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Remote Sens., Volume 10, Issue 7 (July 2018)

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Open AccessArticle Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting
Remote Sens. 2018, 10(7), 1156; https://doi.org/10.3390/rs10071156 (registering DOI)
Received: 18 May 2018 / Revised: 10 July 2018 / Accepted: 19 July 2018 / Published: 21 July 2018
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Abstract
Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. When there are only a few labeled pixels for training and a skewed class label distribution, this task becomes very challenging because of the increased risk of overfitting
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Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. When there are only a few labeled pixels for training and a skewed class label distribution, this task becomes very challenging because of the increased risk of overfitting when training a classifier. In this paper, we show that in this setting, a convolutional neural network with a single hidden layer can achieve state-of-the-art performance when three tricks are used: a spectral-locality-aware regularization term and smoothing- and label-based data augmentation. The shallow network architecture prevents overfitting in the presence of many features and few training samples. The locality-aware regularization forces neighboring wavelengths to have similar contributions to the features generated during training. The new data augmentation procedure favors the selection of pixels in smaller classes, which is beneficial for skewed class label distributions. The accuracy of the proposed method is assessed on five publicly available hyperspectral images, where it achieves state-of-the-art results. As other spectral-spatial classification methods, we use the entire image (labeled and unlabeled pixels) to infer the class of its unlabeled pixels. To investigate the positive bias induced by the use of the entire image, we propose a new learning setting where unlabeled pixels are not used for building the classifier. Results show the beneficial effect of the proposed tricks also in this setting and substantiate the advantages of using labeled and unlabeled pixels from the image for hyperspectral image classification. Full article
(This article belongs to the Section Remote Sensing Image Processing)
Open AccessArticle TerraSAR-X Time Series Fill a Gap in Spaceborne Snowmelt Monitoring of Small Arctic Catchments—A Case Study on Qikiqtaruk (Herschel Island), Canada
Remote Sens. 2018, 10(7), 1155; https://doi.org/10.3390/rs10071155 (registering DOI)
Received: 4 June 2018 / Revised: 14 July 2018 / Accepted: 20 July 2018 / Published: 21 July 2018
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Abstract
The timing of snowmelt is an important turning point in the seasonal cycle of small Arctic catchments. The TerraSAR-X (TSX) satellite mission is a synthetic aperture radar system (SAR) with high potential to measure the high spatiotemporal variability of snow cover extent (SCE)
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The timing of snowmelt is an important turning point in the seasonal cycle of small Arctic catchments. The TerraSAR-X (TSX) satellite mission is a synthetic aperture radar system (SAR) with high potential to measure the high spatiotemporal variability of snow cover extent (SCE) and fractional snow cover (FSC) on the small catchment scale. We investigate the performance of multi-polarized and multi-pass TSX X-Band SAR data in monitoring SCE and FSC in small Arctic tundra catchments of Qikiqtaruk (Herschel Island) off the Yukon Coast in the Western Canadian Arctic. We applied a threshold based segmentation on ratio images between TSX images with wet snow and a dry snow reference, and tested the performance of two different thresholds. We quantitatively compared TSX- and Landsat 8-derived SCE maps using confusion matrices and analyzed the spatiotemporal dynamics of snowmelt from 2015 to 2017 using TSX, Landsat 8 and in situ time lapse data. Our data showed that the quality of SCE maps from TSX X-Band data is strongly influenced by polarization and to a lesser degree by incidence angle. VH polarized TSX data performed best in deriving SCE when compared to Landsat 8. TSX derived SCE maps from VH polarization detected late lying snow patches that were not detected by Landsat 8. Results of a local assessment of TSX FSC against the in situ data showed that TSX FSC accurately captured the temporal dynamics of different snow melt regimes that were related to topographic characteristics of the studied catchments. Both in situ and TSX FSC showed a longer snowmelt period in a catchment with higher contributions of steep valleys and a shorter snowmelt period in a catchment with higher contributions of upland terrain. Landsat 8 had fundamental data gaps during the snowmelt period in all 3 years due to cloud cover. The results also revealed that by choosing a positive threshold of 1 dB, detection of ice layers due to diurnal temperature variations resulted in a more accurate estimation of snow cover than a negative threshold that detects wet snow alone. We find that TSX X-Band data in VH polarization performs at a comparable quality to Landsat 8 in deriving SCE maps when a positive threshold is used. We conclude that TSX data polarization can be used to accurately monitor snowmelt events at high temporal and spatial resolution, overcoming limitations of Landsat 8, which due to cloud related data gaps generally only indicated the onset and end of snowmelt. Full article
(This article belongs to the Special Issue Ten Years of TerraSAR-X—Scientific Results)
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Open AccessArticle SfM-Based Method to Assess Gorgonian Forests (Paramuricea clavata (Cnidaria, Octocorallia))
Remote Sens. 2018, 10(7), 1154; https://doi.org/10.3390/rs10071154 (registering DOI)
Received: 21 June 2018 / Revised: 11 July 2018 / Accepted: 17 July 2018 / Published: 21 July 2018
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Abstract
Animal forests promote marine habitats morphological complexity and functioning. The red gorgonian, Paramuricea clavata, is a key structuring species of the Mediterranean coralligenous habitat and an indicator species of climate effects on habitat functioning. P. clavata metrics such as population structure, morphology
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Animal forests promote marine habitats morphological complexity and functioning. The red gorgonian, Paramuricea clavata, is a key structuring species of the Mediterranean coralligenous habitat and an indicator species of climate effects on habitat functioning. P. clavata metrics such as population structure, morphology and biomass inform on the overall health of coralligenous habitats, but the estimation of these metrics is time and cost consuming, and often requires destructive sampling. As a consequence, the implementation of long-term and wide-area monitoring programmes is limited. This study proposes a novel and transferable Structure from Motion (SfM) based method for the estimation of gorgonian population structure (i.e., maximal height, density, abundance), morphometries (i.e., maximal width, fan surface) and biomass (i.e., coenenchymal Dry Weight, Ash Free Dried Weight). The method includes the estimation of a novel metric (3D canopy surface) describing the gorgonian forest as a mosaic of planes generated by fitting multiple 5 cm × 5 cm facets to a SfM generated point cloud. The performance of the method is assessed for two different cameras (GoPro Hero4 and Sony NEX7). Results showed that for highly dense populations (17 colonies/m2), the SfM-method had lower accuracies in estimating the gorgonians density for both cameras (60% to 89%) than for medium to low density populations (14 and 7 colonies/m2) (71% to 100%). Results for the validation of the method showed that the correlation between ground truth and SfM estimates for maximal height, maximal width and fan surface were between R2 = 0.63 and R2 = 0.9, and R2 = 0.99 for coenenchymal surface estimation. The methodological approach was used to estimate the biomass of the gorgonian population within the study area and across the coralligenous habitat between −25 to −40 m depth in the Portofino Marine Protected Area. For that purpose, the coenenchymal surface of sampled colonies was obtained and used for the calculations. Results showed biomass values of dry weight and ash free dry weight of 220 g and 32 g for the studied area and to 365 kg and 55 Kg for the coralligenous habitat in the Marine Protected Area. This study highlighted the feasibility of the methodology for the quantification of P. clavata metrics as well as the potential of the SfM-method to improve current predictions of the status of the coralligenous habitat in the Mediterranean sea and overall management of threatened ecosystems. Full article
(This article belongs to the Section Ocean Remote Sensing)
Open AccessTechnical Note Estimation of Gap Fraction and Foliage Clumping in Forest Canopies
Remote Sens. 2018, 10(7), 1153; https://doi.org/10.3390/rs10071153 (registering DOI)
Received: 14 May 2018 / Revised: 31 May 2018 / Accepted: 18 July 2018 / Published: 21 July 2018
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Abstract
The gap fractions of three mature hemi-boreal forest stands in Estonia were estimated using the LAI-2000 plant canopy analyzer ( LI-COR Biosciences, Lincoln, NE, USA) , the TRAC instrument (Miami, FL, USA), Cajanus’ tube, hemispherical photos, as well as terrestrial (TLS) and airborne
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The gap fractions of three mature hemi-boreal forest stands in Estonia were estimated using the LAI-2000 plant canopy analyzer ( LI-COR Biosciences, Lincoln, NE, USA) , the TRAC instrument (Miami, FL, USA), Cajanus’ tube, hemispherical photos, as well as terrestrial (TLS) and airborne (ALS) laser scanners. ALS measurements with an 8-year interval confirmed that changes in the structure of mature forest stands are slow, and that measurements in the same season Full article
(This article belongs to the Special Issue Optical Remote Sensing of Boreal Forests)
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Open AccessFeature PaperArticle Sentinel-1 InSAR Measurements of Elevation Changes over Yedoma Uplands on Sobo-Sise Island, Lena Delta
Remote Sens. 2018, 10(7), 1152; https://doi.org/10.3390/rs10071152 (registering DOI)
Received: 31 May 2018 / Revised: 15 July 2018 / Accepted: 20 July 2018 / Published: 21 July 2018
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Abstract
Yedoma—extremely ice-rich permafrost with massive ice wedges formed during the Late Pleistocene—is vulnerable to thawing and degradation under climate warming. Thawing of ice-rich Yedoma results in lowering of surface elevations. Quantitative knowledge about surface elevation changes helps us to understand the freeze-thaw processes
[...] Read more.
Yedoma—extremely ice-rich permafrost with massive ice wedges formed during the Late Pleistocene—is vulnerable to thawing and degradation under climate warming. Thawing of ice-rich Yedoma results in lowering of surface elevations. Quantitative knowledge about surface elevation changes helps us to understand the freeze-thaw processes of the active layer and the potential degradation of Yedoma deposits. In this study, we use C-band Sentinel-1 InSAR measurements to map the elevation changes over ice-rich Yedoma uplands on Sobo-Sise Island, Lena Delta with frequent revisit observations (as short as six or 12 days). We observe significant seasonal thaw subsidence during summer months and heterogeneous inter-annual elevation changes from 2016–17. We also observe interesting patterns of stronger seasonal thaw subsidence on elevated flat Yedoma uplands by comparing to the surrounding Yedoma slopes. Inter-annual analyses from 2016–17 suggest that our observed positive surface elevation changes are likely caused by the delayed progression of the thaw season in 2017, associated with mean annual air temperature fluctuations. Full article
(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions)
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Open AccessArticle Aboveground Forest Biomass Estimation Combining L- and P-Band SAR Acquisitions
Remote Sens. 2018, 10(7), 1151; https://doi.org/10.3390/rs10071151 (registering DOI)
Received: 8 June 2018 / Revised: 16 July 2018 / Accepted: 19 July 2018 / Published: 20 July 2018
PDF Full-text (7059 KB)
Abstract
While considerable research has focused on using either L-band or P-band SAR (Synthetic Aperture Radar) on their own for forest biomass retrieval, the use of the two bands simultaneously to improve forest biomass retrieval remains less explored. In this paper, we make use
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While considerable research has focused on using either L-band or P-band SAR (Synthetic Aperture Radar) on their own for forest biomass retrieval, the use of the two bands simultaneously to improve forest biomass retrieval remains less explored. In this paper, we make use of L- and P-band airborne SAR and in situ data measured in the field together with laser scanning data acquired over one hemi-boreal (Remningstorp) and one boreal (Krycklan) forest study area in Sweden. We fit statistical models to different combinations of topographic-corrected SAR backscatter and forest heights estimated from PolInSAR for the biomass estimation, and evaluate retrieval performance in terms of R2 and using 10-fold cross-validation. The study shows that specific combinations of radar observables from L- and P-band lead to biomass predictions that are more accurate in comparison with single-band retrievals. The correlations and accuracies between the combinations of SAR features and aboveground biomass are consistent across the two study areas, whereas the retrieval performance varied for individual bands. P-band-based retrievals were more accurate than L-band for the hemi-boreal Remningstorp site and less accurate than L-band for the boreal Krycklan site. The aboveground biomass levels as well as the ground topography differ between the two sites. The results suggest that P-band is more sensitive to higher biomass and L-band to lower biomass forests. The forest height from PolInSAR improved the results at L-band in the higher biomass substantially, whereas no improvement was observed at P-band in both study areas. These results are relevant in the context of combining information over boreal forests from future low-frequency SAR missions such as the European Space Agency (ESA) BIOMASS mission, which will operate at P-band, and future L-band missions planned by several space agencies. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessTechnical Note How Well Can Global Precipitation Measurement (GPM) Capture Hurricanes? Case Study: Hurricane Harvey
Remote Sens. 2018, 10(7), 1150; https://doi.org/10.3390/rs10071150 (registering DOI)
Received: 12 June 2018 / Revised: 18 July 2018 / Accepted: 18 July 2018 / Published: 20 July 2018
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Abstract
Hurricanes and other severe coastal storms have become more frequent and destructive during recent years. Hurricane Harvey, one of the most extreme events in recent history, advanced as a category IV storm and brought devastating rainfall to the Houston, TX, region during 25–29
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Hurricanes and other severe coastal storms have become more frequent and destructive during recent years. Hurricane Harvey, one of the most extreme events in recent history, advanced as a category IV storm and brought devastating rainfall to the Houston, TX, region during 25–29 August 2017. It inflicted damage of more than $125 billion to the state of Texas infrastructure and caused multiple fatalities in a very short period of time. Rainfall totals from Harvey during the 5-day period were among the highest ever recorded in the United States. Study of this historical devastating event can lead to better preparation and effective reduction of far-reaching consequences of similar events. Precipitation products based on satellites observations can provide valuable information needed to understand the evolution of such devastating storms. In this study, the ability of recent Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM-IMERG) final-run product to capture the magnitudes and spatial (0.1° × 0.1°)/temporal (hourly) patterns of rainfall resulting from hurricane Harvey was evaluated. Hourly gridded rainfall estimates by ground radar (4 × 4 km) were used as a reference dataset. Basic and probabilistic statistical indices of the satellite rainfall products were examined. The results indicated that the performance of IMERG product was satisfactory in detecting the spatial variability of the storm. It reconstructed precipitation with nearly 62% accuracy, although it systematically under-represented rainfall in coastal areas and over-represented rainfall over the high-intensity regions. Moreover, while the correlation between IMERG and radar products was generally high, it decreased significantly at and around the storm core. Full article
(This article belongs to the Special Issue Remote Sensing of Precipitation)
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Open AccessArticle Intercomparison of Three Two-Source Energy Balance Models for Partitioning Evaporation and Transpiration in Semiarid Climates
Remote Sens. 2018, 10(7), 1149; https://doi.org/10.3390/rs10071149 (registering DOI)
Received: 10 May 2018 / Revised: 9 July 2018 / Accepted: 18 July 2018 / Published: 20 July 2018
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Abstract
Evaporation (E) and transpiration (T) information is crucial for precise water resources planning and management in arid and semiarid areas. Two-source energy balance (TSEB) methods based on remotely-sensed land surface temperature provide an important modeling approach for estimating evapotranspiration (ET) and its components
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Evaporation (E) and transpiration (T) information is crucial for precise water resources planning and management in arid and semiarid areas. Two-source energy balance (TSEB) methods based on remotely-sensed land surface temperature provide an important modeling approach for estimating evapotranspiration (ET) and its components of E and T. Approaches for accurate decomposition of the component temperature and E/T partitioning from ET based on TSEB requires careful investigation. In this study, three TSEB models are used: (i) the TSEB model with the Priestley-Taylor equation, i.e., TSEB-PT; (ii) the TSEB model using the Penman-Monteith equation, i.e., TSEB-PM, and (iii) the TSEB using component temperatures derived from vegetation fractional cover and land surface temperature (VFC/LST) space, i.e., TSEB-TC-TS. These models are employed to investigate the impact of component temperature decomposition on E/T partitioning accuracy. Validation was conducted in the large-scale campaign of Heihe Watershed Allied Telemetry Experimental Research-Multi-Scale Observation Experiment on Evapotranspiration (HiWATER-MUSOEXE) in the northwest of China, and results showed that root mean square errors (RMSEs) of latent and sensible heat fluxes were respectively lower than 76 W/m2 and 50 W/m2 for all three approaches. Based on the measurements from the stable oxygen and hydrogen isotopes system at the Daman superstation, it was found that all three models slightly overestimated the ratio of E/ET. In addition, discrepancies in E/T partitioning among the three models were observed in the kernel experimental area of MUSOEXE. Further intercomparison indicated that different temperature decomposition methods were responsible for the observed discrepancies in E/T partitioning. The iterative procedure adopted by TSEB-PT and TSEB-PM produced higher LEC and lower TC when compared to TSEB-TC-TS. Overall, this work provides valuable insights into understanding the performances of TSEB models with different temperature decomposition mechanisms over semiarid regions. Full article
(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))
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Open AccessArticle Large-Scale Accurate Reconstruction of Buildings Employing Point Clouds Generated from UAV Imagery
Remote Sens. 2018, 10(7), 1148; https://doi.org/10.3390/rs10071148 (registering DOI)
Received: 27 May 2018 / Revised: 9 July 2018 / Accepted: 16 July 2018 / Published: 20 July 2018
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Abstract
High-density point clouds are valuable and detailed sources of data for different processes related to photogrammetry. We explore the knowledge-based generation of accurate large-scale three-dimensional (3D) models of buildings employing point clouds derived from UAV-based photogrammetry. A new two-level segmentation approach based on
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High-density point clouds are valuable and detailed sources of data for different processes related to photogrammetry. We explore the knowledge-based generation of accurate large-scale three-dimensional (3D) models of buildings employing point clouds derived from UAV-based photogrammetry. A new two-level segmentation approach based on efficient RANdom SAmple Consensus (RANSAC) shape detection is developed to segment potential facades and roofs of the buildings and extract their footprints. In the first level, the cylinder primitive is implemented to trim point clouds and split buildings, and the second level of the segmentation produces planar segments. The efficient RANSAC algorithm is enhanced in sizing up the segments via point-based analyses for both levels of segmentation. Then, planar modelling is carried out employing contextual knowledge through a new constrained least squares method. New evaluation criteria are proposed based on conceptual knowledge. They can examine the abilities of the approach in reconstruction of footprints, 3D models, and planar segments in addition to detection of over/under segmentation. Evaluation of the 3D models proves that the geometrical accuracy of LoD3 is achieved, since the average horizontal and vertical accuracy of the reconstructed vertices of roofs and footprints are better than (0.24, 0.23) m, (0.19, 0.17) m for the first dataset, and (0.35, 0.37) m, (0.28, 0.24) m for the second dataset. Full article
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Open AccessArticle Radiation Component Calculation and Energy Budget Analysis for the Korean Peninsula Region
Remote Sens. 2018, 10(7), 1147; https://doi.org/10.3390/rs10071147 (registering DOI)
Received: 31 May 2018 / Revised: 10 July 2018 / Accepted: 19 July 2018 / Published: 20 July 2018
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Abstract
In this study, a radiation component calculation algorithm was developed using channel data from the Himawari-8 Advanced Himawari Imager (AHI) and meteorological data from the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS). In addition, the energy budget of the Korean
[...] Read more.
In this study, a radiation component calculation algorithm was developed using channel data from the Himawari-8 Advanced Himawari Imager (AHI) and meteorological data from the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS). In addition, the energy budget of the Korean Peninsula region in 2016 was calculated and its regional differences were analyzed. Radiation components derived using the algorithm were calibrated using the broadband radiation component data from the Clouds and the Earth’s Radiant Energy System (CERES) to improve their accuracy. The calculated radiation components and the CERES data showed an annual mean percent bias of less than 3.5% and a high correlation coefficient of over 0.98. The energy budget of the Korean Peninsula region was −2.4 Wm−2 at the top of the atmosphere (RT), −14.5 Wm−2 at the surface (RS), and 12.1 Wm−2 in the atmosphere (RA), with regional energy budget differences. The Seoul region had a high surface temperature (289.5 K) and a RS of −33.4 Wm−2 (surface emission), whereas the Sokcho region had a low surface temperature (284.7 K) and a RS of 5.0 Wm−2 (surface absorption), for a difference of 38.5 Wm−2. In short, regions with relatively high surface temperatures tended to show energy emission, and regions with relatively low surface temperatures tended to show energy absorption. Such regional energy imbalances can cause weather and climate changes and bring about meteorological disasters, and thus research on detecting energy budget changes must be continued. Full article
(This article belongs to the Special Issue Earth Radiation Budget)
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Open AccessFeature PaperArticle Subsidence Evolution of the Firenze–Prato–Pistoia Plain (Central Italy) Combining PSI and GNSS Data
Remote Sens. 2018, 10(7), 1146; https://doi.org/10.3390/rs10071146 (registering DOI)
Received: 1 June 2018 / Revised: 10 July 2018 / Accepted: 18 July 2018 / Published: 20 July 2018
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Abstract
Subsidence phenomena, as well as landslides and floods, are one of the main geohazards affecting the Tuscany region (central Italy). The monitoring of related ground deformations plays a key role in their management to avoid problems for buildings and infrastructure. In this scenario,
[...] Read more.
Subsidence phenomena, as well as landslides and floods, are one of the main geohazards affecting the Tuscany region (central Italy). The monitoring of related ground deformations plays a key role in their management to avoid problems for buildings and infrastructure. In this scenario, Earth observation offers a better solution in terms of costs and benefits than traditional techniques (e.g., GNSS (Global Navigation Satellite System) or levelling networks), especially for wide area applications. In this work, the subsidence-related ground motions in the Firenze–Prato–Pistoia plain were back-investigated to track the evolution of displacement from 2003 to 2017 by means of multi-interferometric analysis of ENVISAT and Sentinel-1 imagery combined with GNSS data. The resulting vertical deformation velocities are aligned to the European Terrestrial Reference System 89 (ETRS89) datum and can be considered real velocity of displacement. The vertical ground deformation maps derived by ENVISAT and Sentinel-1 data, corrected with the GNSS, show how the area affected by subsidence for the period 2003–2010 and the period 2014–2017 evolved. The differences between the two datasets in terms of the extension and velocity values were analysed and then associated with the geological setting of the basin and external factors, e.g., new greenhouses and nurseries. This analysis allowed for reconstructing the evolution of the subsidence for the area of interest showing an increment of ground deformation in the historic centre of Pistoia Town, a decrement of subsidence in the nursery area between Pistoia and Prato cities, and changes in the industrial sector close to Prato. Full article
(This article belongs to the Special Issue Radar Interferometry for Geohazards)
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Open AccessArticle Supervised Classification of Built-Up Areas in Sub-Saharan African Cities Using Landsat Imagery and OpenStreetMap
Remote Sens. 2018, 10(7), 1145; https://doi.org/10.3390/rs10071145 (registering DOI)
Received: 19 June 2018 / Revised: 12 July 2018 / Accepted: 16 July 2018 / Published: 20 July 2018
PDF Full-text (14504 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The Landsat archives have been made freely available in 2008, allowing the production of high resolution built-up maps at the regional or global scale. In this context, most of the classification algorithms rely on supervised learning to tackle the heterogeneity of the urban
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The Landsat archives have been made freely available in 2008, allowing the production of high resolution built-up maps at the regional or global scale. In this context, most of the classification algorithms rely on supervised learning to tackle the heterogeneity of the urban environments. However, at a large scale, the process of collecting training samples becomes a huge project in itself. This leads to a growing interest from the remote sensing community toward Volunteered Geographic Information (VGI) projects such as OpenStreetMap (OSM). Despite the spatial heterogeneity of its contribution patterns, OSM provides an increasing amount of information on the earth’s surface. More interestingly, the community has moved beyond street mapping to collect a wider range of spatial data such as building footprints, land use, or points of interest. In this paper, we propose a classification method that makes use of OSM to automatically collect training samples for supervised learning of built-up areas. To take into account a wide range of potential issues, the approach is assessed in ten Sub-Saharan African urban areas from various demographic profiles and climates. The obtained results are compared with: (1) existing high resolution global urban maps such as the Global Human Settlement Layer (GHSL) or the Human Built-up and Settlements Extent (HBASE); and (2) a supervised classification based on manually digitized training samples. The results suggest that automated supervised classifications based on OSM can provide performances similar to manual approaches, provided that OSM training samples are sufficiently available and correctly pre-processed. Moreover, the proposed method could reach better results in the near future, given the increasing amount and variety of information in the OSM database. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation II)
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Open AccessArticle Multitemporal Classification of River Floodplain Vegetation Using Time Series of UAV Images
Remote Sens. 2018, 10(7), 1144; https://doi.org/10.3390/rs10071144
Received: 10 July 2018 / Accepted: 17 July 2018 / Published: 19 July 2018
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Abstract
The functions of river floodplains often conflict spatially, for example, water conveyance during peak discharge and diverse riparian ecology. Such functions are often associated with floodplain vegetation. Frequent monitoring of floodplain land cover is necessary to capture the dynamics of this vegetation. However,
[...] Read more.
The functions of river floodplains often conflict spatially, for example, water conveyance during peak discharge and diverse riparian ecology. Such functions are often associated with floodplain vegetation. Frequent monitoring of floodplain land cover is necessary to capture the dynamics of this vegetation. However, low classification accuracies are found with existing methods, especially for relatively similar vegetation types, such as grassland and herbaceous vegetation. Unmanned aerial vehicle (UAV) imagery has great potential to improve the classification of these vegetation types owing to its high spatial resolution and flexibility in image acquisition timing. This study aimed to evaluate the increase in classification accuracy obtained using multitemporal UAV images versus single time step data on floodplain land cover classification and to assess the effect of varying the number and timing of imagery acquisition moments. We obtained a dataset of multitemporal UAV imagery and field reference observations and applied object-based Random Forest classification (RF) to data of different time step combinations. High overall accuracies (OA) exceeding 90% were found for the RF of floodplain land cover, with six vegetation classes and four non-vegetation classes. Using two or more time steps compared with a single time step increased the OA from 96.9% to 99.3%. The user’s accuracies of the classes with large similarity, such as natural grassland and herbaceous vegetation, also exceeded 90%. The combination of imagery from June and September resulted in the highest OA (98%) for two time steps. Our method is a practical and highly accurate solution for monitoring areas of a few square kilometres. For large-scale monitoring of floodplains, the same method can be used, but with data from airborne platforms covering larger extents. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Variations in Remotely-Sensed Phytoplankton Size Structure of a Cyclonic Eddy in the Southwest Indian Ocean
Remote Sens. 2018, 10(7), 1143; https://doi.org/10.3390/rs10071143
Received: 1 May 2018 / Revised: 1 June 2018 / Accepted: 2 June 2018 / Published: 19 July 2018
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Abstract
Phytoplankton size classes were derived from weekly-averaged MODIS Aqua chlorophyll a data over the southwest Indian Ocean in order to assess changes in surface phytoplankton community structure within a cyclonic eddy as it propagated across the Mozambique Basin in 2013. Satellite altimetry was
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Phytoplankton size classes were derived from weekly-averaged MODIS Aqua chlorophyll a data over the southwest Indian Ocean in order to assess changes in surface phytoplankton community structure within a cyclonic eddy as it propagated across the Mozambique Basin in 2013. Satellite altimetry was used to identify and track the southwesterly movement of the eddy from its origin off Madagascar in mid-June until mid-October, when it eventually merged with the Agulhas Current along the east coast of South Africa. Nano- and picophytoplankton comprised most of the community in the early phase of the eddy development in June, but nanophytoplankton then dominated in austral winter (July and August). Microphytoplankton was entrained into the eddy by horizontal advection from the southern Madagascar shelf, increasing the proportion of microphytoplankton to 23% when the chlorophyll a levels reached a peak of 0.36 mg·m−3 in the third week of July. Chlorophyll a levels declined to <0.2 mg·m−3 in austral spring (September and October) as the eddy propagated further to the southwest. Picophytoplankton dominated the community during the spring period, accounting for >50% of the population. As far as is known, this is the first study to investigate temporal changes in chlorophyll a and community structure in a cyclonic eddy propagating across an ocean basin in the southwest Indian Ocean. Full article
(This article belongs to the Special Issue Remote Sensing of Ocean Colour)
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Open AccessArticle Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
Remote Sens. 2018, 10(7), 1142; https://doi.org/10.3390/rs10071142
Received: 27 May 2018 / Revised: 16 July 2018 / Accepted: 18 July 2018 / Published: 19 July 2018
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Abstract
Spatial and temporal data fusion approaches have been developed to fuse reflectance imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS), which have complementary spatial and temporal sampling characteristics. The approach relies on using Landsat and MODIS image pairs that are acquired
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Spatial and temporal data fusion approaches have been developed to fuse reflectance imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS), which have complementary spatial and temporal sampling characteristics. The approach relies on using Landsat and MODIS image pairs that are acquired on the same day to estimate Landsat-scale reflectance on other MODIS dates. Previous studies have revealed that the accuracy of data fusion results partially depends on the input image pair used. The selection of the optimal image pair to achieve better prediction of surface reflectance has not been fully evaluated. This paper assesses the impacts of Landsat-MODIS image pair selection on the accuracy of the predicted land surface reflectance using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) over different landscapes. MODIS images from the Aqua and Terra platforms were paired with images from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) to make different pair image combinations. The accuracy of the predicted surface reflectance at 30 m resolution was evaluated using the observed Landsat data in terms of mean absolute difference, root mean square error and correlation coefficient. Results show that the MODIS pair images with smaller view zenith angles produce better predictions. As expected, the image pair closer to the prediction date during a short prediction period produce better prediction results. For prediction dates distant from the pair date, the predictability depends on the temporal and spatial variability of land cover type and phenology. The prediction accuracy for forests is higher than for crops in our study areas. The Normalized Difference Vegetation Index (NDVI) for crops is overestimated during the non-growing season when using an input image pair from the growing season, while NDVI is slightly underestimated during the growing season when using an image pair from the non-growing season. Two automatic pair selection strategies are evaluated. Results show that the strategy of selecting the MODIS pair date image that most highly correlates with the MODIS image on the prediction date produces more accurate predictions than the nearest date strategy. This study demonstrates that data fusion results can be improved if appropriate image pairs are used. Full article
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