Next Issue
Previous Issue

E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Table of Contents

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

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
View options order results:
result details:
Displaying articles 1-54
Export citation of selected articles as:
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 11 | Viewed by 2347 | 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 [...] Read more.
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)
Figures

Graphical abstract

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 16 | Viewed by 2767 | 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 [...] Read more.
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
Figures

Graphical abstract

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 13 | Viewed by 3165 | 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 [...] Read more.
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))
Figures

Graphical abstract

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 24 | Viewed by 3673 | 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 [...] Read more.
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 34 | Viewed by 3676 | 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, [...] Read more.
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)
Figures

Graphical abstract

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 17 | Viewed by 2549 | 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 [...] Read more.
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
Figures

Graphical abstract

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 | Viewed by 2478 | 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 [...] Read more.
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
Figures

Graphical abstract

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 6 | Viewed by 2627 | 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 [...] Read more.
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
Figures

Graphical abstract

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 14 | Viewed by 2726 | 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 [...] Read more.
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)
Figures

Graphical abstract

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 8 | Viewed by 3207 | 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 [...] Read more.
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))
Figures

Graphical abstract

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 3 | Viewed by 2289 | 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 [...] Read more.
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
Figures

Graphical abstract

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 21 | Viewed by 4572 | 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 [...] Read more.
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
Figures

Graphical abstract

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 13 | Viewed by 5383 | 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 [...] Read more.
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
Figures

Graphical abstract

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 9 | Viewed by 2671 | 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 [...] Read more.
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
Figures

Graphical abstract

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 22 | Viewed by 4665 | 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 [...] Read more.
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)
Figures

Graphical abstract

Open AccessArticle
Can I Trust My One-Class Classification?
Remote Sens. 2014, 6(9), 8779-8802; https://doi.org/10.3390/rs6098779
Received: 22 May 2014 / Revised: 11 September 2014 / Accepted: 12 September 2014 / Published: 19 September 2014
Cited by 26 | Viewed by 3407 | PDF Full-text (2798 KB) | HTML Full-text | XML Full-text
Abstract
Contrary to binary and multi-class classifiers, the purpose of a one-class classifier for remote sensing applications is to map only one specific land use/land cover class of interest. Training these classifiers exclusively requires reference data for the class of interest, while training data [...] Read more.
Contrary to binary and multi-class classifiers, the purpose of a one-class classifier for remote sensing applications is to map only one specific land use/land cover class of interest. Training these classifiers exclusively requires reference data for the class of interest, while training data for other classes is not required. Thus, the acquisition of reference data can be significantly reduced. However, one-class classification is fraught with uncertainty and full automatization is difficult, due to the limited reference information that is available for classifier training. Thus, a user-oriented one-class classification strategy is proposed, which is based among others on the visualization and interpretation of the one-class classifier outcomes during the data processing. Careful interpretation of the diagnostic plots fosters the understanding of the classification outcome, e.g., the class separability and suitability of a particular threshold. In the absence of complete and representative validation data, which is the fact in the context of a real one-class classification application, such information is valuable for evaluation and improving the classification. The potential of the proposed strategy is demonstrated by classifying different crop types with hyperspectral data from Hyperion. Full article
Figures

Graphical abstract

Open AccessArticle
Inversion of Aerosol Optical Depth Based on the CCD and IRS Sensors on the HJ-1 Satellites
Remote Sens. 2014, 6(9), 8760-8778; https://doi.org/10.3390/rs6098760
Received: 31 July 2014 / Accepted: 10 September 2014 / Published: 19 September 2014
Cited by 7 | Viewed by 2589 | PDF Full-text (9126 KB) | HTML Full-text | XML Full-text
Abstract
To perform a high-resolution aerosol optical depth (AOD) inversion from the HJ-1 satellites, a dark pixel algorithm utilizing the HJ-1 satellite data was developed based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) algorithm. By analyzing the relationship between the apparent reflectance from the 1.65 [...] Read more.
To perform a high-resolution aerosol optical depth (AOD) inversion from the HJ-1 satellites, a dark pixel algorithm utilizing the HJ-1 satellite data was developed based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) algorithm. By analyzing the relationship between the apparent reflectance from the 1.65 μm and 2.1 μm channels of MODIS, a method for estimating albedo using the 1.65 μm channel data of the HJ-1 satellites was established, and a high-resolution AOD inversion in the Chengdu region based on the HJ-1 satellite was completed. A comparison of the inversion results with CE318 measured data produced a correlation of 0.957, respectively, with an absolute error of 0.106. An analysis of the AOD inversion results from different aerosol models showed that the rural aerosol model was suitable as a general model for establishing an aerosol inversion look-up table for the Chengdu region. Full article
(This article belongs to the Special Issue Aerosol and Cloud Remote Sensing)
Figures

Graphical abstract

Open AccessArticle
Assessing Consistency of Five Global Land Cover Data Sets in China
Remote Sens. 2014, 6(9), 8739-8759; https://doi.org/10.3390/rs6098739
Received: 10 June 2014 / Revised: 7 August 2014 / Accepted: 9 September 2014 / Published: 18 September 2014
Cited by 27 | Viewed by 4729 | PDF Full-text (5638 KB) | HTML Full-text | XML Full-text
Abstract
Global land cover mapping with high accuracy is essential to downstream researches. Five global land cover data sets derived from moderate-resolution satellites, i.e., Global Land Cover Characterization (GLCC), University of Maryland land cover product (UMd), Global Land Cover 2000 project data (GLC2000), [...] Read more.
Global land cover mapping with high accuracy is essential to downstream researches. Five global land cover data sets derived from moderate-resolution satellites, i.e., Global Land Cover Characterization (GLCC), University of Maryland land cover product (UMd), Global Land Cover 2000 project data (GLC2000), MODIS Land Cover product (MODIS LC), and GLOBCOVER land cover product (GlobCover), have been widely used in many researches. However, these data sets were produced using different data sources and class definitions, which led to high uncertainty and inconsistency when using them. This study looked into the consistencies and discrepancies among the five data sets in China. All of the compared data sets were aggregated to consistent spatial resolution and extent, along with a 12-class thematic classification schema; intercomparisons among five datasets and each with reference data GLCD-2005 were performed. Results show reasonable agreement across the five data sets over China in terms of the dominating land cover types like Grassland and Cropland; while discrepancies of Forest classes, particularly Shrubland and Wetland among them are great. Additionally, GLC2000 has the highest agreement with GLCD-2005; MODIS LC gets the highest map-specific consistency compared with others; whereas UMd has the lowest agreement with GLCD-2005, but also has the lowest map-specific consistency. Full article
Figures

Graphical abstract

Open AccessArticle
Assessing Seasonal Backscatter Variations with Respect to Uncertainties in Soil Moisture Retrieval in Siberian Tundra Regions
Remote Sens. 2014, 6(9), 8718-8738; https://doi.org/10.3390/rs6098718
Received: 30 June 2014 / Revised: 26 August 2014 / Accepted: 2 September 2014 / Published: 17 September 2014
Cited by 13 | Viewed by 2913 | PDF Full-text (5463 KB) | HTML Full-text | XML Full-text
Abstract
Knowledge of surface hydrology is essential for many applications, including studies that aim to understand permafrost response to changing climate and the associated feedback mechanisms. Advanced remote sensing techniques make it possible to retrieve a range of land-surface variables, including radar retrieved soil [...] Read more.
Knowledge of surface hydrology is essential for many applications, including studies that aim to understand permafrost response to changing climate and the associated feedback mechanisms. Advanced remote sensing techniques make it possible to retrieve a range of land-surface variables, including radar retrieved soil moisture (SSM). It has been pointed out before that soil moisture retrieval from satellite data can be challenging at high latitudes, which correspond to remote areas where ground data are scarce and the applicability of satellite data of this type is essential. This study investigates backscatter variability other than associated with changing soil moisture in order to examine the possible impact on soil moisture retrieval. It focuses on issues specific to SSM retrieval in the Arctic, notably variations related to tundra lakes. ENVISAT Advanced Synthetic Aperture Radar (ASAR) Wide Swath (WS, 120 m) data are used to understand and quantify impacts on Metop (AAdvanced Scatterometer (ASCAT, 25 km) soil moisture retrieval during the snow free period. Sites of interest are chosen according to ASAR WS availability, high or low agreement between output from the land surface model ORCHIDEE and ASCAT derived SSM. Backscatter variations are analyzed with respect to the ASCAT footprint area. It can be shown that the low model agreement is related to water fraction in most cases. No difference could be detected between periods with floating ice (in snow off situation) and ice free periods at the chosen sites. The mean footprint backscatter is however impacted by partial short term surface roughness change. The water fraction correlates with backscatter deviations (relative to a smooth water surface reference image) within the ASCAT footprint areas (R = 0.91) Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
Figures

Graphical abstract

Open AccessArticle
Mapping Levees Using LiDAR Data and Multispectral Orthoimages in the Nakdong River Basins, South Korea
Remote Sens. 2014, 6(9), 8696-8717; https://doi.org/10.3390/rs6098696
Received: 29 April 2014 / Revised: 3 September 2014 / Accepted: 3 September 2014 / Published: 16 September 2014
Cited by 8 | Viewed by 2462 | PDF Full-text (10167 KB) | HTML Full-text | XML Full-text
Abstract
Mapping levees is important for analyzing levee surfaces, assessing levee stability, etc. Historically, mapping levees has been carried out using ground surveying methods or only one type of remote sensing dataset. This research aims to map levees using airborne topographic LiDAR data and [...] Read more.
Mapping levees is important for analyzing levee surfaces, assessing levee stability, etc. Historically, mapping levees has been carried out using ground surveying methods or only one type of remote sensing dataset. This research aims to map levees using airborne topographic LiDAR data and multispectral orthoimages taken in the Nakdong River Basins. Levee surfaces consist of multiple objects with different geometric and spectral patterns. This research investigates different methods for identifying multiple levee components, such as major objects and eroded areas. Multiple geometric analysis approaches such as the slope classification method, and elevation and area analysis are used to identify the levee crown, berm, slope surfaces, and the eroded area, with different geometric patterns using the LiDAR data. Next, a spectral analysis approach, such as the clustering algorithm, is used to identify the major objects with different spectral patterns on the identified components using multispectral orthoimages. Finally, multiple levee components, including major objects and eroded areas, are identified. The accuracy of the results shows that the various components on the levee surfaces are well identified using the proposed methodology. The obtained results are applied for evaluating the physical condition of the levees in the study area. Full article
Figures

Graphical abstract

Open AccessArticle
Ancient Maya Regional Settlement and Inter-Site Analysis: The 2013 West-Central Belize LiDAR Survey
Remote Sens. 2014, 6(9), 8671-8695; https://doi.org/10.3390/rs6098671
Received: 16 July 2014 / Revised: 25 August 2014 / Accepted: 3 September 2014 / Published: 16 September 2014
Cited by 36 | Viewed by 6677 | PDF Full-text (23886 KB) | HTML Full-text | XML Full-text
Abstract
During April and May 2013, a total of 1057 km2 of LiDAR was flown by NCALM for a consortium of archaeologists working in West-central Belize, making this the largest surveyed area within the Mayan lowlands. Encompassing the Belize Valley and the Vaca [...] Read more.
During April and May 2013, a total of 1057 km2 of LiDAR was flown by NCALM for a consortium of archaeologists working in West-central Belize, making this the largest surveyed area within the Mayan lowlands. Encompassing the Belize Valley and the Vaca Plateau, West-central Belize is one of the most actively researched parts of the Maya lowlands; however, until this effort, no comprehensive survey connecting all settlement had been conducted. Archaeological projects have investigated at least 18 different sites within this region. Thus, a large body of archaeological research provides both the temporal and spatial parameters for the varied ancient Maya centers that once occupied this area; importantly, these data can be used to help interpret the collected LiDAR data. The goal of the 2013 LiDAR campaign was to gain information on the distribution of ancient Maya settlement and sites on the landscape and, particularly, to determine how the landscape was used between known centers. The data that were acquired through the 2013 LiDAR campaign have significance for interpreting both the composition and limits of ancient Maya political units. This paper presents the initial results of these new data and suggests a developmental model for ancient Maya polities. Full article
(This article belongs to the Special Issue New Perspectives of Remote Sensing for Archaeology)
Figures

Graphical abstract

Open AccessArticle
An Assessment of Methods and Remote-Sensing Derived Covariates for Regional Predictions of 1 km Daily Maximum Air Temperature
Remote Sens. 2014, 6(9), 8639-8670; https://doi.org/10.3390/rs6098639
Received: 5 June 2014 / Revised: 12 August 2014 / Accepted: 25 August 2014 / Published: 16 September 2014
Cited by 13 | Viewed by 3003 | PDF Full-text (7418 KB) | HTML Full-text | XML Full-text
Abstract
The monitoring and prediction of biodiversity and environmental changes is constrained by the availability of accurate and spatially contiguous climatic variables at fine temporal and spatial grains. In this study, we evaluate best practices for generating gridded, one-kilometer resolution, daily maximum air temperature [...] Read more.
The monitoring and prediction of biodiversity and environmental changes is constrained by the availability of accurate and spatially contiguous climatic variables at fine temporal and spatial grains. In this study, we evaluate best practices for generating gridded, one-kilometer resolution, daily maximum air temperature surfaces in a regional context, the state of Oregon, USA. Covariates used in the interpolation include remote sensing derived elevation, aspect, canopy height, percent forest cover and MODIS Land Surface Temperature (LST). Because of missing values, we aggregated daily LST values as long term (2000–2010) monthly climatologies to leverage its spatial detail in the interpolation. We predicted temperature with three methods—Universal Kriging, Geographically Weighted Regression (GWR) and Generalized Additive Models (GAM)—and assessed predictions using meteorological stations over 365 days in 2010. We find that GAM is least sensitive to overtraining (overfitting) and results in lowest errors in term of distance to closest training stations. Mean elevation, LST, and distance to ocean are flagged most frequently as significant covariates among all daily predictions. Results indicate that GAM with latitude, longitude and elevation is the top model but that LST has potential in providing additional fine-grained spatial structure related to land cover effects. The study also highlights the need for more rigorous methods and data to evaluate the spatial structure and fine grained accuracy of predicted surfaces. Full article
Figures

Graphical abstract

Open AccessArticle
ARCTIS — A MATLAB® Toolbox for Archaeological Imaging Spectroscopy
Remote Sens. 2014, 6(9), 8617-8638; https://doi.org/10.3390/rs6098617
Received: 3 June 2014 / Revised: 7 July 2014 / Accepted: 28 July 2014 / Published: 16 September 2014
Cited by 14 | Viewed by 4627 | PDF Full-text (13784 KB) | HTML Full-text | XML Full-text
Abstract
Imaging spectroscopy acquires imagery in hundreds or more narrow contiguous spectral bands. This offers unprecedented information for archaeological research. To extract the maximum of useful archaeological information from it, however, a number of problems have to be solved. Major problems relate to data [...] Read more.
Imaging spectroscopy acquires imagery in hundreds or more narrow contiguous spectral bands. This offers unprecedented information for archaeological research. To extract the maximum of useful archaeological information from it, however, a number of problems have to be solved. Major problems relate to data redundancy and the visualization of the large amount of data. This makes data mining approaches necessary, as well as efficient data visualization tools. Additional problems relate to data quality. Indeed, the upwelling electromagnetic radiation is recorded in small spectral bands that are only about ten nanometers wide. The signal received by the sensor is, thus quite low compared to sensor noise and possible atmospheric perturbations. The often small, instantaneous field of view (IFOV)—essential for archaeologically relevant imaging spectrometer datasets—further limits the useful signal stemming from the ground. The combination of both effects makes radiometric smoothing techniques mandatory. The present study details the functionality of a MATLAB®-based toolbox, called ARCTIS (ARChaeological Toolbox for Imaging Spectroscopy), for filtering, enhancing, analyzing, and visualizing imaging spectrometer datasets. The toolbox addresses the above-mentioned problems. Its Graphical User Interface (GUI) is designed to allow non-experts in remote sensing to extract a wealth of information from imaging spectroscopy for archaeological research. ARCTIS will be released under creative commons license, free of charge, via website (http://luftbildarchiv.univie.ac.at). Full article
(This article belongs to the Special Issue New Perspectives of Remote Sensing for Archaeology)
Figures

Graphical abstract

Open AccessArticle
Inter-Calibration of Satellite Passive Microwave Land Observations from AMSR-E and AMSR2 Using Overlapping FY3B-MWRI Sensor Measurements
Remote Sens. 2014, 6(9), 8594-8616; https://doi.org/10.3390/rs6098594
Received: 22 May 2014 / Revised: 1 September 2014 / Accepted: 10 September 2014 / Published: 16 September 2014
Cited by 25 | Viewed by 2911 | PDF Full-text (2269 KB) | HTML Full-text | XML Full-text
Abstract
The development and continuity of consistent long-term data records from similar overlapping satellite observations is critical for global monitoring and environmental change assessments. We developed an empirical approach for inter-calibration of satellite microwave brightness temperature (Tb) records over land from [...] Read more.
The development and continuity of consistent long-term data records from similar overlapping satellite observations is critical for global monitoring and environmental change assessments. We developed an empirical approach for inter-calibration of satellite microwave brightness temperature (Tb) records over land from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Microwave Scanning Radiometer 2 (AMSR2) using overlapping Tb observations from the Microwave Radiation Imager (MWRI). Double Differencing (DD) calculations revealed significant AMSR2 and MWRI biases relative to AMSR-E. Pixel-wise linear relationships were established from overlapping Tb records and used for calibrating MWRI and AMSR2 records to the AMSR-E baseline. The integrated multi-sensor Tb record was largely consistent over the major global vegetation and climate zones; sensor biases were generally well calibrated, though residual Tb differences inherent to different sensor configurations were still present. Daily surface air temperature estimates from the calibrated AMSR2 Tb inputs also showed favorable accuracy against independent measurements from 142 global weather stations (R2 ≥ 0.75, RMSE ≤ 3.64 °C), but with slightly lower accuracy than the AMSR-E baseline (R2 ≥ 0.78, RMSE ≤ 3.46 °C). The proposed method is promising for generating consistent, uninterrupted global land parameter records spanning the AMSR-E and continuing AMSR2 missions. Full article
Figures

Graphical abstract

Open AccessArticle
Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada
Remote Sens. 2014, 6(9), 8565-8593; https://doi.org/10.3390/rs6098565
Received: 10 June 2014 / Revised: 20 August 2014 / Accepted: 22 August 2014 / Published: 11 September 2014
Cited by 28 | Viewed by 4116 | PDF Full-text (27180 KB) | HTML Full-text | XML Full-text
Abstract
In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The [...] Read more.
In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
Figures

Graphical abstract

Open AccessArticle
How Reliable is the MODIS Land Cover Product for Crop Mapping Sub-Saharan Agricultural Landscapes?
Remote Sens. 2014, 6(9), 8541-8564; https://doi.org/10.3390/rs6098541
Received: 25 June 2014 / Revised: 27 August 2014 / Accepted: 4 September 2014 / Published: 11 September 2014
Cited by 22 | Viewed by 3063 | PDF Full-text (2314 KB) | HTML Full-text | XML Full-text
Abstract
Accurate cropland maps at the global and local scales are crucial for scientists, government and nongovernment agencies, farmers and other stakeholders, particularly in food-insecure regions, such as Sub-Saharan Africa. In this study, we aim to qualify the crop classes of the MODIS Land [...] Read more.
Accurate cropland maps at the global and local scales are crucial for scientists, government and nongovernment agencies, farmers and other stakeholders, particularly in food-insecure regions, such as Sub-Saharan Africa. In this study, we aim to qualify the crop classes of the MODIS Land Cover Product (LCP) in Sub-Saharan Africa using FAO (Food and Agricultural Organisation) and AGRHYMET (AGRiculture, Hydrology and METeorology) statistical data of agriculture and a sample of 55 very-high-resolution images. In terms of cropland acreage and dynamics, we found that the correlation between the statistical data and MODIS LCP decreases when we localize the spatial scale (from R2 = 0.86 *** at the national scale to R2 = 0.26 *** at two levels below the national scale). In terms of the cropland spatial distribution, our findings indicate a strong relationship between the user accuracy and the fragmentation of the agricultural landscape, as measured by the MODIS LCP; the accuracy decreases as the crop fraction increases. In addition, thanks to the Pareto boundary method, we were able to isolate and quantify the part of the MODIS classification error that could be directly linked to the performance of the adopted classification algorithm. Finally, based on these results, (i) a regional map of the MODIS LCP user accuracy estimates for cropland classes was produced for the entire Sub-Saharan region; this map presents a better accuracy in the western part of the region (43%–70%) compared to the eastern part (17%–43%); (ii) Theoretical user and producer accuracies for a given set of spatial resolutions were provided; the simulated future Sentinel-2 system would provide theoretical 99% user and producer accuracies given the landscape pattern of the region. Full article
(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)
Figures

Graphical abstract

Open AccessArticle
Evaluation of Satellite Retrievals of Ocean Chlorophyll-a in the California Current
Remote Sens. 2014, 6(9), 8524-8540; https://doi.org/10.3390/rs6098524
Received: 14 July 2014 / Revised: 28 August 2014 / Accepted: 3 September 2014 / Published: 11 September 2014
Cited by 21 | Viewed by 2901 | PDF Full-text (2860 KB) | HTML Full-text | XML Full-text
Abstract
Retrievals of ocean surface chlorophyll-a concentration (Chla) by multiple ocean color satellite sensors (SeaWiFS, MODIS-Terra, MODIS-Aqua, MERIS, VIIRS) using standard algorithms were evaluated in the California Current using a large archive of in situ measurements. Over the full range of in situ Chla, [...] Read more.
Retrievals of ocean surface chlorophyll-a concentration (Chla) by multiple ocean color satellite sensors (SeaWiFS, MODIS-Terra, MODIS-Aqua, MERIS, VIIRS) using standard algorithms were evaluated in the California Current using a large archive of in situ measurements. Over the full range of in situ Chla, all sensors produced a coefficient of determination (R2) between 0.79 and 0.88 and a median absolute percent error (MdAPE) between 21% and 27%. However, at in situ Chla > 1 mg m3, only products from MERIS (both the ESA produced algal_1 and NASA produced chlor_a) maintained reasonable accuracy (R2 from 0.74 to 0.52 and MdAPE from 23% to 31%, respectively), while the other sensors had R2 below 0.5 and MdAPE higher than 36%. We show that the low accuracy at medium and high Chla is caused by the poor retrieval of remote sensing reflectance. Full article
(This article belongs to the Special Issue Remote Sensing of Phytoplankton)
Figures

Graphical abstract

Open AccessArticle
Plant Species Discrimination in a Tropical Wetland Using In Situ Hyperspectral Data
Remote Sens. 2014, 6(9), 8494-8523; https://doi.org/10.3390/rs6098494
Received: 27 June 2014 / Revised: 17 August 2014 / Accepted: 2 September 2014 / Published: 10 September 2014
Cited by 29 | Viewed by 2926 | PDF Full-text (9741 KB) | HTML Full-text | XML Full-text
Abstract
We investigated the use of full-range (400–2,500 nm) hyperspectral data obtained by sampling foliar reflectances to discriminate 46 plant species in a tropical wetland in Jamaica. A total of 47 spectral variables, including derivative spectra, spectral vegetation indices, spectral position variables, normalized spectra [...] Read more.
We investigated the use of full-range (400–2,500 nm) hyperspectral data obtained by sampling foliar reflectances to discriminate 46 plant species in a tropical wetland in Jamaica. A total of 47 spectral variables, including derivative spectra, spectral vegetation indices, spectral position variables, normalized spectra and spectral absorption features, were used for classifying the 46 species. The Mann–Whitney U-test, paired one-way ANOVA, principal component analysis (PCA), random forest (RF) and a wrapper approach with a support vector machine were used as feature selection methods. Linear discriminant analysis (LDA), an artificial neural network (ANN) and a generalized linear model fitted with elastic net penalties (GLMnet) were then used for species separation. For comparison, the RF classifier (denoted as RFa) was also used to separate the species by using all reflectance spectra and spectral indices, respectively, without applying any feature selection. The RFa classifier was able to achieve 91.8% and 84.8% accuracy with importance-ranked spectral indices and reflectance spectra, respectively. The GLMnet classifier produced the lowest overall accuracies for feature-selected reflectance spectra data (52–77%) when compared with the LDA and ANN methods. However, when feature-selected spectral indices were used, the GLMnet produced overall accuracies ranging from 79 to 88%, which were the highest among the three classifiers that used feature-selected data. A total of 12 species recorded a 100% producer accuracy, but with spectral indices, and an additional 8 species had perfect producer accuracies, regardless of the input features. The results of this study suggest that the GLMnet classifier can be used, particularly on feature-selected spectral indices, to discern vegetation in wetlands. However, it might be more efficient to use RFa without feature-selected variables, especially for spectral indices. Full article
Figures

Graphical abstract

Open AccessReview
Monitoring Depth of Shallow Atmospheric Boundary Layer to Complement LiDAR Measurements Affected by Partial Overlap
Remote Sens. 2014, 6(9), 8468-8493; https://doi.org/10.3390/rs6098468
Received: 24 March 2014 / Revised: 3 September 2014 / Accepted: 4 September 2014 / Published: 10 September 2014
Cited by 21 | Viewed by 2873 | PDF Full-text (991 KB) | HTML Full-text | XML Full-text
Abstract
There is compelling evidence that the incomplete laser beam receiver field-of-view overlap (i.e., partial overlap) of ground-based vertically-pointing aerosol LiDAR restricts the observational range for detecting aerosol layer boundaries to a certain height above the LiDAR. This height varies from one [...] Read more.
There is compelling evidence that the incomplete laser beam receiver field-of-view overlap (i.e., partial overlap) of ground-based vertically-pointing aerosol LiDAR restricts the observational range for detecting aerosol layer boundaries to a certain height above the LiDAR. This height varies from one to few hundreds of meters, depending on the transceiver geometry. The range, or height of full overlap, is defined as the minimum distance at which the laser beam is completely imaged onto the detector through the field stop in the receiver optics. Thus, the LiDAR signal below the height of full overlap remains erroneous. In effect, it is not possible to derive the atmospheric boundary layer (ABL) top (zi) below the height of full overlap using lidar measurements alone. This problem makes determination of the nocturnal zi almost impossible, as the nocturnal zi is often lower than the minimum possible retrieved height due to incomplete overlap of lidar. Detailed studies of the nocturnal boundary layer or of variability of low zi would require changes in the LiDAR configuration such that a complete transceiver overlap could be achieved at a much lower height. Otherwise, improvements in the system configuration or deployment (e.g., scanning LiDAR) are needed. However, these improvements are challenging due to the instrument configuration and the need for Raman channel signal, eye-safe laser transmitter for scanning deployment, etc. This paper presents a brief review of some of the challenges and opportunities in overcoming the partial overlap of the LiDAR transceiver to determine zi below the height of full-overlap using complementary approaches to derive low zi. A comprehensive discussion focusing on four different techniques is presented. These are based on the combined (1) ceilometer and LiDAR; (2) tower-based trace gas (e.g., CO2) concentration profiles and LiDAR measurements; (3) 222Rn budget approach and LiDAR-derived results; and (4) encroachment model and LiDAR observations. Full article
Figures

Graphical abstract

Open AccessArticle
A Bidimensional Empirical Mode Decomposition Method for Fusion of Multispectral and Panchromatic Remote Sensing Images
Remote Sens. 2014, 6(9), 8446-8467; https://doi.org/10.3390/rs6098446
Received: 14 April 2014 / Revised: 1 September 2014 / Accepted: 2 September 2014 / Published: 5 September 2014
Cited by 16 | Viewed by 3260 | PDF Full-text (12318 KB) | HTML Full-text | XML Full-text
Abstract
This article focuses on the image fusion of high-resolution panchromatic and multispectral images. We propose a new image fusion method based on a Hue-Saturation-Value (HSV) color space model and bidimensional empirical mode decomposition (BEMD), by integrating high-frequency component of panchromatic image into multispectral [...] Read more.
This article focuses on the image fusion of high-resolution panchromatic and multispectral images. We propose a new image fusion method based on a Hue-Saturation-Value (HSV) color space model and bidimensional empirical mode decomposition (BEMD), by integrating high-frequency component of panchromatic image into multispectral image and optimizing the BEMD in decreasing sifting time, simplifying extrema point locating and more efficient interpolation. This new method has been tested with a panchromatic image (SPOT, 10-m resolution) and a multispectral image (TM, 28-m resolution). Visual and quantitative assessment methods are applied to evaluate the quality of the fused images. The experimental results show that the proposed method provided superior performance over conventional fusion algorithms in improving the quality of the fused images in terms of visual effectiveness, standard deviation, correlation coefficient, bias index and degree of distortion. Both five different land cover types WorldView-II images and three different sensor combinations (TM/SPOT, WorldView-II, 0.5 m/1 m resolution and IKONOS, 1 m/4 m resolution) validated the robustness of BEMD fusion performance. Both of these results prove the capability of the proposed BEMD method as a robust image fusion method to prevent color distortion and enhance image detail. Full article
(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)
Figures

Graphical abstract

Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top