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[IEEE 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Xiamen, China (2019.12.17-2019.12.20)] 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Ultra-wideband Absorber Based on Graphene Metamaterial
摘要： Surface water is a critical resource in semiarid West-African regions that are frequently exposed to droughts. Natural and artificial wetlands are of high importance for different livelihoods, particularly during the dry season, from October/November until May. However, wetlands largely go unmonitored. In this work, remote sensing is used to monitor wetlands in semiarid Burkina Faso over large areal extents along a gradient of different rainfall and land use characteristics. Time series of data from the Moderate Resolution Imaging Spectrometer (MODIS) from 2000 to 2012 is used for near-infrared (NIR)-based water monitoring using a latitudinal threshold gradient approach. The occurrence of 21 new water bodies with a size larger than 0.5 km2 over the 13-year analysis period results from a postclassification change detection. Yearly cumulative spatiotemporal analysis shows lower water extents in the drought seasons of 2000–2001, 2004–2005, and 2011–2012. Multiple wetlands indicate a positive trend toward a larger yearly maximum area, but a negative trend toward shorter flooding duration. Such a negative trend is observed particularly for natural wetlands. The temporal behavior of five selected case studies demonstrates that monthly negative anomalies of water-covered areas coincide with the occurrence of drought seasons. The successful application of remote sensing time series as a tool to monitor wetlands in semiarid regions is presented, and the potential of novel early warning indicators of drought from remote sensing is demonstrated.
关键词： Sahel,Moderate Resolution Imaging Spectrometer (MODIS),monitoring,drought indicators,Burkina Faso,wetlands,time series,surface water
更新于2025-09-19 17:13:59
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Solution‐Processed Laminated Perovskite Layers for High‐Performance Solar Cells
摘要： In this study, the combined effect of dissolved oxygen (DO) and COD/N on nitrogen (N) removal as well as the corresponding mechanisms were investigated in aerated constructed wetlands (CWs). At each investigated COD/N level, the ammonium removal efficiency increased as DO concentration increased. However, the highest total N removal efficiency occurred at different DO concentration at each COD/N level. The results of functional gene analysis and cyclic N profile studies indicated that DO supply and COD/N influence the N removal performance, which is not only exert a direct effect on nitrification-denitrification process, but also change N removal pathway in intermittent aerated CWs. At a relatively high influent COD/N of 20, the simultaneous nitrification and denitrification (SND) via nitrite was almost the exclusive N removal pathway at all investigated DO concentration. With the decrease of COD/N from 20 to 2 at DO of ～1.8, ～3.5 and ～6.0 mg/L, SND efficiency all decreased, however, its decreasing rate was much higher at relatively high DO level of ～6.0 mg/L than that at DO levels of ～1.8 and ～3.5 mg/L. In comparison, a simultaneously partial nitrification, anammox and denitrification was established at DO of ～0.8 mg/L along with reducing influent COD/N.
关键词： Oxygen,Removal,Intermittent,Aeration,Dissolved,COD/N,Constructed wetlands,Nitrogen
更新于2025-09-19 17:13:59
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Unmanned Aerial Vehicle (UAV) photogrammetry produces accurate high-resolution orthophotos, point clouds and surface models for mapping wetlands
摘要： Unmanned Aerial Vehicle (UAV) photogrammetry has recently become a powerful tool that offers a viable alternative to traditional remote sensing systems, particularly for applications covering relatively small spatial extents. This paper presents results of a study that aimed at investigating the use of UAV photogrammetry as a tool for the mapping of wetlands. A multi-rotor UAV and a digital camera on a motion compensated gimbal mount were utilised for the survey. The survey of the 100ha study area at the Kameelzynkraal farm, Gauteng Province, South Africa took about 2? hours and the generation of the point cloud about 18 hours. Ground control points (GCPs) were positioned across the site to achieve geometrical precision and georeferencing accuracy. Structure from Motion (SfM) computer vision techniques were used to reconstruct the camera positions, terrain features and to derive ultra-high resolution point clouds, orthophotos and 3D models from the multi-view photos. The results of the geometric accuracy of the data based on the 20 GCPs were 0.018m for the overall and 0.0025m for the vertical root mean squared error (RMSE). The results exceeded our expectations and provided valuable, rapid and accurate mapping of wetlands that can be used for wetland studies and thereby support and enhance associated decision making to secure our future.
关键词： wetlands mapping,surface models,high-resolution orthophotos,point clouds,UAV photogrammetry,Structure from Motion
更新于2025-09-10 09:29:36
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Object-based correction of LiDAR DEMs using RTK-GPS data and machine learning modeling in the coastal Everglades
摘要： Light Detection and Ranging (LiDAR) Digital Elevation Models (DEMs) are frequently applied in modeling coastal environments. We present an object-based correction approach for accurate and precise DEMs by integrating LiDAR point data, aerial imagery, and Real Time Kinematic-Global Positioning Systems. Four machine learning techniques (Random Forest, Support Vector Machine, k-Nearest Neighbor, and Artificial Neural Network) were compared with the commonly used bias-correction method. The Random Forest object-based model produced best predictions for two study areas: Nine Mile (Mean Bias Error (MBE) reduced 0.18 to ?0.02 m, Root Mean Square Error (RMSE) reduced 0.22 to 0.08 m) and Flamingo (MBE reduced 0.17 to 0.02 m, RMSE reduced 0.24 to 0.10 m). A Monte Carlo model was developed to combine errors into the object-based machine learning corrected DEMs, and uncertainty maps spatially revealed the likelihood of error. The object-based correction approach provides an attractive alternative to the bias-correction method.
关键词： DEMs,Object-based image analysis,Monte Carlo,LiDAR,Machine learning,Coastal wetlands
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Monitoring of Inundation Dynamics in the North-American Prairie Pothole Region using Sentinel-1 Time Series
摘要： Monitoring of wetland inundation dynamics is important for flood management and the characterisation of hydrological connectivity. SAR-based inundation extent monitoring in wetlands is often challenging due to different factors, such as waves, vegetation cover and wet snow. The presented study targets the mapping of inundation dynamics in the Prairie Pothole Region (PPR) of North Dakota, USA. A 3-year water extent time series was derived from Sentinel-1 SAR data by first delineating permanent water bodies using a clustering approach. In a second step, water body dynamics were mapped using region growing and automatic thresholding. Results suggest that there is considerable potential for mapping surface water dynamics in late spring, summer and autumn, whereas confusion with wet snow may take place in early spring.
关键词： wetlands,remote sensing,SAR,connectivity,Sentinel-1
更新于2025-09-09 09:28:46