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Remote Sens., Volume 8, Issue 10 (October 2016)

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Cover Story (view full-size image) Monitoring vegetation phenology with digital color cameras has become highly popular now that time [...] Read more.
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Open AccessFeature PaperArticle Integration of Aerial Thermal Imagery, LiDAR Data and Ground Surveys for Surface Temperature Mapping in Urban Environments
Remote Sens. 2016, 8(10), 880; https://doi.org/10.3390/rs8100880
Received: 26 August 2016 / Revised: 23 September 2016 / Accepted: 18 October 2016 / Published: 23 October 2016
Cited by 4 | PDF Full-text (2550 KB) | HTML Full-text | XML Full-text
Abstract
A single-band surface temperature retrieval method is proposed, aiming at achieving a better accuracy by exploiting the integration of aerial thermal images with LiDAR data and ground surveys. LiDAR data allow the generation of a high resolution digital surface model and a detailed
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A single-band surface temperature retrieval method is proposed, aiming at achieving a better accuracy by exploiting the integration of aerial thermal images with LiDAR data and ground surveys. LiDAR data allow the generation of a high resolution digital surface model and a detailed modeling of the Sky-View Factor (SVF). Ground surveys of surface temperature and emissivity, instead, are used to estimate the atmospheric parameters involved in the model (through a bounded least square adjustment) and for a first assessment of the accuracy of the results. The RMS of the difference between the surface temperatures computed from the model and measured on the check sites ranges between 0.8 °C and 1.0 °C, depending on the algorithm used to calculate the SVF. Results are in general better than the ones obtained without considering SVF and prove the effectiveness of the integration of different data sources. The proposed approach has the advantage of avoiding the modeling of the atmosphere conditions, which is often difficult to achieve with the desired accuracy; on the other hand, it is highly dependent on the accuracy of the data measured on the ground. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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Open AccessFeature PaperArticle Estimation of Paddy Rice Variables with a Modified Water Cloud Model and Improved Polarimetric Decomposition Using Multi-Temporal RADARSAT-2 Images
Remote Sens. 2016, 8(10), 878; https://doi.org/10.3390/rs8100878
Received: 16 August 2016 / Revised: 12 October 2016 / Accepted: 18 October 2016 / Published: 23 October 2016
Cited by 2 | PDF Full-text (9670 KB) | HTML Full-text | XML Full-text
Abstract
Rice growth monitoring is very important as rice is one of the staple crops of the world. Rice variables as quantitative indicators of rice growth are critical for farming management and yield estimation, and synthetic aperture radar (SAR) has great advantages for monitoring
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Rice growth monitoring is very important as rice is one of the staple crops of the world. Rice variables as quantitative indicators of rice growth are critical for farming management and yield estimation, and synthetic aperture radar (SAR) has great advantages for monitoring rice variables due to its all-weather observation capability. In this study, eight temporal RADARSAT-2 full-polarimetric SAR images were acquired during rice growth cycle and a modified water cloud model (MWCM) was proposed, in which the heterogeneity of the rice canopy in the horizontal direction and its phenological changes were considered when the double-bounce scattering between the rice canopy and the underlying surface was firstly considered as well. Then, three scattering components from an improved polarimetric decomposition were coupled with the MWCM, instead of the backscattering coefficients. Using a genetic algorithm, eight rice variables were estimated, such as the leaf area index (LAI), rice height (h), and the fresh and dry biomass of ears (Fe and De). The accuracy validation showed the MWCM was suitable for the estimation of rice variables during the whole growth season. The validation results showed that the MWCM could predict the temporal behaviors of the rice variables well during the growth cycle (R2 > 0.8). Compared with the original water cloud model (WCM), the relative errors of rice variables with the MWCM were much smaller, especially in the vegetation phase (approximately 15% smaller). Finally, it was discussed that the MWCM could be used, theoretically, for extensive applications since the empirical coefficients in the MWCM were determined in general cases, but more applications of the MWCM are necessary in future work. Full article
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Open AccessFeature PaperArticle InSAR Observation and Numerical Modeling of the Earth-Dam Displacement of Shuibuya Dam (China)
Remote Sens. 2016, 8(10), 877; https://doi.org/10.3390/rs8100877
Received: 30 June 2016 / Revised: 19 September 2016 / Accepted: 13 October 2016 / Published: 23 October 2016
Cited by 2 | PDF Full-text (13628 KB) | HTML Full-text | XML Full-text
Abstract
How to accurately determine the mechanical parameters of rockfill is one of the key issues of concrete-face rockfill dams. Parameter back-analysis using internal or external monitoring data has been proven to be an efficient way to solve this problem. However, traditional internal or
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How to accurately determine the mechanical parameters of rockfill is one of the key issues of concrete-face rockfill dams. Parameter back-analysis using internal or external monitoring data has been proven to be an efficient way to solve this problem. However, traditional internal or external monitoring methods have limitations in efficiency and long-term monitoring. In this paper, the displacement of the Shuibuya concrete-face rockfill dam is monitored by the space-borne Interferometric Synthetic Aperture Radar (InSAR) time series method. Using the InSAR results and the finite element method, the back-analysis of the mechanical parameters of the rockfill dam is investigated, and the back-analysis results of InSAR and levelling are compared. A high correlation of 0.99 for the displacement results generated from InSAR and the levelling offers good agreement between the two methods. The agreement provides confidence that the external InSAR monitoring measurement allows producing a reliable back-analysis and captures the displacement properties of the dam. Based on the identified parameters from the InSAR results, the dam displacement is predicted. The prediction of the maximum settlement of the dam is 2.332 m by the end of 2020, according to the dam displacement characteristics, which agrees well with the results derived from the recorded internal monitoring data. Therefore, the external monitoring results from the InSAR observation can be used as a supplement for traditional monitoring methods to analyse the parameters of the dam. Full article
(This article belongs to the Special Issue Earth Observations for Geohazards) Printed Edition available
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Open AccessFeature PaperArticle The Influences of Climate Change and Human Activities on Vegetation Dynamics in the Qinghai-Tibet Plateau
Remote Sens. 2016, 8(10), 876; https://doi.org/10.3390/rs8100876
Received: 30 June 2016 / Revised: 28 September 2016 / Accepted: 18 October 2016 / Published: 23 October 2016
Cited by 14 | PDF Full-text (10856 KB) | HTML Full-text | XML Full-text
Abstract
Grasslands occupy nearly three quarters of the land surface of the Qinghai-Tibet plateau (QTP) and play a critical role in regulating the ecological functions of the QTP. Ongoing climate change and human interference have greatly affected grasslands on the QTP. Differentiating human-induced and
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Grasslands occupy nearly three quarters of the land surface of the Qinghai-Tibet plateau (QTP) and play a critical role in regulating the ecological functions of the QTP. Ongoing climate change and human interference have greatly affected grasslands on the QTP. Differentiating human-induced and climate-driven vegetation changes is vital for both ecological understanding and the management of husbandry. In this study, we employed statistical analysis of annual records, various sources of remote sensing data, and an ecosystem process model to calculate the relative contribution of climate and human activities to vegetation vigor on the QTP. The temperature, precipitation and the intensity and spatial pattern of livestock grazing differed between the periods prior to and after the year 2000, which led to different vegetation dynamics. Overall, increased temperature and enhanced precipitation favored vegetation growth. However, their combined effects exhibited strong spatial heterogeneity. Specifically, increased temperature restrained vegetation growth in dry steppe regions during a period of slightly increasing precipitation from 1986 to 2000 and in meadow regions during a period of precipitation decline during 2000–2011, thereby making precipitation a dominant factor. An increase in precipitation tended to enhance vegetation growth in wet meadow regions during warm periods, and temperature was the limiting factor in Tibet during dry periods. The dominant role played by climate and human activities differed with location and targeted time period. Areas dominated by human activities are much smaller than those dominated by climate. The effects of grazing on grassland pasture were more obvious under unfavorable climate conditions than under suitable ones. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessArticle Satellite Survey of Inner Seas: Oil Pollution in the Black and Caspian Seas
Remote Sens. 2016, 8(10), 875; https://doi.org/10.3390/rs8100875
Received: 5 June 2016 / Revised: 4 October 2016 / Accepted: 17 October 2016 / Published: 23 October 2016
Cited by 4 | PDF Full-text (17531 KB) | HTML Full-text | XML Full-text
Abstract
The paper discusses our studies of oil pollution in the Black and Caspian Seas. The research was based on a multi-sensor approach on satellite survey data. A combined analysis of oil film signatures in satellite synthetic aperture radar (SAR) and optical imagery was
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The paper discusses our studies of oil pollution in the Black and Caspian Seas. The research was based on a multi-sensor approach on satellite survey data. A combined analysis of oil film signatures in satellite synthetic aperture radar (SAR) and optical imagery was performed. Maps of oil spills detected in satellite imagery of the whole aquatic area of the Black Sea and the Middle and the Southern Caspian Sea are created. Areas of the heaviest pollution are outlined. It is shown that the main types of sea surface oil pollution are ship discharges and natural marine hydrocarbon seepages. For each type of pollution and each sea, regions of regular pollution occurrence were determined, polluted areas were estimated, and specific manifestation features were revealed. Long-term observations demonstrate that in recent years, illegal wastewater discharges into the Black Sea have become very common, which raises serious environmental issues. Manifestations of seabed hydrocarbon seepages were also detected in the Black Sea, primarily in its eastern part. The patterns of surface oil pollution of the Caspian Sea differ considerably from those observed in the Black Sea. They are largely determined by presence of big seabed oil and gas deposits. The dependence of surface oil SAR signatures on wind/wave conditions is discussed. The impact of dynamic and circulation processes on oil films drift and spread is investigated. A large amount of the data available allowed us to make some generalizations and obtain statistically significant results on spatial and temporal variability of various surface film manifestations.The examples and numerical data we provide on ship spills and seabed seepages reflect the influence of the pollution on the sea environment. Full article
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Open AccessArticle Hybrid-SAR Technique: Joint Analysis Using Phase-Based and Amplitude-Based Methods for the Xishancun Giant Landslide Monitoring
Remote Sens. 2016, 8(10), 874; https://doi.org/10.3390/rs8100874
Received: 29 June 2016 / Revised: 9 October 2016 / Accepted: 17 October 2016 / Published: 23 October 2016
Cited by 5 | PDF Full-text (9363 KB) | HTML Full-text | XML Full-text
Abstract
Early detection and early warning are of great importance in giant landslide monitoring because of the unexpectedness and concealed nature of large-scale landslides. In China, the western mountainous areas are prone to landslides and feature many giant complex landslides, especially following the Wenchuan
[...] Read more.
Early detection and early warning are of great importance in giant landslide monitoring because of the unexpectedness and concealed nature of large-scale landslides. In China, the western mountainous areas are prone to landslides and feature many giant complex landslides, especially following the Wenchuan Earthquake in 2008. This work concentrates on a new technique, known as the “hybrid-SAR technique”, that combines both phase-based and amplitude-based methods to detect and monitor large-scale landslides in Li County, Sichuan Province, southwestern China. This work aims to develop a robust methodological approach to promptly identify diverse landslides with different deformation magnitudes, sliding modes and slope geometries, even when the available satellite data are limited. The phase-based and amplitude-based techniques are used to obtain the landslide displacements from six TerraSAR-X Stripmap descending scenes acquired from November 2014 to March 2015. Furthermore, the application circumstances and influence factors of hybrid-SAR are evaluated according to four aspects: (1) quality of terrain visibility to the radar sensor; (2) landslide deformation magnitude and different sliding mode; (3) impact of dense vegetation cover; and (4) sliding direction sensitivity. The results achieved from hybrid-SAR are consistent with in situ measurements. This new hybrid-SAR technique for complex giant landslide research successfully identified representative movement areas, e.g., an extremely slow earthflow and a creeping region with a displacement rate of 1 cm per month and a typical rotational slide with a displacement rate of 2–3 cm per month downwards and towards the riverbank. Hybrid-SAR allows for a comprehensive and preliminary identification of areas with significant movement and provides reliable data support for the forecasting and monitoring of landslides. Full article
(This article belongs to the Special Issue Earth Observations for Geohazards) Printed Edition available
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Open AccessArticle Separability Analysis of Sentinel-2A Multi-Spectral Instrument (MSI) Data for Burned Area Discrimination
Remote Sens. 2016, 8(10), 873; https://doi.org/10.3390/rs8100873
Received: 12 July 2016 / Revised: 17 October 2016 / Accepted: 18 October 2016 / Published: 22 October 2016
Cited by 9 | PDF Full-text (10220 KB) | HTML Full-text | XML Full-text
Abstract
Biomass burning is a global phenomenon and systematic burned area mapping is of increasing importance for science and applications. With high spatial resolution and novelty in band design, the recently launched Sentinel-2A satellite provides a new opportunity for moderate spatial resolution burned area
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Biomass burning is a global phenomenon and systematic burned area mapping is of increasing importance for science and applications. With high spatial resolution and novelty in band design, the recently launched Sentinel-2A satellite provides a new opportunity for moderate spatial resolution burned area mapping. This study examines the performance of the Sentinel-2A Multi Spectral Instrument (MSI) bands and derived spectral indices to differentiate between unburned and burned areas. For this purpose, five pairs of pre-fire and post-fire top of atmosphere (TOA reflectance) and atmospherically corrected (surface reflectance) images were studied. The pixel values of locations that were unburned in the first image and burned in the second image, as well as the values of locations that were unburned in both images which served as a control, were compared and the discrimination of individual bands and spectral indices were evaluated using parametric (transformed divergence) and non-parametric (decision tree) approaches. Based on the results, the most suitable MSI bands to detect burned areas are the 20 m near-infrared, short wave infrared and red-edge bands, while the performance of the spectral indices varied with location. The atmospheric correction only significantly influenced the separability of the visible wavelength bands. The results provide insights that are useful for developing Sentinel-2 burned area mapping algorithms. Full article
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Open AccessArticle Interannual Variability in Dry Mixed-Grass Prairie Yield: A Comparison of MODIS, SPOT, and Field Measurements
Remote Sens. 2016, 8(10), 872; https://doi.org/10.3390/rs8100872
Received: 28 April 2016 / Revised: 17 October 2016 / Accepted: 18 October 2016 / Published: 22 October 2016
PDF Full-text (3971 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Remote sensing is often used to assess rangeland condition and biophysical parameters across large areas. In particular, the relationship between the Normalized Difference Vegetation Index (NDVI) and above-ground biomass can be used to assess rangeland primary productivity (seasonal carbon gain or above-ground biomass
[...] Read more.
Remote sensing is often used to assess rangeland condition and biophysical parameters across large areas. In particular, the relationship between the Normalized Difference Vegetation Index (NDVI) and above-ground biomass can be used to assess rangeland primary productivity (seasonal carbon gain or above-ground biomass “yield”). We evaluated the NDVI–yield relationship for a southern Alberta prairie rangeland, using seasonal trends in NDVI and biomass during the 2009 and 2010 growing seasons, two years with contrasting rainfall regimes. The study compared harvested biomass and NDVI from field spectrometry to NDVI from three satellite platforms: the Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Système Pour l’Observation de la Terre (SPOT 4 and 5). Correlations between ground spectrometry and harvested biomass were also examined for each growing season. The contrasting precipitation patterns were easily captured with satellite NDVI, field NDVI and green biomass measurements. NDVI provided a proxy measure for green plant biomass, and was linearly related to the log of standing green biomass. NDVI phenology clearly detected the green biomass increase at the beginning of each growing season and the subsequent decrease in green biomass at the end of each growing season due to senescence. NDVI–biomass regressions evolved over each growing season due to end-of-season senescence and carryover of dead biomass to the following year. Consequently, mid-summer measurements yielded the strongest correlation (R2 = 0.97) between NDVI and green biomass, particularly when the data were spatially aggregated to better match the satellite sampling scale. Of the three satellite platforms (MODIS Aqua, MODIS Terra, and SPOT), Terra yielded the best agreement with ground-measured NDVI, and SPOT yielded the weakest relationship. When used properly, NDVI from satellite remote sensing can accurately estimate peak-season productivity and detect interannual variation in standing green biomass, and field spectrometry can provide useful validation for satellite data in a biomass monitoring program in this prairie ecosystem. Together, these methods can be used to identify the effects of year-to-year precipitation variability on above-ground biomass in a dry mixed-grass prairie. These findings have clear applications in monitoring yield and productivity, and could be used to support a rangeland carbon monitoring program. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Investigation of Spectral Band Requirements for Improving Retrievals of Phytoplankton Functional Types
Remote Sens. 2016, 8(10), 871; https://doi.org/10.3390/rs8100871
Received: 14 June 2016 / Revised: 5 October 2016 / Accepted: 14 October 2016 / Published: 22 October 2016
Cited by 9 | PDF Full-text (1355 KB) | HTML Full-text | XML Full-text
Abstract
Studying phytoplankton functional types (PFTs) from space is possible due to recent advances in remote sensing. Though a variety of products are available, the limited number of wavelengths available compared to the number of model parameters needed to be retrieved is still a
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Studying phytoplankton functional types (PFTs) from space is possible due to recent advances in remote sensing. Though a variety of products are available, the limited number of wavelengths available compared to the number of model parameters needed to be retrieved is still a major problem in using ocean-color data for PFT retrievals. Here, we investigated which band placement could improve retrievals of three particular PFTs (diatoms, coccolithophores and cyanobacteria). In addition to analyzing dominant spectral features in the absorption spectra of the target PFTs, two previously-developed methods using measured spectra were applied to simulated data. Such a synthetic dataset allowed for significantly increasing the number of scenarios and enabled a full control over parameters causing spectral changes. We evaluated the chosen band placement by applying an adapted ocean reflectance inversion, as utilized in the generalized inherent optical properties (GIOP) retrieval. Results show that the optimal band settings depend on the method applied to determine the bands placement, as well as on the internal variability of the dataset investigated. Therefore, continuous hyperspectral instruments would be most beneficial for discriminating multiple PFTs, though a small improvement in spectral sampling and resolution does not significantly modify the results. Bands, which could be added to future instruments (e.g., Ocean and Land Colour Instrument (OLCI) instrument on the upcoming Sentinel-3B,-3C,-3D, etc., and further satellites) in order to enhance PFT retrieval capabilities, were also determined. Full article
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Open AccessArticle Remote Sensing of Particle Cross-Sectional Area in the Bohai Sea and Yellow Sea: Algorithm Development and Application Implications
Remote Sens. 2016, 8(10), 841; https://doi.org/10.3390/rs8100841
Received: 26 July 2016 / Revised: 23 September 2016 / Accepted: 8 October 2016 / Published: 22 October 2016
Cited by 4 | PDF Full-text (4852 KB) | HTML Full-text | XML Full-text
Abstract
Suspended particles in waters play an important role in determination of optical properties and ocean color remote sensing. To link suspended particles to their optical properties and thereby remote sensing reflectance (Rrs(λ)), cross-sectional area is a key factor.
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Suspended particles in waters play an important role in determination of optical properties and ocean color remote sensing. To link suspended particles to their optical properties and thereby remote sensing reflectance (Rrs(λ)), cross-sectional area is a key factor. Till now, there is still a lack of methodologies for derivation of the particle cross-sectional area concentration (AC) from satellite measurements, which consequently limits potential applications of AC. In this study, we investigated the relationship between AC and Rrs(λ) based on field measurements in the Bohai Sea (BS) and Yellow Sea (YS). Our analysis confirmed the strong dependence of Rrs(λ) on AC and that such dependence is stronger than on mass concentration. Subsequently, a remote sensing algorithm that uses the slope of Rrs(λ) between 490 and 555 nm was developed for retrieval of AC from satellite measurements of the Geostationary Ocean Color Imager (GOCI). In situ evaluations show that the algorithm displays good performance for deriving AC and is robust to uncertainties in Rrs(λ). When the algorithm was applied to satellite data, it performed well, with a coefficient of determination of 0.700, a root mean squared error of 2.126 m−1 and a mean absolute percentage error of 40.7%, and it yielded generally reasonable spatial and temporal distributions of AC in the BS and YS. The satellite-derived AC using our algorithm may offer useful information for modeling the inherent optical properties of suspended particles, deriving the water transparency, estimating the particle composition and possibly improving particle mass concentration estimations in future. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing) Printed Edition available
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Open AccessArticle Scale-Aware Pansharpening Algorithm for Agricultural Fragmented Landscapes
Remote Sens. 2016, 8(10), 870; https://doi.org/10.3390/rs8100870
Received: 8 July 2016 / Revised: 3 October 2016 / Accepted: 11 October 2016 / Published: 21 October 2016
Cited by 3 | PDF Full-text (35483 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing (RS) has played an important role in extensive agricultural monitoring and management for several decades. However, the current spatial resolution of satellite imagery does not have enough definition to generalize its use in highly-fragmented agricultural landscapes, which represents a significant percentage
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Remote sensing (RS) has played an important role in extensive agricultural monitoring and management for several decades. However, the current spatial resolution of satellite imagery does not have enough definition to generalize its use in highly-fragmented agricultural landscapes, which represents a significant percentage of the world’s total cultivated surface. To characterize and analyze this type of landscape, multispectral (MS) images with high and very high spatial resolutions are required. Multi-source image fusion algorithms are normally used to improve the spatial resolution of images with a medium spatial resolution. In particular, pansharpening (PS) methods allow one to produce high-resolution MS images through a coherent integration of spatial details from a panchromatic (PAN) image with spectral information from an MS. The spectral and spatial quality of source images must be preserved to be useful in RS tasks. Different PS strategies provide different trade-offs between the spectral and the spatial quality of the fused images. Considering that agricultural landscape images contain many levels of significant structures and edges, the PS algorithms based on filtering processes must be scale-aware and able to remove different levels of detail in any input images. In this work, a new PS methodology based on a rolling guidance filter (RGF) is proposed. The main contribution of this new methodology is to produce artifact-free pansharpened images, improving the MS edges with a scale-aware approach. Three images have been used, and more than 150 experiments were carried out. An objective comparison with widely-used methodologies shows the capability of the proposed method as a powerful tool to obtain pansharpened images preserving the spatial and spectral information. Full article
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Open AccessArticle Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping
Remote Sens. 2016, 8(10), 869; https://doi.org/10.3390/rs8100869
Received: 1 September 2016 / Revised: 28 September 2016 / Accepted: 11 October 2016 / Published: 21 October 2016
Cited by 10 | PDF Full-text (30369 KB) | HTML Full-text | XML Full-text
Abstract
Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones,
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Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observations in a cost-effective way. Digital cameras can be used as a relascope device to measure basal area, a forest density variable that is closely related to biomass. In this paper, we present the Relasphone mobile application with extensive accuracy assessment in two mixed forest sites from different biomes. Basal area measurements in Finland (boreal zone) were in good agreement with reference forest inventory plot data on pine ( R 2 = 0 . 75 , R M S E = 5 . 33 m 2 /ha), spruce ( R 2 = 0 . 75 , R M S E = 6 . 73 m 2 /ha) and birch ( R 2 = 0 . 71 , R M S E = 4 . 98 m 2 /ha), with total relative R M S E ( % ) = 29 . 66 % . In Durango, Mexico (temperate zone), Relasphone stem volume measurements were best for pine ( R 2 = 0 . 88 , R M S E = 32 . 46 m 3 /ha) and total stem volume ( R 2 = 0 . 87 , R M S E = 35 . 21 m 3 /ha). Relasphone data were then successfully utilized as the only reference data in combination with optical satellite images to produce biomass maps. The Relasphone concept has been validated for future use by citizens in other locations. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Open AccessArticle Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake
Remote Sens. 2016, 8(10), 868; https://doi.org/10.3390/rs8100868
Received: 5 August 2016 / Revised: 26 September 2016 / Accepted: 17 October 2016 / Published: 20 October 2016
Cited by 10 | PDF Full-text (9400 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing continues to be an invaluable tool in earthquake damage assessments and emergency response. This study evaluates the effectiveness of multilayer feedforward neural networks, radial basis neural networks, and Random Forests in detecting earthquake damage caused by the 2010 Port-au-Prince, Haiti 7.0
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Remote sensing continues to be an invaluable tool in earthquake damage assessments and emergency response. This study evaluates the effectiveness of multilayer feedforward neural networks, radial basis neural networks, and Random Forests in detecting earthquake damage caused by the 2010 Port-au-Prince, Haiti 7.0 moment magnitude (Mw) event. Additionally, textural and structural features including entropy, dissimilarity, Laplacian of Gaussian, and rectangular fit are investigated as key variables for high spatial resolution imagery classification. Our findings show that each of the algorithms achieved nearly a 90% kernel density match using the United Nations Operational Satellite Applications Programme (UNITAR/UNOSAT) dataset as validation. The multilayer feedforward network was able to achieve an error rate below 40% in detecting damaged buildings. Spatial features of texture and structure were far more important in algorithmic classification than spectral information, highlighting the potential for future implementation of machine learning algorithms which use panchromatic or pansharpened imagery alone. Full article
(This article belongs to the Special Issue Earth Observations for Geohazards) Printed Edition available
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Open AccessArticle Effect of Protection Level in the Hydroperiod of Water Bodies on Doñana’s Aeolian Sands
Remote Sens. 2016, 8(10), 867; https://doi.org/10.3390/rs8100867
Received: 28 July 2016 / Revised: 11 October 2016 / Accepted: 13 October 2016 / Published: 20 October 2016
Cited by 4 | PDF Full-text (7200 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Mediterranean temporary ponds on Doñana’s aeolian sands form an extensive system of small dynamic water bodies, dependent on precipitation and groundwater, of considerable importance for biodiversity conservation. Different areas of the aeolian sands have received different levels of environmental protection since 1969, and
[...] Read more.
Mediterranean temporary ponds on Doñana’s aeolian sands form an extensive system of small dynamic water bodies, dependent on precipitation and groundwater, of considerable importance for biodiversity conservation. Different areas of the aeolian sands have received different levels of environmental protection since 1969, and this has influenced the degree of conservation and the flooding dynamic of these temporary surface waters. We use the Landsat series of satellite images from 1985 to 2014 to study the temporal dynamic of small temporary water bodies on the aeolian sands in relation to the protection level and to distance to water abstraction pressures from agriculture and residential areas. The results show that even with small and ephemeral water bodies optical remote sensing time-series are an effective way to study their flooding temporal dynamics. The protected areas of the aeolian sands hold a better preserved system of temporary ponds, with a flooding dynamic that fluctuates with precipitation. The unprotected area shows an increase in mean hydroperiod duration, and surface flooded, and a decline in hydroperiod variability. This seems to be due to the creation of irrigation ponds and the artificialization of the flooding regime of the natural temporary ponds, that either receive excess irrigation water or dry-up due to the lowering of the groundwater table level. Although a decline in hydroperiod duration of temporary ponds is seen as negative to the system, an increase in hydroperiod of surface waters due to artificialization, or a decline in variability cannot be considered as positive compensatory effects. Full article
(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)
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Open AccessArticle Guidance Index for Shallow Landslide Hazard Analysis
Remote Sens. 2016, 8(10), 866; https://doi.org/10.3390/rs8100866
Received: 9 August 2016 / Revised: 29 September 2016 / Accepted: 14 October 2016 / Published: 20 October 2016
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Abstract
Rainfall-induced shallow landslides are one of the most frequent hazards on slanted terrains. Intense storms with high-intensity and long-duration rainfall have high potential to trigger rapidly moving soil masses due to changes in pore water pressure and seepage forces. Nevertheless, regardless of the
[...] Read more.
Rainfall-induced shallow landslides are one of the most frequent hazards on slanted terrains. Intense storms with high-intensity and long-duration rainfall have high potential to trigger rapidly moving soil masses due to changes in pore water pressure and seepage forces. Nevertheless, regardless of the intensity and/or duration of the rainfall, shallow landslides are influenced by antecedent soil moisture conditions. As of this day, no system exists that dynamically interrelates these two factors on large scales. This work introduces a Shallow Landslide Index (SLI) as the first implementation of antecedent soil moisture conditions for the hazard analysis of shallow rainfall-induced landslides. The proposed mathematical algorithm is built using a logistic regression method that systematically learns from a comprehensive landslide inventory. Initially, root-soil moisture and rainfall measurements modeled from AMSR-E and TRMM respectively, are used as proxies to develop the index. The input dataset is randomly divided into training and verification sets using the Hold-Out method. Validation results indicate that the best-fit model predicts the highest number of cases correctly at 93.2% accuracy. Consecutively, as AMSR-E and TRMM stopped working in October 2011 and April 2015 respectively, root-soil moisture and rainfall measurements modeled by SMAP and GPM are used to develop models that calculate the SLI for 10, 7, and 3 days. The resulting models indicate a strong relationship (78.7%, 79.6%, and 76.8% respectively) between the predictors and the predicted value. The results also highlight important remaining challenges such as adequate information for algorithm functionality and satellite based data reliability. Nevertheless, the experimental system can potentially be used as a dynamic indicator of the total amount of antecedent moisture and rainfall (for a given duration of time) needed to trigger a shallow landslide in a susceptible area. It is indicated that the SLI algorithm can be re-built for other regions where deterministic studies are not feasible. This represents a significant step towards rainfall-induced shallow landslide hazard readiness. Full article
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
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