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Remote Sens., Volume 6, Issue 9 (September 2014), Pages 7857-9144

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Open AccessArticle Changes in Spring Phenology in the Three-Rivers Headwater Region from 1999 to 2013
Remote Sens. 2014, 6(9), 9130-9144; https://doi.org/10.3390/rs6099130
Received: 19 March 2014 / Revised: 1 September 2014 / Accepted: 15 September 2014 / Published: 24 September 2014
Cited by 9 | PDF Full-text (2233 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation phenology is considered a sensitive indicator of terrestrial ecosystem response to global climate change. We used a satellite-derived normalized difference vegetation index to investigate the spatiotemporal changes in the green-up date over the Three-Rivers Headwater Region (TRHR) from 1999 to 2013 and
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Vegetation phenology is considered a sensitive indicator of terrestrial ecosystem response to global climate change. We used a satellite-derived normalized difference vegetation index to investigate the spatiotemporal changes in the green-up date over the Three-Rivers Headwater Region (TRHR) from 1999 to 2013 and characterized their driving forces using climatic data sets. A significant advancement trend was observed throughout the entire study area from 1999 to 2013 with a linear tendency of 6.3 days/decade (p < 0.01); the largest advancement trend was over the Yellow River source region (8.6 days/decade, p < 0.01). Spatially, the green-up date increased from the southeast to the northwest, and the green-up date of 87.4% of pixels fell between the 130th and 150th Julian day. Additionally, about 91.5% of the study area experienced advancement in the green-up date, of which 80.2%, mainly distributed in areas of vegetation coverage increase, experienced a significant advance. Moreover, it was found that the green-up date and its trend were significantly correlated with altitude. Statistical analyses showed that a 1-°C increase in spring temperature would induce an advancement in the green-up date of 4.2 days. We suggest that the advancement of the green-up date in the TRHR might be attributable principally to warmer and wetter springs. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
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Open AccessArticle Mapping Layers of Clay in a Vertical Geological Surface Using Hyperspectral Imagery: Variability in Parameters of SWIR Absorption Features under Different Conditions of Illumination
Remote Sens. 2014, 6(9), 9104-9129; https://doi.org/10.3390/rs6099104
Received: 1 June 2014 / Revised: 16 September 2014 / Accepted: 17 September 2014 / Published: 24 September 2014
Cited by 15 | PDF Full-text (4489 KB) | HTML Full-text | XML Full-text
Abstract
Hyperspectral imagery of a vertical mine face acquired from a field-based platform is used to evaluate the effects of different conditions of illumination on absorption feature parameters wavelength position, depth and width. Imagery was acquired at different times of the day under direct
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Hyperspectral imagery of a vertical mine face acquired from a field-based platform is used to evaluate the effects of different conditions of illumination on absorption feature parameters wavelength position, depth and width. Imagery was acquired at different times of the day under direct solar illumination and under diffuse illumination imposed by cloud cover. Imagery acquired under direct solar illumination did not show large amounts of variability in any absorption feature parameter; however, imagery acquired under cloud caused changes in absorption feature parameters. These included the introduction of a spurious absorption feature at wavelengths > 2250 nm and a shifting of the wavelength position of specific clay absorption features to longer or shorter wavelengths. Absorption feature depth increased. The spatial patterns of clay absorption in imagery acquired under similar conditions of direct illumination were preserved but not in imagery acquired under cloud. Kaolinite, ferruginous smectite and nontronite were identified and mapped on the mine face. Results were validated by comparing them with predictions from x-ray diffraction and laboratory hyperspectral imagery of samples acquired from the mine face. These results have implications for the collection of hyperspectral data from field-based platforms. Full article
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Open AccessArticle Multi-Level Spatial Analysis for Change Detection of Urban Vegetation at Individual Tree Scale
Remote Sens. 2014, 6(9), 9086-9103; https://doi.org/10.3390/rs6099086
Received: 27 May 2014 / Revised: 28 August 2014 / Accepted: 10 September 2014 / Published: 23 September 2014
Cited by 9 | PDF Full-text (7583 KB) | HTML Full-text | XML Full-text
Abstract
Spurious change is a common problem in urban vegetation change detection by using multi-temporal remote sensing images of high resolution. This usually results from the false-absent and false-present vegetation patches in an obscured and/or shaded scene. The presented approach focuses on object-based change
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Spurious change is a common problem in urban vegetation change detection by using multi-temporal remote sensing images of high resolution. This usually results from the false-absent and false-present vegetation patches in an obscured and/or shaded scene. The presented approach focuses on object-based change detection with joint use of spatial and spectral information, referring to it as multi-level spatial analyses. The analyses are conducted in three phases: (1) The pixel-level spatial analysis is performed by adding the density dimension into a multi-feature space for classification to indicate the spatial dependency between pixels; (2) The member-level spatial analysis is conducted by the self-adaptive morphology to readjust the incorrectly classified members according to the spatial dependency between members; (3) The object-level spatial analysis is reached by the self-adaptive morphology involved with the additional rule of sharing boundaries. Spatial analysis at this level will help detect spurious change objects according to the spatial dependency between objects. It is revealed that the error from the automatically extracted vegetation objects with the pixel- and member-level spatial analyses is no more than 2.56%, compared with 12.15% without spatial analysis. Moreover, the error from the automatically detected spurious changes with the object-level spatial analysis is no higher than 3.26% out of all the dynamic vegetation objects, meaning that the fully automatic detection of vegetation change at a joint maximum error of 5.82% can be guaranteed. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle High-Density LiDAR Mapping of the Ancient City of Mayapán
Remote Sens. 2014, 6(9), 9064-9085; https://doi.org/10.3390/rs6099064
Received: 23 July 2014 / Revised: 8 September 2014 / Accepted: 9 September 2014 / Published: 23 September 2014
Cited by 18 | PDF Full-text (35723 KB) | HTML Full-text | XML Full-text
Abstract
A 2013 survey of a 40 square kilometer area surrounding Mayapán, Yucatan, Mexico used high-density LiDAR data to map prehispanic architecture and related natural features. Most of the area is covered by low canopy dense forest vegetation over karstic hilly terrain that impedes
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A 2013 survey of a 40 square kilometer area surrounding Mayapán, Yucatan, Mexico used high-density LiDAR data to map prehispanic architecture and related natural features. Most of the area is covered by low canopy dense forest vegetation over karstic hilly terrain that impedes full coverage archaeological survey. We used LiDAR at 40 laser points per square meter to generate a bare earth digital elevation model (DEM). Results were evaluated with comparisons to previously mapped areas and with traditional archaeological survey methods for 38 settlement clusters outside of the city wall. Ground checking employed full coverage survey of selected 500 m grid squares, as well as documentation of the chronology and detail of new public and domestic settlement features and cenotes. Results identify the full extent of continued, contemporary Postclassic settlement (A.D. 1150–1450) outside of the city wall to at least 500 meters to the east, north, and west. New data also reveal an extensive modified landscape of terraformed residential hills, rejolladas, and dense settlement dating from Preclassic through Classic Periods. The LiDAR data also allow for the identification of rooms, benches, and stone property walls and lanes within the city. Full article
(This article belongs to the Special Issue New Perspectives of Remote Sensing for Archaeology)
Open AccessArticle Defining the Spatial Resolution Requirements for Crop Identification Using Optical Remote Sensing
Remote Sens. 2014, 6(9), 9034-9063; https://doi.org/10.3390/rs6099034
Received: 23 June 2014 / Revised: 1 September 2014 / Accepted: 4 September 2014 / Published: 23 September 2014
Cited by 29 | PDF Full-text (13082 KB) | HTML Full-text | XML Full-text
Abstract
The past decades have seen an increasing demand for operational monitoring of crop conditions and food production at local to global scales. To properly use satellite Earth observation for such agricultural monitoring, high temporal revisit frequency over vast geographic areas is necessary. However,
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The past decades have seen an increasing demand for operational monitoring of crop conditions and food production at local to global scales. To properly use satellite Earth observation for such agricultural monitoring, high temporal revisit frequency over vast geographic areas is necessary. However, this often limits the spatial resolution that can be used. The challenge of discriminating pixels that correspond to a particular crop type, a prerequisite for crop specific agricultural monitoring, remains daunting when the signal encoded in pixels stems from several land uses (mixed pixels), e.g., over heterogeneous landscapes where individual fields are often smaller than individual pixels. The question of determining the optimal pixel sizes for an application such as crop identification is therefore naturally inclined towards finding the coarsest acceptable pixel sizes, so as to potentially benefit from what instruments with coarser pixels can offer. To answer this question, this study builds upon and extends a conceptual framework to quantitatively define pixel size requirements for crop identification via image classification. This tool can be modulated using different parameterizations to explore trade-offs between pixel size and pixel purity when addressing the question of crop identification. Results over contrasting landscapes in Central Asia demonstrate that the task of finding the optimum pixel size does not have a “one-size-fits-all” solution. The resulting values for pixel size and purity that are suitable for crop identification proved to be specific to a given landscape, and for each crop they differed across different landscapes. Over the same time series, different crops were not identifiable simultaneously in the season and these requirements further changed over the years, reflecting the different agro-ecological conditions the crops are growing in. Results indicate that sensors like MODIS (250 m) could be suitable for identifying major crop classes in the study sites, whilst sensors like Landsat (30 m) should be considered for object-based classification. The proposed framework is generic and can be applied to any agricultural landscape, thereby potentially serving to guide recommendations for designing dedicated EO missions that can satisfy the requirements in terms of pixel size to identify and discriminate crop types. Full article
(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)
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Open AccessArticle Automatic Road Centerline Extraction from Imagery Using Road GPS Data
Remote Sens. 2014, 6(9), 9014-9033; https://doi.org/10.3390/rs6099014
Received: 20 June 2014 / Revised: 25 August 2014 / Accepted: 2 September 2014 / Published: 23 September 2014
Cited by 12 | PDF Full-text (12012 KB) | HTML Full-text | XML Full-text
Abstract
Road centerline extraction from imagery constitutes a key element in numerous geospatial applications, which has been addressed through a variety of approaches. However, most of the existing methods are not capable of dealing with challenges such as different road shapes, complex scenes, and
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Road centerline extraction from imagery constitutes a key element in numerous geospatial applications, which has been addressed through a variety of approaches. However, most of the existing methods are not capable of dealing with challenges such as different road shapes, complex scenes, and variable resolutions. This paper presents a novel method for road centerline extraction from imagery in a fully automatic approach that addresses the aforementioned challenges by exploiting road GPS data. The proposed method combines road color feature with road GPS data to detect road centerline seed points. After global alignment of road GPS data, a novel road centerline extraction algorithm is developed to extract each individual road centerline in local regions. Through road connection, road centerline network is generated as the final output. Extensive experiments demonstrate that our proposed method can rapidly and accurately extract road centerline from remotely sensed imagery. Full article
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Open AccessArticle Narrowband Bio-Indicator Monitoring of Temperate Forest Carbon Fluxes in Northeastern China
Remote Sens. 2014, 6(9), 8986-9013; https://doi.org/10.3390/rs6098986
Received: 3 June 2014 / Revised: 5 September 2014 / Accepted: 10 September 2014 / Published: 22 September 2014
Cited by 5 | PDF Full-text (6048 KB) | HTML Full-text | XML Full-text | Correction
Abstract
Developments in hyperspectral remote sensing techniques during the last decade have enabled the use of narrowband indices to evaluate the role of forest ecosystem variables in estimating carbon (C) fluxes. In this study, narrowband bio-indicators derived from EO-1 Hyperion data were investigated to
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Developments in hyperspectral remote sensing techniques during the last decade have enabled the use of narrowband indices to evaluate the role of forest ecosystem variables in estimating carbon (C) fluxes. In this study, narrowband bio-indicators derived from EO-1 Hyperion data were investigated to determine whether they could capture the temporal variation and estimate the spatial variability of forest C fluxes derived from eddy covariance tower data. Nineteen indices were divided into four categories of optical indices: broadband, chlorophyll, red edge, and light use efficiency. Correlation tests were performed between the selected vegetation indices, gross primary production (GPP), and ecosystem respiration (Re). Among the 19 indices, five narrowband indices (Chlorophyll Index RedEdge 710, scaled photochemical reflectance index (SPRI)*enhanced vegetation index (EVI), SPRI*normalized difference vegetation index (NDVI), MCARI/OSAVI[705, 750] and the Vogelmann Index), and one broad band index (EVI) had R-squared values with a good fit for GPP and Re. The SPRI*NDVI has the highest significant coefficients of determination with GPP and Re (R2 = 0.86 and 0.89, p < 0.0001, respectively). SPRI*NDVI was used in atmospheric inverse modeling at regional scales for the estimation of C fluxes. We compared the GPP spatial patterns inversed from our model with corresponding results from the Vegetation Photosynthesis Model (VPM), the Boreal Ecosystems Productivity Simulator model, and MODIS MOD17A2 products. The inversed GPP spatial patterns from our model of SPRI*NDVI had good agreement with the output from the VPM model. The normalized difference nitrogen index was well correlated with measured C net ecosystem exchange. Our findings indicated that narrowband bio-indicators based on EO-1 Hyperion images could be used to predict regional C flux variations for Northeastern China’s temperate broad-leaved Korean pine forest ecosystems. Full article
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Open AccessArticle Derivation of Land Surface Albedo at High Resolution by Combining HJ-1A/B Reflectance Observations with MODIS BRDF Products
Remote Sens. 2014, 6(9), 8966-8985; https://doi.org/10.3390/rs6098966
Received: 21 April 2014 / Revised: 17 July 2014 / Accepted: 18 July 2014 / Published: 22 September 2014
Cited by 5 | PDF Full-text (1041 KB) | HTML Full-text | XML Full-text
Abstract
Land surface albedo is an essential parameter for monitoring global/regional climate and land surface energy balance. Although many studies have been conducted on global or regional land surface albedo using various remote sensing data over the past few decades, land surface albedo product
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Land surface albedo is an essential parameter for monitoring global/regional climate and land surface energy balance. Although many studies have been conducted on global or regional land surface albedo using various remote sensing data over the past few decades, land surface albedo product with a high spatio–temporal resolution is currently very scarce. This paper proposes a method for deriving land surface albedo with a high spatio–temporal resolution (space: 30 m and time: 2–4 days). The proposed method works by combining the land surface reflectance data at 30 m spatial resolution obtained from the charge-coupled devices in the Huanjing-1A and -1B (HJ-1A/B) satellites with the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface bidirectional reflectance distribution function (BRDF) parameters product (MCD43A1), which is at a spatial resolution of 500 m. First, the land surface BRDF parameters for HJ-1A/B land surface reflectance with a spatial–temporal resolutions of 30 m and 2–4 day are calculated on the basis of the prior knowledge from the MODIS BRDF product; then, the calculated high resolution BRDF parameters are integrated over the illuminating/viewing hemisphere to produce the white- and black-sky albedos at 30 m resolution. These results form the basis for the final land surface albedo derivation by accounting for the proportion of direct and diffuse solar radiation arriving at the ground. The albedo retrieved by this novel method is compared with MODIS land surface albedo products, as well as with ground measurements. The results show that the derived land surface albedo during the growing season of 2012 generally achieved a mean absolute accuracy of ±0.044, and a root mean square error of 0.039, confirming the effectiveness of the newly proposed method. Full article
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Open AccessArticle Large Differences in Terrestrial Vegetation Production Derived from Satellite-Based Light Use Efficiency Models
Remote Sens. 2014, 6(9), 8945-8965; https://doi.org/10.3390/rs6098945
Received: 20 May 2014 / Revised: 28 August 2014 / Accepted: 10 September 2014 / Published: 22 September 2014
Cited by 12 | PDF Full-text (11381 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Terrestrial gross primary production (GPP) is the largest global CO2 flux and determines other ecosystem carbon cycle variables. Light use efficiency (LUE) models may have the most potential to adequately address the spatial and temporal dynamics of GPP, but recent studies have
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Terrestrial gross primary production (GPP) is the largest global CO2 flux and determines other ecosystem carbon cycle variables. Light use efficiency (LUE) models may have the most potential to adequately address the spatial and temporal dynamics of GPP, but recent studies have shown large model differences in GPP simulations. In this study, we investigated the GPP differences in the spatial and temporal patterns derived from seven widely used LUE models at the global scale. The result shows that the global annual GPP estimates over the period 2000–2010 varied from 95.10 to 139.71 Pg C∙yr1 among models. The spatial and temporal variation of global GPP differs substantially between models, due to different model structures and dominant environmental drivers. In almost all models, water availability dominates the interannual variability of GPP over large vegetated areas. Solar radiation and air temperature are not the primary controlling factors for interannual variability of global GPP estimates for most models. The disagreement among the current LUE models highlights the need for further model improvement to quantify the global carbon cycle. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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Open AccessArticle 1982–2010 Trends of Light Use Efficiency and Inherent Water Use Efficiency in African vegetation: Sensitivity to Climate and Atmospheric CO2 Concentrations
Remote Sens. 2014, 6(9), 8923-8944; https://doi.org/10.3390/rs6098923
Received: 29 April 2014 / Revised: 19 July 2014 / Accepted: 25 August 2014 / Published: 22 September 2014
Cited by 6 | PDF Full-text (2700 KB) | HTML Full-text | XML Full-text
Abstract
Light and water use by vegetation at the ecosystem level, are key components for understanding the carbon and water cycles particularly in regions with high climate variability and dry climates such as Africa. The objective of this study is to examine recent trends
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Light and water use by vegetation at the ecosystem level, are key components for understanding the carbon and water cycles particularly in regions with high climate variability and dry climates such as Africa. The objective of this study is to examine recent trends over the last 30 years in Light Use Efficiency (LUE) and inherent Water Use Efficiency (iWUE*) for the major biomes of Africa, including their sensitivities to climate and CO2. LUE and iWUE* trends are analyzed using a combination of NOAA-AVHRR NDVI3g and fAPAR3g, and a data-driven model of monthly evapotranspiration and Gross Primary Productivity (based on flux tower measurements and remote sensing fAPAR, yet with no flux tower data in Africa) and the ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms) process-based land surface model driven by variable CO2 and two different gridded climate fields. The iWUE* data product increases by 10%–20% per decade during the 1982–2010 period over the northern savannas (due to positive trend of vegetation productivity) and the central African forest (due to positive trend of vapor pressure deficit). In contrast to the iWUE*, the LUE trends are not statistically significant. The process-based model simulations only show a positive linear trend in iWUE* and LUE over the central African forest. Additionally, factorial model simulations were conducted to attribute trends in iWUE and LUE to climate change and rising CO2 concentrations. We found that the increase of atmospheric CO2 by 52.8 ppm during the period of study explains 30%–50% of the increase in iWUE* and >90% of the LUE trend over the central African forest. The modeled iWUE* trend exhibits a high sensitivity to the climate forcing and environmental conditions, whereas the LUE trend has a smaller sensitivity to the selected climate forcing. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Open AccessArticle A Comparison of Model-Assisted Estimators to Infer Land Cover/Use Class Area Using Satellite Imagery
Remote Sens. 2014, 6(9), 8904-8922; https://doi.org/10.3390/rs6098904
Received: 11 April 2014 / Revised: 18 July 2014 / Accepted: 9 September 2014 / Published: 19 September 2014
Cited by 1 | PDF Full-text (2686 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing provides timely, economic, and objective data over a large area and has become the main data source for land cover/use area estimation. However, the classification results cannot be directly used to calculate the area of a given land cover/use type because
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Remote sensing provides timely, economic, and objective data over a large area and has become the main data source for land cover/use area estimation. However, the classification results cannot be directly used to calculate the area of a given land cover/use type because of classification errors. The main purpose of this study is to explore the performance and stability of several model-assisted estimators in various overall accuracies of classification and sampling fractions. In this study, the confusion matrix calibration direct estimator, confusion matrix calibration inverse estimator, ratio estimator, and simple regression estimator were implemented to infer the areas of several land cover classes using simple random sampling without replacement in two experiments: a case study using simulation data based on RapidEye images and that using actual RapidEye and Huan Jing (HJ)-1A images. In addition, the simple estimator using a simple random sample without replacement was regarded as a basic estimator. The comparison results suggested that the confusion matrix calibration estimators, ratio estimator, and simple regression estimator could provide more accurate and stable estimates than the simple random sampling estimator. In addition, high-quality classification data played a positive role in the estimation, and the confusion matrix inverse estimators were more sensitive to the classification accuracy. In the simulated experiment, the average deviation of a confusion matrix calibration inverse estimator decreased by about 0.195 with the increasing overall accuracy of classification; otherwise, the variation of the other three model-assisted estimators was 0.102. Moreover, the simple regression estimator was slightly superior to the confusion matrix calibration estimators and required fewer sample units under acceptable classification accuracy levels of 70%–90%. Full article
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Open AccessArticle Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover
Remote Sens. 2014, 6(9), 8878-8903; https://doi.org/10.3390/rs6098878
Received: 10 June 2014 / Revised: 3 September 2014 / Accepted: 10 September 2014 / Published: 19 September 2014
Cited by 18 | PDF Full-text (7008 KB) | HTML Full-text | XML Full-text
Abstract
We introduce and test a new method to detect annual forest cover loss from time series estimates of percent tree cover. Our approach is founded on two realistic assumptions: (1) land cover disturbances are rare events over large geographic areas that occur within
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We introduce and test a new method to detect annual forest cover loss from time series estimates of percent tree cover. Our approach is founded on two realistic assumptions: (1) land cover disturbances are rare events over large geographic areas that occur within a short time frame; and (2) spatially discrete land cover disturbances are continuous processes over time. Applying statistically rigorous algorithms, we first detect disturbance pixels as outliers of an underlying chi-square distribution. Then, we fit nonlinear, logistic curves for each identified change pixel to simultaneously characterize the magnitude and timing of the disturbance. Our method is applied using the yearly Vegetation Continuous Fields (VCF) tree cover product from Moderate Resolution Imaging Spectroradiometer (MODIS), and the resulting disturbance-year estimates are evaluated using a large sample of Landsat-based forest disturbance data. Temporal accuracy is ~65% at 250-m, annual resolution and increases to >85% when temporal resolution is relaxed to ±1 yr. The r2 of MODIS VCF-based disturbance rates against Landsat ranges from 0.7 to 0.9 at 5-km spatial resolution. The general approach developed in this study can be potentially applied at a global scale and to other land cover types characterized as continuous variables from satellite data. Full article
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Open AccessArticle Comparison of Latent Heat Flux Using Aerodynamic Methods and Using the Penman–Monteith Method with Satellite-Based Surface Energy Balance
Remote Sens. 2014, 6(9), 8844-8877; https://doi.org/10.3390/rs6098844
Received: 8 April 2014 / Revised: 30 August 2014 / Accepted: 3 September 2014 / Published: 19 September 2014
Cited by 11 | PDF Full-text (15397 KB) | HTML Full-text | XML Full-text
Abstract
A surface energy balance was conducted to calculate the latent heat flux (λE) using aerodynamic methods and the Penman–Monteith (PM) method. Computations were based on gridded weather and Landsat satellite reflected and thermal data. The surface energy balance facilitated a comparison of impacts
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A surface energy balance was conducted to calculate the latent heat flux (λE) using aerodynamic methods and the Penman–Monteith (PM) method. Computations were based on gridded weather and Landsat satellite reflected and thermal data. The surface energy balance facilitated a comparison of impacts of different parameterizations and assumptions, while calculating λE over large areas through the use of remote sensing. The first part of the study compares the full aerodynamic method for estimating latent heat flux against the appropriately parameterized PM method with calculation of bulk surface resistance (rs). The second part of the study compares the appropriately parameterized PM method against the PM method, with various relaxations on parameters. This study emphasizes the use of separate aerodynamic equations (latent heat flux and sensible heat flux) against the combined Penman–Monteith equation to calculate λE when surface temperature (Ts) is much warmer than air temperature (Ta), as will occur under water stressed conditions. The study was conducted in southern Idaho for a 1000-km2 area over a range of land use classes and for two Landsat satellite overpass dates. The results show discrepancies in latent heat flux (λE) values when the PM method is used with simplifications and relaxations, compared to the appropriately parameterized PM method and full aerodynamic method. Errors were particularly significant in areas of sparse vegetation where differences between Ts and Ta were high. The maximum RMSD between the correct PM method and simplified PM methods was about 56 W/m2 in sparsely vegetated sagebrush desert where the same surface resistance was applied. Full article
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Open AccessArticle Multi-Chromatic Analysis of SAR Images for Coherent Target Detection
Remote Sens. 2014, 6(9), 8822-8843; https://doi.org/10.3390/rs6098822
Received: 8 July 2014 / Revised: 10 September 2014 / Accepted: 10 September 2014 / Published: 19 September 2014
Cited by 7 | PDF Full-text (20695 KB) | HTML Full-text | XML Full-text
Abstract
This work investigates the possibility of performing target analysis through the Multi-Chromatic Analysis (MCA), a technique that basically explores the information content of sub-band images obtained by processing portions of the range spectrum of a synthetic aperture radar (SAR) image. According to the
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This work investigates the possibility of performing target analysis through the Multi-Chromatic Analysis (MCA), a technique that basically explores the information content of sub-band images obtained by processing portions of the range spectrum of a synthetic aperture radar (SAR) image. According to the behavior of the SAR signal at the different sub-bands, MCA allows target classification. Two strategies have been experimented by processing TerraSAR-X images acquired over the Venice Lagoon, Italy: one exploiting the phase of interferometric sub-band pairs, the other using the spectral coherence derived by computing the coherence between sub-band images of a single SAR acquisition. The first approach introduces the concept of frequency-persistent scatterers (FPS), which is complementary to that of the time-persistent scatterers (PS). FPS and PS populations have been derived and analyzed to evaluate the respective characteristics and the physical nature of the targets. Spectral coherence analysis has been applied to vessel detection, according to the property that, in presence of a random distribution of surface scatterers, as for open sea surfaces, spectral coherence is expected to be proportional to sub-band intersection, while in presence of manmade structures it is preserved anyhow. First results show that spectral coherence is well preserved even for very small vessels, and can be used as a complementary information channel to constrain vessel detection in addition to classical Constant False Alarm Rate techniques based on the sole intensity channel. Full article
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Open AccessArticle Radiometric Calibration Methodology of the Landsat 8 Thermal Infrared Sensor
Remote Sens. 2014, 6(9), 8803-8821; https://doi.org/10.3390/rs6098803
Received: 6 August 2014 / Revised: 10 September 2014 / Accepted: 10 September 2014 / Published: 19 September 2014
Cited by 14 | PDF Full-text (711 KB) | HTML Full-text | XML Full-text
Abstract
The science-focused mission of the Landsat 8 Thermal Infrared Sensor (TIRS) requires that it have an accurate radiometric calibration. A calibration methodology was developed to convert the raw output from the instrument into an accurate at-aperture radiance. The methodology is based on measurements
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The science-focused mission of the Landsat 8 Thermal Infrared Sensor (TIRS) requires that it have an accurate radiometric calibration. A calibration methodology was developed to convert the raw output from the instrument into an accurate at-aperture radiance. The methodology is based on measurements obtained during component-level and instrument-level characterization testing. The radiometric accuracy from the pre-flight measurements was estimated to be approximately 0.7%. The calibration parameters determined pre-flight were updated during the post-launch checkout period by utilizing the on-board calibration sources and Earth scene data. These relative corrections were made to adjust for differences between the pre-flight and the on-orbit performance of the instrument, thereby correcting large striping artifacts observed in Earth imagery. Despite this calibration correction, banding artifacts (low frequency variation in the across-track direction) have been observed in certain uniform Earth scenes, but not in other uniform scenes. In addition, the absolute calibration performance determined from vicarious measurements have revealed a time-varying error to the absolute radiance reported by TIRS. These issues were determined to not be caused by the calibration process developed for the instrument. Instead, an investigation has revealed that stray light is affecting the recorded signal from the Earth. The varying optical stray light effect is an ongoing subject of evaluation and investigation, and a correction strategy is being devised that will be added to the calibration process. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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