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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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19 pages, 13416 KiB  
Article
Antarctic Supraglacial Lake Detection Using Landsat 8 and Sentinel-2 Imagery: Towards Continental Generation of Lake Volumes
by Mahsa Moussavi, Allen Pope, Anna Ruth W. Halberstadt, Luke D. Trusel, Leanne Cioffi and Waleed Abdalati
Remote Sens. 2020, 12(1), 134; https://doi.org/10.3390/rs12010134 - 1 Jan 2020
Cited by 71 | Viewed by 11541
Abstract
Melt and supraglacial lakes are precursors to ice shelf collapse and subsequent accelerated ice sheet mass loss. We used data from the Landsat 8 and Sentinel-2 satellites to develop a threshold-based method for detection of lakes found on the Antarctic ice shelves, calculate [...] Read more.
Melt and supraglacial lakes are precursors to ice shelf collapse and subsequent accelerated ice sheet mass loss. We used data from the Landsat 8 and Sentinel-2 satellites to develop a threshold-based method for detection of lakes found on the Antarctic ice shelves, calculate their depths and thus their volumes. To achieve this, we focus on four key areas: the Amery, Roi Baudouin, Nivlisen, and Riiser-Larsen ice shelves, which are all characterized by extensive surface meltwater features. To validate our products, we compare our results against those obtained by an independent method based on a supervised classification scheme (e.g., Random Forest algorithm). Additional verification is provided by manual inspection of results for nearly 1000 Landsat 8 and Sentinel-2 images. Our dual-sensor approach will enable constructing high-resolution time series of lake volumes. Therefore, to ensure interoperability between the two datasets, we evaluate depths from contemporaneous Landsat 8 and Sentinel-2 image pairs. Our assessments point to a high degree of correspondence, producing an average R2 value of 0.85, no bias, and an average RMSE of 0.2 m. We demonstrate our method’s ability to characterize lake evolution by presenting first evidence of drainage events outside of the Antarctic Peninsula on the Amery Ice shelf. The methods presented here pave the way to upscaling throughout the Landsat 8 and Sentinel-2 observational record across Antarctica to produce a first-ever continental dataset of supraglacial lake volumes. Such a dataset will improve our understanding of the influence of surface hydrology on ice shelf stability, and thus, future projections of Antarctica’s contribution to sea level rise. Full article
(This article belongs to the Section Environmental Remote Sensing)
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15 pages, 2238 KiB  
Article
Potential of Night-Time Lights to Measure Regional Inequality
by Kinga Ivan, Iulian-Horia Holobâcă, József Benedek and Ibolya Török
Remote Sens. 2020, 12(1), 33; https://doi.org/10.3390/rs12010033 - 20 Dec 2019
Cited by 52 | Viewed by 8268
Abstract
Night-time lights satellite images provide a new opportunity to measure regional inequality in real-time by developing the Night Light Development Index (NLDI). The NLDI was extracted using the Gini coefficient approach based on population and night light spatial distribution in Romania. Night-time light [...] Read more.
Night-time lights satellite images provide a new opportunity to measure regional inequality in real-time by developing the Night Light Development Index (NLDI). The NLDI was extracted using the Gini coefficient approach based on population and night light spatial distribution in Romania. Night-time light data were calculated using a grid with a 0.15 km2 area, based on Defense Meteorological Satellite Program (DMSP) /Operational Linescan System (OLS satellite imagery for the 1992–2013 period and based on the National Polar-orbiting Partnership–Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) satellite imagery for the 2014–2018 period. Two population density grids were created at the level of equal cells (0.15 km2) using ArcGIS and PostgreSQL software, and census data from 1992 and 2011. Subsequently, based on this data and using the Gini index approach, the Night Light Development Index (NLDI) was calculated within the MATLAB software. The NLDI was obtained for 42 administrative counties (nomenclature of territorial units for statistics level 3 (NUTS-3 units)) for the 1992–2018 period. The statistical relationship between the NLDI and the socio-economic, demographic, and geographic variables highlighted a strong indirect relationship with local tax income and gross domestic product (GDP) per capita. The polynomial model proved to be better in estimating income based on the NLDI and R2 coefficients showed a significant improvement in total variation explained compared to the linear regression model. The NLDI calculated on the basis of night-time lights satellite images proved to be a good proxy for measuring regional inequalities. Therefore, it can play a crucial role in monitoring the progress made in the implementation of Sustainable Development Goal 10 (reduced inequalities). Full article
(This article belongs to the Special Issue EO Solutions to Support Countries Implementing the SDGs)
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18 pages, 6109 KiB  
Article
Feasibility of Burned Area Mapping Based on ICESAT−2 Photon Counting Data
by Meng Liu, Sorin Popescu and Lonesome Malambo
Remote Sens. 2020, 12(1), 24; https://doi.org/10.3390/rs12010024 - 19 Dec 2019
Cited by 35 | Viewed by 6046
Abstract
Accurately mapping burned areas is crucial for the analysis of carbon emissions and wildfire risk as well as understanding the effects of climate change on forest structure. Burned areas have predominantly been mapped using optical remote sensing images. However, the structural changes due [...] Read more.
Accurately mapping burned areas is crucial for the analysis of carbon emissions and wildfire risk as well as understanding the effects of climate change on forest structure. Burned areas have predominantly been mapped using optical remote sensing images. However, the structural changes due to fire also offer opportunities for mapping burned areas using three-dimensional (3D) datasets such as Light detection and ranging (LiDAR). This study focuses on the feasibility of using photon counting LiDAR data from National Aeronautics and Space Administration’s (NASA) Ice, Cloud, and land Elevation Satellite-2 (ICESat−2) mission to differentiate vegetation structure in burned and unburned areas and ultimately classify burned areas along mapped ground tracks. The ICESat−2 mission (launched in September 2018) provides datasets such as geolocated photon data (ATL03), which comprises precise latitude, longitude and elevation of each point where a photon interacts with land surface, and derivative products such as the Land Water Vegetation Elevation product (ATL08), which comprises estimated terrain and canopy height information. For analysis, 24 metrics such as the average, median and standard deviation of canopy height were derived from ATL08 data over forests burned by recent fires in 2018 in northern California and western New Mexico. A reference burn map was derived from Sentinel−2 images based on the differenced Normalized Burn Ratio (dNBR) index. A landcover map based on Sentinel−2 images was employed to remove non-forest classes. Landsat 8 based dNBR image and landcover map were also used for comparison. Next, ICESat−2 data of forest samples were classified into burned and unburned ATL08 100-m segments by both Random Forest classification and logistic regression. Both Sentinel−2 derived and Landsat 8 derived ATL08 samples got high classification accuracy, 83% versus 76%. Moreover, the resulting classification accuracy by Random Forest and logistic regression reached 83% and 74%, respectively. Among the 24 ICESat−2 metrics, apparent surface reflectance and the number of canopy photons were the most important. Furthermore, burn severity of each ATL08 segment was also estimated with Random Forest regression. R2 of predicted burn severity to observed dNBR is 0.61 with significant linear relationship and moderate correlation (r = 0.78). Overall, the reasonably high accuracies achieved in this study demonstrate the feasibility of employing ICESat−2 data in burned forest classification, opening avenues for improved estimation of burned biomass and carbon emissions from a 3D perspective. Full article
(This article belongs to the Special Issue Lidar Remote Sensing of Forest Structure, Biomass and Dynamics)
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19 pages, 1016 KiB  
Article
Remote Sensing Big Data Classification with High Performance Distributed Deep Learning
by Rocco Sedona, Gabriele Cavallaro, Jenia Jitsev, Alexandre Strube, Morris Riedel and Jón Atli Benediktsson
Remote Sens. 2019, 11(24), 3056; https://doi.org/10.3390/rs11243056 - 17 Dec 2019
Cited by 37 | Viewed by 7790
Abstract
High-Performance Computing (HPC) has recently been attracting more attention in remote sensing applications due to the challenges posed by the increased amount of open data that are produced daily by Earth Observation (EO) programs. The unique parallel computing environments and programming techniques that [...] Read more.
High-Performance Computing (HPC) has recently been attracting more attention in remote sensing applications due to the challenges posed by the increased amount of open data that are produced daily by Earth Observation (EO) programs. The unique parallel computing environments and programming techniques that are integrated in HPC systems are able to solve large-scale problems such as the training of classification algorithms with large amounts of Remote Sensing (RS) data. This paper shows that the training of state-of-the-art deep Convolutional Neural Networks (CNNs) can be efficiently performed in distributed fashion using parallel implementation techniques on HPC machines containing a large number of Graphics Processing Units (GPUs). The experimental results confirm that distributed training can drastically reduce the amount of time needed to perform full training, resulting in near linear scaling without loss of test accuracy. Full article
(This article belongs to the Special Issue Analysis of Big Data in Remote Sensing)
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18 pages, 10412 KiB  
Article
TanDEM-X Forest Mapping Using Convolutional Neural Networks
by Antonio Mazza, Francescopaolo Sica, Paola Rizzoli and Giuseppe Scarpa
Remote Sens. 2019, 11(24), 2980; https://doi.org/10.3390/rs11242980 - 12 Dec 2019
Cited by 36 | Viewed by 4591
Abstract
In this work, we face the problem of forest mapping from TanDEM-X data by means of Convolutional Neural Networks (CNNs). Our study aims to highlight the relevance of domain-related features for the extraction of the information of interest thanks to their joint nonlinear [...] Read more.
In this work, we face the problem of forest mapping from TanDEM-X data by means of Convolutional Neural Networks (CNNs). Our study aims to highlight the relevance of domain-related features for the extraction of the information of interest thanks to their joint nonlinear processing through CNN. In particular, we focus on the main InSAR features as the backscatter, coherence, and volume decorrelation, as well as the acquisition geometry through the local incidence angle. By using different state-of-the-art CNN architectures, our experiments consistently demonstrate the great potential of deep learning in data fusion for information extraction in the context of synthetic aperture radar signal processing and specifically for the task of forest mapping from TanDEM-X images. We compare three state-of-the-art CNN architectures, such as ResNet, DenseNet, and U-Net, obtaining a large performance gain over the baseline approach for all of them, with the U-Net solution being the most effective one. Full article
(This article belongs to the Special Issue SAR for Forest Mapping)
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32 pages, 14875 KiB  
Article
Overall Methodology Design for the United States National Land Cover Database 2016 Products
by Suming Jin, Collin Homer, Limin Yang, Patrick Danielson, Jon Dewitz, Congcong Li, Zhe Zhu, George Xian and Danny Howard
Remote Sens. 2019, 11(24), 2971; https://doi.org/10.3390/rs11242971 - 11 Dec 2019
Cited by 236 | Viewed by 14931
Abstract
The National Land Cover Database (NLCD) 2016 provides a suite of data products, including land cover and land cover change of the conterminous United States from 2001 to 2016, at two- to three-year intervals. The development of this product is part of an [...] Read more.
The National Land Cover Database (NLCD) 2016 provides a suite of data products, including land cover and land cover change of the conterminous United States from 2001 to 2016, at two- to three-year intervals. The development of this product is part of an effort to meet the growing demand for longer temporal duration and more frequent, accurate, and consistent land cover and change information. To accomplish this, we designed a new land cover strategy and developed comprehensive methods, models, and procedures for NLCD 2016 implementation. Major steps in the new procedures consist of data preparation, land cover change detection and classification, theme-based postprocessing, and final integration. Data preparation includes Landsat imagery selection, cloud detection, and cloud filling, as well as compilation and creation of more than 30 national-scale ancillary datasets. Land cover change detection includes single-date water and snow/ice detection algorithms and models, two-date multi-index integrated change detection models, and long-term multi-date change algorithms and models. The land cover classification includes seven-date training data creation and 14-run classifications. Pools of training data for change and no-change areas were created before classification based on integrated information from ancillary data, change-detection results, Landsat spectral and temporal information, and knowledge-based trajectory analysis. In postprocessing, comprehensive models for each land cover theme were developed in a hierarchical order to ensure the spatial and temporal coherence of land cover and land cover changes over 15 years. An initial accuracy assessment on four selected Landsat path/rows classified with this method indicates an overall accuracy of 82.0% at an Anderson Level II classification and 86.6% at the Anderson Level I classification after combining the primary and alternate reference labels. This methodology was used for the operational production of NLCD 2016 for the Conterminous United States, with final produced products available for free download. Full article
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24 pages, 3953 KiB  
Article
Evaluation of GPM-era Global Satellite Precipitation Products over Multiple Complex Terrain Regions
by Yagmur Derin, Emmanouil Anagnostou, Alexis Berne, Marco Borga, Brice Boudevillain, Wouter Buytaert, Che-Hao Chang, Haonan Chen, Guy Delrieu, Yung Chia Hsu, Waldo Lavado-Casimiro, Bastian Manz, Semu Moges, Efthymios I. Nikolopoulos, Dejene Sahlu, Franco Salerno, Juan-Pablo Rodríguez-Sánchez, Humberto J. Vergara and Koray K. Yilmaz
Remote Sens. 2019, 11(24), 2936; https://doi.org/10.3390/rs11242936 - 7 Dec 2019
Cited by 87 | Viewed by 7269
Abstract
The great success of the Tropical Rainfall Measuring Mission (TRMM) and its successor Global Precipitation Measurement (GPM) has accelerated the development of global high-resolution satellite-based precipitation products (SPP). However, the quantitative accuracy of SPPs has to be evaluated before using these datasets in [...] Read more.
The great success of the Tropical Rainfall Measuring Mission (TRMM) and its successor Global Precipitation Measurement (GPM) has accelerated the development of global high-resolution satellite-based precipitation products (SPP). However, the quantitative accuracy of SPPs has to be evaluated before using these datasets in water resource applications. This study evaluates the following GPM-era and TRMM-era SPPs based on two years (2014–2015) of reference daily precipitation data from rain gauge networks in ten mountainous regions: Integrated Multi-SatellitE Retrievals for GPM (IMERG, version 05B and version 06B), National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction Center Morphing Method (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and Multi-Source Weighted-Ensemble Precipitation (MSWEP), which represents a global precipitation data-blending product. The evaluation is performed at daily and annual temporal scales, and at 0.1 deg grid resolution. It is shown that GSMaPV07 surpass the performance of IMERGV06B Final for almost all regions in terms of systematic and random error metrics. The new orographic rainfall classification in the GSMaPV07 algorithm is able to improve the detection of orographic rainfall, the rainfall amounts, and error metrics. Moreover, IMERGV05B showed significantly better performance, capturing the lighter and heavier precipitation values compared to IMERGV06B for almost all regions due to changes conducted to the morphing, where motion vectors are derived using total column water vapor for IMERGV06B. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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14 pages, 9476 KiB  
Article
Use of Remote Sensing, Geophysical Techniques and Archaeological Excavations to Define the Roman Amphitheater of Torreparedones (Córdoba, Spain)
by Antonio Monterroso-Checa, Teresa Teixidó, Massimo Gasparini, José Antonio Peña, Santiago Rodero, Juan Carlos Moreno and José Antonio Morena
Remote Sens. 2019, 11(24), 2937; https://doi.org/10.3390/rs11242937 - 7 Dec 2019
Cited by 17 | Viewed by 6254
Abstract
Non-destructive techniques are widely used to explore and detect burial remains in archaeological sites. In this study, we present two sets of sensors, aerial and geophysics, that we have combined to analyze a 2 ha sector of ground in the Torreparedones Archaeological Park [...] Read more.
Non-destructive techniques are widely used to explore and detect burial remains in archaeological sites. In this study, we present two sets of sensors, aerial and geophysics, that we have combined to analyze a 2 ha sector of ground in the Torreparedones Archaeological Park located in Cordoba, Spain. Aerial platforms were used in a first step to identify a Roman amphitheater located near the Roman city. To ensure greater reliability and to rule out geological causes, a geophysical survey was subsequently carried out. Magnetic gradiometer, electrical resistivity tomography (ERT), and ground-penetrating radar (GPR) methods were also used to confirm the existence of this structure, define the geometry and, to the greatest possible extent, determine the degree of preservation of this construction. The adverse conditions for data acquisition was one of the main constraints, since the area of interest was an almond plantation which conditioned geophysical profiles. In addition, due to the low dielectric and magnetic contrast between the structures and the embedding material, meticulous data processing was required. In order to obtain further evidence of this amphitheater and to corroborate the aerial images and the geophysical models, an archaeological excavation was carried out. The results confirmed the cross-validation with the predicted non-destructive models. Therefore, this work can serve as an example to be used prior to conservation actions to investigate the suburbs and landscapes near similar roman cities in Spain. Full article
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32 pages, 16817 KiB  
Article
Evaluation of UAV LiDAR for Mapping Coastal Environments
by Yi-Chun Lin, Yi-Ting Cheng, Tian Zhou, Radhika Ravi, Seyyed Meghdad Hasheminasab, John Evan Flatt, Cary Troy and Ayman Habib
Remote Sens. 2019, 11(24), 2893; https://doi.org/10.3390/rs11242893 - 4 Dec 2019
Cited by 128 | Viewed by 15239
Abstract
Unmanned Aerial Vehicle (UAV)-based remote sensing techniques have demonstrated great potential for monitoring rapid shoreline changes. With image-based approaches utilizing Structure from Motion (SfM), high-resolution Digital Surface Models (DSM), and orthophotos can be generated efficiently using UAV imagery. However, image-based mapping yields relatively [...] Read more.
Unmanned Aerial Vehicle (UAV)-based remote sensing techniques have demonstrated great potential for monitoring rapid shoreline changes. With image-based approaches utilizing Structure from Motion (SfM), high-resolution Digital Surface Models (DSM), and orthophotos can be generated efficiently using UAV imagery. However, image-based mapping yields relatively poor results in low textured areas as compared to those from LiDAR. This study demonstrates the applicability of UAV LiDAR for mapping coastal environments. A custom-built UAV-based mobile mapping system is used to simultaneously collect LiDAR and imagery data. The quality of LiDAR, as well as image-based point clouds, are investigated and compared over different geomorphic environments in terms of their point density, relative and absolute accuracy, and area coverage. The results suggest that both UAV LiDAR and image-based techniques provide high-resolution and high-quality topographic data, and the point clouds generated by both techniques are compatible within a 5 to 10 cm range. UAV LiDAR has a clear advantage in terms of large and uniform ground coverage over different geomorphic environments, higher point density, and ability to penetrate through vegetation to capture points below the canopy. Furthermore, UAV LiDAR-based data acquisitions are assessed for their applicability in monitoring shoreline changes over two actively eroding sandy beaches along southern Lake Michigan, Dune Acres, and Beverly Shores, through repeated field surveys. The results indicate a considerable volume loss and ridge point retreat over an extended period of one year (May 2018 to May 2019) as well as a short storm-induced period of one month (November 2018 to December 2018). The foredune ridge recession ranges from 0 m to 9 m. The average volume loss at Dune Acres is 18.2 cubic meters per meter and 12.2 cubic meters per meter within the one-year period and storm-induced period, respectively, highlighting the importance of episodic events in coastline changes. The average volume loss at Beverly Shores is 2.8 cubic meters per meter and 2.6 cubic meters per meter within the survey period and storm-induced period, respectively. Full article
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18 pages, 8924 KiB  
Article
Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data
by Leandro Parente, Evandro Taquary, Ana Paula Silva, Carlos Souza and Laerte Ferreira
Remote Sens. 2019, 11(23), 2881; https://doi.org/10.3390/rs11232881 - 3 Dec 2019
Cited by 60 | Viewed by 11874
Abstract
The rapid growth of satellites orbiting the planet is generating massive amounts of data for Earth science applications. Concurrently, state-of-the-art deep-learning-based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the image processing of satellite remote sensing. Within [...] Read more.
The rapid growth of satellites orbiting the planet is generating massive amounts of data for Earth science applications. Concurrently, state-of-the-art deep-learning-based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the image processing of satellite remote sensing. Within this context, this study evaluated, based on thousands of PlanetScope images obtained over a 12-month period, the performance of three machine learning approaches (random forest, long short-term memory-LSTM, and U-Net). We applied these approaches to mapped pasturelands in a Central Brazil region. The deep learning algorithms were implemented using TensorFlow, while the random forest utilized the Google Earth Engine platform. The accuracy assessment presented F1 scores for U-Net, LSTM, and random forest of, respectively, 96.94%, 98.83%, and 95.53% in the validation data, and 94.06%, 87.97%, and 82.57% in the test data, indicating a better classification efficiency using the deep learning approaches. Although the use of deep learning algorithms depends on a high investment in calibration samples and the generalization of these methods requires further investigations, our results suggest that the neural network architectures developed in this study can be used to map large geographic regions that consider a wide variety of satellite data (e.g., PlanetScope, Sentinel-2, Landsat-8). Full article
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21 pages, 6101 KiB  
Article
Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France
by Daniel Chiyeka Shamambo, Bertrand Bonan, Jean-Christophe Calvet, Clément Albergel and Sebastian Hahn
Remote Sens. 2019, 11(23), 2842; https://doi.org/10.3390/rs11232842 - 29 Nov 2019
Cited by 21 | Viewed by 4669
Abstract
This paper investigates to what extent soil moisture and vegetation density information can be extracted from the Advanced Scatterometer (ASCAT) satellite-derived radar backscatter (σ°) in a data assimilation context. The impact of independent estimates of the surface soil moisture (SSM) and [...] Read more.
This paper investigates to what extent soil moisture and vegetation density information can be extracted from the Advanced Scatterometer (ASCAT) satellite-derived radar backscatter (σ°) in a data assimilation context. The impact of independent estimates of the surface soil moisture (SSM) and leaf area index (LAI) of diverse vegetation types on ASCAT σ° observations is simulated over southwestern France using the water cloud model (WCM). The LAI and SSM variables used by the WCM are derived from satellite observations and from the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model, respectively. They permit the calibration of the four parameters of the WCM describing static soil and vegetation characteristics. A seasonal analysis of the model scores shows that the WCM has shortcomings over karstic areas and wheat croplands. In the studied area, the Klaus windstorm in January 2009 damaged a large fraction of the Landes forest. The ability of the WCM to represent the impact of Klaus and to simulate ASCAT σ° observations in contrasting land-cover conditions is explored. The difference in σ° observations between the forest zone affected by the storm and the bordering agricultural areas presents a marked seasonality before the storm. The difference is small in the springtime (from March to May) and large in the autumn (September to November) and wintertime (December to February). After the storm, hardly any seasonality was observed over four years. This study shows that the WCM is able to simulate this extreme event. It is concluded that the WCM could be used as an observation operator for the assimilation of ASCAT σ° observations into the ISBA land surface model. Full article
(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)
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13 pages, 2298 KiB  
Letter
Comparison of Hyperspectral Versus Traditional Field Measurements of Fractional Ground Cover in the Australian Arid Zone
by Claire Fisk, Kenneth D. Clarke and Megan M. Lewis
Remote Sens. 2019, 11(23), 2825; https://doi.org/10.3390/rs11232825 - 28 Nov 2019
Cited by 4 | Viewed by 3929
Abstract
The collection of high-quality field measurements of ground cover is critical for calibration and validation of fractional ground cover maps derived from satellite imagery. Field-based hyperspectral ground cover sampling is a potential alternative to traditional in situ techniques. This study aimed to develop [...] Read more.
The collection of high-quality field measurements of ground cover is critical for calibration and validation of fractional ground cover maps derived from satellite imagery. Field-based hyperspectral ground cover sampling is a potential alternative to traditional in situ techniques. This study aimed to develop an effective sampling design for spectral ground cover surveys in order to estimate fractional ground cover in the Australian arid zone. To meet this aim, we addressed two key objectives: (1) Determining how spectral surveys and traditional step-point sampling compare when conducted at the same spatial scale and (2) comparing these two methods to current Australian satellite-derived fractional cover products. Across seven arid, sparsely vegetated survey sites, six 500-m transects were established. Ground cover reflectance was recorded taking continuous hyperspectral readings along each transect while step-point surveys were conducted along the same transects. Both measures of ground cover were converted into proportions of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil for each site. Comparisons were made of the proportions of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil derived from both in situ methods as well as MODIS and Landsat fractional cover products. We found strong correlations between fractional cover derived from hyperspectral and step-point sampling conducted at the same spatial scale at our survey sites. Comparison of the in situ measurements and image-derived fractional cover products showed that overall, the Landsat product was strongly related to both in situ methods for non-photosynthetic vegetation and bare soil whereas the MODIS product was strongly correlated with both in situ methods for photosynthetic vegetation. This study demonstrates the potential of the spectral transect method, both in its ability to produce results comparable to the traditional transect measures, but also in its improved objectivity and relative logistic ease. Future efforts should be made to include spectral ground cover sampling as part of Australia’s plan to produce calibration and validation datasets for remotely sensed products. Full article
(This article belongs to the Special Issue Remote Sensing Data Interpretation and Validation)
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19 pages, 2675 KiB  
Article
Active SLAM for Autonomous Underwater Exploration
by Narcís Palomeras, Marc Carreras and Juan Andrade-Cetto
Remote Sens. 2019, 11(23), 2827; https://doi.org/10.3390/rs11232827 - 28 Nov 2019
Cited by 30 | Viewed by 6728
Abstract
Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater [...] Read more.
Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps. Full article
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22 pages, 7514 KiB  
Article
Accurate Calibration Scheme for a Multi-Camera Mobile Mapping System
by Ehsan Khoramshahi, Mariana Batista Campos, Antonio Maria Garcia Tommaselli, Niko Vilijanen, Teemu Mielonen, Harri Kaartinen, Antero Kukko and Eija Honkavaara
Remote Sens. 2019, 11(23), 2778; https://doi.org/10.3390/rs11232778 - 25 Nov 2019
Cited by 16 | Viewed by 6582
Abstract
Mobile mapping systems (MMS) are increasingly used for many photogrammetric and computer vision applications, especially encouraged by the fast and accurate geospatial data generation. The accuracy of point position in an MMS is mainly dependent on the quality of calibration, accuracy of sensor [...] Read more.
Mobile mapping systems (MMS) are increasingly used for many photogrammetric and computer vision applications, especially encouraged by the fast and accurate geospatial data generation. The accuracy of point position in an MMS is mainly dependent on the quality of calibration, accuracy of sensor synchronization, accuracy of georeferencing and stability of geometric configuration of space intersections. In this study, we focus on multi-camera calibration (interior and relative orientation parameter estimation) and MMS calibration (mounting parameter estimation). The objective of this study was to develop a practical scheme for rigorous and accurate system calibration of a photogrammetric mapping station equipped with a multi-projective camera (MPC) and a global navigation satellite system (GNSS) and inertial measurement unit (IMU) for direct georeferencing. The proposed technique is comprised of two steps. Firstly, interior orientation parameters of each individual camera in an MPC and the relative orientation parameters of each cameras of the MPC with respect to the first camera are estimated. In the second step the offset and misalignment between MPC and GNSS/IMU are estimated. The global accuracy of the proposed method was assessed using independent check points. A correspondence map for a panorama is introduced that provides metric information. Our results highlight that the proposed calibration scheme reaches centimeter-level global accuracy for 3D point positioning. This level of global accuracy demonstrates the feasibility of the proposed technique and has the potential to fit accurate mapping purposes. Full article
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14 pages, 7257 KiB  
Article
RealPoint3D: Generating 3D Point Clouds from a Single Image of Complex Scenarios
by Yan Xia, Cheng Wang, Yusheng Xu, Yu Zang, Weiquan Liu, Jonathan Li and Uwe Stilla
Remote Sens. 2019, 11(22), 2644; https://doi.org/10.3390/rs11222644 - 13 Nov 2019
Cited by 13 | Viewed by 6331
Abstract
Generating 3D point clouds from a single image has attracted full attention from researchers in the field of multimedia, remote sensing and computer vision. With the recent proliferation of deep learning, various deep models have been proposed for the 3D point cloud generation. [...] Read more.
Generating 3D point clouds from a single image has attracted full attention from researchers in the field of multimedia, remote sensing and computer vision. With the recent proliferation of deep learning, various deep models have been proposed for the 3D point cloud generation. However, they require objects to be captured with absolutely clean backgrounds and fixed viewpoints, which highly limits their application in the real environment. To guide 3D point cloud generation, we propose a novel network, RealPoint3D, to integrate prior 3D shape knowledge into the network. Taking additional 3D information, RealPoint3D can handle 3D object generation from a single real image captured from any viewpoint and complex background. Specifically, provided a query image, we retrieve the nearest shape model from a pre-prepared 3D model database. Then, the image, together with the retrieved shape model, is fed into RealPoint3D to generate a fine-grained 3D point cloud. We evaluated the proposed RealPoint3D on the ShapeNet dataset and ObjectNet3D dataset for the 3D point cloud generation. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework achieves superior performance. Furthermore, our proposed framework works well for real images in complex backgrounds (the image has the remaining objects in addition to the reconstructed object, and the reconstructed object may be occluded or truncated) with various viewing angles. Full article
(This article belongs to the Special Issue Point Cloud Processing in Remote Sensing)
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31 pages, 3075 KiB  
Review
A Review of the Applications of Remote Sensing in Fire Ecology
by David M. Szpakowski and Jennifer L. R. Jensen
Remote Sens. 2019, 11(22), 2638; https://doi.org/10.3390/rs11222638 - 12 Nov 2019
Cited by 192 | Viewed by 24931
Abstract
Wildfire plays an important role in ecosystem dynamics, land management, and global processes. Understanding the dynamics associated with wildfire, such as risks, spatial distribution, and effects is important for developing a clear understanding of its ecological influences. Remote sensing technologies provide a means [...] Read more.
Wildfire plays an important role in ecosystem dynamics, land management, and global processes. Understanding the dynamics associated with wildfire, such as risks, spatial distribution, and effects is important for developing a clear understanding of its ecological influences. Remote sensing technologies provide a means to study fire ecology at multiple scales using an efficient and quantitative method. This paper provides a broad review of the applications of remote sensing techniques in fire ecology. Remote sensing applications related to fire risk mapping, fuel mapping, active fire detection, burned area estimates, burn severity assessment, and post-fire vegetation recovery monitoring are discussed. Emphasis is given to the roles of multispectral sensors, lidar, and emerging UAS technologies in mapping, analyzing, and monitoring various environmental properties related to fire activity. Examples of current and past research are provided, and future research trends are discussed. In general, remote sensing technologies provide a low-cost, multi-temporal means for conducting local, regional, and global-scale fire ecology research, and current research is rapidly evolving with the introduction of new technologies and techniques which are increasing accuracy and efficiency. Future research is anticipated to continue to build upon emerging technologies, improve current methods, and integrate novel approaches to analysis and classification. Full article
(This article belongs to the Special Issue Remote Sensing Approaches to Biogeographical Applications)
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34 pages, 8255 KiB  
Review
Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products
by Stefan Mayr, Claudia Kuenzer, Ursula Gessner, Igor Klein and Martin Rutzinger
Remote Sens. 2019, 11(22), 2616; https://doi.org/10.3390/rs11222616 - 8 Nov 2019
Cited by 29 | Viewed by 7694
Abstract
Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an [...] Read more.
Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided. Full article
(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)
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23 pages, 9793 KiB  
Article
Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data
by Markus Immitzer, Martin Neuwirth, Sebastian Böck, Harald Brenner, Francesco Vuolo and Clement Atzberger
Remote Sens. 2019, 11(22), 2599; https://doi.org/10.3390/rs11222599 - 6 Nov 2019
Cited by 167 | Viewed by 9499
Abstract
Detailed knowledge about tree species composition is of great importance for forest management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites provide data with unprecedented spectral, spatial and temporal resolution. Here, we investigated the potential benefits of using high temporal resolution [...] Read more.
Detailed knowledge about tree species composition is of great importance for forest management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites provide data with unprecedented spectral, spatial and temporal resolution. Here, we investigated the potential benefits of using high temporal resolution data for classification of five coniferous and seven broadleaved tree species in a diverse Central European Forest. To run the classification, 18 cloud-free S2 acquisitions were analyzed in a two-step approach. The available scenes were first used to stratify the study area into six broad land-cover classes. Subsequently, additional classification models were created separately for the coniferous and the broadleaved forest strata. To permit a deeper analytical insight in the benefits of multi-temporal datasets for species identification, classification models were developed taking into account all 262,143 possible permutations of the 18 S2 scenes. Each model was fine-tuned using a stepwise recursive feature reduction. The additional use of vegetation indices improved the model performances by around 5 percentage points. Individual mono-temporal tree species accuracies range from 48.1% (January 2017) to 78.6% (June 2017). Compared to the best mono-temporal results, the multi-temporal analysis approach improves the out-of-bag overall accuracy from 72.9% to 85.7% for the broadleaved and from 83.8% to 95.3% for the coniferous tree species, respectively. Remarkably, a combination of six–seven scenes achieves a model quality equally high as the model based on all data; images from April until August proved most important. The classes European Beech and European Larch attain the highest user’s accuracies of 96.3% and 95.9%, respectively. The most important spectral variables to distinguish between tree species are located in the Red (coniferous) and short wave infrared (SWIR) bands (broadleaved), respectively. Overall, the study highlights the high potential of multi-temporal S2 data for species-level classifications in Central European forests. Full article
(This article belongs to the Special Issue Mapping Tree Species Diversity)
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10 pages, 1759 KiB  
Letter
Distinguishing Photosynthetic and Non-Photosynthetic Vegetation: How Do Traditional Observations and Spectral Classification Compare?
by Claire Fisk, Kenneth D. Clarke, Steven Delean and Megan M. Lewis
Remote Sens. 2019, 11(21), 2589; https://doi.org/10.3390/rs11212589 - 4 Nov 2019
Cited by 7 | Viewed by 3956
Abstract
Remotely sensed ground cover maps are routinely validated using field data collected by observers who classify ground cover into defined categories such as photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil (BS), and rock. There is an element of subjectivity to the classification [...] Read more.
Remotely sensed ground cover maps are routinely validated using field data collected by observers who classify ground cover into defined categories such as photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil (BS), and rock. There is an element of subjectivity to the classification of PV and NPV, and classifications may differ between observers. An alternative is to estimate ground cover based on in situ hyperspectral reflectance measurements (HRM). This study examines observer consistency when classifying vegetation samples of wheat (Triticum aestivum var. Gladius) covering the full range of photosynthetic activity, from completely senesced (0% PV) to completely green (100% PV), as photosynthetic or non-photosynthetic. We also examine how the classification of spectra of the same vegetation samples compares to the observer results. We collected HRM and photographs, over two months, to capture the transition of wheat leaves from 100% PV to 100% NPV. To simulate typical field methodology, observers viewed the photographs and classified each leaf as either PV or NPV, while spectral unmixing was used to decompose the HRM of the leaves into proportions of PV and NPV. The results showed that when a leaf was ≤25% or ≥75% PV observers tended to agree, and assign the leaf to the expected category. However, as leaves transitioned from PV to NPV (i.e., PV ≥ 25% but ≤ 75%) observers’ decisions differed more widely and their classifications showed little agreement with the spectral proportions of PV and NPV. This has significant implications for the reliability of data collected using binary methods in areas containing a significant proportion of vegetation in this intermediate range such as the over/underestimation of PV and NPV vegetation and how reliably this data can then be used to validate remotely sensed products. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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18 pages, 82144 KiB  
Article
Pixel Tracking to Estimate Rivers Water Flow Elevation Using Cosmo-SkyMed Synthetic Aperture Radar Data
by Filippo Biondi, Angelica Tarpanelli, Pia Addabbo, Carmine Clemente and Danilo Orlando
Remote Sens. 2019, 11(21), 2574; https://doi.org/10.3390/rs11212574 - 2 Nov 2019
Cited by 9 | Viewed by 5064
Abstract
The lack of availability of historical and reliable river water level information is an issue that can be overcome through the exploitation of modern satellite remote sensing systems. This research has the objective of contributing in solving the information-gap problem of river flow [...] Read more.
The lack of availability of historical and reliable river water level information is an issue that can be overcome through the exploitation of modern satellite remote sensing systems. This research has the objective of contributing in solving the information-gap problem of river flow monitoring through a synthetic aperture radar (SAR) signal processing technique that has the capability to perform water flow elevation estimation. This paper proposes the application of a new method for the design of a robust procedure to track over the time double-bounce reflections from bridges crossing rivers to measure the gap space existing between the river surface and bridges. Specifically, the difference in position between the single and double bounce is suitably measured over the time. Simulated and satellite temporal series of SAR data from COSMO-SkyMed data are compared to the ground measurements recorded for three gauges sites over the Po and Tiber Rivers, Italy. The obtained performance indices confirm the effectiveness of the method in the estimation of water level also in narrow or ungauged rivers. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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26 pages, 12791 KiB  
Article
Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas
by Sepideh Tavakkoli Piralilou, Hejar Shahabi, Ben Jarihani, Omid Ghorbanzadeh, Thomas Blaschke, Khalil Gholamnia, Sansar Raj Meena and Jagannath Aryal
Remote Sens. 2019, 11(21), 2575; https://doi.org/10.3390/rs11212575 - 2 Nov 2019
Cited by 147 | Viewed by 14682
Abstract
Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining [...] Read more.
Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining landslide susceptibility and risk. Satellite data have been widely used for such investigations—next to data from airborne or unmanned aerial vehicle (UAV)-borne campaigns and Digital Elevation Models (DEMs). We have developed a methodology that incorporates object-based image analysis (OBIA) with three machine learning (ML) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection. We identified the optimal scale parameters (SP) and used them for multi-scale segmentation and further analysis. We evaluated the resulting objects using the object pureness index (OPI), object matching index (OMI), and object fitness index (OFI) measures. We then applied two different methods to optimize the landslide detection task: (a) an ensemble method of stacking that combines the different ML methods for improving the performance, and (b) Dempster–Shafer theory (DST), to combine the multi-scale segmentation and classification results. Through the combination of three ML methods and the multi-scale approach, the framework enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal. PlanetScope optical satellite images and a DEM were used, along with the derived landslide conditioning factors. Different accuracy assessment measures were used to compare the results against a field-based landslide inventory. All ML methods yielded the highest overall accuracies ranging from 83.3% to 87.2% when using objects with the optimal SP compared to other SPs. However, applying DST to combine the multi-scale results of each ML method significantly increased the overall accuracies to almost 90%. Overall, the integration of OBIA with ML methods resulted in appropriate landslide detections, but using the optimal SP and ML method is crucial for success. Full article
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
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27 pages, 9303 KiB  
Article
Modeling Quiet Solar Luminosity Variability from TSI Satellite Measurements and Proxy Models during 1980–2018
by Nicola Scafetta, Richard C. Willson, Jae N. Lee and Dong L. Wu
Remote Sens. 2019, 11(21), 2569; https://doi.org/10.3390/rs11212569 - 1 Nov 2019
Cited by 20 | Viewed by 13451
Abstract
A continuous record of direct total solar irradiance (TSI) observations began with a series of satellite experiments in 1978. This record requires comparisons of overlapping satellite observations with adequate relative precisions to provide useful long term TSI trend information. Herein we briefly review [...] Read more.
A continuous record of direct total solar irradiance (TSI) observations began with a series of satellite experiments in 1978. This record requires comparisons of overlapping satellite observations with adequate relative precisions to provide useful long term TSI trend information. Herein we briefly review the active cavity radiometer irradiance monitor physikalisch-meteorologisches observatorium davos (ACRIM-PMOD) TSI composite controversy regarding how the total solar irradiance (TSI) has evolved since 1978 and about whether TSI significantly increased or slightly decreased from 1980 to 2000. The main question is whether TSI increased or decreased during the so-called ACRIM-gap period from 1989 to 1992. There is significant discrepancy between TSI proxy models and observations before and after the gap, which requires a careful revisit of the data analysis and modeling performed during the ACRIM-gap period. In this study, we use three recently proposed TSI proxy models that do not present any TSI increase during the ACRIM-gap, and show that they agree with the TSI data only from 1996 to 2016. However, these same models significantly diverge from the observations from 1981 and 1996. Thus, the scaling errors must be different between the two periods, which suggests errors in these models. By adjusting the TSI proxy models to agree with the data patterns before and after the ACRIM-gap, we found that these models miss a slowly varying TSI component. The adjusted models suggest that the quiet solar luminosity increased from the 1986 to the 1996 TSI minimum by about 0.45 W/m2 reaching a peak near 2000 and decreased by about 0.15 W/m2 from the 1996 to the 2008 TSI cycle minimum. This pattern is found to be compatible with the ACRIM TSI composite and confirms the ACRIM TSI increasing trend from 1980 to 2000, followed by a long-term decreasing trend since. Full article
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19 pages, 9615 KiB  
Article
Upper Ocean Response to Two Sequential Tropical Cyclones over the Northwestern Pacific Ocean
by Jue Ning, Qing Xu, Tao Feng, Han Zhang and Tao Wang
Remote Sens. 2019, 11(20), 2431; https://doi.org/10.3390/rs11202431 - 19 Oct 2019
Cited by 18 | Viewed by 4396
Abstract
The upper ocean thermodynamic and biological responses to two sequential tropical cyclones (TCs) over the Northwestern Pacific Ocean were investigated using multi-satellite datasets, in situ observations and numerical model outputs. During Kalmaegi and Fung-Wong, three distinct cold patches were observed at sea surface. [...] Read more.
The upper ocean thermodynamic and biological responses to two sequential tropical cyclones (TCs) over the Northwestern Pacific Ocean were investigated using multi-satellite datasets, in situ observations and numerical model outputs. During Kalmaegi and Fung-Wong, three distinct cold patches were observed at sea surface. The locations of these cold patches are highly correlated with relatively shallower depth of the 26 °C isotherm and mixed layer depth (MLD) and lower upper ocean heat content. The enhancement of surface chlorophyll a (chl-a) concentration was detected in these three regions as well, mainly due to the TC-induced mixing and upwelling as well as the terrestrial runoff. Moreover, the pre-existing ocean cyclonic eddy (CE) has been found to significantly modulate the magnitude of surface cooling and chl-a increase. With the deepening of the MLD on the right side of TCs, the temperature of the mixed layer decreased and the salinity increased. The sequential TCs had superimposed effects on the upper ocean response. The possible causes of sudden track change in sequential TCs scenario were also explored. Both atmospheric and oceanic conditions play noticeable roles in abrupt northward turning of the subsequent TC Fung-Wong. Full article
(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)
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15 pages, 10739 KiB  
Article
New Global View of Above-Cloud Absorbing Aerosol Distribution Based on CALIPSO Measurements
by Wenzhong Zhang, Shumei Deng, Tao Luo, Yang Wu, Nana Liu, Xuebin Li, Yinbo Huang and Wenyue Zhu
Remote Sens. 2019, 11(20), 2396; https://doi.org/10.3390/rs11202396 - 16 Oct 2019
Cited by 3 | Viewed by 3386
Abstract
Above-low-level-cloud aerosols (ACAs) have gradually gained more interest in recent years; however, the combined aerosol–cloud radiation effects are not well understood. The uncertainty about the radiative effects of aerosols above cloud mainly stems from the lack of comprehensive and accurate retrieval of aerosols [...] Read more.
Above-low-level-cloud aerosols (ACAs) have gradually gained more interest in recent years; however, the combined aerosol–cloud radiation effects are not well understood. The uncertainty about the radiative effects of aerosols above cloud mainly stems from the lack of comprehensive and accurate retrieval of aerosols and clouds for ACA scenes. In this study, an improved ACA identification and retrieval methodology was developed to provide a new global view of the ACA distribution by combining three-channel CALIOP (The Cloud–Aerosol Lidar with Orthogonal Polarization) observations. The new method can reliably identify and retrieve both thin and dense ACA layers, providing consistent results between the day- and night-time retrieval of ACAs. Then, new four-year (2007 to 2010) global ACA datasets were built, and new seasonal mean views of global ACA occurrence, optical depth, and geometrical thickness were presented and analyzed. Further discussion on the relative position of ACAs to low clouds showed that the mean distance between the ACA layer and the low cloud deck over the tropical Atlantic region is less than 0.2 km. This indicates that the ACAs over this region are more likely to be mixed with low-level clouds, thereby possibly influencing the cloud microphysics over this region, contrary to findings reported from previous studies. The results not only help us better understand global aerosol transportation and aerosol–cloud interactions but also provide useful information for model evaluation and improvements. Full article
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21 pages, 37154 KiB  
Article
Extracting Khmer Rouge Irrigation Networks from Pre-Landsat 4 Satellite Imagery Using Vegetation Indices
by Corrine Coakley, Mandy Munro-Stasiuk, James A. Tyner, Sokvisal Kimsroy, Chhunly Chhay and Stian Rice
Remote Sens. 2019, 11(20), 2397; https://doi.org/10.3390/rs11202397 - 16 Oct 2019
Cited by 8 | Viewed by 4688
Abstract
Often discussed, the spatial extent and scope of the Khmer Rouge irrigation network has not been previously mapped on a national scale. Although low resolution, early Landsat images can identify water features accurately when using vegetation indices. We discuss the methods involved in [...] Read more.
Often discussed, the spatial extent and scope of the Khmer Rouge irrigation network has not been previously mapped on a national scale. Although low resolution, early Landsat images can identify water features accurately when using vegetation indices. We discuss the methods involved in mapping historic irrigation on a national scale, as well as comparing the performance of several vegetation indices at irrigation detection. Irrigation was a critical component of the Communist Part of Kampuchea (CPK)’s plan to transform Cambodia into an ideal communist society, aimed at providing surplus for the nation by tripling rice production. Of the three indices used, normalized difference, corrected transformed, and Thiam’s transformed vegetation indexes, (NDVI, CTVI, and TTVI respectively), the CTVI provided the clearest images of water storage and transport. This method for identifying anthropogenic water features proved highly accurate, despite low spatial resolution. We were successful in locating and identifying both water storage and irrigation canals from the time that the CPK regime was in power. In many areas these canals and reservoirs are no longer visible, even with high resolution modern satellites. Most of the structures built at this time experienced some collapse, either during the CPK regime or soon after, however many have been rehabilitated and are still in use, in at least a partial capacity. Full article
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24 pages, 2871 KiB  
Communication
RadCalNet: A Radiometric Calibration Network for Earth Observing Imagers Operating in the Visible to Shortwave Infrared Spectral Range
by Marc Bouvet, Kurtis Thome, Béatrice Berthelot, Agnieszka Bialek, Jeffrey Czapla-Myers, Nigel P. Fox, Philippe Goryl, Patrice Henry, Lingling Ma, Sébastien Marcq, Aimé Meygret, Brian N. Wenny and Emma R. Woolliams
Remote Sens. 2019, 11(20), 2401; https://doi.org/10.3390/rs11202401 - 16 Oct 2019
Cited by 175 | Viewed by 10691
Abstract
Vicarious calibration approaches using in situ measurements saw first use in the early 1980s and have since improved to keep pace with the evolution of the radiometric requirements of the sensors that are being calibrated. The advantage of in situ measurements for vicarious [...] Read more.
Vicarious calibration approaches using in situ measurements saw first use in the early 1980s and have since improved to keep pace with the evolution of the radiometric requirements of the sensors that are being calibrated. The advantage of in situ measurements for vicarious calibration is that they can be carried out with traceable and quantifiable accuracy, making them ideal for interconsistency studies of on-orbit sensors. The recent development of automated sites to collect the in situ data has led to an increase in the available number of datasets for sensor calibration. The current work describes the Radiometric Calibration Network (RadCalNet) that is an effort to provide automated surface and atmosphere in situ data as part of a network including multiple sites for the purpose of optical imager radiometric calibration in the visible to shortwave infrared spectral range. The key goals of RadCalNet are to standardize protocols for collecting data, process to top-of-atmosphere reflectance, and provide uncertainty budgets for automated sites traceable to the international system of units. RadCalNet is the result of efforts by the RadCalNet Working Group under the umbrella of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) and the Infrared Visible Optical Sensors (IVOS). Four radiometric calibration instrumented sites located in the USA, France, China, and Namibia are presented here that were used as initial sites for prototyping and demonstrating RadCalNet. All four sites rely on collection of data for assessing the surface reflectance as well as atmospheric data over that site. The data are converted to top-of-atmosphere reflectance within RadCalNet and provided through a web portal to allow users to either radiometrically calibrate or verify the calibration of their sensors of interest. Top-of-atmosphere reflectance data with associated uncertainties are available at 10 nm intervals over the 400 nm to 1000 nm spectral range at 30 min intervals for a nadir-viewing geometry. An example is shown demonstrating how top-of-atmosphere data from RadCalNet can be used to determine the interconsistency between two sensors. Full article
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50 pages, 8303 KiB  
Review
Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review
by Matthew G. Cooper and Laurence C. Smith
Remote Sens. 2019, 11(20), 2405; https://doi.org/10.3390/rs11202405 - 16 Oct 2019
Cited by 16 | Viewed by 10538
Abstract
The Greenland Ice Sheet is now the largest land ice contributor to global sea level rise, largely driven by increased surface meltwater runoff from the ablation zone, i.e., areas of the ice sheet where annual mass losses exceed gains. This small but critically [...] Read more.
The Greenland Ice Sheet is now the largest land ice contributor to global sea level rise, largely driven by increased surface meltwater runoff from the ablation zone, i.e., areas of the ice sheet where annual mass losses exceed gains. This small but critically important area of the ice sheet has expanded in size by ~50% since the early 1960s, and satellite remote sensing is a powerful tool for monitoring the physical processes that influence its surface mass balance. This review synthesizes key remote sensing methods and scientific findings from satellite remote sensing of the Greenland Ice Sheet ablation zone, covering progress in (1) radar altimetry, (2) laser (lidar) altimetry, (3) gravimetry, (4) multispectral optical imagery, and (5) microwave and thermal imagery. Physical characteristics and quantities examined include surface elevation change, gravimetric mass balance, reflectance, albedo, and mapping of surface melt extent and glaciological facies and zones. The review concludes that future progress will benefit most from methods that combine multi-sensor, multi-wavelength, and cross-platform datasets designed to discriminate the widely varying surface processes in the ablation zone. Specific examples include fusing laser altimetry, radar altimetry, and optical stereophotogrammetry to enhance spatial measurement density, cross-validate surface elevation change, and diagnose radar elevation bias; employing dual-frequency radar, microwave scatterometry, or combining radar and laser altimetry to map seasonal snow depth; fusing optical imagery, radar imagery, and microwave scatterometry to discriminate between snow, liquid water, refrozen meltwater, and bare ice near the equilibrium line altitude; combining optical reflectance with laser altimetry to map supraglacial lake, stream, and crevasse bathymetry; and monitoring the inland migration of snowlines, surface melt extent, and supraglacial hydrologic features. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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43 pages, 11195 KiB  
Review
Detection of Archaeological Looting from Space: Methods, Achievements and Challenges
by Deodato Tapete and Francesca Cigna
Remote Sens. 2019, 11(20), 2389; https://doi.org/10.3390/rs11202389 - 15 Oct 2019
Cited by 62 | Viewed by 10448
Abstract
Illegal excavations in archaeological heritage sites (namely “looting”) are a global phenomenon. Satellite images are nowadays massively used by archaeologists to systematically document sites affected by looting. In parallel, remote sensing scientists are increasingly developing processing methods with a certain degree of automation [...] Read more.
Illegal excavations in archaeological heritage sites (namely “looting”) are a global phenomenon. Satellite images are nowadays massively used by archaeologists to systematically document sites affected by looting. In parallel, remote sensing scientists are increasingly developing processing methods with a certain degree of automation to quantify looting using satellite imagery. To capture the state-of-the-art of this growing field of remote sensing, in this work 47 peer-reviewed research publications and grey literature are reviewed, accounting for: (i) the type of satellite data used, i.e., optical and synthetic aperture radar (SAR); (ii) properties of looting features utilized as proxies for damage assessment (e.g., shape, morphology, spectral signature); (iii) image processing workflows; and (iv) rationale for validation. Several scholars studied looting even prior to the conflicts recently affecting the Middle East and North Africa (MENA) region. Regardless of the method used for looting feature identification (either visual/manual, or with the aid of image processing), they preferred very high resolution (VHR) optical imagery, mainly black-and-white panchromatic, or pansharpened multispectral, whereas SAR is being used more recently by specialist image analysts only. Yet the full potential of VHR and high resolution (HR) multispectral information in optical imagery is to be exploited, with limited research studies testing spectral indices. To fill this gap, a range of looted sites across the MENA region are presented in this work, i.e., Lisht, Dashur, and Abusir el Malik (Egypt), and Tell Qarqur, Tell Jifar, Sergiopolis, Apamea, Dura Europos, and Tell Hizareen (Syria). The aim is to highlight: (i) the complementarity of HR multispectral data and VHR SAR with VHR optical imagery, (ii) usefulness of spectral profiles in the visible and near-infrared bands, and (iii) applicability of methods for multi-temporal change detection. Satellite data used for the demonstration include: HR multispectral imagery from the Copernicus Sentinel-2 constellation, VHR X-band SAR data from the COSMO-SkyMed mission, VHR panchromatic and multispectral WorldView-2 imagery, and further VHR optical data acquired by GeoEye-1, IKONOS-2, QuickBird-2, and WorldView-3, available through Google Earth. Commonalities between the different image processing methods are examined, alongside a critical discussion about automation in looting assessment, current lack of common practices in image processing, achievements in managing the uncertainty in looting feature interpretation, and current needs for more dissemination and user uptake. Directions toward sharing and harmonization of methodologies are outlined, and some proposals are made with regard to the aspects that the community working with satellite images should consider, in order to define best practices of satellite-based looting assessment. Full article
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51 pages, 5899 KiB  
Review
Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics
by Angela Lausch, Jussi Baade, Lutz Bannehr, Erik Borg, Jan Bumberger, Sabine Chabrilliat, Peter Dietrich, Heike Gerighausen, Cornelia Glässer, Jorg M. Hacker, Dagmar Haase, Thomas Jagdhuber, Sven Jany, András Jung, Arnon Karnieli, Roland Kraemer, Mohsen Makki, Christian Mielke, Markus Möller, Hannes Mollenhauer, Carsten Montzka, Marion Pause, Christian Rogass, Offer Rozenstein, Christiane Schmullius, Franziska Schrodt, Martin Schrön, Karsten Schulz, Claudia Schütze, Christian Schweitzer, Peter Selsam, Andrew K. Skidmore, Daniel Spengler, Christian Thiel, Sina C. Truckenbrodt, Michael Vohland, Robert Wagner, Ute Weber, Ulrike Werban, Ute Wollschläger, Steffen Zacharias and Michael E. Schaepmanadd Show full author list remove Hide full author list
Remote Sens. 2019, 11(20), 2356; https://doi.org/10.3390/rs11202356 - 11 Oct 2019
Cited by 56 | Viewed by 21861
Abstract
In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. [...] Read more.
In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits. Full article
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16 pages, 5702 KiB  
Letter
Variability of Major Aerosol Types in China Classified Using AERONET Measurements
by Lu Zhang and Jing Li
Remote Sens. 2019, 11(20), 2334; https://doi.org/10.3390/rs11202334 - 9 Oct 2019
Cited by 26 | Viewed by 4131
Abstract
Aerosol type is a critical piece of information in both aerosol forcing estimation and passive satellite remote sensing. However, the major aerosol types in China and their variability is still less understood. This work uses direct sun measurements and inversion derived parameters from [...] Read more.
Aerosol type is a critical piece of information in both aerosol forcing estimation and passive satellite remote sensing. However, the major aerosol types in China and their variability is still less understood. This work uses direct sun measurements and inversion derived parameters from 47 sites within the Aerosol Robotic Network (AERONET) in China, with more than 39,000 records obtained between April 1998 and January 2017, to identify dominant aerosol types using two independent methods, namely, K means and Self Organizing Map (SOM). In total, we define four aerosol types, namely, desert dust, scattering mixed, absorbing mixed and scattering fine, based on their optical and microphysical characteristics. Seasonally, dust aerosols mainly occur in the spring and over North and Northwest China; scattering mixed are more common in the spring and summer, whereas absorbing aerosols mostly occur in the autumn and winter during heating period, and scattering fine aerosols have their highest occurrence frequency in summer over East China. Based on their spatial and temporal distribution, we also generate seasonal aerosol type maps that can be used for passive satellite retrieval. Compared with the global models used in most satellite retrieval algorithms, the unique feature of East Asian aerosols is the curved single scattering albedo spectrum, which could be related to the mixing of black carbon with dust or organic aerosols. Full article
(This article belongs to the Special Issue Urban Air Quality Monitoring using Remote Sensing)
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19 pages, 7554 KiB  
Article
Regional Atmospheric Aerosol Pollution Detection Based on LiDAR Remote Sensing
by Xin Ma, Chengyi Wang, Ge Han, Yue Ma, Song Li, Wei Gong and Jialin Chen
Remote Sens. 2019, 11(20), 2339; https://doi.org/10.3390/rs11202339 - 9 Oct 2019
Cited by 32 | Viewed by 5989
Abstract
Atmospheric aerosol is one of the major factors that cause environmental pollution. Light detection and ranging (LiDAR) is an effective remote sensing tool for aerosol observation. In order to provide a comprehensive understanding of the aerosol pollution from the physical perspective, this study [...] Read more.
Atmospheric aerosol is one of the major factors that cause environmental pollution. Light detection and ranging (LiDAR) is an effective remote sensing tool for aerosol observation. In order to provide a comprehensive understanding of the aerosol pollution from the physical perspective, this study investigated regional atmospheric aerosol pollution through the integration of measurements, including LiDAR, satellite, and ground station observations and combined the backward trajectory tracking model. First, the horizontal distribution of atmospheric aerosol wa obtained by a whole-day working scanning micro-pulse LiDAR placed on a residential building roof. Another micro-pulse LiDAR was arranged at a distance from the scanning LiDAR to provide the vertical distribution information of aerosol. A new method combining the slope and Fernald methods was then proposed for the retrieval of the horizontal aerosol extinction coefficient. Finally, whole-day data, including the LiDAR data, the satellite remote sensing data, meteorological data, and backward trajectory tracking model, were selected to reveal the vertical and horizontal distribution characteristics of aerosol pollution and to provide some evidence of the potential pollution sources in the regional area. Results showed that the aerosol pollutants in the district on this specific day were mainly produced locally and distributed below 2.0 km. Six areas with high aerosol concentration were detected in the scanning area, showing that the aerosol pollution was mainly obtained from local life, transportation, and industrial activities. Correlation analysis with the particulate matter data of the ground air quality national control station verified the accuracy of the LiDAR detection results and revealed the effectiveness of LiDAR detection of atmospheric aerosol pollution. Full article
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14 pages, 2543 KiB  
Article
Quantifying Tidal Fluctuations in Remote Sensing Infrared SST Observations
by Cristina González-Haro, Aurélien Ponte and Emmanuelle Autret
Remote Sens. 2019, 11(19), 2313; https://doi.org/10.3390/rs11192313 - 4 Oct 2019
Cited by 4 | Viewed by 3612
Abstract
The expected amplitude of fixed-point sea surface temperature (SST) fluctuations induced by barotropic and baroclinic tidal flows is estimated from tidal current atlases and SST observations. The fluctuations considered are the result of the advection of pre-existing SST fronts by tidal currents. They [...] Read more.
The expected amplitude of fixed-point sea surface temperature (SST) fluctuations induced by barotropic and baroclinic tidal flows is estimated from tidal current atlases and SST observations. The fluctuations considered are the result of the advection of pre-existing SST fronts by tidal currents. They are thus confined to front locations and exhibit fine-scale spatial structures. The amplitude of these tidally induced SST fluctuations is proportional to the scalar product of SST frontal gradients and tidal currents. Regional and global estimations of these expected amplitudes are presented. We predict barotropic tidal motions produce SST fluctuations that may reach amplitudes of 0.3 K. Baroclinic (internal) tides produce SST fluctuations that may reach values that are weaker than 0.1 K. The amplitudes and the detectability of tidally induced fluctuations of SST are discussed in the light of expected SST fluctuations due to other geophysical processes and instrumental (pixel) noise. We conclude that actual observations of tidally induced SST fluctuations are a challenge with present-day observing systems. Full article
(This article belongs to the Special Issue Ten Years of Remote Sensing at Barcelona Expert Center)
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18 pages, 5458 KiB  
Article
The Effect of Mineral Sediments on Satellite Chlorophyll-a Retrievals from Line-Height Algorithms Using Red and Near-Infrared Bands
by Chuiqing Zeng and Caren Binding
Remote Sens. 2019, 11(19), 2306; https://doi.org/10.3390/rs11192306 - 3 Oct 2019
Cited by 23 | Viewed by 4563
Abstract
Red and near-infrared line-height algorithms such as the maximum chlorophyll index (MCI) are often considered optimal for remote sensing of chlorophyll-a (Chl-a) in turbid eutrophic waters, under the assumption of minimal influence from mineral sediments. This study investigated the impact of mineral turbidity [...] Read more.
Red and near-infrared line-height algorithms such as the maximum chlorophyll index (MCI) are often considered optimal for remote sensing of chlorophyll-a (Chl-a) in turbid eutrophic waters, under the assumption of minimal influence from mineral sediments. This study investigated the impact of mineral turbidity on line-height algorithms using MCI as a primary example. Inherent optical properties from two turbid eutrophic lakes were used to simulate reflectance spectra. The simulated results: (1) confirmed a non-linear relationship between Chl-a and MCI; (2) suggested optimal use of the MCI at Chl-a < ~100 mg/m3 and saturation of the index at Chl-a ~300 mg/m3; (3) suggested significant variability in the MCI:Chl-a relationship due to mineral scattering, resulting in an RMSE in predicted Chl-a of ~23 mg/m3; and (4) revealed elevated Chl a retrievals and potential false positive algal bloom reports for sediment concentrations > 20 g/m3. A novel approach combining both MCI and its baseline slope, MCIslope reduced the RMSE to ~5 mg/m3. A quality flag based on MCIslope was proposed to mask erroneously high Chl-a retrievals and reduce the risk of false positive bloom reports in highly turbid waters. Observations suggest the approach may be valuable for all line-height-based Chl-a algorithms. Full article
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41 pages, 6009 KiB  
Review
Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
by Vincenzo Levizzani and Elsa Cattani
Remote Sens. 2019, 11(19), 2301; https://doi.org/10.3390/rs11192301 - 2 Oct 2019
Cited by 115 | Viewed by 20766
Abstract
The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater [...] Read more.
The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater flow, and plant uptake. For a correct closure of the global water cycle, observations are needed of all these processes with a global perspective. In particular, precipitation requires continuous monitoring, as it is the most important component of the cycle, especially under changing climatic conditions. Passive and active sensors on board meteorological and environmental satellites now make reasonably complete data available that allow better measurements of precipitation to be made from space, in order to improve our understanding of the cycle’s acceleration/deceleration under current and projected climate conditions. The article aims to draw an up-to-date picture of the current status of observations of precipitation from space, with an outlook to the near future of the satellite constellation, modeling applications, and water resource management. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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43 pages, 12218 KiB  
Article
Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument
by Alexander Kokhanovsky, Maxim Lamare, Olaf Danne, Carsten Brockmann, Marie Dumont, Ghislain Picard, Laurent Arnaud, Vincent Favier, Bruno Jourdain, Emmanuel Le Meur, Biagio Di Mauro, Teruo Aoki, Masashi Niwano, Vladimir Rozanov, Sergey Korkin, Sepp Kipfstuhl, Johannes Freitag, Maria Hoerhold, Alexandra Zuhr, Diana Vladimirova, Anne-Katrine Faber, Hans Christian Steen-Larsen, Sonja Wahl, Jonas K. Andersen, Baptiste Vandecrux, Dirk van As, Kenneth D. Mankoff, Michael Kern, Eleonora Zege and Jason E. Boxadd Show full author list remove Hide full author list
Remote Sens. 2019, 11(19), 2280; https://doi.org/10.3390/rs11192280 - 29 Sep 2019
Cited by 64 | Viewed by 11835
Abstract
The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and [...] Read more.
The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo. Full article
(This article belongs to the Special Issue Remote Sensing: 10th Anniversary)
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32 pages, 9319 KiB  
Review
Monitoring Beach Topography and Nearshore Bathymetry Using Spaceborne Remote Sensing: A Review
by Edward Salameh, Frédéric Frappart, Rafael Almar, Paulo Baptista, Georg Heygster, Bertrand Lubac, Daniel Raucoules, Luis Pedro Almeida, Erwin W. J. Bergsma, Sylvain Capo, Marcello De Michele, Deborah Idier, Zhen Li, Vincent Marieu, Adrien Poupardin, Paulo A. Silva, Imen Turki and Benoit Laignel
Remote Sens. 2019, 11(19), 2212; https://doi.org/10.3390/rs11192212 - 21 Sep 2019
Cited by 121 | Viewed by 11857
Abstract
With high anthropogenic pressure and the effects of climate change (e.g., sea level rise) on coastal regions, there is a greater need for accurate and up-to-date information about the topography of these systems. Reliable topography and bathymetry information are fundamental parameters for modelling [...] Read more.
With high anthropogenic pressure and the effects of climate change (e.g., sea level rise) on coastal regions, there is a greater need for accurate and up-to-date information about the topography of these systems. Reliable topography and bathymetry information are fundamental parameters for modelling the morpho-hydrodynamics of coastal areas, for flood forecasting, and for coastal management. Traditional methods such as ground, ship-borne, and airborne surveys suffer from limited spatial coverage and temporal sampling due to logistical constraints and high costs which limit their ability to provide the needed information. The recent advancements of spaceborne remote sensing techniques, along with their ability to acquire data over large spatial areas and to provide high frequency temporal monitoring, has made them very attractive for topography and bathymetry mapping. In this review, we present an overview of the current state of spaceborne-based remote sensing techniques used to estimate the topography and bathymetry of beaches, intertidal, and nearshore areas. We also provide some insights about the potential of these techniques when using data provided by new and future satellite missions. Full article
(This article belongs to the Special Issue Satellite Derived Bathymetry)
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43 pages, 65401 KiB  
Article
Automatic Methodology to Detect the Coastline from Landsat Images with a New Water Index Assessed on Three Different Spanish Mediterranean Deltas
by Sandra Paola Viaña-Borja and Miguel Ortega-Sánchez
Remote Sens. 2019, 11(18), 2186; https://doi.org/10.3390/rs11182186 - 19 Sep 2019
Cited by 47 | Viewed by 8046
Abstract
Due to the importance of coastline detection in coastal studies, different methods have been developed in recent decades in accordance with the evolution of measuring techniques such as remote sensing. This work proposes an automatic methodology with new water indexes to detect the [...] Read more.
Due to the importance of coastline detection in coastal studies, different methods have been developed in recent decades in accordance with the evolution of measuring techniques such as remote sensing. This work proposes an automatic methodology with new water indexes to detect the coastline from different multispectral Landsat images; the methodology is applied to three Spanish deltas in the Mediterranean Sea. The new water indexes use surface reflectance rather than top-of-atmosphere reflectance from blue and shortwave infrared (SWIR 2) Landsat bands. A total of 621 sets of images were analyzed from three different Landsat sensors with a moderate spatial resolution of 30 m. Our proposal, which was compared to the most commonly used water indexes, showed outstanding performance in automatic detection of the coastline in 96% of the data analyzed, which also reached the minimum value of bias of 0.91 m and a standard deviation ranging from ±4.7 and ±7.29 m in some cases in contrast to the existing values. Bicubic interpolation was evaluated for a simple sub-pixel analysis to assess its capability in improving the accuracy of coastline extraction. Our methodology represents a step forward in automatic coastline detection that can be applied to micro-tidal coastal sites with different land covers using many multi-sensor satellite images. Full article
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16 pages, 1590 KiB  
Article
Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach
by Hamid Dashti, Andrew Poley, Nancy F. Glenn, Nayani Ilangakoon, Lucas Spaete, Dar Roberts, Josh Enterkine, Alejandro N. Flores, Susan L. Ustin and Jessica J. Mitchell
Remote Sens. 2019, 11(18), 2141; https://doi.org/10.3390/rs11182141 - 14 Sep 2019
Cited by 16 | Viewed by 5691
Abstract
The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor [...] Read more.
The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)
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22 pages, 3188 KiB  
Article
The GFZ GRACE RL06 Monthly Gravity Field Time Series: Processing Details and Quality Assessment
by Christoph Dahle, Michael Murböck, Frank Flechtner, Henryk Dobslaw, Grzegorz Michalak, Karl Hans Neumayer, Oleh Abrykosov, Anton Reinhold, Rolf König, Roman Sulzbach and Christoph Förste
Remote Sens. 2019, 11(18), 2116; https://doi.org/10.3390/rs11182116 - 11 Sep 2019
Cited by 97 | Viewed by 10143
Abstract
Time-variable gravity field models derived from observations of the Gravity Recovery and Climate Experiment (GRACE) mission, whose science operations phase ended in June 2017 after more than 15 years, enabled a multitude of studies of Earth’s surface mass transport processes and climate change. [...] Read more.
Time-variable gravity field models derived from observations of the Gravity Recovery and Climate Experiment (GRACE) mission, whose science operations phase ended in June 2017 after more than 15 years, enabled a multitude of studies of Earth’s surface mass transport processes and climate change. The German Research Centre for Geosciences (GFZ), routinely processing such monthly gravity fields as part of the GRACE Science Data System, has reprocessed the complete GRACE mission and released an improved GFZ GRACE RL06 monthly gravity field time series. This study provides an insight into the processing strategy of GFZ RL06 which has been considerably changed with respect to previous GFZ GRACE releases, and modifications relative to the precursor GFZ RL05a are described. The quality of the RL06 gravity field models is analyzed and discussed both in the spectral and spatial domain in comparison to the RL05a time series. All results indicate significant improvements of about 40% in terms of reduced noise. It is also shown that the GFZ RL06 time series is a step forward in terms of consistency, and that errors of the gravity field coefficients are more realistic. These findings are confirmed as well by independent validation of the monthly GRACE models, as done in this work by means of ocean bottom pressure in situ observations and orbit tests with the GOCE satellite. Thus, the GFZ GRACE RL06 time series allows for a better quantification of mass changes in the Earth system. Full article
(This article belongs to the Special Issue Remote Sensing by Satellite Gravimetry)
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16 pages, 7447 KiB  
Article
Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale
by M. Razu Ahmed, Quazi K. Hassan, Masoud Abdollahi and Anil Gupta
Remote Sens. 2019, 11(18), 2101; https://doi.org/10.3390/rs11182101 - 9 Sep 2019
Cited by 26 | Viewed by 4691
Abstract
Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based [...] Read more.
Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based medium-term (i.e., four-day) forest fire danger forecasting system (FFDFS) based on an existing framework, and applied the system over the forested regions in the northern Alberta, Canada. Hence, we first employed moderate resolution imaging spectroradiometer (MODIS)-derived daily land surface temperature (Ts) and surface reflectance products along with the annual land cover to generate three four-day composite for Ts, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) at 500 m spatial resolution for the next four days over the forest-dominant regions. Upon generating these four-day composites, we calculated the variable-specific mean values to determine variable-specific fire danger maps with two danger classes (i.e., high and low). Then, by assuming the cloud-contaminated pixels as the low fire danger areas, we combined these three danger maps to generate a four-day fire danger map with four danger classes (i.e., low, moderate, high, and very high) over our study area of interest, which was further enhanced by incorporation of a human-caused static fire danger map. Finally, the four-day scale fire danger maps were evaluated using observed/ground-based forest fire occurrences during the 2015–2017 fire seasons. The results revealed that our proposed system was able to detect about 75% of the fire events in the top two danger classes (i.e., high and very high). The system was also able to predict the 2016 Horse River wildfire, the worst fire event in Albertian and Canadian history, with about 67% agreement. The higher accuracy outputs from our proposed model indicated that it could be implemented in the operational management, which would be very useful for lessening the adverse impact of such fire events. Full article
(This article belongs to the Special Issue Environmental Modelling and Remote Sensing)
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19 pages, 7248 KiB  
Article
Global Detection of Long-Term (1982–2017) Burned Area with AVHRR-LTDR Data
by Gonzalo Otón, Rubén Ramo, Joshua Lizundia-Loiola and Emilio Chuvieco
Remote Sens. 2019, 11(18), 2079; https://doi.org/10.3390/rs11182079 - 5 Sep 2019
Cited by 41 | Viewed by 5855
Abstract
This paper presents the first global burned area (BA) product derived from the land long term data record (LTDR), a long-term 0.05-degree resolution dataset generated from advanced very high resolution radiometer (AVHRR) images. Daily images were combined in monthly composites using the maximum [...] Read more.
This paper presents the first global burned area (BA) product derived from the land long term data record (LTDR), a long-term 0.05-degree resolution dataset generated from advanced very high resolution radiometer (AVHRR) images. Daily images were combined in monthly composites using the maximum temperature criterion to enhance the burned signal and eliminate clouds and artifacts. A synthetic BA index was created to improve the detection of the BA signal. This index included red and near infrared reflectance, surface temperature, two spectral indices, and their temporal differences. Monthly models were generated using the random forest classifier, using the twelve monthly composites of each year as the predictors. Training data were obtained from the NASA MCD64A1 collection 6 product (500 m spatial resolution) for eight years of the overlapping period (2001–2017). This included some years with low and high fire occurrence. Results were tested with the remaining eight years. Pixels classified as burned were converted to burned proportions using the MCD64A1 product. The final product (named FireCCILT10) estimated BA in 0.05-degree cells for the 1982 to 2017 period (excluding 1994, due to input data gaps). This product is the longest global BA currently available, extending almost 20 years back from the existing NASA and ESA BA products. BA estimations from the FireCCILT10 product were compared with those from the MCD64A1 product for continental regions, obtaining high correlation values (r2 > 0.9), with better agreement in tropical regions rather than boreal regions. The annual average of BA of the time series was 3.12 Mkm2. Tropical Africa had the highest proportion of burnings, accounting for 74.37% of global BA. Spatial trends were found to be similar to existing global BA products, but temporal trends showed unstable annual variations, most likely linked to the changes in the AVHRR sensor and orbital decays of the NOAA satellites. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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39 pages, 9661 KiB  
Article
NWCSAF High Resolution Winds (NWC/GEO-HRW) Stand-Alone Software for Calculation of Atmospheric Motion Vectors and Trajectories
by Javier García-Pereda, José Miguel Fernández-Serdán, Óscar Alonso, Adrián Sanz, Rocío Guerra, Cristina Ariza, Inés Santos and Laura Fernández
Remote Sens. 2019, 11(17), 2032; https://doi.org/10.3390/rs11172032 - 29 Aug 2019
Cited by 7 | Viewed by 5138
Abstract
The High Resolution Winds (NWC/GEO-HRW) software is developed by the EUMETSAT Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting (NWCSAF). It is part of a stand-alone software package for the calculation of meteorological products with geostationary satellite data (NWC/GEO). [...] Read more.
The High Resolution Winds (NWC/GEO-HRW) software is developed by the EUMETSAT Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting (NWCSAF). It is part of a stand-alone software package for the calculation of meteorological products with geostationary satellite data (NWC/GEO). NWCSAF High Resolution Winds provides a detailed calculation of Atmospheric Motion Vectors (AMVs) and Trajectories, locally and in near real time, using as input geostationary satellite image data, NWP model data, and OSTIA sea surface temperature data. The whole NWC/GEO software package can be obtained after registration at the NWCSAF Helpdesk, www.nwcsaf.org, where users also find support and help for its use. NWC/GEO v2018.1 software version, available since autumn 2019, is able to process MSG, Himawari-8/9, GOES-N, and GOES-R satellite series images, so that AMVs and trajectories can be calculated all throughout the planet Earth with the same algorithm and quality. Considering other equivalent meteorological products, in the ‘2014 and 2018 AMV Intercomparison Studies’ NWCSAF High Resolution Winds compared very positively with six other AMV algorithms for both MSG and Himawari-8/9 satellites. Finally, the Coordination Group for Meteorological Satellites (CGMS) recognized in its ‘2012 Meeting Report’: (1) NWCSAF High Resolution Winds fulfills the requirements to be a portable stand-alone AMV calculation software due to its easy installation and usability. (2) It has been successfully adopted by some CGMS members and serves as an important tool for development. It is modular, well documented, and well suited as stand-alone AMV software. (3) Although alternatives exist as portable stand-alone AMV calculation software, they are not as advanced in terms of documentation and do not have an existing Helpdesk. Full article
(This article belongs to the Special Issue Satellite-Derived Wind Observations)
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28 pages, 6253 KiB  
Article
Impacts of Radiometric Uncertainty and Weather-Related Surface Conditions on Soil Moisture Retrievals with Sentinel-1
by Harm-Jan F. Benninga, Rogier van der Velde and Zhongbo Su
Remote Sens. 2019, 11(17), 2025; https://doi.org/10.3390/rs11172025 - 28 Aug 2019
Cited by 28 | Viewed by 4705
Abstract
The radiometric uncertainty of Synthetic Aperture Radar (SAR) observations and weather-related surface conditions caused by frozen conditions, snow and intercepted rain affect the backscatter ( σ 0 ) observations and limit the accuracy of soil moisture retrievals. This study estimates Sentinel-1’s radiometric uncertainty, [...] Read more.
The radiometric uncertainty of Synthetic Aperture Radar (SAR) observations and weather-related surface conditions caused by frozen conditions, snow and intercepted rain affect the backscatter ( σ 0 ) observations and limit the accuracy of soil moisture retrievals. This study estimates Sentinel-1’s radiometric uncertainty, identifies the effects of weather-related surface conditions on σ 0 and investigates their impact on soil moisture retrievals for various conditions regarding soil moisture, surface roughness and incidence angle. Masking rules for the surface conditions that disturb σ 0 were developed based on meteorological measurements and timeseries of Sentinel-1 observations collected over five forests, five meadows and five cultivated fields in the eastern part of the Netherlands. The Sentinel-1 σ 0 observations appear to be affected by frozen conditions below an air temperature of 1 C , snow during Sentinel-1’s morning overpasses on meadows and cultivated fields and interception after more than 1.8 m m of rain in the 12 h preceding a Sentinel-1 overpass, whereas dew was not found to be of influence. After the application of these masking rules, the radiometric uncertainty was estimated by the standard deviation of the seasonal anomalies timeseries of the Sentinel-1 forest σ 0 observations. By spatially averaging the σ 0 observations, the Sentinel-1 radiometric uncertainty improves from 0.85 dB for a surface area of 0.25 h a to 0.30 dB for 10 h a for the VV polarization and from 0.89 dB to 0.36 dB for the VH polarization, following approximately an inverse square root dependency on the surface area over which the σ 0 observations are averaged. Deviations in σ 0 were combined with the σ 0 sensitivity to soil moisture as simulated with the Integral Equation Method (IEM) surface scattering model, which demonstrated that both the disturbing effects by the weather-related surface conditions (if not masked) and radiometric uncertainty have a significant impact on the soil moisture retrievals from Sentinel-1. The soil moisture retrieval uncertainty due to radiometric uncertainty ranges from 0.01 m 3 m 3 up to 0.17 m 3 m 3 for wet soils and small surface areas. The impacts on soil moisture retrievals are found to be weakly dependent on the surface roughness and the incidence angle, and strongly dependent on the surface area (or the σ 0 disturbance caused by a weather-related surface condition for a specific land cover type) and the soil moisture itself. Full article
(This article belongs to the Special Issue Soil Moisture Retrieval using Radar Remote Sensing Sensors)
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15 pages, 2675 KiB  
Article
Environmental Differences between Migratory and Resident Ungulates—Predicting Movement Strategies in Rocky Mountain Mule Deer (Odocoileus hemionus) with Remotely Sensed Plant Phenology, Snow, and Land Cover
by Benjamin Robb, Qiongyu Huang, Joseph O. Sexton, David Stoner and Peter Leimgruber
Remote Sens. 2019, 11(17), 1980; https://doi.org/10.3390/rs11171980 - 22 Aug 2019
Cited by 6 | Viewed by 4768
Abstract
Migration is a valuable life history strategy for many species because it enables individuals to exploit spatially and temporally variable resources. Globally, the prevalence of species’ migratory behavior is decreasing as individuals forgo migration to remain resident year-round, an effect hypothesized to result [...] Read more.
Migration is a valuable life history strategy for many species because it enables individuals to exploit spatially and temporally variable resources. Globally, the prevalence of species’ migratory behavior is decreasing as individuals forgo migration to remain resident year-round, an effect hypothesized to result from anthropogenic changes to landscape dynamics. Efforts to conserve and restore migrations require an understanding of the ecological characteristics driving the behavioral tradeoff between migration and residence. We identified migratory and resident behaviors of 42 mule deer (Odocoileus hemionus) based on GPS locations and correlated their locations to remotely sensed indicators of forage quality, land cover, snow cover, and human land use. The model classified mule deer seasonal migratory and resident niches with an overall accuracy of 97.8% and cross-validated accuracy of 81.2%. The distance to development was the most important variable in discriminating in which environments these behaviors occur, with resident niche space most often closer to developed areas than migratory niches. Additionally, snow cover in December was important for discriminating summer migratory niches. This approach demonstrates the utility of niche analysis based on remotely sensed environmental datasets and provides empirical evidence of human land use impacts on large-scale wildlife migrations. Full article
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18 pages, 4833 KiB  
Article
Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast
by Jorge Vazquez-Cuervo, Jose Gomez-Valdes, Marouan Bouali, Luis E. Miranda, Tom Van der Stocken, Wenqing Tang and Chelle Gentemann
Remote Sens. 2019, 11(17), 1964; https://doi.org/10.3390/rs11171964 - 21 Aug 2019
Cited by 44 | Viewed by 6677
Abstract
Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products by comparing with buoy measurements, do not allow for evaluating the impact of mesoscale-to-submesoscale variability. We present the validation of remotely sensed SST and SSS data against the [...] Read more.
Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products by comparing with buoy measurements, do not allow for evaluating the impact of mesoscale-to-submesoscale variability. We present the validation of remotely sensed SST and SSS data against the unmanned surface vehicle (USV)—called Saildrone—measurements from the 60 day 2018 Baja California campaign. More specifically, biases and root mean square differences (RMSDs) were calculated between USV-derived SST and SSS values, and six satellite-derived SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and three SSS (JPLSMAP, RSS40, RSS70) products. Biases between the USV SST and OSTIA/CMC/DMI were approximately zero, while MUR showed a bias of 0.3 °C. The OSTIA showed the smallest RMSD of 0.39 °C, while DMI had the largest RMSD of 0.5 °C. An RMSD of 0.4 °C between Saildrone SST and the satellite-derived products could be explained by the diurnal and sub-daily variability in USV SST, which currently cannot be resolved by remote sensing measurements. SSS showed fresh biases of 0.1 PSU for JPLSMAP and 0.2 PSU and 0.3 PSU for RMSS40 and RSS70 respectively. SST and SSS showed peaks in coherence at 100 km, most likely associated with the variability of the California Current System. Full article
(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)
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36 pages, 5410 KiB  
Review
Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
by Jinyang Du, Jennifer D. Watts, Lingmei Jiang, Hui Lu, Xiao Cheng, Claude Duguay, Mary Farina, Yubao Qiu, Youngwook Kim, John S. Kimball and Paolo Tarolli
Remote Sens. 2019, 11(16), 1952; https://doi.org/10.3390/rs11161952 - 20 Aug 2019
Cited by 54 | Viewed by 12564
Abstract
Cold regions, including high-latitude and high-altitude landscapes, are experiencing profound environmental changes driven by global warming. With the advance of earth observation technology, remote sensing has become increasingly important for detecting, monitoring, and understanding environmental changes over vast and remote regions. This paper [...] Read more.
Cold regions, including high-latitude and high-altitude landscapes, are experiencing profound environmental changes driven by global warming. With the advance of earth observation technology, remote sensing has become increasingly important for detecting, monitoring, and understanding environmental changes over vast and remote regions. This paper provides an overview of recent achievements, challenges, and opportunities for land remote sensing of cold regions by (a) summarizing the physical principles and methods in remote sensing of selected key variables related to ice, snow, permafrost, water bodies, and vegetation; (b) highlighting recent environmental nonstationarity occurring in the Arctic, Tibetan Plateau, and Antarctica as detected from satellite observations; (c) discussing the limits of available remote sensing data and approaches for regional monitoring; and (d) exploring new opportunities from next-generation satellite missions and emerging methods for accurate, timely, and multi-scale mapping of cold regions. Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions)
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22 pages, 7199 KiB  
Article
An Analysis of Ground-Point Classifiers for Terrestrial LiDAR
by Kevin C. Roberts, John B. Lindsay and Aaron A. Berg
Remote Sens. 2019, 11(16), 1915; https://doi.org/10.3390/rs11161915 - 16 Aug 2019
Cited by 19 | Viewed by 6712
Abstract
Previous literature has compared the performance of existing ground point classification (GPC) techniques on airborne LiDAR (ALS) data (LiDAR—light detection and ranging); however, their performance when applied to terrestrial LiDAR (TLS) data has not yet been addressed. This research tested the classification accuracy [...] Read more.
Previous literature has compared the performance of existing ground point classification (GPC) techniques on airborne LiDAR (ALS) data (LiDAR—light detection and ranging); however, their performance when applied to terrestrial LiDAR (TLS) data has not yet been addressed. This research tested the classification accuracy of five openly-available GPC algorithms on seven TLS datasets: Zhang et al.’s inverted cloth simulation (CSF), Kraus and Pfeiffer’s hierarchical weighted robust interpolation classifier (HWRI), Axelsson’s progressive TIN densification filter (TIN), Evans and Hudak’s multiscale curvature classification (MCC), and Vosselman’s modified slope-based filter (MSBF). Classification performance was analyzed using the kappa index of agreement (KIA) and rasterized spatial distribution of classification accuracy datasets generated through comparisons with manually classified reference datasets. The results identified a decrease in classification accuracy for the CSF and HWRI classification of low vegetation, for the HWRI and MCC classifications of variably sloped terrain, for the HWRI and TIN classifications of low outlier points, and for the TIN and MSBF classifications of off-terrain (OT) points without any ground points beneath. Additionally, the results show that while no single algorithm was suitable for use on all datasets containing varying terrain characteristics and OT object types, in general, a mathematical-morphology/slope-based method outperformed other methods, reporting a kappa score of 0.902. Full article
(This article belongs to the Special Issue Remote Sensing: 10th Anniversary)
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17 pages, 6855 KiB  
Article
Integration of Machine Learning and Open Access Geospatial Data for Land Cover Mapping
by Mohammad Mardani, Hossein Mardani, Lorenzo De Simone, Samuel Varas, Naoki Kita and Takafumi Saito
Remote Sens. 2019, 11(16), 1907; https://doi.org/10.3390/rs11161907 - 15 Aug 2019
Cited by 20 | Viewed by 5838
Abstract
In-time and accurate monitoring of land cover and land use are essential tools for countries to achieve sustainable food production. However, many developing countries are struggling to efficiently monitor land resources due to the lack of financial support and limited access to adequate [...] Read more.
In-time and accurate monitoring of land cover and land use are essential tools for countries to achieve sustainable food production. However, many developing countries are struggling to efficiently monitor land resources due to the lack of financial support and limited access to adequate technology. This study aims at offering a solution to fill in such a gap in developing countries, by developing a land cover solution that is free of costs. A fully automated framework for land cover mapping was developed using 10-m resolution open access satellite images and machine learning (ML) techniques for the African country of Lesotho. Sentinel-2 satellite images were accessed through Google Earth Engine (GEE) for initial processing and feature extraction at a national level. Also, Food and Agriculture Organization’s land cover of Lesotho (FAO LCL) data were used to train a support vector machine (SVM) and bagged trees (BT) classifiers. SVM successfully classified urban and agricultural lands with 62 and 67% accuracy, respectively. Also, BT could classify the two categories with 81 and 65% accuracy, correspondingly. The trained models could provide precise LC maps in minutes or hours. they can also be utilized as a viable solution for developing countries as an alternative to traditional geographic information system (GIS) methods, which are often labor intensive, require acquisition of very high-resolution commercial satellite imagery, time consuming and call for high budgets. Full article
(This article belongs to the Special Issue Multitemporal Land Cover and Land Use Mapping)
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24 pages, 9901 KiB  
Article
Crop Monitoring Using Sentinel-1 Data: A Case Study from The Netherlands
by Saeed Khabbazan, Paul Vermunt, Susan Steele-Dunne, Lexy Ratering Arntz, Caterina Marinetti, Dirk van der Valk, Lorenzo Iannini, Ramses Molijn, Kees Westerdijk and Corné van der Sande
Remote Sens. 2019, 11(16), 1887; https://doi.org/10.3390/rs11161887 - 13 Aug 2019
Cited by 144 | Viewed by 16256
Abstract
Agriculture is of huge economic significance in The Netherlands where the provision of real-time, reliable information on crop development is essential to support the transition towards precision agriculture. Optical imagery can provide invaluable insights into crop growth and development but is severely hampered [...] Read more.
Agriculture is of huge economic significance in The Netherlands where the provision of real-time, reliable information on crop development is essential to support the transition towards precision agriculture. Optical imagery can provide invaluable insights into crop growth and development but is severely hampered by cloud cover. This case study in the Flevopolder illustrates the potential value of Sentinel-1 for monitoring five key crops in The Netherlands, namely sugar beet, potato, maize, wheat and English rye grass. Time series of radar backscatter from the European Space Agency’s Sentinel-1 Mission are analyzed and compared to ground measurements including phenological stage and height. Temporal variations in backscatter data reflect changes in water content and structure associated with phenological development. Emergence and closure dates are estimated from the backscatter time series and validated against a photo archive. Coherence data are compared to Normalized Difference Vegetation Index (NDVI) and ground data, illustrating that the sudden increase in coherence is a useful indicator of harvest. The results presented here demonstrate that Sentinel-1 data have significant potential value to monitor growth and development of key Dutch crops. Furthermore, the guaranteed availability of Sentinel-1 imagery in clouded conditions ensures the reliability of data to meet the monitoring needs of farmers, food producers and regulatory bodies. Full article
(This article belongs to the Special Issue Radar Remote Sensing for Agriculture)
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18 pages, 4971 KiB  
Article
Impacts of Land-Use Changes on Soil Erosion in Water–Wind Crisscross Erosion Region of China
by Jie Wang, Weiwei Zhang and Zengxiang Zhang
Remote Sens. 2019, 11(14), 1732; https://doi.org/10.3390/rs11141732 - 23 Jul 2019
Cited by 13 | Viewed by 5794
Abstract
Soil erosion affects food production, biodiversity, biogeochemical cycles, hydrology, and climate. Land-use changes accelerated by intensive human activities are a dominant anthropogenic factor inducing soil erosion globally. However, the impacts of land-use-type changes on soil erosion dynamics over a continuous period for constructing [...] Read more.
Soil erosion affects food production, biodiversity, biogeochemical cycles, hydrology, and climate. Land-use changes accelerated by intensive human activities are a dominant anthropogenic factor inducing soil erosion globally. However, the impacts of land-use-type changes on soil erosion dynamics over a continuous period for constructing a sustainable ecological environment has not been systematically quantified. This study investigates the spatial–temporal dynamics of land-use change and soil erosion across a specific area in China with water–wind crisscross erosion during three periods: 1995–1999, 2000–2005, and 2005–2010. We analyzed the impacts of each land-use-type conversion on the intensity changes of soil erosion caused by water and wind, respectively. The major findings include: (1) land-use change in the water–wind crisscross erosion region of China was characterized as cultivated land expansion at the main cost of grassland during 1995–2010; (2) the strongest land-use change moved westward in space from the central Loess Plateau area in 1995–2005 to the western piedmont alluvial area in 2005–2010; (3) soil erosion area is continuously increasing, but the trend is declining from the late 1990s to the late 2000s; (4) the soil conservation capability of land-use types in water–wind crisscross erosion regions could be compiled from high to low as high coverage grasslands, medium coverage grasslands, paddy, drylands, low coverage grasslands, built-up lands, unused land of sandy lands, the Gobi Desert, and bare soil. These findings could provide some insights for executing reasonable land-use approaches to balance human demands and environment sustainability. Full article
(This article belongs to the Special Issue Remote Sensing of Human-Environment Interactions)
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