Remote Sensing

18 pages, 3651 KiB

Open AccessArticle

Improvement of Remote Sensing-Based Assessment of Defoliation of Pinus spp. Caused by Thaumetopoea pityocampa Denis and Schiffermüller and Related Environmental Drivers in Southeastern Spain

by Javier Pérez-Romero, Rafael María Navarro-Cerrillo, Guillermo Palacios-Rodriguez, Cristina Acosta and Francisco Javier Mesas-Carrascosa

Remote Sens. 2019, 11(14), 1736; https://doi.org/10.3390/rs11141736 - 23 Jul 2019

Cited by 10 | Viewed by 4553

Abstract

This study used Landsat temporal series to describe defoliation levels due to the Pine Processionary Moth (PPM) in Pinus forests of southeastern Andalusia (Spain), utilizing Google Earth Engine. A combination of remotely sensed data and field survey data was used to detect the [...] Read more.

This study used Landsat temporal series to describe defoliation levels due to the Pine Processionary Moth (PPM) in Pinus forests of southeastern Andalusia (Spain), utilizing Google Earth Engine. A combination of remotely sensed data and field survey data was used to detect the defoliation levels of different Pinus spp. and the main environmental drivers of the defoliation due to the PPM. Four vegetation indexes were also calculated for remote sensing defoliation assessment, both inside the stand and in a 60-m buffer area. In the area of study, all Pinus species are affected by defoliation due to the PPM, with a cyclic behavior that has been increasing in frequency in recent years. Defoliation levels were practically equal for all species, with a high increase in defoliation levels 2 and 3 since 2014. The Moisture Stress Index (MSI) and Normalized Difference Infrared Index (NDII) exhibited similar overall (p < 0.001) accuracy in the assessment of defoliation due to the PPM. The synchronization of NDII-defoliation data had a similar pattern for all together and individual Pinus species, showing the ability of this index to adjust the model parameters based on the characteristics of specific defoliation levels. Using Landsat-based NDII-defoliation maps and interpolated environmental data, we have shown that the PPM defoliation in southeastern Spain is driven by the minimum temperature in February and the precipitation in June, March, September, and October. Therefore, the NDII-defoliation assessment seems to be a general index that can be applied to forests in other areas. The trends of NDII-defoliation related to environmental variables showed the importance of summer drought stress in the expansion of the PPM on Mediterranean Pinus species. Our results confirm the potential of Landsat time-series data in the assessment of PPM defoliation and the spatiotemporal patterns of the PPM; hence, these data are a powerful tool that can be used to develop a fully operational system for the monitoring of insect damage. Full article

(This article belongs to the Special Issue Google Earth Engine and Cloud Computing Platforms: Methods and Applications in Big Geo Data Science)

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19 pages, 4876 KiB

Open AccessEditor’s ChoiceArticle

Giving Ecological Meaning to Satellite-Derived Fire Severity Metrics across North American Forests

by Sean A. Parks, Lisa M. Holsinger, Michael J. Koontz, Luke Collins, Ellen Whitman, Marc-André Parisien, Rachel A. Loehman, Jennifer L. Barnes, Jean-François Bourdon, Jonathan Boucher, Yan Boucher, Anthony C. Caprio, Adam Collingwood, Ron J. Hall, Jane Park, Lisa B. Saperstein, Charlotte Smetanka, Rebecca J. Smith and Nick Soverel

Remote Sens. 2019, 11(14), 1735; https://doi.org/10.3390/rs11141735 - 23 Jul 2019

Cited by 62 | Viewed by 11278

Abstract

Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying [...] Read more.

Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R² of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions. Full article

(This article belongs to the Section Forest Remote Sensing)

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16 pages, 3401 KiB

Open AccessArticle

Advantages of Geostationary Satellites for Ionospheric Anomaly Studies: Ionospheric Plasma Depletion Following a Rocket Launch

by Giorgio Savastano, Attila Komjathy, Esayas Shume, Panagiotis Vergados, Michela Ravanelli, Olga Verkhoglyadova, Xing Meng and Mattia Crespi

Remote Sens. 2019, 11(14), 1734; https://doi.org/10.3390/rs11141734 - 23 Jul 2019

Cited by 26 | Viewed by 5305

Abstract

In this study, we analyzed signals transmitted by the U.S. Wide Area Augmentation System (WAAS) geostationary (GEO) satellites using the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithm in a simulated real-time scenario, to characterize the ionospheric response to the 24 August 2017 [...] Read more.

In this study, we analyzed signals transmitted by the U.S. Wide Area Augmentation System (WAAS) geostationary (GEO) satellites using the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithm in a simulated real-time scenario, to characterize the ionospheric response to the 24 August 2017 Falcon 9 rocket launch from Vandenberg Air Force Base in California. VARION is a real-time Global Navigation Satellites Systems (GNSS)-based algorithm that can be used to detect various ionospheric disturbances associated with natural hazards, such as tsunamis and earthquakes. A noise reduction algorithm was applied to the VARION-GEO solutions to remove the satellite-dependent noise term. Our analysis showed that the interactions of the exhaust plume with the ionospheric plasma depleted the total electron content (TEC) to a level comparable with nighttime TEC values. During this event, the geometry of the satellite-receiver link is such that GEO satellites measured the depleted plasma hole before any GPS satellites. We estimated that the ionosphere relaxed back to a pre-perturbed state after about 3 h, and the hole propagated with a mean speed of about 600 m/s over a region of 700 km in radius. We conclude that the VARION-GEO approach can provide important ionospheric TEC real-time measurements, which are not affected by the motion of the ionospheric pierce points (IPPs). Furthermore, the VARION-GEO measurements experience a steady noise level throughout the entire observation period, making this technique particularly useful to augment and enhance the capabilities of well-established GNSS-based ionosphere remote sensing techniques and future ionospheric-based early warning systems. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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29 pages, 9733 KiB

Open AccessArticle

Quantifying the Impacts of Land-Use and Climate on Carbon Fluxes Using Satellite Data across Texas, U.S.

by Ram L. Ray, Ademola Ibironke, Raghava Kommalapati and Ali Fares

Remote Sens. 2019, 11(14), 1733; https://doi.org/10.3390/rs11141733 - 23 Jul 2019

Cited by 6 | Viewed by 5545

Abstract

Climate change and variability, soil types and soil characteristics, animal and microbial communities, and photosynthetic plants are the major components of the ecosystem that affect carbon sequestration potential of any location. This study used NASA’s Soil Moisture Active Passive (SMAP) Level 4 carbon [...] Read more.

Climate change and variability, soil types and soil characteristics, animal and microbial communities, and photosynthetic plants are the major components of the ecosystem that affect carbon sequestration potential of any location. This study used NASA’s Soil Moisture Active Passive (SMAP) Level 4 carbon products, gross primary productivity (GPP), and net ecosystem exchange (NEE) to quantify their spatial and temporal variabilities for selected terrestrial ecosystems across Texas during the 2015–2018 study period. These SMAP carbon products are available at 9 km spatial resolution on a daily basis. The ten selected SMAP grids are located in seven climate zones and dominated by five major land uses (developed, crop, forest, pasture, and shrub). Results showed CO₂ emissions and uptake were affected by land-use and climatic conditions across Texas. It was also observed that climatic conditions had more impact on CO₂ emissions and uptake than land-use in this state. On average, South Central Plains and East Central Texas Plains ecoregions of East Texas and Western Gulf Coastal Plain ecoregion of Upper Coast climate zones showed higher GPP flux and potential carbon emissions and uptake than other climate zones across the state, whereas shrubland on the Trans Pecos climate zone showed lower GPP flux and carbon emissions/uptake. Comparison of GPP and NEE distribution maps between 2015 and 2018 confirmed substantial changes in carbon emissions and uptake across Texas. These results suggest that SMAP carbon products can be used to study the terrestrial carbon cycle at regional to global scales. Overall, this study helps to understand the impacts of climate, land-use, and ecosystem dynamics on the terrestrial carbon cycle. Full article

(This article belongs to the Special Issue Terrestrial Carbon Cycle)

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18 pages, 4971 KiB

Open AccessEditor’s ChoiceArticle

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 10 | Viewed by 4576

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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20 pages, 14803 KiB

Open AccessArticle

Revealing Kunming’s (China) Historical Urban Planning Policies Through Local Climate Zones

by Stéphanie Vandamme, Matthias Demuzere, Marie-Leen Verdonck, Zhiming Zhang and Frieke Van Coillie

Remote Sens. 2019, 11(14), 1731; https://doi.org/10.3390/rs11141731 - 22 Jul 2019

Cited by 21 | Viewed by 5795

Abstract

Over the last decade, Kunming has been subject to a strong urbanisation driven by rapid economic growth and socio-economic, topographical and proximity factors. As this urbanisation is expected to continue in the future, it is important to understand its environmental impacts and the [...] Read more.

Over the last decade, Kunming has been subject to a strong urbanisation driven by rapid economic growth and socio-economic, topographical and proximity factors. As this urbanisation is expected to continue in the future, it is important to understand its environmental impacts and the role that spatial planning strategies and urbanisation regulations can play herein. This is addressed by (1) quantifying the cities’ expansion and intra-urban restructuring using Local Climate Zones (LCZs) for three periods in time (2005, 2011 and 2017) based on the World Urban Database and Access Portal Tool (WUDAPT) protocol, and (2) cross-referencing observed land-use and land-cover changes with existing planning regulations. The results of the surveys on urban development show that, between 2005 and 2011, the city showed spatial expansion, whereas between 2011 and 2017, densification mainly occurred within the existing urban extent. Between 2005 and 2017, the fraction of open LCZs increased, with the largest increase taking place between 2011 and 2017. The largest decrease was seen for low the plants (LCZ D) and agricultural greenhouse (LCZ H) categories. As the potential of LCZs as, for example, a heat stress assessment tool has been shown elsewhere, understanding the relation between policy strategies and LCZ changes is important to take rational urban planning strategies toward sustainable city development. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Urban Climatology)

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29 pages, 2600 KiB

Open AccessArticle

Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions

by Alexandra Runge and Guido Grosse

Remote Sens. 2019, 11(14), 1730; https://doi.org/10.3390/rs11141730 - 22 Jul 2019

Cited by 20 | Viewed by 5572

Abstract

The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering [...] Read more.

The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering a variety of techniques to assess and monitor landscape changes, a persistent cloud cover decreases the amount of usable images considerably. However, combining data from multiple platforms promises to increase the number of images drastically. We therefore assess the comparability of Landsat-8 and Sentinel-2 imagery and the possibility to use both Landsat and Sentinel-2 images together in time series analyses, achieving a temporally-dense data coverage in Arctic-Boreal regions. We determined overlapping same-day acquisitions of Landsat-8 and Sentinel-2 images for three representative study sites in Eastern Siberia. We then compared the Landsat-8 and Sentinel-2 pixel-pairs, downscaled to 60 m, of corresponding bands and derived the ordinary least squares regression for every band combination. The acquired coefficients were used for spectral bandpass adjustment between the two sensors. The spectral band comparisons showed an overall good fit between Landsat-8 and Sentinel-2 images already. The ordinary least squares regression analyses underline the generally good spectral fit with intercept values between 0.0031 and 0.056 and slope values between 0.531 and 0.877. A spectral comparison after spectral bandpass adjustment of Sentinel-2 values to Landsat-8 shows a nearly perfect alignment between the same-day images. The spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to Landsat-8 very well in Eastern Siberian Arctic-Boreal landscapes. After spectral adjustment, Landsat and Sentinel-2 data can be used to create temporally-dense time series and be applied to assess permafrost landscape changes in Eastern Siberia. Remaining differences between the sensors can be attributed to several factors including heterogeneous terrain, poor cloud and cloud shadow masking, and mixed pixels. Full article

(This article belongs to the Section Environmental Remote Sensing)

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19 pages, 6518 KiB

Open AccessArticle

Mapping Hydrothermal Zoning Pattern of Porphyry Cu Deposit Using Absorption Feature Parameters Calculated from ASTER Data

by Mengjuan Wu, Kefa Zhou, Quan Wang and Jinlin Wang

Remote Sens. 2019, 11(14), 1729; https://doi.org/10.3390/rs11141729 - 22 Jul 2019

Cited by 13 | Viewed by 3371

Abstract

Identifying hydrothermal zoning pattern associated with porphyry copper deposit is important for indicating its economic potential. Traditional approaches like systematic sampling and conventional geological mapping are time-consuming and labor extensive, and with limitations for providing small scale information. Recent developments suggest that remote [...] Read more.

Identifying hydrothermal zoning pattern associated with porphyry copper deposit is important for indicating its economic potential. Traditional approaches like systematic sampling and conventional geological mapping are time-consuming and labor extensive, and with limitations for providing small scale information. Recent developments suggest that remote sensing is a powerful tool for mapping and interpreting the spatial pattern of porphyry Cu deposit. In this study, we integrated in situ spectral measurement taken at the Yudai copper deposit in the Kalatag district, northwestern China, information obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), as well as the spectra of samples (hand-specimen) measured using an Analytical Spectral Device (ASD) FieldSpec4 high-resolution spectrometer in laboratory, to map the hydrothermal zoning pattern of the copper deposit. Results proved that the common statistical approaches, such as relative band depth and Principle Component Analysis (PCA), were unable to identify the pattern accurately. To address the difficulty, we introduced a curve-fitting technique for ASTER shortwave infrared data to simulate Al(OH)-bearing, Fe/Mg(OH)-bearing, and carbonate minerals absorption features, respectively. The results indicate that the absorption feature parameters can effectively locate the ore body inside the research region, suggesting the absorption feature parameters have great potentials to delineate hydrothermal zoning pattern of porphyry Cu deposit. We foresee the method being widely used in the future. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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14 pages, 5346 KiB

Open AccessArticle

A New Approach to Earth’s Gravity Field Modeling Using GPS-Derived Kinematic Orbits and Baselines

by Xiang Guo and Qile Zhao

Remote Sens. 2019, 11(14), 1728; https://doi.org/10.3390/rs11141728 - 21 Jul 2019

Cited by 6 | Viewed by 2967

Abstract

Earth’s gravity field recovery from GPS observations collected by low earth orbiting (LEO) satellites is a well-established technique, and kinematic orbits are commonly used for that purpose. Nowadays, more and more satellites are flying in close formations. The GPS-derived kinematic baselines between them [...] Read more.

Earth’s gravity field recovery from GPS observations collected by low earth orbiting (LEO) satellites is a well-established technique, and kinematic orbits are commonly used for that purpose. Nowadays, more and more satellites are flying in close formations. The GPS-derived kinematic baselines between them can reach millimeter precision, which is more precise than the centimeter-level kinematic orbits. Thus, it has long been expected that the more precise kinematic baselines can deliver better gravity field solutions. However, this expectation has not been met yet in practice. In this study, we propose a new approach to gravity field modeling, in which kinematic orbits of the reference satellite and baseline vectors between the reference satellite and its accompanying satellite are jointly inverted. To validate the added value, data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are used. We derive kinematic orbits and inter-satellite baselines of the twin GRACE satellites from the GPS data collected in the year of 2010. Then two sets of monthly gravity field solutions up to degree and order 60 are produced. One is derived from kinematic orbits of the twin GRACE satellites (‘orbit approach’). The other is derived from kinematic orbits of GRACE A and baseline vectors between GRACE A and B (‘baseline approach’). Analysis of observation postfit residuals shows that noise in the kinematic baselines is notably lower than the kinematic orbits by 50, 47 and 43% for the along-track, cross-track and radial components, respectively. Regarding the gravity field solutions, analysis in the spectral domain shows that noise of the gravity field solutions beyond degree 10 can be significantly reduced when the baseline approach is applied, with cumulative errors up to degree 60 being reduced by 34%, when compared to the orbit approach. In the spatial domain, the recovered mass changes with the baseline approach are more consistent with those inferred from the K-Band Ranging based solutions. Our results demonstrate that the proposed baseline approach is able to provide better gravity field solutions than the orbit approach. The findings may facilitate, among others, bridging the gap between GRACE and GRACE Follow-On satellite mission. Full article

(This article belongs to the Special Issue Remote Sensing by Satellite Gravimetry)

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29 pages, 10244 KiB

Open AccessArticle

Automatic Building Outline Extraction from ALS Point Clouds by Ordered Points Aided Hough Transform

by Elyta Widyaningrum, Ben Gorte and Roderik Lindenbergh

Remote Sens. 2019, 11(14), 1727; https://doi.org/10.3390/rs11141727 - 21 Jul 2019

Cited by 32 | Viewed by 7307

Abstract

Many urban applications require building polygons as input. However, manual extraction from point cloud data is time- and labor-intensive. Hough transform is a well-known procedure to extract line features. Unfortunately, current Hough-based approaches lack flexibility to effectively extract outlines from arbitrary buildings. We [...] Read more.

Many urban applications require building polygons as input. However, manual extraction from point cloud data is time- and labor-intensive. Hough transform is a well-known procedure to extract line features. Unfortunately, current Hough-based approaches lack flexibility to effectively extract outlines from arbitrary buildings. We found that available point order information is actually never used. Using ordered building edge points allows us to present a novel ordered points–aided Hough Transform (OHT) for extracting high quality building outlines from an airborne LiDAR point cloud. First, a Hough accumulator matrix is constructed based on a voting scheme in parametric line space (θ, r). The variance of angles in each column is used to determine dominant building directions. We propose a hierarchical filtering and clustering approach to obtain accurate line based on detected hotspots and ordered points. An Ordered Point List matrix consisting of ordered building edge points enables the detection of line segments of arbitrary direction, resulting in high-quality building roof polygons. We tested our method on three different datasets of different characteristics: one new dataset in Makassar, Indonesia, and two benchmark datasets in Vaihingen, Germany. To the best of our knowledge, our algorithm is the first Hough method that is highly adaptable since it works for buildings with edges of different lengths and arbitrary relative orientations. The results prove that our method delivers high completeness (between 90.1% and 96.4%) and correctness percentages (all over 96%). The positional accuracy of the building corners is between 0.2–0.57 m RMSE. The quality rate (89.6%) for the Vaihingen-B benchmark outperforms all existing state of the art methods. Other solutions for the challenging Vaihingen-A dataset are not yet available, while we achieve a quality score of 93.2%. Results with arbitrary directions are demonstrated on the complex buildings around the EYE museum in Amsterdam. Full article

(This article belongs to the Section Urban Remote Sensing)

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19 pages, 77826 KiB

Open AccessArticle

Automatic Vector-Based Road Structure Mapping Using Multibeam LiDAR

by Junqiao Zhao, Xudong He, Jun Li, Tiantian Feng, Chen Ye and Lu Xiong

Remote Sens. 2019, 11(14), 1726; https://doi.org/10.3390/rs11141726 - 21 Jul 2019

Cited by 8 | Viewed by 4383

Abstract

The high-definition map (HD-map) of road structures is crucial for the safe planning and control of autonomous vehicles. However, generating and updating such maps requires intensive manual work. Simultaneous localization and mapping (SLAM) is able to automatically build and update a map of [...] Read more.

The high-definition map (HD-map) of road structures is crucial for the safe planning and control of autonomous vehicles. However, generating and updating such maps requires intensive manual work. Simultaneous localization and mapping (SLAM) is able to automatically build and update a map of the environment. Nevertheless, there is still a lack of SLAM method for generating vector-based road structure maps. In this paper, we propose a vector-based SLAM method for the road structure mapping using vehicle-mounted multibeam LiDAR. We propose using polylines as the primary mapping element instead of grid maps or point clouds because the vector-based representation is lightweight and precise. We explored the following: (1) the extraction and vectorization of road structures based on multiframe probabilistic fusion; (2) the efficient vector-based matching between frames of road structures; (3) the loop closure and optimization based on the pose-graph; and (4) the global reconstruction of the vector map. One specific road structure, the road boundary, is taken as an example. We applied the proposed mapping method to three road scenes, ranging from hundreds of meters to over ten kilometers and the results are automatically generated vector-based road boundary maps. The average absolute pose error of the trajectory in the mapping is 1.83 m without the aid of high-precision GPS. Full article

(This article belongs to the Section Urban Remote Sensing)

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14 pages, 24464 KiB

Open AccessArticle

Deep Fully Convolutional Networks for Cadastral Boundary Detection from UAV Images

by Xue Xia, Claudio Persello and Mila Koeva

Remote Sens. 2019, 11(14), 1725; https://doi.org/10.3390/rs11141725 - 20 Jul 2019

Cited by 38 | Viewed by 5946

Abstract

There is a growing demand for cheap and fast cadastral mapping methods to face the challenge of 70% global unregistered land rights. As traditional on-site field surveying is time-consuming and labor intensive, imagery-based cadastral mapping has in recent years been advocated by fit-for-purpose [...] Read more.

There is a growing demand for cheap and fast cadastral mapping methods to face the challenge of 70% global unregistered land rights. As traditional on-site field surveying is time-consuming and labor intensive, imagery-based cadastral mapping has in recent years been advocated by fit-for-purpose (FFP) land administration. However, owing to the semantic gap between the high-level cadastral boundary concept and low-level visual cues in the imagery, improving the accuracy of automatic boundary delineation remains a major challenge. In this research, we use imageries acquired by Unmanned Aerial Vehicles (UAV) to explore the potential of deep Fully Convolutional Networks (FCNs) for cadastral boundary detection in urban and semi-urban areas. We test the performance of FCNs against other state-of-the-art techniques, including Multi-Resolution Segmentation (MRS) and Globalized Probability of Boundary (gPb) in two case study sites in Rwanda. Experimental results show that FCNs outperformed MRS and gPb in both study areas and achieved an average accuracy of 0.79 in precision, 0.37 in recall and 0.50 in F-score. In conclusion, FCNs are able to effectively extract cadastral boundaries, especially when a large proportion of cadastral boundaries are visible. This automated method could minimize manual digitization and reduce field work, thus facilitating the current cadastral mapping and updating practices. Full article

(This article belongs to the Special Issue Remote Sensing for Land Administration)

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25 pages, 8538 KiB

Open AccessArticle

Monitoring of Nitrogen and Grain Protein Content in Winter Wheat Based on Sentinel-2A Data

by Haitao Zhao, Xiaoyu Song, Guijun Yang, Zhenhai Li, Dongyan Zhang and Haikuan Feng

Remote Sens. 2019, 11(14), 1724; https://doi.org/10.3390/rs11141724 - 20 Jul 2019

Cited by 39 | Viewed by 5604

Abstract

Grain protein content (GPC) is an important indicator of wheat quality. Earlier estimation of wheat GPC based on remote sensing provided effective decision to adapt optimized strategies for grain harvest, which is of great significance for agricultural production. The objectives of this field [...] Read more.

Grain protein content (GPC) is an important indicator of wheat quality. Earlier estimation of wheat GPC based on remote sensing provided effective decision to adapt optimized strategies for grain harvest, which is of great significance for agricultural production. The objectives of this field study are: (i) To assess the ability of spectral vegetation indices (VIs) of Sentinel 2 data to detect the wheat nitrogen (N) attributes related to the grain quality of winter wheat production, and (ii) to examine the accuracy of wheat N status and GPC estimation models based on different VIs and wheat nitrogen parameters across Analytical Spectra Devices (ASD) and Unmanned Aerial Vehicle (UAV) hyper-spectral data-simulated sentinel data and the real Sentinel-2 data. In this study, four nitrogen parameters at the wheat anthesis stage, including plant nitrogen accumulation (PNA), plant nitrogen content (PNC), leaf nitrogen accumulation (LNA), and leaf nitrogen content (LNC), were evaluated for their relationship between spectral parameters and GPC. Then, a multivariate linear regression method was used to establish the wheat nitrogen and GPC estimation model through simulated Sentinel-2A VIs. The coefficients of determination (

R^{2}

) of four nitrogen parameter models were all greater than 0.7. The minimum

R^{2}

of the prediction model of wheat GPC constructed by four nitrogen parameters combined with VIs was 0.428 and the highest

R^{2}

was 0.467. The normalized root mean square error (nRMSE) of the four nitrogen estimation models ranged from 26.333% to 29.530% when verified by the ground-measured data collected from the Beijing suburbs, and the corresponding nRMSE for the GPC-predicted models ranged from 17.457% to 52.518%. The accuracy of the estimated model was verified by UAV hyper-spectral data which had resized to different spatial resolution collected from the National Experimental Station for Precision Agriculture. The normalized root mean square error (nRMSE) of the four nitrogen estimation models ranged from 16.9% to 37.8%, and the corresponding nRMSE for the GPC-predicted models ranged from 12.3% to 13.2%. The relevant models were also verified by Sentinel-2A data collected in 2018 while the minimum nRMSE for GPC invert model based on PNA was 7.89% and the maximum nRMSE of the GPC model based on LNC was 12.46% in Renqiu district, Hebei province. The nRMSE for the wheat nitrogen estimation model ranged from 23.200% to 42.790% for LNC and PNC. These data demonstrate that freely available Sentinel-2 imagery can be used as an important data source for wheat nutrition and grain quality monitoring. Full article

(This article belongs to the Special Issue Earth Observations and Crop Models for Sustainable Agricultural Management)

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18 pages, 6034 KiB

Open AccessArticle

Spatiotemporal Analysis of MODIS NDVI in the Semi-Arid Region of Kurdistan (Iran)

by Mehdi Gholamnia, Reza Khandan, Stefania Bonafoni and Ali Sadeghi

Remote Sens. 2019, 11(14), 1723; https://doi.org/10.3390/rs11141723 - 20 Jul 2019

Cited by 19 | Viewed by 4676

Abstract

In this study, the spatiotemporal behavior of vegetation cover in the Kurdistan province of Iran was analyzed for the first time by TIMESAT and Breaks for Additive Season and Trend (BFAST) algorithms. They were applied on Normalized Vegetation Index (NDVI) time series from [...] Read more.

In this study, the spatiotemporal behavior of vegetation cover in the Kurdistan province of Iran was analyzed for the first time by TIMESAT and Breaks for Additive Season and Trend (BFAST) algorithms. They were applied on Normalized Vegetation Index (NDVI) time series from 2000 to 2016 derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The TIMESAT software package was used to estimate the seasonal parameters of NDVI and their relation to land covers. BFAST was applied for identifying abrupt changes (breakpoints) of NDVI and their magnitudes. The results from TIMESAT and BFAST were first reported separately, and then interpreted together. TMESAT outcomes showed that the lowest and highest amplitudes of NDVI during the whole time period happened in 2008 and 2010. The spatial distribution of the number of breakpoints showed different behaviors in the west and east of the study area, and the breakpoint frequency confirmed the extreme NDVI amplitudes in 2008 and 2010 found by TIMESAT. For the first time in Iran, a correlation analysis between accumulated precipitations and maximum NDVIs (from one to seven months before the NDVI maximum) was conducted. The results showed that precipitation one month before had a higher correlation with the maximum NDVIs in the region. Overall, the results describe the NDVI behavior in terms of greenness, lifetime, abrupt changes for the different land covers, and across the years, suggesting how the northwest and west of the study area can be more susceptible to drought conditions. Full article

(This article belongs to the Special Issue Advances of Multi-Temporal Remote Sensing in Vegetation and Agriculture Research)

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18 pages, 3838 KiB

Open AccessArticle

Evaluating the Variability of Urban Land Surface Temperatures Using Drone Observations

by Joseph Naughton and Walter McDonald

Remote Sens. 2019, 11(14), 1722; https://doi.org/10.3390/rs11141722 - 20 Jul 2019

Cited by 50 | Viewed by 6584

Abstract

Urbanization and climate change are driving increases in urban land surface temperatures that pose a threat to human and environmental health. To address this challenge, we must be able to observe land surface temperatures within spatially complex urban environments. However, many existing remote [...] Read more.

Urbanization and climate change are driving increases in urban land surface temperatures that pose a threat to human and environmental health. To address this challenge, we must be able to observe land surface temperatures within spatially complex urban environments. However, many existing remote sensing studies are based upon satellite or aerial imagery that capture temperature at coarse resolutions that fail to capture the spatial complexities of urban land surfaces that can change at a sub-meter resolution. This study seeks to fill this gap by evaluating the spatial variability of land surface temperatures through drone thermal imagery captured at high-resolutions (13 cm). In this study, flights were conducted using a quadcopter drone and thermal camera at two case study locations in Milwaukee, Wisconsin and El Paso, Texas. Results indicate that land use types exhibit significant variability in their surface temperatures (3.9–15.8 °C) and that this variability is influenced by surface material properties, traffic, weather and urban geometry. Air temperature and solar radiation were statistically significant predictors of land surface temperature (R² 0.37–0.84) but the predictive power of the models was lower for land use types that were heavily impacted by pedestrian or vehicular traffic. The findings from this study ultimately elucidate factors that contribute to land surface temperature variability in the urban environment, which can be applied to develop better temperature mitigation practices to protect human and environmental health. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Land Surface Temperature (LST))

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13 pages, 5761 KiB

Open AccessArticle

Canopy and Terrain Height Retrievals with ICESat-2: A First Look

by Amy L. Neuenschwander and Lori A. Magruder

Remote Sens. 2019, 11(14), 1721; https://doi.org/10.3390/rs11141721 - 20 Jul 2019

Cited by 147 | Viewed by 8030

Abstract

NASA’s Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) launched in fall 2018 and has since collected continuous elevation data over the Earth’s surface. The primary scientific objective is to measure the cryosphere for studies related to land ice and sea ice characteristics. The [...] Read more.

NASA’s Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) launched in fall 2018 and has since collected continuous elevation data over the Earth’s surface. The primary scientific objective is to measure the cryosphere for studies related to land ice and sea ice characteristics. The vantage point from space, however, provides the opportunity to measure global surfaces including oceans, land, and vegetation. The ICESat-2 mission has dedicated products to the represented surface types, including an along-track elevation profile of terrain and canopy heights (ATL08). This study presents the first look at the ATL08 product and the quantitative assessment of the canopy and terrain height retrievals as compared to airborne lidar data. The study also provides qualitative examples of ICESat-2 observations from selected ecosystems to highlight the broad capability of the satellite for vegetation applications. Analysis of the mission’s preliminary ATL08 data product accuracy using an ICESat-2 transect over a vegetated region of Finland indicates a 5 m offset in geolocation knowledge (horizontal accuracy) well within the 6.5 m mission requirement. The vertical RMSE for the terrain and canopy height retrievals for one transect are 0.85 m and 3.2 m respectively. Full article

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11 pages, 4546 KiB

Open AccessFeature PaperTechnical Note

LEO to GEO-SAR Interferences: Modelling and Performance Evaluation

by Antonio Leanza, Marco Manzoni, Andrea Monti-Guarnieri and Marco di Clemente

Remote Sens. 2019, 11(14), 1720; https://doi.org/10.3390/rs11141720 - 20 Jul 2019

Cited by 11 | Viewed by 4115

Abstract

This paper proposes a statistical model to evaluate the impact of the signal backscattered by low Earth orbiting (LEO) synthetic aperture radar (SAR) and received by GEO-stationary orbiting SAR. The model properly accounts for the bistatic backscatter, the number of LEO-SAR satellites and [...] Read more.

This paper proposes a statistical model to evaluate the impact of the signal backscattered by low Earth orbiting (LEO) synthetic aperture radar (SAR) and received by GEO-stationary orbiting SAR. The model properly accounts for the bistatic backscatter, the number of LEO-SAR satellites and their duty cycles. The presence of many sun-synchronous, dawn-dusk satellites creates a 24 h periodic pattern in interference that should be considered in the acquisition plan of future geostationary SAR. The model, implemented by a numerical simulator, allows also the prediction of performance in future scenarios of many LEO-SAR. Examples and evaluations are made here for X band. Full article

(This article belongs to the Special Issue Radio Frequency Interference (RFI) in Microwave Remote Sensing)

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22 pages, 6774 KiB

Open AccessArticle

Tracking the Land Use/Land Cover Change in an Area with Underground Mining and Reforestation via Continuous Landsat Classification

by Jiaxin Mi, Yongjun Yang, Shaoliang Zhang, Shi An, Huping Hou, Yifei Hua and Fuyao Chen

Remote Sens. 2019, 11(14), 1719; https://doi.org/10.3390/rs11141719 - 20 Jul 2019

Cited by 40 | Viewed by 5987

Abstract

Understanding the changes in a land use/land cover (LULC) is important for environmental assessment and land management. However, tracking the dynamic of LULC has proved difficult, especially in large-scale underground mining areas with extensive LULC heterogeneity and a history of multiple disturbances. Additional [...] Read more.

Understanding the changes in a land use/land cover (LULC) is important for environmental assessment and land management. However, tracking the dynamic of LULC has proved difficult, especially in large-scale underground mining areas with extensive LULC heterogeneity and a history of multiple disturbances. Additional research related to the methods in this field is still needed. In this study, we tracked the LULC change in the Nanjiao mining area, Shanxi Province, China between 1987 and 2017 via random forest classifier and continuous Landsat imagery, where years of underground mining and reforestation projects have occurred. We applied a Savitzky–Golay filter and a normalized difference vegetation index (NDVI)-based approach to detect the temporal and spatial change, respectively. The accuracy assessment shows that the random forest classifier has a good performance in this heterogeneous area, with an accuracy ranging from 81.92% to 86.6%, which is also higher than that via support vector machine (SVM), neural network (NN), and maximum likelihood (ML) algorithm. LULC classification results reveal that cultivated forest in the mining area increased significantly after 2004, while the spatial extent of natural forest, buildings, and farmland decreased significantly after 2007. The areas where vegetation was significantly reduced were mainly because of the transformation from natural forest and shrubs into grasslands and bare lands, respectively, whereas the areas with an obvious increase in NDVI were mainly because of the conversion from grasslands and buildings into cultivated forest, especially when villages were abandoned after mining subsidence. A partial correlation analysis demonstrated that the extent of LULC change was significantly related to coal production and reforestation, which indicated the effects of underground mining and reforestation projects on LULC changes. This study suggests that continuous Landsat classification via random forest classifier could be effective in monitoring the long-term dynamics of LULC changes, and provide crucial information and data for the understanding of the driving forces of LULC change, environmental impact assessment, and ecological protection planning in large-scale mining areas. Full article

(This article belongs to the Special Issue Remote Sensing of Human-Environment Interactions)

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13 pages, 3454 KiB

Open AccessArticle

Remote Sensing of Lake Ice Phenology across a Range of Lakes Sizes, ME, USA

by Shuai Zhang and Tamlin M. Pavelsky

Remote Sens. 2019, 11(14), 1718; https://doi.org/10.3390/rs11141718 - 20 Jul 2019

Cited by 29 | Viewed by 4117

Abstract

Remote sensing of ice phenology for small lakes is hindered by a lack of satellite observations with both high temporal and spatial resolutions. By merging multi-source satellite data over individual lakes, we present a new algorithm that successfully estimates ice freeze and thaw [...] Read more.

Remote sensing of ice phenology for small lakes is hindered by a lack of satellite observations with both high temporal and spatial resolutions. By merging multi-source satellite data over individual lakes, we present a new algorithm that successfully estimates ice freeze and thaw timing for lakes with surface areas as small as 0.13 km² and obtains consistent results across a range of lake sizes. We have developed an approach for classifying ice pixels based on the red reflectance band of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, with a threshold calibrated against ice fraction from Landsat Fmask over each lake. Using a filter derived from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) surface air temperature product, we removed outliers in the time series of lake ice fraction. The time series of lake ice fraction was then applied to identify lake ice breakup and freezeup dates. Validation results from over 296 lakes in Maine indicate that the satellite-based lake ice timing detection algorithm perform well, with mean absolute error (MAE) of 5.54 days for breakup dates and 7.31 days for freezeup dates. This algorithm can be applied to lakes worldwide, including the nearly two million lakes with surface area between 0.1 and 1 km². Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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22 pages, 1063 KiB

Open AccessArticle

Reflectance Properties of Hemiboreal Mixed Forest Canopies with Focus on Red Edge and Near Infrared Spectral Regions

by Lea Hallik, Andres Kuusk, Mait Lang and Joel Kuusk

Remote Sens. 2019, 11(14), 1717; https://doi.org/10.3390/rs11141717 - 20 Jul 2019

Cited by 15 | Viewed by 3816

Abstract

This study present the results of airborne top-of-canopy measurements of reflectance spectra in the spectral domain of 350–1050 nm over the hemiboreal mixed forest. We investigated spectral transformations that were originally designed for utilization at very different spectral resolutions. We found that the [...] Read more.

This study present the results of airborne top-of-canopy measurements of reflectance spectra in the spectral domain of 350–1050 nm over the hemiboreal mixed forest. We investigated spectral transformations that were originally designed for utilization at very different spectral resolutions. We found that the estimates of red edge inflection point by two methods—the linear four-point interpolation approach (S2REP) and searching the maximum of the first derivative spectrum (

D_{m a x}

) according to the mathematical definition of red edge inflection point—were well related to each other but S2REP produced a continuously shifting location of red edge inflection point while

D_{m a x}

resulted in a discrete variable with peak jumps between fixed locations around 717 nm and 727 nm for forest canopy (the third maximum at 700 nm appeared only in clearcut areas). We found that, with medium high spectral resolution (bandwidth 10 nm, spectral step 3.3 nm), the in-filling of the O

_{2}

-A Fraunhofer line (

F_{a r e a}

) was very strongly related to single band reflectance factor in NIR spectral region (

ρ

= 0.91, p < 0.001) and not related to Photochemical Reflectance Index (PRI). Stemwood volume, basal area and tree height of dominant layer were negatively correlated with reflectance factors at both visible and NIR spectral region due to the increase in roughness of canopy surface and the amount of shade. Forest age was best related to single band reflectance at NIR region (

ρ

= −0.48, p < 0.001) and the best predictor for allometric LAI was the single band reflectance at red spectral region (

ρ

= −0.52, p < 0.001) outperforming all studied vegetation indices. It suggests that Sentinel-2 MSI bands with higher spatial resolution (10 m pixel size) could be more beneficial than increased spectral resolution for monitoring forest LAI and age. The new index R

_{751}

/R

_{736}

originally developed for leaf chlorophyll content estimation, also performed well at the canopy level and was mainly influenced by the location of red edge inflection point (

ρ

= 0.99, p < 0.001) providing similar info in a simpler mathematical form and using a narrow spectral region very close to the O

_{2}

-A Fraunhofer line. Full article

(This article belongs to the Special Issue Remote Sensing of Boreal Forests)

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14 pages, 4630 KiB

Open AccessArticle

Dasymetric Mapping Using UAV High Resolution 3D Data within Urban Areas

by Carla Rebelo, António Manuel Rodrigues and José António Tenedório

Remote Sens. 2019, 11(14), 1716; https://doi.org/10.3390/rs11141716 - 19 Jul 2019

Cited by 7 | Viewed by 3854

Abstract

Multi-temporal analysis of census small-area microdata is hampered by the fact that census tract shapes do not often coincide between census exercises. Dasymetric mapping techniques provide a workaround that is nonetheless highly dependent on the quality of ancillary data. The objectives of this [...] Read more.

Multi-temporal analysis of census small-area microdata is hampered by the fact that census tract shapes do not often coincide between census exercises. Dasymetric mapping techniques provide a workaround that is nonetheless highly dependent on the quality of ancillary data. The objectives of this work are to: (1) Compare the use of three spatial techniques for the estimation of population according to census tracts: Areal interpolation and dasymetric mapping using control data—building block area (2D) and volume (3D); (2) demonstrate the potential of unmanned aerial vehicle (UAV) technology for the acquisition of control data; (3) perform a sensitivity analysis using Monte Carlo simulations showing the effect of changes in building block volume (3D information) in population estimates. The control data were extracted by a (semi)-automatic solution—3DEBP (3D extraction building parameters) developed using free open source software (FOSS) tools. The results highlight the relevance of 3D for the dasymetric mapping exercise, especially if the variations in height between building blocks are significant. Using low-cost UAV backed systems with a FOSS-only computing framework also proved to be a competent solution with a large scope of potential applications. Full article

(This article belongs to the Special Issue Remote Sensing for Urban Morphology)

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14 pages, 2055 KiB

Open AccessArticle

Using Solar-Induced Chlorophyll Fluorescence Observed by OCO-2 to Predict Autumn Crop Production in China

by Jin Wei, Xuguang Tang, Qing Gu, Min Wang, Mingguo Ma and Xujun Han

Remote Sens. 2019, 11(14), 1715; https://doi.org/10.3390/rs11141715 - 19 Jul 2019

Cited by 18 | Viewed by 4130

Abstract

The remote sensing of solar-induced chlorophyll fluorescence (SIF) has attracted considerable attention as a new monitor of vegetation photosynthesis. Previous studies have revealed the close correlation between SIF and terrestrial gross primary productivity (GPP), and have used SIF to estimate vegetation GPP. This [...] Read more.

The remote sensing of solar-induced chlorophyll fluorescence (SIF) has attracted considerable attention as a new monitor of vegetation photosynthesis. Previous studies have revealed the close correlation between SIF and terrestrial gross primary productivity (GPP), and have used SIF to estimate vegetation GPP. This study investigated the relationship between the Orbiting Carbon Observatory-2 (OCO-2) SIF products at two retrieval bands (SIF757, SIF771) and the autumn crop production in China during the summer of 2015 on different timescales. Subsequently, we evaluated the performance to estimate the autumn crop production of 2016 by using the optimal model developed in 2015. In addition, the OCO-2 SIF was compared with the moderate resolution imaging spectroradiometer (MODIS) vegetation indices (VIs) (normalized difference vegetation index, NDVI; enhanced vegetation index, EVI) for predicting the crop production. All the remotely sensed products exhibited the strongest correlation with autumn crop production in July. The OCO-2 SIF757 estimated autumn crop production best (R² = 0.678, p < 0.01; RMSE = 748.901 ten kilotons; MAE = 567.629 ten kilotons). SIF monitored the crop dynamics better than VIs, although the performances of VIs were similar to SIF. The estimation accuracy was limited by the spatial resolution and discreteness of the OCO-2 SIF products. Our findings demonstrate that SIF is a feasible approach for the crop production estimation and is not inferior to VIs, and suggest that accurate autumn crop production forecasts while using the SIF-based model can be obtained one to two months before the harvest. Furthermore, the proposed method can be widely applied with the development of satellite-based SIF observation technology. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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3 pages, 172 KiB

Open AccessEditorial

Editorial for the Special Issue “Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions”

by Eija Honkavaara, Konstantinos Karantzalos, Xinlian Liang, Erica Nocerino, Ilkka Pölönen and Petri Rönnholm

Remote Sens. 2019, 11(14), 1714; https://doi.org/10.3390/rs11141714 - 19 Jul 2019

Cited by 1 | Viewed by 2378

Abstract

This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the utilization of these technologies in a variety of geospatial applications. The presented results [...] Read more.

This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results when multimodal data was used in object analysis. Full article

(This article belongs to the Special Issue Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions)

24 pages, 15856 KiB

Open AccessFeature PaperArticle

Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification

by Shahab Eddin Jozdani, Brian Alan Johnson and Dongmei Chen

Remote Sens. 2019, 11(14), 1713; https://doi.org/10.3390/rs11141713 - 19 Jul 2019

Cited by 133 | Viewed by 9689

Abstract

With the advent of high-spatial resolution (HSR) satellite imagery, urban land use/land cover (LULC) mapping has become one of the most popular applications in remote sensing. Due to the importance of context information (e.g., size/shape/texture) for classifying urban LULC features, Geographic Object-Based Image [...] Read more.

With the advent of high-spatial resolution (HSR) satellite imagery, urban land use/land cover (LULC) mapping has become one of the most popular applications in remote sensing. Due to the importance of context information (e.g., size/shape/texture) for classifying urban LULC features, Geographic Object-Based Image Analysis (GEOBIA) techniques are commonly employed for mapping urban areas. Regardless of adopting a pixel- or object-based framework, the selection of a suitable classifier is of critical importance for urban mapping. The popularity of deep learning (DL) (or deep neural networks (DNNs)) for image classification has recently skyrocketed, but it is still arguable if, or to what extent, DL methods can outperform other state-of-the art ensemble and/or Support Vector Machines (SVM) algorithms in the context of urban LULC classification using GEOBIA. In this study, we carried out an experimental comparison among different architectures of DNNs (i.e., regular deep multilayer perceptron (MLP), regular autoencoder (RAE), sparse, autoencoder (SAE), variational autoencoder (AE), convolutional neural networks (CNN)), common ensemble algorithms (Random Forests (RF), Bagging Trees (BT), Gradient Boosting Trees (GB), and Extreme Gradient Boosting (XGB)), and SVM to investigate their potential for urban mapping using a GEOBIA approach. We tested the classifiers on two RS images (with spatial resolutions of 30 cm and 50 cm). Based on our experiments, we drew three main conclusions: First, we found that the MLP model was the most accurate classifier. Second, unsupervised pretraining with the use of autoencoders led to no improvement in the classification result. In addition, the small difference in the classification accuracies of MLP from those of other models like SVM, GB, and XGB classifiers demonstrated that other state-of-the-art machine learning classifiers are still versatile enough to handle mapping of complex landscapes. Finally, the experiments showed that the integration of CNN and GEOBIA could not lead to more accurate results than the other classifiers applied. Full article

(This article belongs to the Section Urban Remote Sensing)

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16 pages, 2017 KiB

Open AccessArticle

Assessing the Impact of the Built-Up Environment on Nighttime Lights in China

by Cheng Wang, Haiming Qin, Kaiguang Zhao, Pinliang Dong, Xuebo Yang, Guoqing Zhou and Xiaohuan Xi

Remote Sens. 2019, 11(14), 1712; https://doi.org/10.3390/rs11141712 - 19 Jul 2019

Cited by 8 | Viewed by 3469

Abstract

Figuring out the effect of the built-up environment on artificial light at night is essential for better understanding nighttime luminosity in both socioeconomic and ecological perspectives. However, there are few studies linking artificial surface properties to nighttime light (NTL). This study uses a [...] Read more.

Figuring out the effect of the built-up environment on artificial light at night is essential for better understanding nighttime luminosity in both socioeconomic and ecological perspectives. However, there are few studies linking artificial surface properties to nighttime light (NTL). This study uses a statistical method to investigate effects of construction region environments on nighttime brightness and its variation with building height and regional economic development level. First, we extracted footprint-level target heights from Geoscience Laser Altimeter System (GLAS) waveform light detection and ranging (LiDAR) data. Then, we proposed a set of built-up environment properties, including building coverage, vegetation fraction, building height, and surface-area index, and then extracted these properties from GLAS-derived height, GlobeLand30 land-cover data, and DMSP/OLS radiance-calibrated NTL data. Next, the effects of non-building areas on NTL data were removed based on a supervised method. Finally, linear regression analyses were conducted to analyze the relationships between nighttime lights and built-up environment properties. Results showed that building coverage and vegetation fraction have weak correlations with nighttime lights (R² < 0.2), building height has a moderate correlation with nighttime lights (R² = 0.48), and surface-area index has a significant correlation with nighttime lights (R² = 0.64). The results suggest that surface-area index is a more reasonable measure for estimating light number and intensity of NTL because it takes into account both building coverage and height, i.e., building surface area. Meanwhile, building height contributed to nighttime lights greater than building coverage. Further analysis showed the correlation between NTL and surface-area index becomes stronger with the increase of building height, while it is the weakest when the regional economic development level is the highest. In conclusion, these results can help us better understand the determinants of nighttime lights. Full article

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21 pages, 4086 KiB

Open AccessArticle

An Unsupervised Method to Detect Rock Glacier Activity by Using Sentinel-1 SAR Interferometric Coherence: A Regional-Scale Study in the Eastern European Alps

by Aldo Bertone, Francesco Zucca, Carlo Marin, Claudia Notarnicola, Giovanni Cuozzo, Karl Krainer, Volkmar Mair, Paolo Riccardi, Mattia Callegari and Roberto Seppi

Remote Sens. 2019, 11(14), 1711; https://doi.org/10.3390/rs11141711 - 19 Jul 2019

Cited by 11 | Viewed by 4298

Abstract

Rock glaciers are widespread periglacial landforms in mountain regions like the European Alps. Depending on their ice content, they are characterized by slow downslope displacement due to permafrost creep. These landforms are usually mapped within inventories, but understand their activity is a very [...] Read more.

Rock glaciers are widespread periglacial landforms in mountain regions like the European Alps. Depending on their ice content, they are characterized by slow downslope displacement due to permafrost creep. These landforms are usually mapped within inventories, but understand their activity is a very difficult task, which is frequently accomplished using geomorphological field evidences, direct measurements, or remote sensing approaches. In this work, a powerful method to analyze the rock glaciers’ activity was developed exploiting the synthetic aperture radar (SAR) satellite data. In detail, the interferometric coherence estimated from Sentinel-1 data was used as key indicator of displacement, developing an unsupervised classification method to distinguish moving (i.e., characterized by detectable displacement) from no-moving (i.e., without detectable displacement) rock glaciers. The original application of interferometric coherence, estimated here using the rock glacier outlines as boundaries instead of regular kernel windows, allows describing the activity of rock glaciers at a regional-scale. The method was developed and tested over a large mountainous area located in the Eastern European Alps (South Tyrol and western part of Trentino, Italy) and takes into account all the factors that may limit the effectiveness of the coherence in describing the rock glaciers’ activity. The activity status of more than 1600 rock glaciers was classified by our method, identifying more than 290 rock glaciers as moving. The method was validated using an independent set of rock glaciers whose activity is well-known, obtaining an accuracy of 88%. Our method is replicable over any large mountainous area where rock glaciers are already mapped and makes it possible to compensate for the drawbacks of time-consuming and subjective analysis based on geomorphological evidences or other SAR approaches. Full article

(This article belongs to the Special Issue Remote Sensing of Changing Mountain Environments)

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18 pages, 4813 KiB

Open AccessArticle

Developing Transformation Functions for VENμS and Sentinel-2 Surface Reflectance over Israel

by V.S. Manivasagam, Gregoriy Kaplan and Offer Rozenstein

Remote Sens. 2019, 11(14), 1710; https://doi.org/10.3390/rs11141710 - 19 Jul 2019

Cited by 21 | Viewed by 6096

Abstract

Vegetation and Environmental New micro Spacecraft (VENμS) and Sentinel-2 are both ongoing earth observation missions that provide high-resolution multispectral imagery at 10 m (VENμS) and 10–20 m (Sentinel-2), at relatively high revisit frequencies (two days for VENμS and five days for Sentinel-2). Sentinel-2 [...] Read more.

Vegetation and Environmental New micro Spacecraft (VENμS) and Sentinel-2 are both ongoing earth observation missions that provide high-resolution multispectral imagery at 10 m (VENμS) and 10–20 m (Sentinel-2), at relatively high revisit frequencies (two days for VENμS and five days for Sentinel-2). Sentinel-2 provides global coverage, whereas VENμS covers selected regions, including parts of Israel. To facilitate the combination of these sensors into a unified time-series, a transformation model between them was developed using imagery from the region of interest. For this purpose, same-day acquisitions from both sensor types covering the surface reflectance over Israel, between April 2018 and November 2018, were used in this study. Transformation coefficients from VENμS to Sentinel-2 surface reflectance were produced for their overlapping spectral bands (i.e., visible, red-edge and near-infrared). The performance of these spectral transformation functions was assessed using several methods, including orthogonal distance regression (ODR), the mean absolute difference (MAD), and spectral angle mapper (SAM). Post-transformation, the value of the ODR slopes were close to unity for the transformed VENμS reflectance with Sentinel-2 reflectance, which indicates near-identity of the two datasets following the removal of systemic bias. In addition, the transformation outputs showed better spectral similarity compared to the original images, as indicated by the decrease in SAM from 0.093 to 0.071. Similarly, the MAD was reduced post-transformation in all bands (e.g., the blue band MAD decreased from 0.0238 to 0.0186, and in the NIR it decreased from 0.0491 to 0.0386). Thus, the model helps to combine the images from Sentinel-2 and VENμS into one time-series that facilitates continuous, temporally dense vegetation monitoring. Full article

(This article belongs to the Special Issue Cross-Calibration and Interoperability of Remote Sensing Instruments)

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26 pages, 54047 KiB

Open AccessArticle

Rapid Assessment of a Typhoon Disaster Based on NPP-VIIRS DNB Daily Data: The Case of an Urban Agglomeration along Western Taiwan Straits, China

by Yuanmao Zheng, Guofan Shao, Lina Tang, Yuanrong He, Xiaorong Wang, Yening Wang and Haowei Wang

Remote Sens. 2019, 11(14), 1709; https://doi.org/10.3390/rs11141709 - 19 Jul 2019

Cited by 31 | Viewed by 4909

Abstract

Rapid assessment of natural disasters is essential for disaster analysis and spatially explicit strategic decisions of post-disaster reconstruction but requires timely available data. The recent daily data of the National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) day/night band (DNB) provide new [...] Read more.

Rapid assessment of natural disasters is essential for disaster analysis and spatially explicit strategic decisions of post-disaster reconstruction but requires timely available data. The recent daily data of the National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) day/night band (DNB) provide new opportunities to detect and evaluate natural disasters. Here, we introduce an application of NPP-VIIRS DNB daily data for rapidly assessing the damage of a severe typhoon that struck the urban agglomerations along the western Taiwan Straits in China. Our research explored the methods of rapid identification and extraction of the areas based on changes in nighttime light (NTL) after the typhoon disaster by using a statistical radiation-normalization method. We analyzed the correlations of NTL image derivatives with human population, population density, and gross domestic product (GDP). The strong correlations were found between NTL image light density and population density (R² = 0.83) and between the total nighttime light intensity and GDP (R² = 0.96) at the prefecture level. In addition, we examined the interrelationships between changes in NTL images and the areas affected by the typhoon and proposed a method to predict the affected population. Finally, the affected area and the affected population in the study area could be rapidly retrieved based on the proposed remote sensing method. The overall accuracy was 83.2% for the detection of the affected population after disaster and the recovery rate of the affected area was 86.9% in the third week after the typhoon. This research demonstrates that the NTL image-based change detection method is simple and effective, and further explains that the NPP-VIIRS DNB daily data are useful for rapidly assessing affected areas and affected populations after typhoon disasters, and for timely quantifying the degree of recovery at a large spatial scale. Full article

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21 pages, 8698 KiB

Open AccessArticle

Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery

by Shuang Cao, Yongtao Yu, Haiyan Guan, Daifeng Peng and Wanqian Yan

Remote Sens. 2019, 11(14), 1708; https://doi.org/10.3390/rs11141708 - 19 Jul 2019

Cited by 10 | Viewed by 3584

Abstract

Vehicle detection from remote sensing images plays a significant role in transportation related applications. However, the scale variations, orientation variations, illumination variations, and partial occlusions of vehicles, as well as the image qualities, bring great challenges for accurate vehicle detection. In this paper, [...] Read more.

Vehicle detection from remote sensing images plays a significant role in transportation related applications. However, the scale variations, orientation variations, illumination variations, and partial occlusions of vehicles, as well as the image qualities, bring great challenges for accurate vehicle detection. In this paper, we present an affine-function transformation-based object matching framework for vehicle detection from unmanned aerial vehicle (UAV) images. First, meaningful and non-redundant patches are generated through a superpixel segmentation strategy. Then, the affine-function transformation-based object matching framework is applied to a vehicle template and each of the patches for vehicle existence estimation. Finally, vehicles are detected and located after matching cost thresholding, vehicle location estimation, and multiple response elimination. Quantitative evaluations on two UAV image datasets show that the proposed method achieves an average completeness, correctness, quality, and F1-measure of 0.909, 0.969, 0.883, and 0.938, respectively. Comparative studies also demonstrate that the proposed method achieves compatible performance with the Faster R-CNN and outperforms the other eight existing methods in accurately detecting vehicles of various conditions. Full article

(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)

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18 pages, 15405 KiB

Open AccessArticle

Spatio-Temporal Patterns of Coastal Aquaculture Derived from Sentinel-1 Time Series Data and the Full Landsat Archive

by Dorothee Stiller, Marco Ottinger and Patrick Leinenkugel

Remote Sens. 2019, 11(14), 1707; https://doi.org/10.3390/rs11141707 - 18 Jul 2019

Cited by 41 | Viewed by 5613

Abstract

Asia is the major contributor to global aquaculture production in quantity, accounting for almost 90%. These practices lead to extensive land-use and land-cover changes in coastal areas, and thus harm valuable and sensitive coastal ecosystems. Remote sensing and GIS technologies contribute to the [...] Read more.

Asia is the major contributor to global aquaculture production in quantity, accounting for almost 90%. These practices lead to extensive land-use and land-cover changes in coastal areas, and thus harm valuable and sensitive coastal ecosystems. Remote sensing and GIS technologies contribute to the mapping and monitoring of changes in aquaculture, providing essential information for coastal management applications. This study aims to investigate aquaculture expansion and spatio-temporal dynamics in two Chinese river deltas over three decades: the Yellow River Delta (YRD) and the Pearl River Delta (PRD). Long-term patterns of aquaculture change are extracted based on combining a reference layer on existing aquaculture ponds for 2015 derived from Sentinel-1 data with annual information on water bodies extracted from the long-term Landsat archive. Furthermore, the suitability of the proposed approach to be applied on a global scale is tested based on exploiting the Global Surface Water (GSW) dataset. We found enormous increases in aquaculture area for the investigated target deltas: an 18.6-fold increase for the YRD (1984–2016), and a 4.1-fold increase for the PRD (1990–2016). Furthermore, we detect hotspots of aquaculture expansion based on linear regression analyses for the deltas, indicating that hotspots are located in coastal regions for the YRD and along the Pearl River in the PRD. A comparison with high-resolution Google Earth data demonstrates that the proposed approach can detect spatio-temporal changes of aquaculture at an overall accuracy of 89%. The presented approach has the potential to be applied to larger spatial scales covering a time period of more than three decades. This is crucial to define appropriate management strategies to reduce the environmental impacts of aquaculture expansion, which are expected to increase in the future. Full article

(This article belongs to the Special Issue Remote Sensing for Fisheries and Aquaculture)

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Remote Sens., Volume 11, Issue 14 (July-2 2019) – 106 articles

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