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 9, Issue 6 (June 2017)

  • 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.
Cover Story (view full-size image) As part of the Copernicus programme of the European Commission (EC), the European Space Agency [...] Read more.
View options order results:
result details:
Displaying articles 1-128
Export citation of selected articles as:
Open AccessArticle
A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion
Remote Sens. 2017, 9(6), 639; https://doi.org/10.3390/rs9060639
Received: 10 May 2017 / Revised: 9 June 2017 / Accepted: 16 June 2017 / Published: 21 June 2017
Cited by 3 | Viewed by 1762 | PDF Full-text (5669 KB) | HTML Full-text | XML Full-text
Abstract
The standard ratio of spatial resolution between bands for high resolution satellites is 1:4, which is typical when combining images obtained from the same sensor. However, the cost of simultaneously purchasing a set of panchromatic and multispectral images is still relatively high. There [...] Read more.
The standard ratio of spatial resolution between bands for high resolution satellites is 1:4, which is typical when combining images obtained from the same sensor. However, the cost of simultaneously purchasing a set of panchromatic and multispectral images is still relatively high. There is therefore a need to develop methods of data fusion of very high resolution panchromatic imagery with low-cost multispectral data (e.g., Landsat). Combining high resolution images with low resolution images broadens the scope of use of satellite data, however, it is also accompanied by the problem of a large ratio between spatial resolutions, which results in large spectral distortions in the merged images. The authors propose a modification of the panchromatic image in such a way that it includes the spectral and spatial information from both the panchromatic and multispectral images to improve the quality of spectral data integration. This fusion is done based on a weighted average. The weight is determined using a coefficient, which determines the ratio of the amount of information contained in the corresponding pixels of the integrated images. The effectiveness of the author’s algorithm had been tested for six of the most popular fusion methods. The proposed methodology is ideal mainly for statistical and numerical methods, especially Principal Component Analysis and Gram-Schmidt. The author’s algorithm makes it possible to lower the root mean square error by up to 20% for the Principal Component Analysis. The spectral quality was also increased, especially for the spectral bands extending beyond the panchromatic image, where the correlation rose by 18% for the Gram-Schmidt orthogonalization. Full article
Figures

Graphical abstract

Open AccessArticle
The Application of ALOS/PALSAR InSAR to Measure Subsurface Penetration Depths in Deserts
Remote Sens. 2017, 9(6), 638; https://doi.org/10.3390/rs9060638
Received: 14 April 2017 / Revised: 9 June 2017 / Accepted: 16 June 2017 / Published: 21 June 2017
Cited by 3 | Viewed by 2084 | PDF Full-text (12415 KB) | HTML Full-text | XML Full-text
Abstract
Spaceborne Synthetic Aperture Radar (SAR) interferometry has been utilised to acquire high-resolution Digital Elevation Models (DEMs) with wide coverage, particularly for persistently cloud-covered regions where stereophotogrammetry is hard to apply. Since the discovery of sand buried drainage systems by the Shuttle Imaging Radar-A [...] Read more.
Spaceborne Synthetic Aperture Radar (SAR) interferometry has been utilised to acquire high-resolution Digital Elevation Models (DEMs) with wide coverage, particularly for persistently cloud-covered regions where stereophotogrammetry is hard to apply. Since the discovery of sand buried drainage systems by the Shuttle Imaging Radar-A (SIR-A) L-band mission in 1982, radar images have been exploited to map subsurface features beneath a sandy cover of extremely low loss and low bulk humidity in some hyper-arid regions such as from the Japanese Earth Resources Satellite 1 (JERS-1) and Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR). Therefore, we hypothesise that a Digital Elevation Model (DEM) derived by InSAR in hyper-arid regions is likely to represent a subsurface elevation model, especially for lower frequency radar systems, such as the L-band system (1.25 GHz). In this paper, we compare the surface appearance of radar images (L-band and C-band) with that of optical images to demonstrate their different abilities to show subsurface features. Moreover, we present an application of L-band InSAR to measure penetration depths in the eastern Sahara Desert. We demonstrate how the retrieved L-band InSAR DEM appears to be of a consistently 1–2 m lower elevation than the C-band Shuttle Radar Topography Mission (SRTM) DEM over sandy covered areas, which indicates the occurrence of penetration and confirms previous studies. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Figures

Graphical abstract

Open AccessFeature PaperArticle
A Stepwise Calibration of Global DMSP/OLS Stable Nighttime Light Data (1992–2013)
Remote Sens. 2017, 9(6), 637; https://doi.org/10.3390/rs9060637
Received: 16 May 2017 / Revised: 19 June 2017 / Accepted: 19 June 2017 / Published: 21 June 2017
Cited by 7 | Viewed by 1965 | PDF Full-text (4755 KB) | HTML Full-text | XML Full-text
Abstract
The Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) stable nighttime light (NTL) data provide a wide range of potentials for studying global and regional dynamics, such as urban sprawl and electricity consumption. However, due to the lack of on-board calibration, it requires [...] Read more.
The Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) stable nighttime light (NTL) data provide a wide range of potentials for studying global and regional dynamics, such as urban sprawl and electricity consumption. However, due to the lack of on-board calibration, it requires inter-annual calibration for these practical applications. In this study, we proposed a stepwise calibration approach to generate a temporally consistent NTL time series from 1992 to 2013. First, the temporal inconsistencies in the original NTL time series were identified. Then, a stepwise calibration scheme was developed to systematically improve the over- and under- estimation of NTL images derived from particular satellites and years, by making full use of the temporally neighbored image as a reference for calibration. After the stepwise calibration, the raw NTL series were improved with a temporally more consistent trend. Meanwhile, the magnitude of the global sum of NTL is maximally maintained in our results, as compared to the raw data, which outperforms previous conventional calibration approaches. The normalized difference index indicates that our approach can achieve a good agreement between two satellites in the same year. In addition, the analysis between the calibrated NTL time series and other socioeconomic indicators (e.g., gross domestic product and electricity consumption) confirms the good performance of the proposed stepwise calibration. The calibrated NTL time series can serve as useful inputs for NTL related dynamic studies, such as global urban extent change and energy consumption. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
Figures

Graphical abstract

Open AccessArticle
Saliency Analysis via Hyperparameter Sparse Representation and Energy Distribution Optimization for Remote Sensing Images
Remote Sens. 2017, 9(6), 636; https://doi.org/10.3390/rs9060636
Received: 10 May 2017 / Revised: 11 June 2017 / Accepted: 16 June 2017 / Published: 21 June 2017
Cited by 5 | Viewed by 1434 | PDF Full-text (1515 KB) | HTML Full-text | XML Full-text
Abstract
In an effort to detect the region-of-interest (ROI) of remote sensing images with complex data distributions, sparse representation based on dictionary learning has been utilized, and has proved able to process high dimensional data adaptively and efficiently. In this paper, a visual attention [...] Read more.
In an effort to detect the region-of-interest (ROI) of remote sensing images with complex data distributions, sparse representation based on dictionary learning has been utilized, and has proved able to process high dimensional data adaptively and efficiently. In this paper, a visual attention model uniting hyperparameter sparse representation with energy distribution optimization is proposed for analyzing saliency and detecting ROIs in remote sensing images. A dictionary learning algorithm based on biological plausibility is adopted to generate the sparse feature space. This method only focuses on finite features, instead of various considerations of feature complexity and massive parameter tuning in other dictionary learning algorithms. In another portion of the model, aimed at obtaining the saliency map, the contribution of each feature is evaluated in a sparse feature space and the coding length of each feature is accumulated. Finally, we calculate the segmentation threshold using the saliency map and obtain the binary mask to separate the ROI from the original images. Experimental results show that the proposed model achieves better performance in saliency analysis and ROI detection for remote sensing images. Full article
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
Figures

Graphical abstract

Open AccessArticle
Linking Spaceborne and Ground Observations of Autumn Foliage Senescence in Southern Québec, Canada
Remote Sens. 2017, 9(6), 630; https://doi.org/10.3390/rs9060630
Received: 26 April 2017 / Revised: 6 June 2017 / Accepted: 15 June 2017 / Published: 21 June 2017
Cited by 1 | Viewed by 1392 | PDF Full-text (3972 KB) | HTML Full-text | XML Full-text
Abstract
Autumn senescence progresses over several weeks during which leaves change their colors. The onset of leaf coloring and its progression have environmental and economic consequences, however, very few efforts have been devoted to monitoring regional foliage color change in autumn using remote sensing [...] Read more.
Autumn senescence progresses over several weeks during which leaves change their colors. The onset of leaf coloring and its progression have environmental and economic consequences, however, very few efforts have been devoted to monitoring regional foliage color change in autumn using remote sensing imagery. This study aimed to monitor the progression of autumn phenology using satellite remote sensing across a region in Southern Québec, Canada, where phenological observations are frequently performed in autumn across a large number of sites, and to evaluate the satellite retrievals against these in-situ observations. We used a temporally-normalized time-series of Normalized Difference Vegetation Index (NDVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to monitor the different phases of autumn foliage during 2011–2015, and compared the results with ground observations from 38 locations. Since the NDVI time-series is separately normalized per pixel, the outcome is a time-series of foliage coloration status that is independent of the land cover. The results show a significant correlation between the timing of peak autumn coloration to elevation and latitude, but not to longitude, and suggest that temperature is likely a main driver of variation in autumn foliage progression. The interannual coloration phase differences for MODIS retrievals are larger than for ground observations, but most ground site observations correlate significantly with MODIS retrievals. The mean absolute error for the timing of all foliage phases is smaller than the frequency of both ground observation reports and the frequency of the MODIS NDVI time-series, and therefore considered acceptable. Despite this, the observations at four of the ground sites did not correspond well with the MODIS retrievals, and therefore we conclude that further methodological refinements to improve the quality of the time series are required for MODIS spatial monitoring of autumn phenology over Québec to be operationally employed in a reliable manner. Full article
(This article belongs to the Section Forest Remote Sensing)
Figures

Graphical abstract

Open AccessArticle
Seasonal and Interannual Variability of Columbia Glacier, Alaska (2011–2016): Ice Velocity, Mass Flux, Surface Elevation and Front Position
Remote Sens. 2017, 9(6), 635; https://doi.org/10.3390/rs9060635
Received: 27 March 2017 / Revised: 13 June 2017 / Accepted: 15 June 2017 / Published: 20 June 2017
Cited by 4 | Viewed by 1891 | PDF Full-text (8778 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Alaskan glaciers are among the largest contributors to sea-level rise outside the polar ice sheets. The contributions include dynamic discharge from marine-terminating glaciers which depends on the seasonally variable ice velocity. Columbia Glacier is a large marine-terminating glacier located in Southcentral Alaska that [...] Read more.
Alaskan glaciers are among the largest contributors to sea-level rise outside the polar ice sheets. The contributions include dynamic discharge from marine-terminating glaciers which depends on the seasonally variable ice velocity. Columbia Glacier is a large marine-terminating glacier located in Southcentral Alaska that has been exhibiting pronounced retreat since the early 1980s. Since 2010, the glacier has split into two branches, the main branch and the west branch. We derived a 5-year record of surface velocity, mass flux (ice discharge), surface elevation and changes in front position using a dense time series of TanDEM-X synthetic aperture radar data (2011–2016). We observed distinct seasonal velocity patterns at both branches. At the main branch, the surface velocity peaked during late winter to midsummer but reached a minimum between late summer and fall. Its near-front velocity reached up to 14 m day−1 in May 2015 and was at its lowest speed of ~1 m day−1 in October 2012. Mass flux via the main branch was strongly controlled by the seasonal and interannual fluctuations of its velocity. The west branch also exhibited seasonal velocity variations with comparably lower magnitudes. The role of subglacial hydrology on the ice velocities of Columbia Glacier is already known from the published field measurements during summers of 1987. Our observed variability in its ice velocities on a seasonal basis also suggest that they are primarily controlled by the seasonal transition of the subglacial drainage system from an inefficient to an efficient and channelized drainage networks. However, abrupt velocity increase events for short periods (2014–2015 and 2015–2016 at the main branch, and 2013–2014 at the west branch) appear to be associated with strong near-front thinning and frontal retreat. This needs further investigation on the role of other potential controlling mechanisms. On the technological side, this study demonstrates the potential of high-resolution X-band SAR missions with a short revisit interval to examine glaciological variables and controlling processes. Full article
(This article belongs to the Special Issue Remote Sensing of Glaciers)
Figures

Graphical abstract

Open AccessArticle
A New Stereo Pair Disparity Index (SPDI) for Detecting Built-Up Areas from High-Resolution Stereo Imagery
Remote Sens. 2017, 9(6), 633; https://doi.org/10.3390/rs9060633
Received: 30 March 2017 / Revised: 10 June 2017 / Accepted: 15 June 2017 / Published: 20 June 2017
Cited by 5 | Viewed by 1720 | PDF Full-text (11770 KB) | HTML Full-text | XML Full-text
Abstract
Within-class spectral variation and between-class spectral confusion in remotely sensed imagery degrades the performance of built-up area detection when using planar texture, shape, and spectral features. Terrain slopes and building heights extracted from auxiliary data, such as Digital Surface Models (DSMs) however, can [...] Read more.
Within-class spectral variation and between-class spectral confusion in remotely sensed imagery degrades the performance of built-up area detection when using planar texture, shape, and spectral features. Terrain slopes and building heights extracted from auxiliary data, such as Digital Surface Models (DSMs) however, can improve the results. Stereo imagery incorporates height information unlike single remotely sensed images. In this study, a new Stereo Pair Disparity Index (SPDI) for indicating built-up areas is calculated from stereo-extracted disparity information. Further, a new method of detecting built-up areas from stereo pairs is proposed based on the SPDI, using disparity information to establish the relationship between two images of a stereo pair. As shown in the experimental results for two stereo pairs covering different scenes with diverse urban settings, the SPDI effectively differentiates between built-up and non-built-up areas. Our proposed method achieves higher accuracy built-up area results from stereo images than the traditional method for single images, and two other widely-applied DSM-based methods for stereo images. Our approach is suitable for spaceborne and airborne stereo pairs and triplets. Our research introduces a new effective height feature (SPDI) for detecting built-up areas from stereo imagery with no need for DSMs. Full article
(This article belongs to the Special Issue Remote Sensing for 3D Urban Morphology)
Figures

Figure 1

Open AccessArticle
Exploring the Potential of Spectral Classification in Estimation of Soil Contaminant Elements
Remote Sens. 2017, 9(6), 632; https://doi.org/10.3390/rs9060632
Received: 18 April 2017 / Revised: 13 June 2017 / Accepted: 14 June 2017 / Published: 20 June 2017
Cited by 5 | Viewed by 1558 | PDF Full-text (14901 KB) | HTML Full-text | XML Full-text
Abstract
Soil contamination by arsenic and heavy metals is an increasingly severe environmental problem. Efficiently investigation of soil contamination is the premise of soil protection and further the foundation of food security. Visible and near-infrared reflectance spectroscopy (VNIRS) has been widely used in soil [...] Read more.
Soil contamination by arsenic and heavy metals is an increasingly severe environmental problem. Efficiently investigation of soil contamination is the premise of soil protection and further the foundation of food security. Visible and near-infrared reflectance spectroscopy (VNIRS) has been widely used in soil science, due to its rapidity and convenience. With different spectrally active soil characteristics, soil reflectance spectra exhibit distinctive curve forms, which may limit the application of VNIRS in estimating contaminant elements in soil. Consequently, spectral clustering was applied to explore the potential of classification in estimating soil contaminant elements. Spectral clustering based on different distance measure methods and elements with different contamination levels were exploited. In this study, soil samples were collected from Hunan Province, China and 74 reflectance spectra of air-dried soil samples over 350–2500 nm were used to predict nickel (Ni) and zinc (Zn) concentrations. Spectral clustering was achieved by K-means clustering based on squared Euclidean distance and Cosine of spectral angle, respectively. The prediction model was calibrated with the combination of Genetic algorithm and partial least squares regression (GA-PLSR). The prediction accuracy shows that the prediction of Ni and Zn concentrations in soil was improved to different extents by the two clustering methods and the clustering based on squared Euclidean distance had better performance over the clustering relied on Cosine of the spectral angle. The result reveals the potential of spectral classification in predicting soil Ni and Zn concentrations. A selected subset of the 74 soil spectra was used to further explore the potential of spectral classification in estimating Zn concentrations. The prediction was dramatically improved by clustering based on squared Euclidean distance. Additionally, analysis on distance measure methods indicates that Euclidean distance is more suitable to describe the difference between the collected soil reflectance spectra, which brought the better performance of the clustering based on squared Euclidean distance. Full article
Figures

Graphical abstract

Open AccessArticle
Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds
Remote Sens. 2017, 9(6), 631; https://doi.org/10.3390/rs9060631
Received: 20 February 2017 / Revised: 18 May 2017 / Accepted: 13 June 2017 / Published: 20 June 2017
Cited by 2 | Viewed by 1788 | PDF Full-text (8378 KB) | HTML Full-text | XML Full-text
Abstract
A low-cost unmanned aerial platform (UAV) equipped with RGB (Red, Green, Blue) and thermographic sensors is used for the acquisition of all the data needed for the automatic detection and evaluation of thermal pathologies on photovoltaic (PV) surfaces and geometric defects in the [...] Read more.
A low-cost unmanned aerial platform (UAV) equipped with RGB (Red, Green, Blue) and thermographic sensors is used for the acquisition of all the data needed for the automatic detection and evaluation of thermal pathologies on photovoltaic (PV) surfaces and geometric defects in the mounting on photovoltaic power stations. RGB imagery is used for the generation of a georeferenced 3D point cloud through digital image preprocessing, photogrammetric and computer vision algorithms. The point cloud is complemented with temperature values measured by the thermographic sensor and with intensity values derived from the RGB data in order to obtain a multidimensional product (5D: 3D geometry plus temperature and intensity on the visible spectrum). A segmentation workflow based on the proper integration of several state-of-the-art geomatic and mathematic techniques is applied to the 5D product for the detection and sizing of thermal pathologies and geometric defects in the mounting in the PV panels. It consists of a three-step segmentation procedure, involving first the geometric information, then the radiometric (RGB) information, and last the thermal data. No configuration of parameters is required. Thus, the methodology presented contributes to the automation of the inspection of PV farms, through the maximization of the exploitation of the data acquired in the different spectra (visible and thermal infrared bands). Results of the proposed workflow were compared with a ground truth generated according to currently established protocols and complemented with a topographic survey. The proposed methodology was able to detect all pathologies established by the ground truth without adding any false positives. Discrepancies in the measurement of damaged surfaces regarding established ground truth, which can reach the 5% of total panel surface for the visual inspection by an expert operator, decrease with the proposed methodology under the 2%. The geometric evaluation of the facilities presents discrepancies regarding the ground truth lower than one degree for angular parameters (azimuth and tilt) and lower than 0.05 m2 for the area of each solar panel. Full article
Figures

Graphical abstract

Open AccessFeature PaperArticle
One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California
Remote Sens. 2017, 9(6), 629; https://doi.org/10.3390/rs9060629
Received: 10 May 2017 / Revised: 12 June 2017 / Accepted: 14 June 2017 / Published: 20 June 2017
Cited by 16 | Viewed by 3086 | PDF Full-text (20385 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this study, a 1-D Convolutional Neural Network (CNN) architecture was developed, trained and utilized to classify single (summer) and three seasons (spring, summer, fall) of hyperspectral imagery over the San Francisco Bay Area, California for the year 2015. For comparison, the Random [...] Read more.
In this study, a 1-D Convolutional Neural Network (CNN) architecture was developed, trained and utilized to classify single (summer) and three seasons (spring, summer, fall) of hyperspectral imagery over the San Francisco Bay Area, California for the year 2015. For comparison, the Random Forests (RF) and Support Vector Machine (SVM) classifiers were trained and tested with the same data. In order to support space-based hyperspectral applications, all analyses were performed with simulated Hyperspectral Infrared Imager (HyspIRI) imagery. Three-season data improved classifier overall accuracy by 2.0% (SVM), 1.9% (CNN) to 3.5% (RF) over single-season data. The three-season CNN provided an overall classification accuracy of 89.9%, which was comparable to overall accuracy of 89.5% for SVM. Both three-season CNN and SVM outperformed RF by over 7% overall accuracy. Analysis and visualization of the inner products for the CNN provided insight to distinctive features within the spectral-temporal domain. A method for CNN kernel tuning was presented to assess the importance of learned features. We concluded that CNN is a promising candidate for hyperspectral remote sensing applications because of the high classification accuracy and interpretability of its inner products. Full article
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
Figures

Graphical abstract

Open AccessArticle
Estimation of Surface NO2 Volume Mixing Ratio in Four Metropolitan Cities in Korea Using Multiple Regression Models with OMI and AIRS Data
Remote Sens. 2017, 9(6), 627; https://doi.org/10.3390/rs9060627
Received: 25 April 2017 / Revised: 15 June 2017 / Accepted: 15 June 2017 / Published: 18 June 2017
Cited by 4 | Viewed by 1462 | PDF Full-text (4303 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Surface NO2 volume mixing ratio (VMR) at a specific time (13:45 Local time) (NO2 VMRST) and monthly mean surface NO2 VMR (NO2 VMRM) are estimated for the first time using three regression models with Ozone [...] Read more.
Surface NO2 volume mixing ratio (VMR) at a specific time (13:45 Local time) (NO2 VMRST) and monthly mean surface NO2 VMR (NO2 VMRM) are estimated for the first time using three regression models with Ozone Monitoring Instrument (OMI) data in four metropolitan cities in South Korea: Seoul, Gyeonggi, Daejeon, and Gwangju. Relationships between the surface NO2 VMR obtained from in situ measurements (NO2 VMRIn-situ) and tropospheric NO2 vertical column density obtained from OMI from 2007 to 2013 were developed using regression models that also include boundary layer height (BLH) from Atmospheric Infrared Sounder (AIRS) and surface pressure, temperature, dew point, and wind speed and direction. The performance of the regression models is evaluated via comparison with the NO2 VMRIn-situ for two validation years (2006 and 2014). Of the three regression models, a multiple regression model shows the best performance in estimating NO2 VMRST and NO2 VMRM. In the validation period, the average correlation coefficient (R), slope, mean bias (MB), mean absolute error (MAE), root mean square error (RMSE), and percent difference between NO2 VMRIn-situ and NO2 VMRST estimated by the multiple regression model are 0.66, 0.41, −1.36 ppbv, 6.89 ppbv, 8.98 ppbv, and 31.50%, respectively, while the average corresponding values for the other two models are 0.75, 0.41, −1.40 ppbv, 3.59 ppbv, 4.72 ppbv, and 16.59%, respectively. All three models have similar performance for NO2 VMRM, with average R, slope, MB, MAE, RMSE, and percent difference between NO2 VMRIn-situ and NO2 VMRM of 0.74, 0.49, −1.90 ppbv, 3.93 ppbv, 5.05 ppbv, and 18.76%, respectively. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle
Modeling the Spatiotemporal Dynamics of Gross Domestic Product in China Using Extended Temporal Coverage Nighttime Light Data
Remote Sens. 2017, 9(6), 626; https://doi.org/10.3390/rs9060626
Received: 12 May 2017 / Revised: 12 June 2017 / Accepted: 15 June 2017 / Published: 18 June 2017
Cited by 5 | Viewed by 1861 | PDF Full-text (4578 KB) | HTML Full-text | XML Full-text
Abstract
Nighttime light data derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) in conjunction with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) possess great potential for measuring the dynamics of Gross Domestic Product (GDP) at large scales. [...] Read more.
Nighttime light data derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) in conjunction with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) possess great potential for measuring the dynamics of Gross Domestic Product (GDP) at large scales. The temporal coverage of the DMSP-OLS data spans between 1992 and 2013, while the NPP-VIIRS data are available from 2012. Integrating the two datasets to produce a time series of continuous and consistently monitored data since the 1990s is of great significance for the understanding of the dynamics of long-term economic development. In addition, since economic developmental patterns vary with physical environment and geographical location, the quantitative relationship between nighttime lights and GDP should be designed for individual regions. Through a case study in China, this study made an attempt to integrate the DMSP-OLS and NPP-VIIRS datasets, as well as to identify an optimal model for long-term spatiotemporal GDP dynamics in different regions of China. Based on constructed regression relationships between total nighttime lights (TNL) data from the DMSP-OLS and NPP-VIIRS data in provincial units (R2 = 0.9648, P < 0.001), the temporal coverage of nighttime light data was extended from 1992 to the present day. Furthermore, three models (the linear model, quadratic polynomial model and power function model) were applied to model the spatiotemporal dynamics of GDP in China from 1992 to 2015 at both the country level and provincial level using the extended temporal coverage data. Our results show that the linear model is optimal at the country level with a mean absolute relative error (MARE) of 11.96%. The power function model is optimal in 22 of the 31 provinces and the quadratic polynomial model is optimal in 7 provinces, whereas the linear model is optimal only in two provinces. Thus, our approach demonstrates the potential to accurately and timely model long-term spatiotemporal GDP dynamics using an integration of DMSP-OLS and NPP-VIIRS data. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
Figures

Graphical abstract

Open AccessLetter
A New Approach to Performing Bundle Adjustment for Time Series UAV Images 3D Building Change Detection
Remote Sens. 2017, 9(6), 625; https://doi.org/10.3390/rs9060625
Received: 22 March 2017 / Revised: 14 June 2017 / Accepted: 16 June 2017 / Published: 17 June 2017
Cited by 4 | Viewed by 1729 | PDF Full-text (1294 KB) | HTML Full-text | XML Full-text
Abstract
Successful change detection in multi-temporal images relies on high spatial co-registration accuracy. However, co-registration accuracy alone cannot meet the needs of change detection when using several ground control points to separately geo-reference multi-temporal images from unmanned aerial vehicles (UAVs). This letter reports on [...] Read more.
Successful change detection in multi-temporal images relies on high spatial co-registration accuracy. However, co-registration accuracy alone cannot meet the needs of change detection when using several ground control points to separately geo-reference multi-temporal images from unmanned aerial vehicles (UAVs). This letter reports on a new approach to perform bundle adjustment—named united bundle adjustment (UBA)—to solve this co-registration problem for change detection in multi-temporal UAV images. In UBA, multi-temporal UAV images are matched with each other to construct a unified tie point net. One single bundle adjustment process is performed on the unified tie point net, placing every image into the same coordinate system and thus automatically accomplishing spatial co-registration. We then perform change detection using both orthophotos and three-dimensional height information derived from dense image matching techniques. Experimental results show that UBA co-registration accuracy is higher than the accuracy of commonly-used approaches for multi-temporal UAV images. Our proposed preprocessing method extends the capacities of consumer-level UAVs so they can eventually meet the growing need for automatic building change detection and dynamic monitoring using only RGB band images. Full article
Figures

Graphical abstract

Open AccessArticle
Spatiotemporal Variation in Particulate Organic Carbon Based on Long-Term MODIS Observations in Taihu Lake, China
Remote Sens. 2017, 9(6), 624; https://doi.org/10.3390/rs9060624
Received: 8 May 2017 / Revised: 26 May 2017 / Accepted: 15 June 2017 / Published: 17 June 2017
Cited by 3 | Viewed by 1712 | PDF Full-text (7360 KB) | HTML Full-text | XML Full-text
Abstract
In situ measured values of particulate organic carbon (POC) in Taihu Lake and remote sensing reflectance observed by three satellite courses from 2014 to 2015 were used to develop an near infrared-red (NIR-Red) empirical algorithm of POC for the Moderate Resolution Imaging Spectroradiometer [...] Read more.
In situ measured values of particulate organic carbon (POC) in Taihu Lake and remote sensing reflectance observed by three satellite courses from 2014 to 2015 were used to develop an near infrared-red (NIR-Red) empirical algorithm of POC for the Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) satellite image. The performance of the POC algorithm is highly consistent with the in situ measured POC, with root mean square error percentage (RMSPs) of 38.9% and 31.5% for two independent validations, respectively. The MODIS-derived POC also shows an acceptable result, with RMSPs of 53.6% and 61.0% for two periods of match-up data. POC from 2005 to 2007 is much higher than it is from 2002 to 2004 and 2008 to 2013, due to a large area of algal bloom. Riverine flux is an important source of POC in Taihu Lake, especially in the lake’s bank and bays. The influence of a terrigenous source of POC can reach the center lake during periods of heavy precipitation. Sediment resuspension is also a source of POC in the lake due to the area’s high dynamic ratio (25.4) and wind speed. The source of POC in an inland shallow lake is particularly complex, and additional research on POC is needed to more clearly reveal its variation in inland water. Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
Figures

Graphical abstract

Open AccessArticle
Land Cover, Land Use, and Climate Change Impacts on Endemic Cichlid Habitats in Northern Tanzania
Remote Sens. 2017, 9(6), 623; https://doi.org/10.3390/rs9060623
Received: 27 March 2017 / Revised: 6 June 2017 / Accepted: 8 June 2017 / Published: 17 June 2017
Cited by 2 | Viewed by 3748 | PDF Full-text (8844 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Freshwater ecosystems are among the most threatened on Earth, facing environmental and anthropogenic pressures often surpassing their terrestrial counterparts. Land use and land cover change (LUCC) such as degradation and fragmentation of the terrestrial landscape negatively impacts aquatic ecosystems. Satellite imagery allows for [...] Read more.
Freshwater ecosystems are among the most threatened on Earth, facing environmental and anthropogenic pressures often surpassing their terrestrial counterparts. Land use and land cover change (LUCC) such as degradation and fragmentation of the terrestrial landscape negatively impacts aquatic ecosystems. Satellite imagery allows for an impartial assessment of the past to determine habitat alterations. It can also be used as a forecasting tool in the development of species conservation strategies through models based on ecological factors extracted from imagery. In this study, we analyze Landsat time sequences (1984–2015) to quantify LUCC around three freshwater ecosystems with endemic cichlids in Tanzania. In addition, we examine population growth, agricultural expansion, and climate change as stressors that impact the habitats. We found that the natural vegetation cover surrounding Lake Chala decreased from 15.5% (1984) to 3.5% (2015). At Chemka Springs, we observed a decrease from 7.4% to 3.5% over the same period. While Lake Natron had minimal LUCC, severe climate change impacts have been forecasted for the region. Subsurface water data from the Gravity Recovery and Climate Experiment (GRACE) satellite observations further show a decrease in water resources for the study areas, which could be exacerbated by increased need from a growing population and an increase in agricultural land use. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
Figures

Graphical abstract

Open AccessArticle
Impact of Atmospheric Inversion Effects on Solar-Induced Chlorophyll Fluorescence: Exploitation of the Apparent Reflectance as a Quality Indicator
Remote Sens. 2017, 9(6), 622; https://doi.org/10.3390/rs9060622
Received: 3 May 2017 / Revised: 9 June 2017 / Accepted: 13 June 2017 / Published: 16 June 2017
Cited by 6 | Viewed by 2074 | PDF Full-text (5364 KB) | HTML Full-text | XML Full-text
Abstract
In the last decade, significant progress has been made in estimating Solar-Induced chlorophyll Fluorescence (SIF) by passive remote sensing techniques that exploit the oxygen absorption spectral regions. Although the O2–B and the deep O2–A absorption bands present a high [...] Read more.
In the last decade, significant progress has been made in estimating Solar-Induced chlorophyll Fluorescence (SIF) by passive remote sensing techniques that exploit the oxygen absorption spectral regions. Although the O2–B and the deep O2–A absorption bands present a high sensitivity to detect SIF, these regions are also largely influenced by atmospheric effects. Therefore, an accurate Atmospheric Correction (AC) process is required to measure SIF from oxygen bands. In this regard, the suitability of a two-step approach, i.e., first an AC and second a Spectral Fitting technique to disentangle SIF from reflected light, has been evaluated. One of the advantages of the two-step approach resides in the derived intermediate products provided prior to SIF estimation, such as surface apparent reflectance. Results suggest that errors introduced in the AC, e.g., related to the characterization of aerosol optical properties, are propagated into systematic residual errors in the apparent reflectance. However, of interest is that these errors can be easily detected in the oxygen bands thanks to the high spectral resolution required to measure SIF. To illustrate this, the predictive power of the apparent reflectance spectra to detect and correct inaccuracies in the aerosols characterization is assessed by using a simulated database with SCOPE and MODTRAN radiative transfer models. In 75% of cases, the aerosol optical thickness, the Angstrom coefficient and the scattering asymmetry factor are corrected with a relative error below of 0.5%, 8% and 3%, respectively. To conclude with, and in view of future SIF monitoring satellite missions such as FLEX, the analysis of the apparent reflectance can entail a valuable quality indicator to detect and correct errors in the AC prior to the SIF estimation. Full article
Figures

Graphical abstract

Open AccessArticle
Four-Component Model-Based Decomposition for Ship Targets Using PolSAR Data
Remote Sens. 2017, 9(6), 621; https://doi.org/10.3390/rs9060621
Received: 20 March 2017 / Revised: 26 May 2017 / Accepted: 13 June 2017 / Published: 16 June 2017
Cited by 3 | Viewed by 1344 | PDF Full-text (5983 KB) | HTML Full-text | XML Full-text
Abstract
Scattering mechanism (SM) analysis is a promising technique for ship detection and classification in polarimetric SAR (PolSAR) images. In this paper, a four-component model-based decomposition method incorporating surface, double-bounce, volume and cross-polarized components is proposed for analyzing the SMs of ships. A novel [...] Read more.
Scattering mechanism (SM) analysis is a promising technique for ship detection and classification in polarimetric SAR (PolSAR) images. In this paper, a four-component model-based decomposition method incorporating surface, double-bounce, volume and cross-polarized components is proposed for analyzing the SMs of ships. A novel cross-polarized scattering component capable of discriminating between the HV scattering power generated by the oriented scatterers on ships from that by volume scatterers is proposed as a means to address the problem of volume scattering power overestimation. In the decomposition stage, by taking into account both the real and imaginary parts of the elements [ T ] H V ( 1 , 3 ) and [ T ] H V ( 2 , 3 ) of the observed coherency matrix, the proposed cross-polarized component can preserve the reflection asymmetry information completely, which is an essential property of man-made targets, such as ships. Based on the proposed decomposition method and an analysis of the different SMs between ships and sea clutter, a novel ship detection metric defined as M = ln P d + P c P s is proposed. Experimental results conducted on RadarSat-2 quad-polarimetric data validate the proposed four-component decomposition method as being more suitable for analyzing the SMs of ship targets than the existing helix matrix-based decomposition methods. Additionally, we find that the proposed ship detection metric can effectively enhance the signal-to-clutter ratio (SCR) and improve ship detection performance. Full article
Figures

Figure 1

Open AccessArticle
Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States
Remote Sens. 2017, 9(6), 620; https://doi.org/10.3390/rs9060620
Received: 3 May 2017 / Revised: 1 June 2017 / Accepted: 13 June 2017 / Published: 16 June 2017
Cited by 6 | Viewed by 1465 | PDF Full-text (3127 KB) | HTML Full-text | XML Full-text
Abstract
Degraded air quality by PM2.5 can cause various health problems. Satellite observations provide abundant data for monitoring PM2.5 pollution. While satellite-derived products, such as aerosol optical depth (AOD) and normalized difference vegetation index (NDVI), have been widely used in estimating PM [...] Read more.
Degraded air quality by PM2.5 can cause various health problems. Satellite observations provide abundant data for monitoring PM2.5 pollution. While satellite-derived products, such as aerosol optical depth (AOD) and normalized difference vegetation index (NDVI), have been widely used in estimating PM2.5 concentration, little research was focused on the use of remotely sensed nighttime light (NTL) imagery. This study evaluated the merits of using NTL satellite images in predicting ground-level PM2.5 at a regional scale. Geographically weighted regression (GWR) was employed to estimate the PM2.5 concentration and analyze its relationships with AOD, meteorological variables, and NTL data across the New England region. Observed data in 2013 were used to test the constructed GWR models for PM2.5 prediction. The Vegetation Adjusted NTL Urban Index (VANUI), which incorporates Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI into NTL to overcome the defects of NTL data, was used as a predictor variable for final PM2.5 prediction. Results showed that Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NTL imagery could be an important dataset for more accurately estimating PM2.5 exposure, especially in urbanized and densely populated areas. VANUI data could obviously improve the performance of GWR for the warm season (GWR model with VANUI performed 17% better than GWR model without NDVI and NTL data and 7.26% better than GWR model without NTL data in terms of RMSE), while its improvements were less obvious for the cold season (GWR model with VANUI performed 3.6% better than the GWR model without NDVI and NTL data and 1.83% better than the GWR model without NTL data in terms of RMSE). Moreover, the spatial distribution of the estimated PM2.5 levels clearly revealed patterns consistent with those densely populated areas and high traffic areas, implying a close and positive correlation between VANUI and PM2.5 concentration. In general, the DMSP/OLS NTL satellite imagery is promising for providing additional information for PM2.5 monitoring and prediction. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
Figures

Graphical abstract

Open AccessArticle
2D Normalized Iterative Hard Thresholding Algorithm for Fast Compressive Radar Imaging
Remote Sens. 2017, 9(6), 619; https://doi.org/10.3390/rs9060619
Received: 18 March 2017 / Revised: 9 June 2017 / Accepted: 13 June 2017 / Published: 16 June 2017
Cited by 2 | Viewed by 1445 | PDF Full-text (2104 KB) | HTML Full-text | XML Full-text
Abstract
Compressive radar imaging has attracted considerable attention because it substantially reduces imaging time through directly compressive sampling. However, a problem that must be addressed for compressive radar imaging systems is the high computational complexity of reconstruction of sparse signals. In this paper, a [...] Read more.
Compressive radar imaging has attracted considerable attention because it substantially reduces imaging time through directly compressive sampling. However, a problem that must be addressed for compressive radar imaging systems is the high computational complexity of reconstruction of sparse signals. In this paper, a novel algorithm, called two-dimensional (2D) normalized iterative hard thresholding (NIHT) or 2D-NIHT algorithm, is proposed to directly reconstruct radar images in the matrix domain. The reconstruction performance of 2D-NIHT algorithm was validated by an experiment on recovering a synthetic 2D sparse signal, and the superiority of the 2D-NIHT algorithm to the NIHT algorithm was demonstrated by a comprehensive comparison of its reconstruction performance. Moreover, to be used in compressive radar imaging systems, a 2D sampling model was also proposed to compress the range and azimuth data simultaneously. The practical application of the 2D-NIHT algorithm in radar systems was validated by recovering two radar scenes with noise at different signal-to-noise ratios, and the results showed that the 2D-NIHT algorithm could reconstruct radar scenes with a high probability of exact recovery in the matrix domain. In addition, the reconstruction performance of the 2D-NIHT algorithm was compared with four existing efficient reconstruction algorithms using the two radar scenes, and the results illustrated that, compared to the other algorithms, the 2D-NIHT algorithm could dramatically reduce the computational complexity in signal reconstruction and successfully reconstruct 2D sparse images with a high probability of exact recovery. Full article
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
Figures

Graphical abstract

Open AccessArticle
Convolutional Neural Networks Based Hyperspectral Image Classification Method with Adaptive Kernels
Remote Sens. 2017, 9(6), 618; https://doi.org/10.3390/rs9060618
Received: 10 May 2017 / Revised: 6 June 2017 / Accepted: 14 June 2017 / Published: 16 June 2017
Cited by 8 | Viewed by 2491 | PDF Full-text (1980 KB) | HTML Full-text | XML Full-text
Abstract
Hyperspectral image (HSI) classification aims at assigning each pixel a pre-defined class label, which underpins lots of vision related applications, such as remote sensing, mineral exploration and ground object identification, etc. Lots of classification methods thus have been proposed for better hyperspectral imagery [...] Read more.
Hyperspectral image (HSI) classification aims at assigning each pixel a pre-defined class label, which underpins lots of vision related applications, such as remote sensing, mineral exploration and ground object identification, etc. Lots of classification methods thus have been proposed for better hyperspectral imagery interpretation. Witnessing the success of convolutional neural networks (CNNs) in the traditional images based classification tasks, plenty of efforts have been made to leverage CNNs to improve HSI classification. An advanced CNNs architecture uses the kernels generated from the clustering method, such as a K-means network uses K-means to generate the kernels. However, the above methods are often obtained heuristically (e.g., the number of kernels should be assigned manually), and how to data-adaptively determine the number of convolutional kernels (i.e., filters), and thus generate the kernels that better represent the data, are seldom studied in existing CNNs based HSI classification methods. In this study, we propose a new CNNs based HSI classification method where the convolutional kernels can be automatically learned from the data through clustering without knowing the cluster number. With those data-adaptive kernels, the proposed CNNs method achieves better classification results. Experimental results from the datasets demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
Figures

Graphical abstract

Open AccessTechnical Note
Flood Inundation Mapping from Optical Satellite Images Using Spatiotemporal Context Learning and Modest AdaBoost
Remote Sens. 2017, 9(6), 617; https://doi.org/10.3390/rs9060617
Received: 20 March 2017 / Revised: 8 June 2017 / Accepted: 10 June 2017 / Published: 16 June 2017
Cited by 2 | Viewed by 1655 | PDF Full-text (22900 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Due to its capacity for temporal and spatial coverage, remote sensing has emerged as a powerful tool for mapping inundation. Many methods have been applied effectively in remote sensing flood analysis. Generally, supervised methods can achieve better precision than unsupervised. However, human intervention [...] Read more.
Due to its capacity for temporal and spatial coverage, remote sensing has emerged as a powerful tool for mapping inundation. Many methods have been applied effectively in remote sensing flood analysis. Generally, supervised methods can achieve better precision than unsupervised. However, human intervention makes its results subjective and difficult to obtain automatically, which is important for disaster response. In this work, we propose a novel procedure combining spatiotemporal context learning method and Modest AdaBoost classifier, which aims to extract inundation in an automatic and accurate way. First, the context model was built with images to calculate the confidence value of each pixel, which represents the probability of the pixel remaining unchanged. Then, the pixels with the highest probabilities, which we define as ‘permanent pixels’, were used as samples to train the Modest AdaBoost classifier. By applying the strong classifier to the target scene, an inundation map can be obtained. The proposed procedure is validated using two flood cases with different sensors, HJ-1A CCD and GF-4 PMS. Qualitative and quantitative evaluation results showed that the proposed procedure can achieve accurate and robust mapping results. Full article
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
Figures

Figure 1

Open AccessArticle
Remotely Monitoring Ecosystem Water Use Efficiency of Grassland and Cropland in China’s Arid and Semi-Arid Regions with MODIS Data
Remote Sens. 2017, 9(6), 616; https://doi.org/10.3390/rs9060616
Received: 27 April 2017 / Revised: 27 May 2017 / Accepted: 13 June 2017 / Published: 16 June 2017
Cited by 6 | Viewed by 1489 | PDF Full-text (3531 KB) | HTML Full-text | XML Full-text
Abstract
Scarce water resources are available in the arid and semi-arid areas of Northwest China, where significant water-related challenges will be faced in the coming decades. Quantitative evaluations of the spatio-temporal dynamics in ecosystem water use efficiency (WUE), as well as the underlying environmental [...] Read more.
Scarce water resources are available in the arid and semi-arid areas of Northwest China, where significant water-related challenges will be faced in the coming decades. Quantitative evaluations of the spatio-temporal dynamics in ecosystem water use efficiency (WUE), as well as the underlying environmental controls, are crucial for predicting future climate change impacts on ecosystem carbon-water interactions and agricultural production. However, these questions remain poorly understood in this typical region. By means of continuous eddy covariance (EC) measurements and time-series MODIS data, this study revealed the distinct seasonal cycles in gross primary productivity (GPP), evapotranspiration (ET), and WUE for both grassland and cropland ecosystems, and the dominant climate factors performed jointly by temperature and precipitation. The MODIS WUE estimates from GPP and ET products can capture the broad trend in WUE variability of grassland, but with large biases for maize cropland, which was mainly ascribed to large uncertainties resulting from both GPP and ET algorithms. Given the excellent biophysical performance of the MODIS-derived enhanced vegetation index (EVI), a new greenness model (GR) was proposed to track the eight-day changes in ecosystem WUE. Seasonal variations and the scatterplots between EC-based WUE and the estimates from time-series EVI data (WUEGR) also certified its prediction accuracy with R2 and RMSE of both grassland and cropland ecosystems over 0.90 and less than 0.30 g kg−1, respectively. The application of the GR model to regional scales in the near future will provide accurate WUE information to support water resource management in dry regions around the world. Full article
Figures

Graphical abstract

Open AccessArticle
The 2013 FLEX—US Airborne Campaign at the Parker Tract Loblolly Pine Plantation in North Carolina, USA
Remote Sens. 2017, 9(6), 612; https://doi.org/10.3390/rs9060612
Received: 4 May 2017 / Revised: 9 June 2017 / Accepted: 11 June 2017 / Published: 16 June 2017
Cited by 9 | Viewed by 2112 | PDF Full-text (10748 KB) | HTML Full-text | XML Full-text
Abstract
The first European Space Agency (ESA) and NASA collaboration in an airborne campaign to support ESA’s FLuorescence EXplorer (FLEX) mission was conducted in North Carolina, USA during September–October 2013 (FLEX-US 2013) at the Parker Tract Loblolly Pine (LP) Plantation (Plymouth, NC, USA). This [...] Read more.
The first European Space Agency (ESA) and NASA collaboration in an airborne campaign to support ESA’s FLuorescence EXplorer (FLEX) mission was conducted in North Carolina, USA during September–October 2013 (FLEX-US 2013) at the Parker Tract Loblolly Pine (LP) Plantation (Plymouth, NC, USA). This campaign combined two unique airborne instrument packages to obtain simultaneous observations of solar-induced fluorescence (SIF), LiDAR-based canopy structural information, visible through shortwave infrared (VSWIR) reflectance spectra, and surface temperature, to advance vegetation studies of carbon cycle dynamics and ecosystem health. We obtained statistically significant results for fluorescence, canopy temperature, and tower fluxes from data collected at four times of day over two consecutive autumn days across an age class chronosequence. Both the red fluorescence (F685) and far-red fluorescence (F740) radiances had highest values at mid-day, but their fluorescence yields exhibited different diurnal responses across LP age classes. The diurnal trends for F685 varied with forest canopy temperature difference (canopy minus air), having a stronger daily amplitude change for young vs. old canopies. The Photochemical Reflectance Index (PRI) was positively correlated with this temperature variable over the diurnal cycle. Tower measurements from mature loblolly stand showed the red/far-red fluorescence ratio was linearly related to canopy light use efficiency (LUE) over the diurnal cycle, but performed even better for the combined morning/afternoon (without midday) observations. This study demonstrates the importance of diurnal observations for interpretation of fluorescence dynamics, the need for red fluorescence to understand canopy physiological processes, and the benefits of combining fluorescence, reflectance, and structure information to clarify canopy function versus structure characteristics for a coniferous forest. Full article
Figures

Graphical abstract

Open AccessArticle
Satellite-Based Method for Estimating the Spatial Distribution of Crop Evapotranspiration: Sensitivity to the Priestley-Taylor Coefficient
Remote Sens. 2017, 9(6), 611; https://doi.org/10.3390/rs9060611
Received: 20 February 2017 / Revised: 30 May 2017 / Accepted: 10 June 2017 / Published: 16 June 2017
Cited by 3 | Viewed by 1479 | PDF Full-text (14378 KB) | HTML Full-text | XML Full-text
Abstract
This work discusses an operational method for actual evapotranspiration (ET) retrieval from remote sensing, considering a minimum quantity of ancillary data. The method consists in a graphical approach based on the Priestley-Taylor (PT) equation, where the dry soil and non-limiting water conditions are [...] Read more.
This work discusses an operational method for actual evapotranspiration (ET) retrieval from remote sensing, considering a minimum quantity of ancillary data. The method consists in a graphical approach based on the Priestley-Taylor (PT) equation, where the dry soil and non-limiting water conditions are defined by land surface temperature (LST) and vegetation index (VI) space, both retrieved from remote sensing. Using ET tower flux measurements and Landsat 5 TM images of an irrigation scheme in southeast Spain, a sensitivity analysis of ET spatial distribution was performed for the period 2009–2011 with respect to: (i) the shape (trapezoidal or rectangular) of the LST-VI space; and (ii) the value of the PT coefficient, α. The results from ground truth validation were satisfactory, both shapes providing similar performances in estimating ET, with root mean square error ~30 W/m2 and relative difference ~10% with respect to tower-based measurements. Importantly, the best fit with ground data was found for α close to 1, a somewhat different value from the commonly used value of 1.27, indicating that substantial error might arise when using the latter value. Overall, our study underlines the importance of a more precise knowledge of the actual value of α coefficient when using ET retrieval methods based on the LST-VI space. Full article
Figures

Graphical abstract

Open AccessArticle
Satellite-Based Inversion and Field Validation of Autotrophic and Heterotrophic Respiration in an Alpine Meadow on the Tibetan Plateau
Remote Sens. 2017, 9(6), 615; https://doi.org/10.3390/rs9060615
Received: 9 May 2017 / Revised: 30 May 2017 / Accepted: 13 June 2017 / Published: 15 June 2017
Viewed by 1717 | PDF Full-text (3379 KB) | HTML Full-text | XML Full-text
Abstract
Alpine meadow ecosystem is among the highest soil carbon density and the most sensitive ecosystem to climate change. Partitioning autotrophic (Ra) and heterotrophic components (Rm) of ecosystem respiration (Re) is critical to evaluating climate change effects on ecosystem carbon cycling. Here we introduce [...] Read more.
Alpine meadow ecosystem is among the highest soil carbon density and the most sensitive ecosystem to climate change. Partitioning autotrophic (Ra) and heterotrophic components (Rm) of ecosystem respiration (Re) is critical to evaluating climate change effects on ecosystem carbon cycling. Here we introduce a satellite-based method, combining MODerate resolution Imaging Spectroradiometer (MODIS) products, eddy covariance (EC) and chamber-based Re components measurements, for estimating carbon dynamics and partitioning of Re from 2009 to 2011 in a typical alpine meadow on the Tibetan Plateau. Six satellite-based gross primary production (GPP) models were employed and compared with GPP_EC, all of which appeared to well explain the temporal GPP_EC trends. However, MODIS versions 6 GPP product (GPP_MOD) and GPP estimation from vegetation photosynthesis model (GPP_VPM) provided the most reliable GPP estimation magnitudes with less than 10% of relative predictive error (RPE) compared to GPP_EC. Thus, they together with MODIS products and GPP_EC were used to estimate Re using the satellite-based method. All satellite-based Re estimations generated an alternative estimation of Re_EC with negligible root mean square errors (RMSEs, g C m−2 day−1) either in the growing season (0.12) or not (0.08). Moreover, chamber-based Re measurements showed that autotrophic contributions to Re (Ra/Re) could be effectively reflected by all these three satellite-based Re partitions. Results showed that the Ra contribution of Re were 27% (10–48%), 43% (22–59%) and 56% (33–76%) from 2009 to 2011, respectively, of which inter-annual variation is mainly attributed to soil water dynamics. This study showed annual temperature sensitivity of Ra (Q10,Ra) with an average of 5.20 was significantly higher than that of Q10,Rm (1.50), and also the inter-annual variation of Q10,Ra (4.14–7.31) was larger than Q10,Rm (1.42–1.60). Therefore, our results suggest that the response of Ra to temperature change is stronger than that of Rm in this alpine meadow. Full article
Figures

Graphical abstract

Open AccessTechnical Note
Tree Stem Diameter Estimation From Volumetric TLS Image Data
Remote Sens. 2017, 9(6), 614; https://doi.org/10.3390/rs9060614
Received: 14 April 2017 / Revised: 31 May 2017 / Accepted: 13 June 2017 / Published: 15 June 2017
Cited by 4 | Viewed by 1231 | PDF Full-text (5950 KB) | HTML Full-text | XML Full-text
Abstract
Recently, a new method on tree stem isolation using volumetric image data from terrestrial laser scans (TLS) has been introduced by the same authors. The method transfers TLS data into a voxel grid data structure and isolates the tree stems from the overall [...] Read more.
Recently, a new method on tree stem isolation using volumetric image data from terrestrial laser scans (TLS) has been introduced by the same authors. The method transfers TLS data into a voxel grid data structure and isolates the tree stems from the overall forest vegetation. While the stem detection method yields on a three dimensional localisation of the tree stems, the present study introduces a supplemental technique, which accurately estimates the diameter at breast height (DBH) from the stem objects. Often, large pieces of the stems are occluded by other vegetation and are only partially represented in the laser scanning data, not covering the complete circumference. Therefore, it was not possible to measure the diameter at 130 cm height directly on the stem imagery. Instead, a method has been developed, which estimated the diameter from the fragmented stem information at the specific cross sections. The stem information was processed in a way, which allowed applying a Hough transform to the image for fitting circles to the cross sections. In contrast to other studies, Hough transform was applied to single stem images with information from other vegetation parts already being removed. Even in cases where only a single and very small fragment of a stem is available, the diameter could be estimated from the curvature. It also has been demonstrated that the image resolution for DBH measurement can be significantly higher than the resolution used for stem isolation in order to increase the precision. Verification of the computed DBH on nine spatially independent test sites showed that applying the Hough transform to single stem cross section images produced accurate results. When excluding the five strongest individual outliers a bias of −0.02 cm, a root mean square error (RMSE) of 2.9 cm and a R 2 of 0.98 were achieved. Full article
Figures

Graphical abstract

Open AccessFeature PaperArticle
Comparing Passive Microwave with Visible-To-Near-Infrared Phenometrics in Croplands of Northern Eurasia
Remote Sens. 2017, 9(6), 613; https://doi.org/10.3390/rs9060613
Received: 22 December 2016 / Revised: 18 May 2017 / Accepted: 12 June 2017 / Published: 15 June 2017
Cited by 2 | Viewed by 1903 | PDF Full-text (7888 KB) | HTML Full-text | XML Full-text
Abstract
Planting and harvesting times drive cropland phenology. There are few datasets that derive explicit phenological metrics, and these datasets use the visible to near infrared (VNIR) spectrum. Many different methods have been used to derive phenometrics such as Start of Season (SOS) and [...] Read more.
Planting and harvesting times drive cropland phenology. There are few datasets that derive explicit phenological metrics, and these datasets use the visible to near infrared (VNIR) spectrum. Many different methods have been used to derive phenometrics such as Start of Season (SOS) and End of Season (EOS), leading to differing results. This discrepancy is partly due to spatial and temporal compositing of the VNIR satellite data to minimize data gaps resulting from cloud cover, atmospheric aerosols, and solar illumination constraints. Phenometrics derived from the downward Convex Quadratic model (CxQ) include Peak Height (PH) and Thermal Time to Peak (TTP), which are more consistent than SOS and EOS because they are minimally affected by snow and frost and other non-vegetation related issues. Here, we have determined PH using the vegetation optical depth (VOD) in three microwave frequencies (6.925, 10.65 and 18.7 GHz) and accumulated growing degree-days derived from AMSR-E (Advanced Microwave Scanning Radiometer on EOS) data at a spatial resolution of 25 km. We focus on 50 AMSR-E cropland pixels in the major grain production areas of Northern Eurasia (Ukraine, southwestern Russia, and northern Kazakhstan) for 2003–2010. We compared the land surface phenologies of AMSR-E VOD and MODIS NDVI data. VOD time series tracked cropland seasonal dynamics similar to that recorded by the NDVI. The coefficients of determination for the CxQ model fit of the NDVI data were high for all sites (0.78 < R2 < 0.99). The 10.65 GHz VOD (VOD1065GHz) achieved the best linear regression fit (R2 = 0.84) with lowest standard error (SEE = 0.128); it is therefore recommended for microwave VOD studies of cropland land surface phenology. Based on an Analysis of Covariance (ANCOVA) analysis, the slopes from the linear regression fit were not significantly different by microwave frequency, whereas the intercepts were significantly different, given the different magnitudes of the VODs. PHs for NDVI and VOD were highly correlated. Despite their strong correspondence, there was generally a lag of AMSR-E PH VOD10.65GHz by about two weeks compared to MODIS peak greenness. To evaluate the utility of the PH determination based on maximum value, we correlated the CxQ derived and maximum value determined PHs of NDVI and found that they were highly correlated with R2 of 0.87, but with a one-week bias. Considering the one-week bias between the two methods, we find that PH of VOD10.65GHz lags PH of NDVI by three weeks. We conclude, therefore, that maximum-value based PH of VOD can be a complementary phenometric for the CxQ model derived PH NDVI, especially in cloud and aerosol obscured regions of the world. Full article
Figures

Graphical abstract

Open AccessArticle
Influence of Plot Size on Efficiency of Biomass Estimates in Inventories of Dry Tropical Forests Assisted by Photogrammetric Data from an Unmanned Aircraft System
Remote Sens. 2017, 9(6), 610; https://doi.org/10.3390/rs9060610
Received: 8 May 2017 / Revised: 5 June 2017 / Accepted: 12 June 2017 / Published: 15 June 2017
Cited by 10 | Viewed by 1322 | PDF Full-text (2152 KB) | HTML Full-text | XML Full-text
Abstract
Applications of unmanned aircraft systems (UASs) to assist in forest inventories have provided promising results in biomass estimation for different forest types. Recent studies demonstrating use of different types of remotely sensed data to assist in biomass estimation have shown that accuracy and [...] Read more.
Applications of unmanned aircraft systems (UASs) to assist in forest inventories have provided promising results in biomass estimation for different forest types. Recent studies demonstrating use of different types of remotely sensed data to assist in biomass estimation have shown that accuracy and precision of estimates are influenced by the size of field sample plots used to obtain reference values for biomass. The objective of this case study was to assess the influence of sample plot size on efficiency of UAS-assisted biomass estimates in the dry tropical miombo woodlands of Malawi. The results of a design-based field sample inventory assisted by three-dimensional point clouds obtained from aerial imagery acquired with a UAS showed that the root mean square errors as well as the standard error estimates of mean biomass decreased as sample plot sizes increased. Furthermore, relative efficiency values over different sample plot sizes were above 1.0 in a design-based and model-assisted inferential framework, indicating that UAS-assisted inventories were more efficient than purely field-based inventories. The results on relative costs for UAS-assisted and pure field-based sample plot inventories revealed that there is a trade-off between inventory costs and required precision. For example, in our study if a standard error of less than approximately 3 Mg ha−1 was targeted, then a UAS-assisted forest inventory should be applied to ensure more cost effective and precise estimates. Future studies should therefore focus on finding optimum plot sizes for particular applications, like for example in projects under the Reducing Emissions from Deforestation and Forest Degradation, plus forest conservation, sustainable management of forest and enhancement of carbon stocks (REDD+) mechanism with different geographical scales. Full article
(This article belongs to the Section Forest Remote Sensing)
Figures

Graphical abstract

Open AccessArticle
Analyzing the Potential Risk of Climate Change on Lyme Disease in Eastern Ontario, Canada Using Time Series Remotely Sensed Temperature Data and Tick Population Modelling
Remote Sens. 2017, 9(6), 609; https://doi.org/10.3390/rs9060609
Received: 15 March 2017 / Revised: 19 May 2017 / Accepted: 2 June 2017 / Published: 15 June 2017
Viewed by 2903 | PDF Full-text (3831 KB) | HTML Full-text | XML Full-text
Abstract
The number of Lyme disease cases (Lyme borreliosis) in Ontario, Canada has increased over the last decade, and that figure is projected to continue to increase. The northern limit of Lyme disease cases has also been progressing northward from the northeastern [...] Read more.
The number of Lyme disease cases (Lyme borreliosis) in Ontario, Canada has increased over the last decade, and that figure is projected to continue to increase. The northern limit of Lyme disease cases has also been progressing northward from the northeastern United States into southeastern Ontario. Several factors such as climate change, changes in host abundance, host and vector migration, or possibly a combination of these factors likely contribute to the emergence of Lyme disease cases in eastern Ontario. This study first determined areas of warming using time series remotely sensed temperature data within Ontario, then analyzed possible spatial-temporal changes in Lyme disease risk in eastern Ontario from 2000 to 2013 due to climate change using tick population modeling. The outputs of the model were validated by using tick surveillance data from 2002 to 2012. Our results indicated areas in Ontario where Lyme disease risk changed from unsustainable to sustainable for sustaining Ixodes scapularis (black-legged tick) populations. This study provides evidence that climate change has facilitated the northward expansion of black-legged tick populations’ geographic range over the past decade. The results demonstrate that remote sensing data can be used to increase the spatial detail for Lyme disease risk mapping and provide risk maps for better awareness of possible Lyme disease cases. Further studies are required to determine the contribution of host migration and abundance on changes in eastern Ontario’s Lyme disease risk. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
Figures

Graphical abstract

Open AccessArticle
Spatio-Temporal Relationships between Optical Information and Carbon Fluxes in a Mediterranean Tree-Grass Ecosystem
Remote Sens. 2017, 9(6), 608; https://doi.org/10.3390/rs9060608
Received: 16 March 2017 / Revised: 24 May 2017 / Accepted: 8 June 2017 / Published: 14 June 2017
Cited by 3 | Viewed by 1586 | PDF Full-text (9193 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Spatio-temporal mismatches between Remote Sensing (RS) and Eddy Covariance (EC) data as well as spatial heterogeneity jeopardize terrestrial Gross Primary Production (GPP) modeling. This article combines: (a) high spatial resolution hyperspectral imagery; (b) EC footprint climatology estimates; and (c) semi-empirical models [...] Read more.
Spatio-temporal mismatches between Remote Sensing (RS) and Eddy Covariance (EC) data as well as spatial heterogeneity jeopardize terrestrial Gross Primary Production (GPP) modeling. This article combines: (a) high spatial resolution hyperspectral imagery; (b) EC footprint climatology estimates; and (c) semi-empirical models of increasing complexity to analyze the impact of these factors on GPP estimation. Analyses are carried out in a Mediterranean Tree-Grass Ecosystem (TGE) that combines vegetation with very different physiologies and structure. Half-hourly GPP (GPPhh) were predicted with relative errors ~36%. Results suggest that, at EC footprint scale, the ecosystem signals are quite homogeneous, despite tree and grass mixture. Models fit using EC and RS data with high degree of spatial and temporal match did not significantly improved models performance; in fact, errors were explained by meteorological variables instead. In addition, the performance of the different models was quite similar. This suggests that none of the models accurately represented light use efficiency or the fraction of absorbed photosynthetically active radiation. This is partly due to model formulation; however, results also suggest that the mixture of the different vegetation types might contribute to hamper such modeling, and should be accounted for GPP models in TGE and other heterogeneous ecosystems. Full article
Figures

Graphical abstract

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