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Remote Sens., Volume 6, Issue 3 (March 2014), Pages 1762-2627

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Open AccessArticle Deciphering the Precision of Stereo IKONOS Canopy Height Models for US Forests with G-LiHT Airborne LiDAR
Remote Sens. 2014, 6(3), 1762-1782; doi:10.3390/rs6031762
Received: 5 November 2013 / Revised: 7 February 2014 / Accepted: 14 February 2014 / Published: 26 February 2014
Cited by 6 | PDF Full-text (1381 KB) | HTML Full-text | XML Full-text
Abstract
Few studies have evaluated the precision of IKONOS stereo data for measuring forest canopy height. The high cost of airborne light detection and ranging (LiDAR) data collection for large area studies and the present lack of a spaceborne instrument lead to the [...] Read more.
Few studies have evaluated the precision of IKONOS stereo data for measuring forest canopy height. The high cost of airborne light detection and ranging (LiDAR) data collection for large area studies and the present lack of a spaceborne instrument lead to the need to explore other low cost options. The US Government currently has access to a large archive of commercial high-resolution imagery, which could be quite valuable to forest structure studies. At 1 m resolution, we here compared canopy height models (CHMs) and height data derived from Goddard’s airborne LiDAR Hyper-spectral and Thermal Imager (G-LiHT) with three types of IKONOS stereo derived digital surface models (DSMs) that estimate CHMs by subtracting National Elevation Data (NED) digital terrain models (DTMs). We found the following in three different forested regions of the US after excluding heterogeneous and disturbed forest samples: (1) G-LiHT DTMs were highly correlated with NED DTMs with R2 > 0.98 and root mean square errors (RMSEs) < 2.96 m; (2) when using one visually identifiable ground control point (GCP) from NED, G-LiHT DSMs and IKONOS DSMs had R2 > 0.84 and RMSEs of 2.7 to 4.1 m; and (3) one GCP CHMs for two study sites had R2 > 0.7 and RMSEs of 2.6 to 3 m where data were collected less than four years apart. Our results suggest that IKONOS stereo data are a useful LiDAR alternative where high-quality DTMs are available. Full article
Open AccessArticle Spatio-Temporal Patterns and Climate Variables Controlling of Biomass Carbon Stock of Global Grassland Ecosystems from 1982 to 2006
Remote Sens. 2014, 6(3), 1783-1802; doi:10.3390/rs6031783
Received: 28 December 2013 / Revised: 10 February 2014 / Accepted: 12 February 2014 / Published: 26 February 2014
Cited by 13 | PDF Full-text (1117 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Grassland ecosystems play an important role in subsistence agriculture and the global carbon cycle. However, the global spatio-temporal patterns and environmental controls of grassland biomass are not well quantified and understood. The goal of this study was to estimate the spatial and [...] Read more.
Grassland ecosystems play an important role in subsistence agriculture and the global carbon cycle. However, the global spatio-temporal patterns and environmental controls of grassland biomass are not well quantified and understood. The goal of this study was to estimate the spatial and temporal patterns of the global grassland biomass and analyze their driving forces using field measurements, Normalized Difference Vegetation Index (NDVI) time series from satellite data, climate reanalysis data, and a satellite-based statistical model. Results showed that the NDVI-based biomass carbon model developed from this study explained 60% of the variance across 38 sites globally. The global carbon stock in grassland aboveground live biomass was 1.05 Pg·C, averaged from 1982 to 2006, and increased at a rate of 2.43 Tg·C·y−1 during this period. Temporal change of the global biomass was significantly and positively correlated with temperature and precipitation. The distribution of biomass carbon density followed the precipitation gradient. The dynamics of regional grassland biomass showed various trends largely determined by regional climate variability, disturbances, and management practices (such as grazing for meat production). The methods and results from this study can be used to monitor the dynamics of grassland aboveground biomass and evaluate grassland susceptibility to climate variability and change, disturbances, and management. Full article
Open AccessArticle Burned Area Detection and Burn Severity Assessment of a Heathland Fire in Belgium Using Airborne Imaging Spectroscopy (APEX)
Remote Sens. 2014, 6(3), 1803-1826; doi:10.3390/rs6031803
Received: 29 December 2013 / Revised: 8 February 2014 / Accepted: 10 February 2014 / Published: 27 February 2014
Cited by 7 | PDF Full-text (1748 KB) | HTML Full-text | XML Full-text
Abstract
Uncontrolled, large fires are a major threat to the biodiversity of protected heath landscapes. The severity of the fire is an important factor influencing vegetation recovery. We used airborne imaging spectroscopy data from the Airborne Prism Experiment (APEX) sensor to: (1) investigate [...] Read more.
Uncontrolled, large fires are a major threat to the biodiversity of protected heath landscapes. The severity of the fire is an important factor influencing vegetation recovery. We used airborne imaging spectroscopy data from the Airborne Prism Experiment (APEX) sensor to: (1) investigate which spectral regions and spectral indices perform best in discriminating burned from unburned areas; and (2) assess the burn severity of a recent fire in the Kalmthoutse Heide, a heathland area in Belgium. A separability index was used to estimate the effectiveness of individual bands and spectral indices to discriminate between burned and unburned land. For the burn severity analysis, a modified version of the Geometrically structured Composite Burn Index (GeoCBI) was developed for the field data collection. The field data were collected in four different vegetation types: Calluna vulgaris-dominated heath (dry heath), Erica tetralix-dominated heath (wet heath), Molinia caerulea (grass-encroached heath), and coniferous woodland. Discrimination between burned and unburned areas differed among vegetation types. For the pooled dataset, bands in the near infrared (NIR) spectral region demonstrated the highest discriminatory power, followed by short wave infrared (SWIR) bands. Visible wavelengths performed considerably poorer. The Normalized Burn Ratio (NBR) outperformed the other spectral indices and the individual spectral bands in discriminating between burned and unburned areas. For the burn severity assessment, all spectral bands and indices showed low correlations with the field data GeoCBI, when data of all pre-fire vegetation types were pooled (R2 maximum 0.41). Analysis per vegetation type, however, revealed considerably higher correlations (R2 up to 0.78). The Mid Infrared Burn Index (MIRBI) had the highest correlations for Molinia and Erica (R2 = 0.78 and 0.42, respectively). In Calluna stands, the Char Soil Index (CSI) achieved the highest correlations, with R2 = 0.65. In Pinus stands, the Normalized Difference Vegetation Index (NDVI) and the red wavelength both had correlations of R2 = 0.64. The results of this study highlight the superior performance of the NBR to discriminate between burned and unburned areas, and the disparate performance of spectral indices to assess burn severity among vegetation types. Consequently, in heathlands, one must consider a stratification per vegetation type to produce more reliable burn severity maps. Full article
(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)
Open AccessArticle A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio
Remote Sens. 2014, 6(3), 1827-1844; doi:10.3390/rs6031827
Received: 8 January 2014 / Revised: 17 February 2014 / Accepted: 24 February 2014 / Published: 27 February 2014
Cited by 13 | PDF Full-text (1799 KB) | HTML Full-text | XML Full-text | Correction
Abstract
Satellite-inferred burn severity data have become increasingly popular over the last decade for management and research purposes. These data typically quantify spectral change between pre-and post-fire satellite images (usually Landsat). There is an active debate regarding which of the two main equations, [...] Read more.
Satellite-inferred burn severity data have become increasingly popular over the last decade for management and research purposes. These data typically quantify spectral change between pre-and post-fire satellite images (usually Landsat). There is an active debate regarding which of the two main equations, the delta normalized burn ratio (dNBR) and its relativized form (RdNBR), is most suitable for quantifying burn severity; each has its critics. In this study, we propose and evaluate a new Landsat-based burn severity metric, the relativized burn ratio (RBR), that provides an alternative to dNBR and RdNBR. For 18 fires in the western US, we compared the performance of RBR to both dNBR and RdNBR by evaluating the agreement of these metrics with field-based burn severity measurements. Specifically, we evaluated (1) the correspondence between each metric and a continuous measure of burn severity (the composite burn index) and (2) the overall accuracy of each metric when classifying into discrete burn severity classes (i.e., unchanged, low, moderate, and high). Results indicate that RBR corresponds better to field-based measurements (average R2 among 18 fires = 0.786) than both dNBR (R2 = 0.761) and RdNBR (R2 = 0.766). Furthermore, the overall classification accuracy achieved with RBR (average among 18 fires = 70.5%) was higher than both dNBR (68.4%) and RdNBR (69.2%). Consequently, we recommend RBR as a robust alternative to both dNBR and RdNBR for measuring and classifying burn severity. Full article
(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)
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Open AccessArticle Multi-Sensor Calibration Studies of AVHRR-Heritage Channel Radiances Using the Simultaneous Nadir Observation Approach
Remote Sens. 2014, 6(3), 1845-1862; doi:10.3390/rs6031845
Received: 26 December 2013 / Revised: 5 February 2014 / Accepted: 9 February 2014 / Published: 27 February 2014
Cited by 4 | PDF Full-text (1523 KB) | HTML Full-text | XML Full-text
Abstract
The European Space Agency project for studies of cloud properties in the Climate Change Initiative programme (ESA-CLOUD-CCI) aims at compiling the longest possible time series of cloud products from one single multispectral sensor—The five-channel Advanced Very High Resolution Radiometer (AVHRR) instrument. A [...] Read more.
The European Space Agency project for studies of cloud properties in the Climate Change Initiative programme (ESA-CLOUD-CCI) aims at compiling the longest possible time series of cloud products from one single multispectral sensor—The five-channel Advanced Very High Resolution Radiometer (AVHRR) instrument. A particular aspect here is to include corresponding products based on other existing (Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Along-Track Scanning Radiometer (AATSR), MEdium Resolution Imaging Spectrometer (MERIS), Visible and Infrared Radiometer Suite (VIIRS)) and future Sea and Land Surface Temperature Radiometer (SLSTR) sensors measuring in similar (AVHRR-heritage) spectral channels. Initial inter-comparisons of the involved AVHRR-heritage channel radiances over a short demonstration period (2007–2009) were performed. Using Aqua-MODIS as reference, AVHRR (NOAA-18), AATSR, and MERIS channel radiances were evaluated using the simultaneous nadir (SNO) approach. Results show generally agreeing radiances within approximately 3% for channels at 0.6 µm and 0.8 µm. Larger deviations (+5%) were found for the corresponding AATSR channel at 0.6 µm. Excessive deviations but with opposite sign were also indicated for AATSR 1.6 µm and MERIS 0.8 µm radiances. Observed differences may largely be attributed to residual temporal and spatial matching differences while excessive AATSR and MERIS deviations are likely partly attributed to incomplete compensation for spectrally varying surface and atmospheric conditions. However, very good agreement was found for all infrared channels among all the studied sensors. Here, deviations were generally less than 0.2% for the measured brightness temperatures with the exception of some remaining non-linear deviations at extreme low and high temperatures. Full article
Open AccessArticle Image-Based Delineation and Classification of Built Heritage Masonry
Remote Sens. 2014, 6(3), 1863-1889; doi:10.3390/rs6031863
Received: 20 December 2013 / Revised: 6 February 2014 / Accepted: 10 February 2014 / Published: 28 February 2014
Cited by 1 | PDF Full-text (5036 KB) | HTML Full-text | XML Full-text
Abstract
Fundación Zain is developing new built heritage assessment protocols. The goal is to objectivize and standardize the analysis and decision process that leads to determining the degree of protection of built heritage in the Basque Country. The ultimate step in this objectivization [...] Read more.
Fundación Zain is developing new built heritage assessment protocols. The goal is to objectivize and standardize the analysis and decision process that leads to determining the degree of protection of built heritage in the Basque Country. The ultimate step in this objectivization and standardization effort will be the development of an information and communication technology (ICT) tool for the assessment of built heritage. This paper presents the ground work carried out to make this tool possible: the automatic, image-based delineation of stone masonry. This is a necessary first step in the development of the tool, as the built heritage that will be assessed consists of stone masonry construction, and many of the features analyzed can be characterized according to the geometry and arrangement of the stones. Much of the assessment is carried out through visual inspection. Thus, this process will be automated by applying image processing on digital images of the elements under inspection. The principal contribution of this paper is the automatic delineation the framework proposed. The other contribution is the performance evaluation of this delineation as the input to a classifier for a geometrically characterized feature of a built heritage object. The element chosen to perform this evaluation is the stone arrangement of masonry walls. The validity of the proposed framework is assessed on real images of masonry walls. Full article
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Open AccessArticle Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product
Remote Sens. 2014, 6(3), 1890-1917; doi:10.3390/rs6031890
Received: 27 December 2013 / Revised: 18 February 2014 / Accepted: 24 February 2014 / Published: 28 February 2014
Cited by 7 | PDF Full-text (1599 KB) | HTML Full-text | XML Full-text
Abstract
Satellite-based remote sensing of active fires is the only practical way to consistently and continuously monitor diurnal fluctuations in biomass burning from regional, to continental, to global scales. Failure to understand, quantify, and communicate the performance of an active fire detection algorithm, [...] Read more.
Satellite-based remote sensing of active fires is the only practical way to consistently and continuously monitor diurnal fluctuations in biomass burning from regional, to continental, to global scales. Failure to understand, quantify, and communicate the performance of an active fire detection algorithm, however, can lead to improper interpretations of the spatiotemporal distribution of biomass burning, and flawed estimates of fuel consumption and trace gas and aerosol emissions. This work evaluates the performance of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Fire Thermal Anomaly (FTA) detection algorithm using seven months of active fire pixels detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) across the Central African Republic (CAR). Results indicate that the omission rate of the SEVIRI FTA detection algorithm relative to MODIS varies spatially across the CAR, ranging from 25% in the south to 74% in the east. In the absence of confounding artifacts such as sunglint, uncertainties in the background thermal characterization, and cloud cover, the regional variation in SEVIRI’s omission rate can be attributed to a coupling between SEVIRI’s low spatial resolution detection bias (i.e., the inability to detect fires below a certain size and intensity) and a strong geographic gradient in active fire characteristics across the CAR. SEVIRI’s commission rate relative to MODIS increases from 9% when evaluated near MODIS nadir to 53% near the MODIS scene edges, indicating that SEVIRI errors of commission at the MODIS scene edges may not be false alarms but rather true fires that MODIS failed to detect as a result of larger pixel sizes at extreme MODIS scan angles. Results from this work are expected to facilitate (i) future improvements to the SEVIRI FTA detection algorithm; (ii) the assimilation of the SEVIRI and MODIS active fire products; and (iii) the potential inclusion of SEVIRI into a network of geostationary sensors designed to achieve global diurnal active fire monitoring. Full article
Open AccessArticle Vicarious Radiometric Calibration of a Multispectral Camera on Board an Unmanned Aerial System
Remote Sens. 2014, 6(3), 1918-1937; doi:10.3390/rs6031918
Received: 16 December 2013 / Revised: 7 February 2014 / Accepted: 19 February 2014 / Published: 28 February 2014
Cited by 12 | PDF Full-text (1008 KB) | HTML Full-text | XML Full-text
Abstract
Combinations of unmanned aerial platforms and multispectral sensors are considered low-cost tools for detailed spatial and temporal studies addressing spectral signatures, opening a broad range of applications in remote sensing. Thus, a key step in this process is knowledge of multi-spectral sensor [...] Read more.
Combinations of unmanned aerial platforms and multispectral sensors are considered low-cost tools for detailed spatial and temporal studies addressing spectral signatures, opening a broad range of applications in remote sensing. Thus, a key step in this process is knowledge of multi-spectral sensor calibration parameters in order to identify the physical variables collected by the sensor. This paper discusses the radiometric calibration process by means of a vicarious method applied to a high-spatial resolution unmanned flight using low-cost artificial and natural covers as control and check surfaces, respectively. Full article
Open AccessArticle Development of a Remote Sensing-Based “Boro” Rice Mapping System
Remote Sens. 2014, 6(3), 1938-1953; doi:10.3390/rs6031938
Received: 19 November 2013 / Revised: 28 January 2014 / Accepted: 24 February 2014 / Published: 3 March 2014
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Abstract
Rice is one of the staple foods across the world, thus information about its production is essential for ensuring food security. Here, our objective was to develop a method for mapping “boro” rice (i.e., cultivated during the months [...] Read more.
Rice is one of the staple foods across the world, thus information about its production is essential for ensuring food security. Here, our objective was to develop a method for mapping “boro” rice (i.e., cultivated during the months January to May) in a Bangladeshi context. In this paper, we used a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived 16-day composite of normalized difference vegetation index (NDVI) at 250 m spatial resolution in conjunction with ancillary datasets (i.e., land use map, crop calendar, and ground-based rice production information) during the period 2007–2012. The proposed method consisted of three procedures: (i) ISODATA clustering and determining the boro rice signatures in temporal dimension using data from the period 2007–2009; (ii) formulating a mathematical model for extracting the boro rice areas using data from the period 2007–2009; and (iii) model calibration using data from the period 2007–2009 and its validation using data from the period 2010–2012. The implementation of the abovementioned procedures revealed reasonable agreements between the model (i.e., MODIS-based) and ground-based estimates of boro rice area at both country (i.e., percentage error in the range −0.83–1.42%) and district levels (i.e., r2 in the range 0.69–0.89) during the period 2010–2012. Our proposed method demonstrated its effectiveness in mapping rice system at the regional/country scale. Full article
(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)
Open AccessArticle Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR
Remote Sens. 2014, 6(3), 1954-1972; doi:10.3390/rs6031954
Received: 31 December 2013 / Revised: 30 January 2014 / Accepted: 24 February 2014 / Published: 4 March 2014
Cited by 3 | PDF Full-text (1124 KB) | HTML Full-text | XML Full-text
Abstract
Aerial and satellite imagery are widely used to assess the severity and impact of wildfires. Light detection and ranging (LiDAR) is a newer remote sensing technology that has demonstrated utility in measuring vegetation structure. Combined use of imagery and LiDAR may improve [...] Read more.
Aerial and satellite imagery are widely used to assess the severity and impact of wildfires. Light detection and ranging (LiDAR) is a newer remote sensing technology that has demonstrated utility in measuring vegetation structure. Combined use of imagery and LiDAR may improve the assessment of wildfire impacts compared to imagery alone. Estimation of tree mortality at the plot scale could serve for more rapid, broad-scale, and lower cost post-fire assessments than feasible through field assessment. We assessed the accuracy of classifying color-infrared imagery in combination with post-fire LiDAR, and with differenced (pre- and post-fire) LiDAR, in estimating plot percent mortality in a second-growth coast redwood forest near Santa Cruz, CA. Percent mortality of trees greater than 25.4 cm DBH in 47 permanent 0.08 ha plots was categorized as low (<25%), moderate (25%–50%), or high (>50%). The model using Normalized Difference Vegetation Index (NDVI) from National Agricultural Imagery Program (NAIP) was 74% accurate; the model using NDVI and post-fire LiDAR was 85% accurate, while the model using NDVI and differenced LiDAR was 83% accurate. The addition of post-fire LiDAR data provided a modest increase in accuracy compared to imagery alone, which may not justify the substantial cost of data acquisition. The method demonstrated could be applied to rapidly estimate tree mortality resulting from wildfires at fine to moderate scale. Full article
(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)
Open AccessArticle Validating and Linking the GIMMS Leaf Area Index (LAI3g) with Environmental Controls in Tropical Africa
Remote Sens. 2014, 6(3), 1973-1990; doi:10.3390/rs6031973
Received: 31 October 2013 / Revised: 24 February 2014 / Accepted: 25 February 2014 / Published: 4 March 2014
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Abstract
The recent Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g product provides a 30-year global times-series of remotely sensed leaf area index (LAI), an essential variable in models of ecosystem process and productivity. In this study, we use a new dataset of [...] Read more.
The recent Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g product provides a 30-year global times-series of remotely sensed leaf area index (LAI), an essential variable in models of ecosystem process and productivity. In this study, we use a new dataset of field-based LAITrue to indirectly validate the GIMMS LAI3g product, LAIavhrr, in East Africa, comparing the distribution properties of LAIavhrr across biomes and environmental gradients with those properties derived for LAITrue. We show that the increase in LAI with vegetation height in natural biomes is captured by both LAIavhrr and LAITrue, but that LAIavhrr overestimates LAI for all biomes except shrubland and cropland. Non-linear responses of LAI to precipitation and moisture indices, whereby leaf area peaks at intermediate values and declines thereafter, are apparent in both LAITrue and LAIavhrr, although LAITrue reaches its maximum at lower values of the respective environmental driver. Socio-economic variables such as governance (protected areas) and population affect both LAI responses, although cause and effect are not always obvious: a positive relationship with human population pressure was detected, but shown to be an artefact of both LAI and human settlement covarying with precipitation. Despite these complexities, targeted field measurements, stratified according to both environmental and socio-economic gradients, could provide crucial data for improving satellite-derived LAI estimates, especially in the human-modified landscapes of tropical Africa. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Evaluation of Multiple Spring Phenological Indicators of Yearly GPP and NEP at Three Canadian Forest Sites
Remote Sens. 2014, 6(3), 1991-2007; doi:10.3390/rs6031991
Received: 27 December 2013 / Accepted: 24 February 2014 / Published: 5 March 2014
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Abstract
Phenological shifts in events such as flowering and bud break are important indicators of ecosystem processes, and are therefore of particular significance for carbon (C) cycle research. Using long-term flux data from three contrasting plant functional type (evergreen and deciduous) boreal forest [...] Read more.
Phenological shifts in events such as flowering and bud break are important indicators of ecosystem processes, and are therefore of particular significance for carbon (C) cycle research. Using long-term flux data from three contrasting plant functional type (evergreen and deciduous) boreal forest sites, we evaluated and compared the responses of annual C fluxes to multiple spring phenological indicators, including the C-uptake period onset (CUP onset), spring temperature (average value from March to May), and satellite-derived enhanced vegetation index (EVI) (average value from March to May). We found that the CUP onset was negatively correlated with annual gross primary production (GPP) for all three sites, but that its predictive strength for annual net ecosystem production (NEP) differed substantially among plant functional types. Spring temperature demonstrated particularly good potential for predicting both annual GPP and NEP for the evergreen sites, but not for the deciduous site. Spring EVI was demonstrated to have potential for predicting annual NEP for all sites. However, both plant functional types confounded the correlation of annual NEP with annual GPP. Although none of these phenological indicators provided consistent insight into annual C fluxes, using various currently available datasets our results remain potentially useful for the assessment of forest C cycling with future climate change. Previous analyses using only a single phenological metric should be considered with caution. Full article
Open AccessArticle Identification of Soil Freezing and Thawing States Using SAR Polarimetry at C-Band
Remote Sens. 2014, 6(3), 2008-2023; doi:10.3390/rs6032008
Received: 29 November 2013 / Revised: 21 February 2014 / Accepted: 24 February 2014 / Published: 5 March 2014
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Abstract
The monitoring of soil freezing and thawing states over large areas is very challenging on ground. In order to investigate the potential and the limitations of space-borne SAR polarimetry at C-band for soil state survey, analyses were conducted on an entire winter [...] Read more.
The monitoring of soil freezing and thawing states over large areas is very challenging on ground. In order to investigate the potential and the limitations of space-borne SAR polarimetry at C-band for soil state survey, analyses were conducted on an entire winter time series of fully polarimetric RADARSAT-2 data from 2011/2012 to identify freezing as well as thawing states within the soil. The polarimetric data were acquired over the Sodankylä test site in Finland together with in situ measurements of the soil and the snow cover. The analyses indicate clearly that the dynamics of the polarimetric entropy and mean scattering alpha angle are directly correlated to soil freezing and thawing states, even under distinct dry snow cover. First modeling attempts using the Extended Bragg soil scattering model justify the observed trends, which indicate surface-like scattering during frozen soil conditions and multiple/volume scattering for thawed soils. Hence, these first investigations at C-band foster motivation to work towards a robust polarimetric detection of soil freezing and thawing states as well as their transition phase. Full article
(This article belongs to the Special Issue Cryospheric Remote Sensing)
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Open AccessArticle Comparison of Eight Techniques for Reconstructing Multi-Satellite Sensor Time-Series NDVI Data Sets in the Heihe River Basin, China
Remote Sens. 2014, 6(3), 2024-2049; doi:10.3390/rs6032024
Received: 26 November 2013 / Revised: 12 February 2014 / Accepted: 14 February 2014 / Published: 6 March 2014
Cited by 14 | PDF Full-text (789 KB) | HTML Full-text | XML Full-text
Abstract
More than 20 techniques have been developed to de-noise time-series vegetation index data from different satellite sensors to reconstruct long time-series data sets. Although many studies have compared Normalized Difference Vegetation Index (NDVI) noise-reduction techniques, few studies have compared these techniques systematically [...] Read more.
More than 20 techniques have been developed to de-noise time-series vegetation index data from different satellite sensors to reconstruct long time-series data sets. Although many studies have compared Normalized Difference Vegetation Index (NDVI) noise-reduction techniques, few studies have compared these techniques systematically and comprehensively. This study tested eight techniques for smoothing different vegetation types using different types of multi-temporal NDVI data (Advanced Very High Resolution Radiometer (AVHRR) (Global Inventory Modeling and Map Studies (GIMMS) and Pathfinder AVHRR Land (PAL), Satellite Pour l’ Observation de la Terre (SPOT) VEGETATION (VGT), and Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra)) with the ultimate purpose of determining the best reconstruction technique for each type of vegetation captured with four satellite sensors. These techniques include the modified best index slope extraction (M-BISE) technique, the Savitzky-Golay (S-G) technique, the mean value iteration filter (MVI) technique, the asymmetric Gaussian (A-G) technique, the double logistic (D-L) technique, the changing-weight filter (CW) technique, the interpolation for data reconstruction (IDR) technique, and the Whittaker smoother (WS) technique. These techniques were evaluated by calculating the root mean square error (RMSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). The results indicate that the S-G, CW, and WS techniques perform better than the other tested techniques, while the IDR, M-BISE, and MVI techniques performed worse than the other techniques. The best de-noise technique varies with different vegetation types and NDVI data sources. The S-G performs best in most situations. In addition, the CW and WS are effective techniques that were exceeded only by the S-G technique. The assessment results are consistent in terms of the three evaluation indexes for GIMMS, PAL, and SPOT data in the study area, but not for the MODIS data. The study will be very helpful for choosing reconstruction techniques for long time-series data sets. Full article
Open AccessArticle Assessing the Temporal Stability of the Accuracy of a Time Series of Burned Area Products
Remote Sens. 2014, 6(3), 2050-2068; doi:10.3390/rs6032050
Received: 22 October 2013 / Revised: 14 February 2014 / Accepted: 27 February 2014 / Published: 6 March 2014
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Abstract
Temporal stability, defined as the change of accuracy through time, is one of the validation aspects required by the Committee on Earth Observation Satellites’ Land Product Validation Subgroup. Temporal stability was evaluated for three burned area products: MCD64, Globcarbon, and fire_cci. Traditional [...] Read more.
Temporal stability, defined as the change of accuracy through time, is one of the validation aspects required by the Committee on Earth Observation Satellites’ Land Product Validation Subgroup. Temporal stability was evaluated for three burned area products: MCD64, Globcarbon, and fire_cci. Traditional accuracy measures, such as overall accuracy and omission and commission error ratios, were computed from reference data for seven years (2001–2007) in seven study sites, located in Angola, Australia, Brazil, Canada, Colombia, Portugal, and South Africa. These accuracy measures served as the basis for the evaluation of temporal stability of each product. Nonparametric tests were constructed to assess different departures from temporal stability, specifically a monotonic trend in accuracy over time (Wilcoxon test for trend), and differences in median accuracy among years (Friedman test). When applied to the three burned area products, these tests did not detect a statistically significant temporal trend or significant differences among years, thus, based on the small sample size of seven sites, there was insufficient evidence to claim these products had temporal instability. Pairwise Wilcoxon tests comparing yearly accuracies provided a measure of the proportion of year-pairs with significant differences and these proportions of significant pairwise differences were in turn used to compare temporal stability between BA products. The proportion of year-pairs with different accuracy (at the 0.05 significance level) ranged from 0% (MCD64) to 14% (fire_cci), computed from the 21 year-pairs available. In addition to the analysis of the three real burned area products, the analyses were applied to the accuracy measures computed for four hypothetical burned area products to illustrate the properties of the temporal stability analysis for different hypothetical scenarios of change in accuracy over time. The nonparametric tests were generally successful at detecting the different types of temporal instability designed into the hypothetical scenarios. The current work presents for the first time methods to quantify the temporal stability of BA product accuracies and to alert product end-users that statistically significant temporal instabilities exist. These methods represent diagnostic tools that allow product users to recognize the potential confounding effect of temporal instability on analysis of fire trends and allow map producers to identify anomalies in accuracy over time that may lead to insights for improving fire products. Additionally, we suggest temporal instabilities that could hypothetically appear, caused by for example by failures or changes in sensor data or classification algorithms. Full article
(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)
Open AccessArticle Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data
Remote Sens. 2014, 6(3), 2069-2083; doi:10.3390/rs6032069
Received: 12 December 2013 / Revised: 5 February 2014 / Accepted: 10 February 2014 / Published: 7 March 2014
Cited by 1 | PDF Full-text (606 KB) | HTML Full-text | XML Full-text
Abstract
This study proposed a new approach to measure the similarity between spectra to discriminate materials and evaluate the performance of parameter-selection procedures. Many pure pixel vector-based similarity measurements have been developed in the past to calculate the distance between two pixel vectors. [...] Read more.
This study proposed a new approach to measure the similarity between spectra to discriminate materials and evaluate the performance of parameter-selection procedures. Many pure pixel vector-based similarity measurements have been developed in the past to calculate the distance between two pixel vectors. However, those methods may not be effective since they do not take full advantage of the spectral correlation. In this study, we adopt Ensemble Empirical Mode Decomposition (EEMD) to decompose the spectrum into serial components and employ these components to improve the performance of spectral discrimination. Performance evaluation was conducted with several commonly used measurements, and the spectral samples used for experimentation were provided by the spectral library of United States Geological Survey (USGS). The experimental results have demonstrated that EEMD can extract the spectral features more effectively than common spectral similarity measurements, and it better characterizes spectral properties. Our experimental results also suggest general rules for selecting noise standard deviation, the number of iterations for EEMD and the collection of Intrinsic Mode Functions (IMFs) for classification. Finally, since EEMD is a time-consuming algorithm, we also implement parallel processing with a Graphics Processing Unit (GPU) to increase the processing speed. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
Open AccessArticle Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests
Remote Sens. 2014, 6(3), 2084-2107; doi:10.3390/rs6032084
Received: 23 December 2013 / Revised: 17 February 2014 / Accepted: 24 February 2014 / Published: 7 March 2014
Cited by 9 | PDF Full-text (1812 KB) | HTML Full-text | XML Full-text
Abstract
The last decade has seen launches of radar satellite missions operating in X-band with the sensors acquiring images with spatial resolutions on the order of 1 m. This study uses digital surface models (DSMs) extracted from stereo synthetic aperture radar images and [...] Read more.
The last decade has seen launches of radar satellite missions operating in X-band with the sensors acquiring images with spatial resolutions on the order of 1 m. This study uses digital surface models (DSMs) extracted from stereo synthetic aperture radar images and a reference airborne laser scanning digital terrain model to calculate the above-ground biomass and tree height. The resulting values are compared to in situ data. Analyses were undertaken at the Swedish test sites Krycklan (64°N) and Remningstorp (58°N), which have different site conditions. The results showed that, for 459 forest stands in Remningstorp, biomass estimation at the stand level could be performed with 22.9% relative root mean square error, while the height estimation showed 9.4%. Many factors influenced the results and it was found that the topography has a significant effect on the generated DSMs and should therefore be taken into consideration when the stand level mean slope is above four degrees. Different tree species did not have a major effect on the models during leaf-on conditions. Moreover, correct estimation within young forest stands was problematic. The intersection angles resulting in the best results were in the range 8–16°. Based on the results in this study, radargrammetry appears to be a promising potential remote sensing technique for future forest applications. Full article
Open AccessArticle Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia
Remote Sens. 2014, 6(3), 2108-2133; doi:10.3390/rs6032108
Received: 14 June 2013 / Revised: 4 February 2014 / Accepted: 19 February 2014 / Published: 7 March 2014
Cited by 9 | PDF Full-text (898 KB) | HTML Full-text | XML Full-text
Abstract
Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation [...] Read more.
Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
Open AccessArticle Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data
Remote Sens. 2014, 6(3), 2134-2153; doi:10.3390/rs6032134
Received: 3 November 2013 / Revised: 10 February 2014 / Accepted: 27 February 2014 / Published: 7 March 2014
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Abstract
Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context [...] Read more.
Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation. Full article
Open AccessArticle Habitat Classification of Temperate Marine Macroalgal Communities Using Bathymetric LiDAR
Remote Sens. 2014, 6(3), 2154-2175; doi:10.3390/rs6032154
Received: 11 November 2013 / Revised: 19 February 2014 / Accepted: 24 February 2014 / Published: 7 March 2014
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Abstract
Here, we evaluated the potential of using bathymetric Light Detection and Ranging (LiDAR) to characterise shallow water (<30 m) benthic habitats of high energy subtidal coastal environments. Habitat classification, quantifying benthic substrata and macroalgal communities, was achieved in this study with the [...] Read more.
Here, we evaluated the potential of using bathymetric Light Detection and Ranging (LiDAR) to characterise shallow water (<30 m) benthic habitats of high energy subtidal coastal environments. Habitat classification, quantifying benthic substrata and macroalgal communities, was achieved in this study with the application of LiDAR and underwater video groundtruth data using automated classification techniques. Bathymetry and reflectance datasets were used to produce secondary terrain derivative surfaces (e.g., rugosity, aspect) that were assumed to influence benthic patterns observed. An automated decision tree classification approach using the Quick Unbiased Efficient Statistical Tree (QUEST) was applied to produce substrata, biological and canopy structure habitat maps of the study area. Error assessment indicated that habitat maps produced were primarily accurate (>70%), with varying results for the classification of individual habitat classes; for instance, producer accuracy for mixed brown algae and sediment substrata, was 74% and 93%, respectively. LiDAR was also successful for differentiating canopy structure of macroalgae communities (i.e., canopy structure classification), such as canopy forming kelp versus erect fine branching algae. In conclusion, habitat characterisation using bathymetric LiDAR provides a unique potential to collect baseline information about biological assemblages and, hence, potential reef connectivity over large areas beyond the range of direct observation. This research contributes a new perspective for assessing the structure of subtidal coastal ecosystems, providing a novel tool for the research and management of such highly dynamic marine environments. Full article
Open AccessArticle Evaluating the Potentials of Sentinel-2 for Archaeological Perspective
Remote Sens. 2014, 6(3), 2176-2194; doi:10.3390/rs6032176
Received: 15 January 2014 / Revised: 14 February 2014 / Accepted: 24 February 2014 / Published: 10 March 2014
Cited by 6 | PDF Full-text (1259 KB) | HTML Full-text | XML Full-text
Abstract
The potentials of the forthcoming new European Space Agency’s (ESA) satellite sensor, Sentinel-2, for archaeological studies was examined in this paper. For this reason, an extensive spectral library of crop marks, acquired through numerous spectroradiometric campaigns, which are related with buried archaeological [...] Read more.
The potentials of the forthcoming new European Space Agency’s (ESA) satellite sensor, Sentinel-2, for archaeological studies was examined in this paper. For this reason, an extensive spectral library of crop marks, acquired through numerous spectroradiometric campaigns, which are related with buried archaeological remains, has been resampled to the spectral characteristics of Sentinel-2. In addition, other existing satellite sensors have been also evaluated (Landsat 5 Thematic Mapper (TM); Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER); IKONOS; Landsat 4 TM; Landsat 7 Enhance Thematic Mapper Plus (ETM+); QuickBird; Satellite Pour l’Observation de la Terre (SPOT); and WorldView-2). The simulated data have been compared with the optimum spectral regions for the detection of crop marks (700 nm and 800 nm). In addition, several existing vegetation indices have been also assessed for all sensors. As it was found, the spectral characteristics of Sentinel-2 are able to better distinguish crop marks compared to other existing satellite sensors. Indeed, as it was found, using a simulated Sentinel-2 image, not only known buried archaeological sites were able to be detected, but also other still unknown sites were able to be revealed. Full article
Open AccessArticle The Delineation of Paleo-Shorelines in the Lake Manyara Basin Using TerraSAR-X Data
Remote Sens. 2014, 6(3), 2195-2212; doi:10.3390/rs6032195
Received: 31 December 2013 / Revised: 18 February 2014 / Accepted: 27 February 2014 / Published: 10 March 2014
Cited by 7 | PDF Full-text (1664 KB) | HTML Full-text | XML Full-text
Abstract
The purpose of this paper is to describe the delineation of paleo-shorelines using high resolution microwave images and digital image processing tools, and with that to contribute to the understanding of the complex landscape evolution of the Lake Manyara Basin. The surroundings [...] Read more.
The purpose of this paper is to describe the delineation of paleo-shorelines using high resolution microwave images and digital image processing tools, and with that to contribute to the understanding of the complex landscape evolution of the Lake Manyara Basin. The surroundings of Lake Manyara are the focus of several paleo-archeological investigations, since the location is close to Olduvai Gorge, where paleo-anthropological findings can be traced back to homo habilis. In the catchment of Lake Manyara two hominin-bearing sites (0.78 to 0.63 Ma), lots of vertebrate fossils and hand axes from different periods were found. Understanding the development and extent of the lake is crucial for understanding the regional paleo-environment of the Quaternary. Morphological structures of shorelines and terraces east of Lake Manyara were identified from TerraSAR-X StripMap images. By applying a Canny edge detector, linear features were extracted and revised for different image acquisitions using a contextual approach. Those features match literature and field references. A digital elevation model of the region was used to map the most distinct paleo-shorelines according to their elevation. Full article
(This article belongs to the Special Issue New Perspectives of Remote Sensing for Archaeology)
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Open AccessArticle A Synergetic Algorithm for Mid-Morning Land Surface Soil and Vegetation Temperatures Estimation Using MSG-SEVIRI Products and TERRA-MODIS Products
Remote Sens. 2014, 6(3), 2213-2238; doi:10.3390/rs6032213
Received: 19 December 2013 / Revised: 21 February 2014 / Accepted: 27 February 2014 / Published: 10 March 2014
Cited by 2 | PDF Full-text (1873 KB) | HTML Full-text | XML Full-text
Abstract
Land surface is normally considered as a mixture of soil and vegetation. Many applications, such as drought monitoring and crop-yield estimation, benefit from accurate retrieval of both soil and vegetation temperatures through satellite observation. A preliminary study has been conducted in this [...] Read more.
Land surface is normally considered as a mixture of soil and vegetation. Many applications, such as drought monitoring and crop-yield estimation, benefit from accurate retrieval of both soil and vegetation temperatures through satellite observation. A preliminary study has been conducted in this study on the estimation of land surface soil and vegetation component temperature using the geostationary satellite data acquired by Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) and TERRA-MODIS data. A synergetic algorithm is proposed to derive soil and vegetation temperatures by using the temporal and spatial information in SEVIRI and MODIS products. The approach is applied to both simulation data and satellite data. For simulation data, the component temperatures are well estimated with root mean squared error (RMSE) close to 0 K. For satellite data application, reasonable spatial distributions of the soil and vegetation temperatures are derived for eight cloud-free days in the Iberian Peninsula from June to August 2009. An evaluation is performed for the estimated vegetation temperature against the near surface air temperature. The correlation analysis between two datasets is found that the R-squareds are from 0.074 to 0.423 and RMSEs are within 4 K. Considering the impact of fraction of vegetation cover (FVC) on the validation, the pixels with FVC less than 30% are excluded in the total data comparison, and an obvious improvement is achieved with R-squared from 0.231 to 0.417 and RMSE from 2.9 K to 2.58 K. The validation indicates that the proposed algorithm is able to provide reasonable estimations of soil and vegetation temperatures. It is a potential way to map soil and vegetation temperature for large areas. Full article
Open AccessArticle Estimating Canopy Characteristics of Inner Mongolia’s Grasslands from Field Spectrometry
Remote Sens. 2014, 6(3), 2239-2254; doi:10.3390/rs6032239
Received: 2 January 2014 / Revised: 26 February 2014 / Accepted: 3 March 2014 / Published: 12 March 2014
Cited by 4 | PDF Full-text (578 KB) | HTML Full-text | XML Full-text
Abstract
This study was designed to estimate the canopy biophysical characteristics of semi-arid grassland ecosystems by using in situ field spectrometry measurements to identify important spectral information for predictions at broader spatial scales. Spectral vegetation indices (VIs), reflectance spectra, continuum removal spectra, and [...] Read more.
This study was designed to estimate the canopy biophysical characteristics of semi-arid grassland ecosystems by using in situ field spectrometry measurements to identify important spectral information for predictions at broader spatial scales. Spectral vegetation indices (VIs), reflectance spectra, continuum removal spectra, and the amplitude of the red edge peak (drre) based on 61 well-replicated field measurements across a large area in Inner Mongolia were used to develop empirical models for estimating four key canopy biophysical features: percent green coverage (PGC), canopy height (H), green aboveground biomass (GBM), and total aboveground biomass (TBM). The results showed that NDVI, EVI, NDSVI, and LSWI were useful for estimating canopy biophysical features, with NDSVI being the most significant variable. The PGC was accurately estimated with spectral reflectance at 441 nm and 2220 nm (R2 = 0.71), while the maximum depth of band (Dc), absorption area (Darea) in the red domain and drre were selected for estimating TBM and GBM (R2 = 0.51 and 0.44). Among the four canopy features, PGC received the highest confidence from all of the models (R2 = 0.81), while H was the most difficult to estimate (R2 = 0.49). Finally, the degree of disturbances and ecosystem types appeared to be a significant variable for model development. Full article
Open AccessArticle Multi-Mission Cross-Calibration of Satellite Altimeters: Constructing a Long-Term Data Record for Global and Regional Sea Level Change Studies
Remote Sens. 2014, 6(3), 2255-2281; doi:10.3390/rs6032255
Received: 10 October 2013 / Revised: 11 February 2014 / Accepted: 19 February 2014 / Published: 12 March 2014
Cited by 19 | PDF Full-text (1013 KB) | HTML Full-text | XML Full-text
Abstract
Climate studies require long data records extending the lifetime of a single remote sensing satellite mission. Precise satellite altimetry exploring global and regional evolution of the sea level has now completed a two decade data record. A consistent long-term data record has [...] Read more.
Climate studies require long data records extending the lifetime of a single remote sensing satellite mission. Precise satellite altimetry exploring global and regional evolution of the sea level has now completed a two decade data record. A consistent long-term data record has to be constructed from a sequence of different, partly overlapping altimeter systems which have to be carefully cross-calibrated. This cross-calibration is realized globally by adjusting an extremely large set of single- and dual-satellite crossover differences performed between all contemporaneous altimeter systems. The total set of crossover differences creates a highly redundant network and enables a robust estimate of radial errors with a dense and rather complete sampling for all altimeter systems analyzed. An iterative variance component estimation is applied to obtain an objective relative weighting between altimeter systems with different performance. The final time series of radial errors is taken to estimate (for each of the altimeter systems) an empirical auto-covariance function. Moreover, the radial errors capture relative range biases and indicate systematic variations in the geo-centering of altimeter satellite orbits. The procedure has the potential to estimate for all altimeter systems the geographically correlated mean errors which is not at all visible in single-satellite crossover differences but maps directly to estimates of the mean sea surface. Full article
Open AccessArticle Early Analysis of Landsat-8 Thermal Infrared Sensor Imagery of Volcanic Activity
Remote Sens. 2014, 6(3), 2282-2295; doi:10.3390/rs6032282
Received: 18 October 2013 / Revised: 21 February 2014 / Accepted: 10 March 2014 / Published: 12 March 2014
Cited by 6 | PDF Full-text (561 KB) | HTML Full-text | XML Full-text
Abstract
The Landsat-8 satellite of the Landsat Data Continuity Mission was launched by the National Aeronautics and Space Administration (NASA) in April 2013. Just weeks after it entered active service, its sensors observed activity at Paluweh Volcano, Indonesia. Given that the image acquired [...] Read more.
The Landsat-8 satellite of the Landsat Data Continuity Mission was launched by the National Aeronautics and Space Administration (NASA) in April 2013. Just weeks after it entered active service, its sensors observed activity at Paluweh Volcano, Indonesia. Given that the image acquired was in the daytime, its shortwave infrared observations were contaminated with reflected solar radiation; however, those of the satellite’s Thermal Infrared Sensor (TIRS) show thermal emission from the volcano’s summit and flanks. These emissions detected in sensor’s band 10 (10.60–11.19 µm) have here been quantified in terms of radiant power, to confirm reports of the actual volcanic processes operating at the time of image acquisition, and to form an initial assessment of the TIRS in its volcanic observation capabilities. Data from band 11 have been neglected as its data have been shown to be unreliable at the time of writing. At the instant of image acquisition, the thermal emission of the volcano was found to be 345 MW. This value is shown to be on the same order of magnitude as similarly timed NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer thermal observations. Given its unique characteristics, the TIRS shows much potential for providing useful, detailed and accurate volcanic observations in the future. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
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Open AccessArticle Pan-Arctic Climate and Land Cover Trends Derived from Multi-Variate and Multi-Scale Analyses (1981–2012)
Remote Sens. 2014, 6(3), 2296-2316; doi:10.3390/rs6032296
Received: 22 November 2013 / Revised: 19 February 2014 / Accepted: 5 March 2014 / Published: 12 March 2014
Cited by 9 | PDF Full-text (1842 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Arctic ecosystems have been afflicted by vast changes in recent decades. Changes in temperature, as well as precipitation, are having an impact on snow cover, vegetation productivity and coverage, vegetation seasonality, surface albedo, and permafrost dynamics. The coupled climate-vegetation change in the [...] Read more.
Arctic ecosystems have been afflicted by vast changes in recent decades. Changes in temperature, as well as precipitation, are having an impact on snow cover, vegetation productivity and coverage, vegetation seasonality, surface albedo, and permafrost dynamics. The coupled climate-vegetation change in the arctic is thought to be a positive feedback in the Earth system, which can potentially further accelerate global warming. This study focuses on the co-occurrence of temperature, precipitation, snow cover, and vegetation greenness trends between 1981 and 2012 in the pan-arctic region based on coarse resolution climate and remote sensing data, as well as ground stations. Precipitation significantly increased during summer and fall. Temperature had the strongest increase during the winter months (twice than during the summer months). The snow water equivalent had the highest trends during the transition seasons of the year. Vegetation greenness trends are characterized by a constant increase during the vegetation-growing period. High spatial resolution remote sensing data were utilized to map structural vegetation changes between 1973 and 2012 for a selected test region in Northern Siberia. An intensification of woody vegetation cover at the taiga-tundra transition area was found. The observed co-occurrence of climatic and ecosystem changes is an example of the multi-scale feedbacks in the arctic ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
Open AccessArticle An Updated Geophysical Model for AMSR-E and SSMIS Brightness Temperature Simulations over Oceans
Remote Sens. 2014, 6(3), 2317-2342; doi:10.3390/rs6032317
Received: 10 December 2013 / Revised: 28 February 2014 / Accepted: 4 March 2014 / Published: 17 March 2014
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Abstract
In this study, we considered the geophysical model for microwave brightness temperature (BT) simulation for the Atmosphere-Ocean System under non-precipitating conditions. The model is presented as a combination of atmospheric absorption and ocean emission models. We validated this model for two satellite [...] Read more.
In this study, we considered the geophysical model for microwave brightness temperature (BT) simulation for the Atmosphere-Ocean System under non-precipitating conditions. The model is presented as a combination of atmospheric absorption and ocean emission models. We validated this model for two satellite instruments—for Advanced Microwave Sounding Radiometer-Earth Observing System (AMSR-E) onboard Aqua satellite and for Special Sensor Microwave Imager/Sounder (SSMIS) onboard F16 satellite of Defense Meteorological Satellite Program (DMSP) series. We compared simulated BT values with satellite BT measurements for different combinations of various water vapor and oxygen absorption models and wind induced ocean emission models. A dataset of clear sky atmospheric and oceanic parameters, collocated in time and space with satellite measurements, was used for the comparison. We found the best model combination, providing the least root mean square error between calculations and measurements. A single combination of models ensured the best results for all considered radiometric channels. We also obtained the adjustments to simulated BT values, as averaged differences between the model simulations and satellite measurements. These adjustments can be used in any research based on modeling data for removing model/calibration inconsistencies. We demonstrated the application of the model by means of the development of the new algorithm for sea surface wind speed retrieval from AMSR-E data. Full article
Open AccessArticle Agricultural Monitoring in Northeastern Ontario, Canada, Using Multi-Temporal Polarimetric RADARSAT-2 Data
Remote Sens. 2014, 6(3), 2343-2371; doi:10.3390/rs6032343
Received: 26 December 2013 / Revised: 21 February 2014 / Accepted: 3 March 2014 / Published: 17 March 2014
Cited by 6 | PDF Full-text (2533 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The purpose of this research is to analyze how changes in acquisition time and incidence angle affect various C-band synthetic aperture radar (SAR) polarimetric intensities, co-polarized phase information, polarimetric response plots and decomposition parameters for various crops typical of Northern Ontario, Canada. [...] Read more.
The purpose of this research is to analyze how changes in acquisition time and incidence angle affect various C-band synthetic aperture radar (SAR) polarimetric intensities, co-polarized phase information, polarimetric response plots and decomposition parameters for various crops typical of Northern Ontario, Canada. We examine how these parameters may be used to monitor the growth stages of five common cash crops, namely, barley (Hordeum vulgare), canola (Brassica napus), oat (Avena sativa), soybean (Glycine max) and wheat (Triticum spp.). In total, nine RADARSAT-2 polarimetric images were analyzed across a 14-week period beginning in June and ending in September 2011 using two incidence angles of approximately 26° and 41°. As expected, the backscatter intensities for all targets were found to show a higher response when acquired at the steeper incidence angle (26°). All cash crop targets showed a rise and fall in backscatter response over the course of the growing season, coinciding with changing growth stages. Slight phase differences were observed for cereal crops, possibly due to one of the polarizations penetrating between the rows allowing double-bounce to occur. The polarimetric response plots and decompositions offered insight into the scattering mechanisms of each crop type, generally showing an increase in volume scattering as the crops reached maturity. Specifically, the contributions of the crops increased towards the volume scattering component and zones 4 and 2, as the crops matured in regards to the Freeman-Durden and Cloude-Pottier decompositions respectively. Overall, soybean and canola showed a more similar response in comparison to the cereal cash crops. Although the study focused on Northern Ontario, it is anticipated that these results would be relevant in investigations of multi-temporal RADARSAT-2 for agricultural zones with similar crop types. Full article
Open AccessArticle Multi-Temporal Polarimetric RADARSAT-2 for Land Cover Monitoring in Northeastern Ontario, Canada
Remote Sens. 2014, 6(3), 2372-2392; doi:10.3390/rs6032372
Received: 24 December 2013 / Revised: 30 January 2014 / Accepted: 10 March 2014 / Published: 17 March 2014
Cited by 15 | PDF Full-text (1994 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
For successful applications of microwave remote sensing endeavors it is essential to understand how surface targets respond to changing synthetic aperture radar (SAR) parameters. The purpose of the study is to examine how two particular parameters, acquisition time and incidence angle, influences [...] Read more.
For successful applications of microwave remote sensing endeavors it is essential to understand how surface targets respond to changing synthetic aperture radar (SAR) parameters. The purpose of the study is to examine how two particular parameters, acquisition time and incidence angle, influences the response from various land use/land cover types (forests, urban infrastructure, surface water and marsh wetland targets) using nine RADARSAT-2 C-band fine-beam (FQ7 and FQ21) fully polarimetric SAR data acquired during the 2011 growing season over northern Ontario, Canada. The results indicate that backscatter from steep incidence angle acquisitions was typically higher than shallow angles. Wetlands showed an increase in HH and HV intensity due to the growth of emergent vegetation over the course of the summer. The forest and urban targets displayed little variation in backscatter over time. The surface water target showed the greatest difference with respect to incidence angle, but was also determined to be the most affected by wind conditions. Analysis of the co-polarized phase difference revealed the urban target as greatly influenced by the incidence angle. The observed phase differences of the wetland target for all acquisitions also suggested evidence of double-bounce interactions, while the forest and surface water targets showed little to no phase difference. In addition, Cloude-Pottier and Freeman-Durden decompositions, when analyzed in conjunction with polarimetric response plots, provided supporting information to confidently identify the various targets and their scattering mechanisms. Full article
Open AccessArticle Multi-Sensor Imaging and Space-Ground Cross-Validation for 2010 Flood along Indus River, Pakistan
Remote Sens. 2014, 6(3), 2393-2407; doi:10.3390/rs6032393
Received: 18 December 2013 / Revised: 26 February 2014 / Accepted: 3 March 2014 / Published: 19 March 2014
Cited by 7 | PDF Full-text (1135 KB) | HTML Full-text | XML Full-text
Abstract
Flood monitoring was conducted using multi-sensor data from space-borne optical, and microwave sensors; with cross-validation by ground-based rain gauges and streamflow stations along the Indus River; Pakistan. First; the optical imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) was processed to delineate [...] Read more.
Flood monitoring was conducted using multi-sensor data from space-borne optical, and microwave sensors; with cross-validation by ground-based rain gauges and streamflow stations along the Indus River; Pakistan. First; the optical imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) was processed to delineate the extent of the 2010 flood along Indus River; Pakistan. Moreover; the all-weather all-time capability of higher resolution imagery from the Advanced Synthetic Aperture Radar (ASAR) is used to monitor flooding in the lower Indus river basin. Then a proxy for river discharge from the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA’s Aqua satellite and rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) are used to study streamflow time series and precipitation patterns. The AMSR-E detected water surface signal was cross-validated with ground-based river discharge observations at multiple streamflow stations along the main Indus River. A high correlation was found; as indicated by a Pearson correlation coefficient of above 0.8 for the discharge gauge stations located in the southwest of Indus River basin. It is concluded that remote-sensing data integrated from multispectral and microwave sensors could be used to supplement stream gauges in sparsely gauged large basins to monitor and detect floods. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
Open AccessArticle Local Vegetation Trends in the Sahel of Mali and Senegal Using Long Time Series FAPAR Satellite Products and Field Measurement (1982–2010)
Remote Sens. 2014, 6(3), 2408-2434; doi:10.3390/rs6032408
Received: 17 January 2014 / Revised: 27 February 2014 / Accepted: 3 March 2014 / Published: 19 March 2014
Cited by 17 | PDF Full-text (5890 KB) | HTML Full-text | XML Full-text
Abstract
Local vegetation trends in the Sahel of Mali and Senegal from Geoland Version 1 (GEOV1) (5 km) and the third generation Global Inventory Modeling and Mapping Studies (GIMMS3g) (8 km) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) time series are studied over [...] Read more.
Local vegetation trends in the Sahel of Mali and Senegal from Geoland Version 1 (GEOV1) (5 km) and the third generation Global Inventory Modeling and Mapping Studies (GIMMS3g) (8 km) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) time series are studied over 29 years. For validation and interpretation of observed greenness trends, two methods are applied: (1) a qualitative approach using in-depth knowledge of the study areas and (2) a quantitative approach by time series of biomass observations and rainfall data. Significant greening trends from 1982 to 2010 are consistently observed in both GEOV1 and GIMMS3g FAPAR datasets. Annual rainfall increased significantly during the observed time period, explaining large parts of FAPAR variations at a regional scale. Locally, GEOV1 data reveals a heterogeneous pattern of vegetation change, which is confirmed by long-term ground data and site visits. The spatial variability in the observed vegetation trends in the Sahel area are mainly caused by varying tree- and land-cover, which are controlled by human impact, soil and drought resilience. A large proportion of the positive trends are caused by the increment in leaf biomass of woody species that has almost doubled since the 1980s due to a tree cover regeneration after a dry-period. This confirms the re-greening of the Sahel, however, degradation is also present and sometimes obscured by greening. GEOV1 as compared to GIMMS3g made it possible to better characterize the spatial pattern of trends and identify the degraded areas in the study region. Full article
Figures

Open AccessArticle An Innovative Curvelet-only-Based Approach for Automated Change Detection in Multi-Temporal SAR Imagery
Remote Sens. 2014, 6(3), 2435-2462; doi:10.3390/rs6032435
Received: 29 November 2013 / Revised: 7 March 2014 / Accepted: 13 March 2014 / Published: 19 March 2014
Cited by 7 | PDF Full-text (1794 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a novel approach for automated image comparison and robust change detection from noisy imagery, such as synthetic aperture radar (SAR) amplitude images. Instead of comparing pixel values and/or pre-classified features this approach clearly highlights structural changes without any preceding [...] Read more.
This paper presents a novel approach for automated image comparison and robust change detection from noisy imagery, such as synthetic aperture radar (SAR) amplitude images. Instead of comparing pixel values and/or pre-classified features this approach clearly highlights structural changes without any preceding segmentation or classification step. The crucial point is the use of the Curvelet transform in order to express the image as composition of several structures instead of numerous individual pixels. Differentiating these structures and weighting their impact according to the image statistics produces a smooth, but detail-preserved change image. The Curvelet-based approach is validated by the standard technique for SAR change detection, the log-ratio with and without additional gamma maximum-a-posteriori (GMAP) speckle filtering, and by the results of human interpreters. The validation proves that the new technique can easily compete with these automated as well as visual interpretation techniques. Finally, a sequence of TerraSAR-X High Resolution Spotlight images of a factory building construction site near Ludwigshafen (Germany) is processed in order to identify single construction stages by the time of the (dis-)appearance of certain objects. Hence, the complete construction monitoring of the whole building and its surroundings becomes feasible. Full article
Open AccessArticle Mapping Crop Cycles in China Using MODIS-EVI Time Series
Remote Sens. 2014, 6(3), 2473-2493; doi:10.3390/rs6032473
Received: 30 December 2013 / Revised: 6 March 2014 / Accepted: 10 March 2014 / Published: 20 March 2014
Cited by 16 | PDF Full-text (1551 KB) | HTML Full-text | XML Full-text
Abstract
As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide [...] Read more.
As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA’s (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data. Full article
Open AccessArticle Spatio-Temporal Assessment of Tuz Gölü, Turkey as a Potential Radiometric Vicarious Calibration Site
Remote Sens. 2014, 6(3), 2494-2513; doi:10.3390/rs6032494
Received: 15 November 2013 / Revised: 5 March 2014 / Accepted: 10 March 2014 / Published: 20 March 2014
Cited by 6 | PDF Full-text (2901 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The paper provides an assessment of Tuz Gölü, a site in Turkey proposed for the radiometric vicarious calibration of satellite sensors, in terms of its spatial homogeneity as expressed in visible and near-infrared (VNIR) wavelengths over a 25-year period (1984–2009). By combining [...] Read more.
The paper provides an assessment of Tuz Gölü, a site in Turkey proposed for the radiometric vicarious calibration of satellite sensors, in terms of its spatial homogeneity as expressed in visible and near-infrared (VNIR) wavelengths over a 25-year period (1984–2009). By combining the coefficient of variation (CV) and Getis statistic (Gi*), a spatially homogenous and temporally stable area at least 720 m × 330 m in size was identified. Analysis of mid-summer Landsat Thematic Mapper (TM) images acquired over the period 1984–2009 showed that the hemispherical-directional reflectance factor of this area had a spatial variability, as defined by the CV, in the range of 0.99% to 3.99% in Landsat TM bands 2–4. This is comparable with the reported variability of other test sites around the world, but this is the first time an area has been shown to have this degree of homogeneity over such a long period of time. Full article
Open AccessArticle Regional Water Balance Based on Remotely Sensed Evapotranspiration and Irrigation: An Assessment of the Haihe Plain, China
Remote Sens. 2014, 6(3), 2514-2533; doi:10.3390/rs6032514
Received: 6 January 2014 / Revised: 12 March 2014 / Accepted: 13 March 2014 / Published: 20 March 2014
Cited by 2 | PDF Full-text (1250 KB) | HTML Full-text | XML Full-text
Abstract
Optimal planning and management of the limited water resources for maximum productivity in agriculture requires quantifying the irrigation applied at a regional scale. However, most efforts involving remote sensing applications in assessing large-scale irrigation applied (IA) have focused on supplying spatial variables [...] Read more.
Optimal planning and management of the limited water resources for maximum productivity in agriculture requires quantifying the irrigation applied at a regional scale. However, most efforts involving remote sensing applications in assessing large-scale irrigation applied (IA) have focused on supplying spatial variables for crop models or studying evapotranspiration (ET) inversions, rather than directly building a remote sensing data-based model to estimate IA. In this study, based on remote sensing data, an IA estimation model together with an ET calculation model (ETWatch) is set up to simulate the spatial distribution of IA in the Haihe Plain of northern China. We have verified this as an effective approach for the simulation of regional IA, being more reflective of regional characteristics and of higher resolution compared to single site-specific results. The results show that annual ET varies from 527 mm to 679 mm and IA varies from 166 mm to 289 mm, with average values of 602 mm and 225 mm, respectively, from 2002 to 2007. We confirm that the region along the Taihang Mountain in Hebei Plain has serious water resource sustainability problems, even while receiving water from the South-North Water Transfer (SNWT) project. This is due to the region’s intensive agricultural production and declining groundwater tables. Water-saving technologies, including more timely and accurate geo-specific IA assessments, may help reduce this threat. Full article
Open AccessArticle Spectral Aging Model Applied to Meteosat First Generation Visible Band
Remote Sens. 2014, 6(3), 2534-2571; doi:10.3390/rs6032534
Received: 29 September 2013 / Revised: 6 February 2014 / Accepted: 19 February 2014 / Published: 20 March 2014
Cited by 2 | PDF Full-text (9147 KB) | HTML Full-text | XML Full-text
Abstract
The Meteosat satellites have been operational since the early eighties, creating so far a continuous time period of observations of more than 30 years. In order to use this data for climate data records, a consistent calibration is necessary between the consecutive [...] Read more.
The Meteosat satellites have been operational since the early eighties, creating so far a continuous time period of observations of more than 30 years. In order to use this data for climate data records, a consistent calibration is necessary between the consecutive instruments. Studies have shown that the Meteosat First Generation (MFG) satellites (1982–2006) suffer from in-flight degradation which is spectral of nature and is not corrected by the official calibration of EUMETSAT. Continuing on previous published work by the same authors, this paper applies the spectral aging model to a set of clear-sky and cloudy targets, and derives the model parameters for all six MFG satellites (Meteosat-2 to -7). Several problems have been encountered, both due to the instrument and due to geophysical occurrences, and these are discussed and illustrated here in detail. The paper shows how the spectral aging model is an improvement compared to the EUMETSAT calibration method with a stability of 1%–2% for Meteosat-4 to -7, which increases up to 6% for ocean sites using the full MFG time period. Full article
Open AccessArticle Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection
Remote Sens. 2014, 6(3), 2572-2600; doi:10.3390/rs6032572
Received: 9 October 2013 / Revised: 13 March 2014 / Accepted: 17 March 2014 / Published: 21 March 2014
Cited by 8 | PDF Full-text (7502 KB) | HTML Full-text | XML Full-text
Abstract
Reliable multi-temporal landslide detection over longer periods of time requires multi-sensor time series data characterized by high internal geometric stability, as well as high relative and absolute accuracy. For this purpose, a new methodology for fully automated co-registration has been developed allowing [...] Read more.
Reliable multi-temporal landslide detection over longer periods of time requires multi-sensor time series data characterized by high internal geometric stability, as well as high relative and absolute accuracy. For this purpose, a new methodology for fully automated co-registration has been developed allowing efficient and robust spatial alignment of standard orthorectified data products originating from a multitude of optical satellite remote sensing data of varying spatial resolution. Correlation-based co-registration uses world-wide available terrain corrected Landsat Level 1T time series data as the spatial reference, ensuring global applicability. The developed approach has been applied to a multi-sensor time series of 592 remote sensing datasets covering an approximately 12,000 km2 area in Southern Kyrgyzstan (Central Asia) strongly affected by landslides. The database contains images acquired during the last 26 years by Landsat (E)TM, ASTER, SPOT and RapidEye sensors. Analysis of the spatial shifts obtained from co-registration has revealed sensor-specific alignments ranging between 5 m and more than 400 m. Overall accuracy assessment of these alignments has resulted in a high relative image-to-image accuracy of 17 m (RMSE) and a high absolute accuracy of 23 m (RMSE) for the whole co-registered database, making it suitable for multi-temporal landslide detection at a regional scale in Southern Kyrgyzstan. Full article
Open AccessArticle Retrieval of Gap Fraction and Effective Plant Area Index from Phase-Shift Terrestrial Laser Scans
Remote Sens. 2014, 6(3), 2601-2627; doi:10.3390/rs6032601
Received: 19 December 2013 / Revised: 11 March 2014 / Accepted: 17 March 2014 / Published: 24 March 2014
Cited by 3 | PDF Full-text (2544 KB) | HTML Full-text | XML Full-text
Abstract
The characterization of canopy structure is crucial for modeling eco-physiological processes. Two commonly used metrics for characterizing canopy structure are the gap fraction and the effective Plant Area Index (PAIe). Both have been successfully retrieved with terrestrial laser scanning. However, a systematic [...] Read more.
The characterization of canopy structure is crucial for modeling eco-physiological processes. Two commonly used metrics for characterizing canopy structure are the gap fraction and the effective Plant Area Index (PAIe). Both have been successfully retrieved with terrestrial laser scanning. However, a systematic assessment of the influence of the laser scan properties on the retrieval of these metrics is still lacking. This study investigated the effects of resolution, measurement speed, and noise compression on the retrieval of gap fraction and PAIe from phase-shift FARO Photon 120 laser scans. We demonstrate that FARO’s noise compression yields gap fractions and PAIe that deviate significantly from those based on scans without noise compression and strongly overestimate Leaf Area Index (LAI) estimates based on litter trap measurements. Scan resolution and measurement speed were also shown to impact gap fraction and PAIe, but this depended on leaf development phase, stand structure, and LAI calculation method. Nevertheless, PAIe estimates based on various scan parameter combinations without noise compression proved to be quite stable. Full article

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Open AccessComment Atoll Rim Expansion or Erosion in Diego Garcia Atoll, Indian Ocean? Comment on Hamylton, S.; East, H. A Geospatial Appraisal of Ecological and Geomorphic Change on Diego Garcia Atoll, Chagos Islands (British Indian Ocean Territory). Remote Sens. 2012, 4, 3444–3461
Remote Sens. 2014, 6(3), 2463-2467; doi:10.3390/rs6032463
Received: 21 November 2013 / Revised: 20 February 2014 / Accepted: 24 February 2014 / Published: 19 March 2014
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Abstract
Hamylton and East [1] reported a remarkably substantial expansion and accretion of land since the 1960s at numerous sites along the island of Diego Garcia atoll. This atoll has a near continuous rim encircling its lagoon, and they reported that the width [...] Read more.
Hamylton and East [1] reported a remarkably substantial expansion and accretion of land since the 1960s at numerous sites along the island of Diego Garcia atoll. This atoll has a near continuous rim encircling its lagoon, and they reported that the width of the island rim has expanded by several tens of metres in many places. Those results contrast markedly with my own near-annual observations on the ground where erosion and increased seawater inundation, rather than land accretion, is evident in many places. Hamylton and East have never visited the atoll but their results have been picked up for various reasons. Therefore I note possible reasons for the substantial discrepancy between their measurements and my own observations, and propose a resolution. Full article
Open AccessReply Response to Sheppard, C. Atoll Rim Expansion or Erosion in Diego Garcia Atoll, Indian Ocean? Comment on Hamylton, S.; East, H. A Geospatial Appraisal of Ecological and Geomorphic Change on Diego Garcia Atoll, Chagos Islands (British Indian Ocean Territory). Remote Sens. 2012, 4, 3444–3461
Remote Sens. 2014, 6(3), 2468-2472; doi:10.3390/rs6032468
Received: 13 March 2014 / Accepted: 17 March 2014 / Published: 19 March 2014
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Abstract
We welcome Sheppard’s comments on our recent assessment of both ecological and geomorphic change at Diego Garcia Atoll in the central Indian Ocean [1]. Whilst our assessment incorporated numerous aspects of change, including movements of the lagoon rim shorelines, changes in the [...] Read more.
We welcome Sheppard’s comments on our recent assessment of both ecological and geomorphic change at Diego Garcia Atoll in the central Indian Ocean [1]. Whilst our assessment incorporated numerous aspects of change, including movements of the lagoon rim shorelines, changes in the terrestrial vegetation on the lagoon rim and amendments to the bathymetry of the lagoon basin through dredging activities [2], this comment solely addresses the estimates of shoreline change. Here we make some brief remarks relating to this shoreline assessment of Diego Garcia and elaborate on some of the complexities of the geomorphic processes that underpin shoreline dynamics. These complexities have important implications for understanding reef island shoreline dynamics, both at this site and globally. Full article

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