2014, 6(12), 12360-12380; https://doi.org/10.3390/rs61212360
Received: 24 September 2014 / Revised: 17 November 2014 / Accepted: 25 November 2014 / Published: 9 December 2014
Cited by 27 | Viewed by 3519 | PDF Full-text (21833 KB) | HTML Full-text | XML Full-text
Abstract
A new supervised burned area mapping software named BAMS (Burned Area Mapping Software) is presented in this paper. The tool was built from standard ArcGISTM libraries. It computes several of the spectral indexes most commonly used in burned area detection and implements [...] Read more.
A new supervised burned area mapping software named BAMS (Burned Area Mapping Software) is presented in this paper. The tool was built from standard ArcGISTM libraries. It computes several of the spectral indexes most commonly used in burned area detection and implements a two-phase supervised strategy to map areas burned between two Landsat multitemporal images. The only input required from the user is the visual delimitation of a few burned areas, from which burned perimeters are extracted. After the discrimination of burned patches, the user can visually assess the results, and iteratively select additional sampling burned areas to improve the extent of the burned patches. The final result of the BAMS program is a polygon vector layer containing three categories: (a) burned perimeters, (b) unburned areas, and (c) non-observed areas. The latter refer to clouds or sensor observation errors. Outputs of the BAMS code meet the requirements of file formats and structure of standard validation protocols. This paper presents the tool’s structure and technical basis. The program has been tested in six areas located in the United States, for various ecosystems and land covers, and then compared against the National Monitoring Trends in Burn Severity (MTBS) Burned Area Boundaries Dataset. Full article
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Open AccessArticle
Automatic Seamline Network Generation for Urban Orthophoto Mosaicking with the Use of a Digital Surface Model
Remote Sens. 2014, 6(12), 12334-12359; https://doi.org/10.3390/rs61212334
Received: 13 October 2014 / Revised: 27 November 2014 / Accepted: 1 December 2014 / Published: 9 December 2014
Cited by 20 | Viewed by 2819 | PDF Full-text (12661 KB) | HTML Full-text | XML Full-text
Abstract
Intelligent seamline selection for image mosaicking is an area of active research in the fields of massive data processing, computer vision, photogrammetry and remote sensing. In mosaicking applications for digital orthophoto maps (DOMs), the visual transition in mosaics is mainly caused by differences [...] Read more.
Intelligent seamline selection for image mosaicking is an area of active research in the fields of massive data processing, computer vision, photogrammetry and remote sensing. In mosaicking applications for digital orthophoto maps (DOMs), the visual transition in mosaics is mainly caused by differences in positioning accuracy, image tone and relief displacement of high ground objects between overlapping DOMs. Among these three factors, relief displacement, which prevents the seamless mosaicking of images, is relatively more difficult to address. To minimize visual discontinuities, many optimization algorithms have been studied for the automatic selection of seamlines to avoid high ground objects. Thus, a new automatic seamline selection algorithm using a digital surface model (DSM) is proposed. The main idea of this algorithm is to guide a seamline toward a low area on the basis of the elevation information in a DSM. Given that the elevation of a DSM is not completely synchronous with a DOM, a new model, called the orthoimage elevation synchronous model (OESM), is derived and introduced. OESM can accurately reflect the elevation information for each DOM unit. Through the morphological processing of the OESM data in the overlapping area, an initial path network is obtained for seamline selection. Subsequently, a cost function is defined on the basis of several measurements, and Dijkstra’s algorithm is adopted to determine the least-cost path from the initial network. Finally, the proposed algorithm is employed for automatic seamline network construction; the effective mosaic polygon of each image is determined, and a seamless mosaic is generated. The experiments with three different datasets indicate that the proposed method meets the requirements for seamline network construction. In comparative trials, the generated seamlines pass through fewer ground objects with low time consumption. Full article
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Open AccessArticle
Spatial Pattern and Temporal Variation Law-Based Multi-Sensor Collaboration Method for Improving Regional Soil Moisture Monitoring Capabilities
Remote Sens. 2014, 6(12), 12309-12333; https://doi.org/10.3390/rs61212309
Received: 23 September 2014 / Revised: 25 November 2014 / Accepted: 28 November 2014 / Published: 9 December 2014
Cited by 5 | Viewed by 3321 | PDF Full-text (8086 KB) | HTML Full-text | XML Full-text
Abstract
Regional soil moisture distributions and changes are critical for agricultural production and environmental modeling. Currently, hundreds of satellite sensors exist with different soil moisture observation capabilities. However, multi-sensor collaborative observation mechanisms for improving regional soil moisture monitoring capabilities are lacking. In this study, [...] Read more.
Regional soil moisture distributions and changes are critical for agricultural production and environmental modeling. Currently, hundreds of satellite sensors exist with different soil moisture observation capabilities. However, multi-sensor collaborative observation mechanisms for improving regional soil moisture monitoring capabilities are lacking. In this study, a Spatial pattern and Temporal variation law-based Multi-sensor Collaboration (STMC) method is proposed to solve this problem. The first component of the STMC method deduces the regional soil moisture distribution and variation patterns based on time stability theory and long-term statistical analyses. The second component of the STMC method detects potential anomalous soil moisture events and immediately triggers the high spatial resolution sensor with the soonest pass-over time. In the detection phase, an anomalous soil moisture judgment (ASMJ) algorithm and high temporal resolution sensors (the Advanced Microwave Scanning Radiometer 2 (AMSR2)) were utilized. Experiments conducted in Hubei province, China, demonstrated that the proposed STMC method was capable of accurately identifying of anomalous soil moisture conditions caused by waterlogging and drought events. Additionally, we observed that the STMC method combined the advantages of different long-term observation, high temporal, and high spatial resolution sensors synergistically for monitoring purposes. Full article
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Open AccessArticle
Landsat-8 Operational Land Imager Radiometric Calibration and Stability
Remote Sens. 2014, 6(12), 12275-12308; https://doi.org/10.3390/rs61212275
Received: 30 July 2014 / Revised: 28 October 2014 / Accepted: 24 November 2014 / Published: 9 December 2014
Cited by 79 | Viewed by 3953 | PDF Full-text (6324 KB) | HTML Full-text | XML Full-text
Abstract
The Landsat-8 Operational Land Imager (OLI) was radiometrically calibrated prior to launch in terms of spectral radiance, using an integrating sphere source traceable to National Institute of Standards and Technology (NIST) standards of spectral irradiance. It was calibrated on-orbit in terms of reflectance [...] Read more.
The Landsat-8 Operational Land Imager (OLI) was radiometrically calibrated prior to launch in terms of spectral radiance, using an integrating sphere source traceable to National Institute of Standards and Technology (NIST) standards of spectral irradiance. It was calibrated on-orbit in terms of reflectance using diffusers characterized prior to launch using NIST traceable standards. The radiance calibration was performed with an uncertainty of ~3%; the reflectance calibration to an uncertainty of ~2%. On-orbit, multiple calibration techniques indicate that the sensor has been stable to better than 0.3% to date, with the exception of the shortest wavelength band, which has degraded about 1.0%. A transfer to orbit experiment conducted using the OLI’s heliostat-illuminated diffuser suggests that some bands increased in sensitivity on transition to orbit by as much as 5%, with an uncertainty of ~2.5%. On-orbit comparisons to other instruments and vicarious calibration techniques show the radiance (without a transfer to orbit adjustment), and reflectance calibrations generally agree with other instruments and ground measurements to within the uncertainties. Calibration coefficients are provided with the data products to convert to either radiance or reflectance units. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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Open AccessArticle
Extraction of Plant Physiological Status from Hyperspectral Signatures Using Machine Learning Methods
Remote Sens. 2014, 6(12), 12247-12274; https://doi.org/10.3390/rs61212247
Received: 17 July 2014 / Revised: 25 November 2014 / Accepted: 27 November 2014 / Published: 8 December 2014
Cited by 21 | Viewed by 3904 | PDF Full-text (1343 KB) | HTML Full-text | XML Full-text
Abstract
The machine learning method, random forest (RF), is applied in order to derive biophysical and structural vegetation parameters from hyperspectral signatures. Hyperspectral data are, among other things, characterized by their high dimensionality and autocorrelation. Common multivariate regression approaches, which usually include only a [...] Read more.
The machine learning method, random forest (RF), is applied in order to derive biophysical and structural vegetation parameters from hyperspectral signatures. Hyperspectral data are, among other things, characterized by their high dimensionality and autocorrelation. Common multivariate regression approaches, which usually include only a limited number of spectral indices as predictors, do not make full use of the available information. In contrast, machine learning methods, such as RF, are supposed to be better suited to extract information on vegetation status. First, vegetation parameters are extracted from hyperspectral signatures simulated with the radiative transfer model, PROSAIL. Second, the transferability of these results with respect to laboratory and field measurements is investigated. In situ observations of plant physiological parameters and corresponding spectra are gathered in the laboratory for summer barley (Hordeum vulgare). Field in situ measurements focus on winter crops over several growing seasons. Chlorophyll content, Leaf Area Index and phenological growth stages are derived from simulated and measured spectra. RF performs very robustly and with a very high accuracy on PROSAIL simulated data. Furthermore, it is almost unaffected by introduced noise and bias in the data. When applied to laboratory data, the prediction accuracy is still good (C\(_{ab}\): \(R^2\) = 0.94/ LAI: \(R^2\) = 0.80/BBCH (Growth stages of mono-and dicotyledonous plants) : \(R^2\) = 0.91), but not as high as for simulated spectra. Transferability to field measurements is given with prediction levels as high as for laboratory data (C\(_{ab}\): \(R^2\) = 0.89/LAI: \(R^2\) = 0.89/BBCH: \(R^2\) = \(\sim\)0.8). Wavelengths for deriving plant physiological status based on simulated and measured hyperspectral signatures are mostly selected from appropriate spectral regions (both field and laboratory): 700–800 nm regressing on C\(_{ab}\) and 800–1300 nm regressing on LAI. Results suggest that the prediction accuracy of vegetation parameters using RF is not hampered by the high dimensionality of hyperspectral signatures (given preceding feature reduction). Wavelengths selected as important for prediction might, however, vary between underlying datasets. The introduction of changing environmental factors (soil, illumination conditions) has some detrimental effect, but more important factors seem to stem from measurement uncertainties and plant geometries. Full article
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