Remote Sensing

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ISSN / EISSN : 20724292 / 20724292
Current Publisher: MDPI (10.3390)
Total articles ≅ 10,186
Google Scholar h5-index: 69
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Rong Li, Xin Mei, Liangfu Chen, Zifeng Wang, Yingying Jing, Lifei Wei
Published: 23 February 2020
Remote Sensing, Volume 12; doi:10.3390/rs12040736

Abstract:Satellite aerosol optical depth (AOD) products have been widely used in estimating fine particulate matter (PM2.5) concentrations near the surface at a regional scale, and perform well compared with ground measurements. However, the influence of limitations such as retrieval frequency and the spatial resolution of satellite AODs on the applicability of predicted PM2.5 values has been rarely considered. With three widely used MODIS AOD products, including Multi-Angle Implementation of Atmospheric Correction (MAIAC), Deep Blue (DB) and Dark Target (DT), here we evaluate the influence of their spatial resolution and sampling frequency by estimating daily PM2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region of northern China during 2017 utilizing a mixed effects model. The daily concentrations of PM2.5 derived from MAIAC, DB and DT AOD all have high correlations (R2: 0.78, 0.8, and 0.78) with the observed values, but the predicted annual PM2.5 exhibits a distinct spatial distribution. DT estimation obviously underestimates annual PM2.5 in polluted areas due to lower sampling of heavy pollution events. By contrast, the retrieval frequency (~40-60%) of MAIAC and DB AOD can represent well annual PM2.5 in nearly all 83 sites tested. However, MAIAC and DB-derived PM2.5 have a larger bias compared with observed values than DT, indicating that the large spatial variation of aerosol properties can exert an influence on the reliability of the statistical AOD-PM2.5 relationship. Also, there is notable difference between MAIAC and DB PM2.5 due to their different cloud screening methods. The MAIAC PM2.5 with high spatial resolution at 1 km can capture much finer hotpots than DB and DT at 10 km. Our results suggest that it is crucial to consider the applicability of satellite-predicted PM2.5 values derived from different aerosol products according to the specific requirements besides modeling the AOD-PM2.5 relationship.
Rebecca Brown, Preston Hartzell, Craig Glennie
Published: 22 February 2020
Remote Sensing, Volume 12; doi:10.3390/rs12040722

Abstract:Geiger-mode and single photon lidar sensors have recently emerged on the commercial market, advertising greater collection efficiency than the traditional linear mode lidar (LML) systems. Non-linear photon detection is a new technology for the geospatial community, and its performance characteristics for surveying and mapping are not yet well understood. Therefore, the geospatial quality of the data produced by one of these new sensors, the Leica SPL100, is examined by comparing the achieved lidar point cloud accuracy, precision, digital elevation model (DEM) generation, canopy penetration, and multiple return generation to a LML point cloud. We find the SPL100 has a lower ranging precision than linear mode lidar and that the precision is more negatively affected by surface properties such as low intensity and high incidence angle. The accuracy of the SPL100 point cloud, however, was found to be comparable to LML for smooth horizontal surfaces. A 1 m resolution SPL100 DEM was also comparable to a corresponding LML DEM, but the SPL100 was observed to have a reduced ability to resolve multiple returns through vegetation when compared to a LML sensor. In its current state, the SPL100 is likely best suited for applications in which the need for collection efficiency outweighs the need for maximum precision and canopy penetration and modeling.
Miguel Noguera, Borja Millán, Juan Pérez-Paredes, Juan Ponce, Arturo Aquino, José Andújar
Published: 22 February 2020
Remote Sensing, Volume 12; doi:10.3390/rs12040723

Abstract:In recent years, many olive orchards, which are a major crop in the Mediterranean basin, have been converted into intensive or super high-density hedgerow systems. This configuration is more efficient in terms of yield per hectare, but at the same time the water requirements are higher than in traditional grove arrangements. Moreover, irrigation regulations have a high environmental (through water use optimization) impact and influence on crop quality and yield. The mapping of (spatio-temporal) variability with conventional water stress assessment methods is impractical due to time and labor constraints, which often involve staff training. To address this problem, this work presents the development of a new low-cost device based on a thermal infrared (IR) sensor for the measurement of olive tree canopy temperature and monitoring of water status. The performance of the developed device was compared to a commercial thermal camera. Furthermore, the proposed device was evaluated in a commercially managed olive orchard, where two different irrigation treatments were established: a full irrigation treatment (FI) and a regulated deficit irrigation (RDC), aimed at covering 100% and 50% of crop evapotranspiration (ETc), respectively. Predawn leaf water potential (ΨPD) and stomatal conductance (gs), two widely accepted indicators for crop water status, were regressed to the measured canopy temperature. The results were promising, reaching a coefficient of determination R2 ≥ 0.80. On the other hand, the crop water stress index (CWSI) was also calculated, resulting in a coefficient of determination R2 ≥ 0.79. The outcomes provided by the developed device support its suitability for fast, low-cost, and reliable estimation of an olive orchard’s water status, even suppressing the need for supervised acquisition of reference temperatures. The newly developed device can be used for water management, reducing water usage, and for overall improvements to olive orchard management.
Neal Pastick, Devendra Dahal, Bruce Wylie, Sujan Parajuli, Stephen Boyte, Zhouting Wu
Published: 22 February 2020
Remote Sensing, Volume 12; doi:10.3390/rs12040725

Abstract:Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. However, few studies have leveraged remote sensing technologies and computing frameworks capable of providing rangeland managers with maps of exotic annual grass cover at relatively high spatiotemporal resolutions and near real-time latencies. Here, we developed a system for automated mapping of invasive annual grass (%) cover using in situ observations, harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables, and machine learning techniques. A robust and automated cloud, cloud shadow, water, and snow/ice masking procedure (mean overall accuracy >81%) was implemented using time-series outlier detection and data mining techniques prior to spatiotemporal interpolation of HLS data via regression tree models (r = 0.94; mean absolute error (MAE) = 0.02). Weekly, cloud-free normalized difference vegetation index (NDVI) image composites (2016–2018) were used to construct a suite of spectral and phenological metrics (e.g., start and end of season dates), consistent with information derived from Moderate Resolution Image Spectroradiometer (MODIS) data. These metrics were incorporated into a data mining framework that accurately (r = 0.83; MAE = 11) modeled and mapped exotic annual grass (%) cover throughout dryland ecosystems in the western United States at a native, 30-m spatial resolution. Our results show that inclusion of weekly HLS time-series data and derived indicators improves our ability to map exotic annual grass cover, as compared to distribution models that use MODIS products or monthly, seasonal, or annual HLS composites as primary inputs. This research fills a critical gap in our ability to effectively assess, manage, and monitor drylands by providing a framework that allows for an accurate and timely depiction of land surface phenology and exotic annual grass cover at spatial and temporal resolutions that are meaningful to local resource managers.
Jordi Isern-Fontanet, Emilio García-Ladona, José Jiménez-Madrid, Estrella Olmedo, Marcos García-Sotillo, Alejandro Orfila, Antonio Turiel
Published: 22 February 2020
Remote Sensing, Volume 12; doi:10.3390/rs12040724

Abstract:Surface currents in the Alboran Sea are characterized by a very fast evolution that is not well captured by altimetric maps due to sampling limitations. On the contrary, satellite infrared measurements provide high resolution synoptic images of the ocean at high temporal rate, allowing to capture the evolution of the flow. The capability of Surface Quasi-Geostrophic (SQG) dynamics to retrieve surface currents from thermal images was evaluated by comparing resulting velocities with in situ observations provided by surface drifters. A difficulty encountered comes from the lack of information about ocean salinity. We propose to exploit the strong relationship between salinity and temperature to identify water masses with distinctive salinity in satellite images and use this information to correct buoyancy. Once corrected, our results show that the SQG approach can retrieve ocean currents slightly better to that of near-real-time currents derived from altimetry in general, but much better in areas badly sampled by altimeters such as the area to the east of the Strait of Gibraltar. Although this area is far from the geostrophic equilibrium, the results show that the good sampling of infrared radiometers allows at least retrieving the direction of ocean currents in this area. The proposed approach can be used in other areas of the ocean for which water masses with distinctive salinity can be identified from satellite observations.
Weitao Yuan, Wangle Zhang, Zhongping Lai, Jingxiong Zhang
Published: 22 February 2020
Remote Sensing, Volume 12; doi:10.3390/rs12040726

Abstract:Parameters of geomorphological characteristics are critical for research on yardangs. However, which are low-cost, accurate, and automatic or semi-automatic methods for extracting these parameters are limited. We present here semi-automatic techniques for this purpose. They are object-based image analysis (OBIA) and Canny edge detection (CED), using free, very high spatial resolution images from Google Earth. We chose yardang fields in Dunhuang of west China to test the methods. Our results showed that the extractions registered an overall accuracy of 92.26% with a Kappa coefficient of agreement of 0.82 at a segmentation scale of 52 using the OBIA method, and the exaction of yardangs had the highest accuracy at medium segmentation scales (138, 145). Using CED, we resampled the experimental image subset to a series of lower spatial resolutions for eliminating noise. The total length of yardang boundaries showed a logarithmically decreasing (R2 = 0.904) trend with decreasing spatial resolution, and there was also a linear relationship between yardang median widths and spatial resolutions (R2 = 0.95). Despite the difficulty of identifying shadows, the CED method achieved an overall accuracy of 89.23%with a kappa coefficient of agreement of 0.72, similar to that of the OBIA method at medium segmentation scale (138).
Manuela Hirschmugl, Janik Deutscher, Carina Sobe, Alexandre Bouvet, Stéphane Mermoz, Mathias Schardt
Published: 22 February 2020
Remote Sensing, Volume 12; doi:10.3390/rs12040727

Abstract:Frequent cloud cover and fast regrowth often hamper topical forest disturbance monitoring with optical data. This study aims at overcoming these limitations by combining dense time series of optical (Sentinel-2 and Landsat 8) and SAR data (Sentinel-1) for forest disturbance mapping at test sites in Peru and Gabon. We compare the accuracies of the individual disturbance maps from optical and SAR time series with the accuracies of the combined map. We further evaluate the detection accuracies by disturbance patch size and by an area-based sampling approach. The results show that the individual optical and SAR based forest disturbance detections are highly complementary, and their combination improves all accuracy measures. The overall accuracies increase by about 3% in both areas, producer accuracies of the disturbed forest class increase by up to 25% in Peru when compared to only using one sensor type. The assessment by disturbance patch size shows that the amount of detections of very small disturbances (< 0.2 ha) can almost be doubled by using both data sets: for Gabon 30% as compared to 15.7–17.5%, for Peru 80% as compared to 48.6–65.7%.
Mario Batubara, Masa-Yuki Yamamoto
Published: 22 February 2020
Remote Sensing, Volume 12; doi:10.3390/rs12040728

Abstract:Thirty infrasound sensors have been operated over Japan since 2015. We developed the irregular array data processing in order to detect and estimate the parameters of the arrival source waves by using infrasound data related to the sequence of the volcanic eruption at Mt. Shinmoedake in March 2018. We found that the apparent velocity at the ground was equal to the acoustic velocity at particular reflection levels. The results were confirmed through a comparison of the findings of the apparent velocity with a wave propagation simulation on the basis of the azimuth, infrasound time arrivals, and the state of the atmospheric background using global atmospheric models. In addition, simple ideas for estimating horizontal wind speeds at certain atmospheric altitudes based on infrasound observation data and their validation and comparison were presented. The calculated upper wind speed and wind observed by radiosonde measurements were found to have a qualitative agreement. Propagation modeling for these events estimated celerities in the propagation direction to the sensors that were consistent with the tropospheric and stratospheric ducting. This study could inspire writers, in particular, and readers, in general, to take advantage of the benefits of infrasound wave remote-sensing for the study of the Earth’s atmospheric dynamics.
Ruchan Dong, Dazhuan Xu, Lichen Jiao, Jin Zhao, Jungang An
Published: 22 February 2020
Remote Sensing, Volume 12; doi:10.3390/rs12040729

Abstract:Current scene classification for high-resolution remote sensing images usually uses deep convolutional neural networks (DCNN) to extract extensive features and adopts support vector machine (SVM) as classifier. DCNN can well exploit deep features but ignore valuable shallow features like texture and directional information; and SVM can hardly train a large amount of samples in an efficient way. This paper proposes a fast deep perception network (FDPResnet) that integrates DCNN and Broad Learning System (BLS), a novel effective learning system, to extract both deep and shallow features and encapsulates a designed DPModel to fuse the two kinds of features. FDPResnet first extracts the shallow and the deep scene features of a remote sensing image through a pre-trained model on residual neural network-101 (Resnet101). Then, it inputs the two kinds of features into a designed deep perception module (DPModel) to obtain a new set of feature vectors that can describe both higher-level semantic and lower-level space information of the image. The DPModel is the key module responsible for dimension reduction and feature fusion. Finally, the obtained new feature vector is input into BLS for training and classification, and we can obtain a satisfactory classification result. A series of experiments are conducted on the challenging NWPU-RESISC45 remote sensing image dataset, and the results demonstrate that our approach outperforms some popular state-of-the-art deep learning methods, and present high-accurate scene classification within a shorter running time.
Haili Sun, Zhengwen Xu, Lianbi Yao, Ruofei Zhong, Liming Du, Hangbin Wu
Published: 22 February 2020
Remote Sensing, Volume 12; doi:10.3390/rs12040730

Abstract:The common statistical methods for rail tunnel deformation and disease detection usually require a large amount of equipment and manpower to achieve full section detection, which are time consuming and inefficient. The development trend in the industry is to use laser scanning for full section detection. In this paper, a design scheme for a tunnel monitoring and measuring system with laser scanning as the main sensor for tunnel environmental disease and deformation analysis is proposed. The system provides functions such as tunnel point cloud collection, section deformation analysis, dislocation analysis, disease extraction, tunnel and track image generation, roaming video generation, etc. Field engineering indicated that the repeatability of the convergence diameter detection of the system can reach ±2 mm, dislocation repeatability can reach ±3 mm, the image resolution is about 0.5 mm/pixel in the ballast part, and the resolution of the inner wall of the tunnel is about 1.5 mm/pixel. The system can include human–computer interaction to extract and label diseases or appurtenances and support the generation of thematic disease maps. The developed system can provide important technical support for deformation and disease detection of rail transit tunnels.