Distribution of land cover has a profound impact on the climate and environment; mapping the land cover patterns from global, regional to local scales are important for scientists and authorities to ...yield better monitoring of the changing world. Satellite remote sensing has been demonstrated as an efficient tool to monitor the land cover patterns for a large spatial extent. Nevertheless, the demand on land cover maps at a finer scale (especially in urban areas) has been raised with evidence by numerous biophysical and socio-economic studies. This paper reviews the small-footprint LiDAR sensor — one of the latest high resolution airborne remote sensing technologies, and its application on urban land cover classification. While most of the early researches focus on the analysis of geometric components of 3D LiDAR data point clouds, there has been an increasing interest in investigating the use of intensity data, waveform data and multi-sensor data to facilitate land cover classification and object recognition in urban environment. In this paper, the advancement of airborne LiDAR technology, including data configuration, feature spaces, classification techniques, and radiometric calibration/correction is reviewed and discussed. The review mainly focuses on the LiDAR studies conducted during the last decade with an emphasis on identification of the approach, analysis of pros and cons, investigating the overall accuracy of the technology, and how the classification results can serve as an input for different urban environmental analyses. Finally, several promising directions for future LiDAR research are highlighted, in hope that it will pave the way for the applications of urban environmental modeling and assessment at a finer scale and a greater extent.
•We review the airborne LiDAR technology to aid in urban land cover classification.•The use of LiDAR height, intensity, waveform, and multi-sensor data is summarized.•The LiDAR data classification and radiometric calibration techniques are identified.•Five other relevant urban environmental applications are presented.•We finally point out five forthcoming research areas that require further efforts.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Nacelle‐mounted, forward‐facing light detection and ranging (LIDAR) technology can deliver benefits to rotor speed regulation and loading reductions of floating offshore wind turbines (FOWTs) when ...assisting with blade pitch control in above‐rated wind speed conditions. Large‐scale wind turbines may be subject to significant variations in structural loads due to differences in the wind profile across the rotor‐swept area. These loading fluctuations can be mitigated by individual pitch control (IPC). This paper presents a novel LIDAR‐assisted feedforward IPC approach that uses each blade's rotor azimuth position to allocate an individual pitch command from a multi‐beam LIDAR. In this study, the source code of OpenFAST wind turbine modelling software was modified to enable LIDAR simulation and LIDAR‐assisted control. The LIDAR simulation modifications were accepted by the National Renewable Energy Laboratory (NREL) and are now present within OpenFAST releases from v3.5 onwards. Simulations of a 15 MW FOWT were performed across the above‐rated wind spectrum. Under a turbulent wind field with an average wind speed of 17 ms−1, the LIDAR‐assisted feedforward IPC delivered up to 54% reductions in the root mean squared errors and standard deviations of key FOWT parameters. Feedforward IPC delivered enhancements of up to 12% over feedforward collective pitch control, relative to the baseline feedback controller. The reductions to the standard deviation and range of the rotor speed may enable structural optimization of the tower, while the reductions in the variations of the loadings present an opportunity for reduced fatigue damage on turbine components and, consequently, a reduction in maintenance expenditure.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Accurate static structure reconstruction and segmentation of non-stationary objects is of vital importance for autonomous navigation applications. These applications assume a LiDAR scan to consist of ...only static structures. In the real world however, LiDAR scans consist of non-stationary dynamic structures — moving and movable objects. Current solutions use segmentation information to isolate and remove moving structures from LiDAR scan. This strategy fails in several important use-cases where segmentation information is not available. In such scenarios, moving objects and objects with high uncertainty in their motion i.e. movable objects, may escape detection. This violates the above assumption. We present MOVES, a novel GAN based adversarial model that segments out moving as well as movable objects in the absence of segmentation information. We achieve this by accurately transforming a dynamic LiDAR scan to its corresponding static scan. This is obtained by replacing dynamic objects and corresponding occlusions with static structures which were occluded by dynamic objects. We leverage corresponding static-dynamic LiDAR pairs. We design a novel discriminator, coupled with a contrastive loss on a smartly selected LiDAR scan triplet. For datasets lacking paired information, we propose MOVES-MMD that integrates Unsupervised Domain Adaptation into the network. We perform rigorous experiments to demonstrate state of the art dynamic to static translation performance on a sparse real world industrial dataset, an urban and a simulated dataset. MOVES also segments out movable and moving objects without using segmentation information. Without utilizing segmentation labels, MOVES performs better than segmentation based navigation baseline in highly dynamic and long LiDAR sequences. The code is available here.
•Accurate background static structures prediction and segmentation of dynamic objects in the absence of segmentation labels.•Replace dynamic objects and occlusions with precise background static structures and segment uncertain movable objects and moving objects accurately.•A Novel GAN with a contrastive paired discriminator trained using smartly selected LiDAR triplets with Hard Negative Mining.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Accurate bathymetric data is essential for marine, coastal ecosystems, and related studies. In the past decades, a lot of studies were investigated to obtain bathymetric data in shallow waters using ...satellite remotely sensed data. Satellite multispectral imagery has been widely used to estimate shallow water depths based on empirical models and physics-based models. However, the in-situ water depth information is essential (as the priori) to use the empirical model in a specific area, which limits its application, especially for remote reefs. In this study, the bathymetric maps in shallow waters were produced based on empirical models with only satellite remotely sensed data (i.e., the new ICESat-2 bathymetric points and Sentinel-2 multispectral imagery). The bathymetric points from the spaceborne ICESat-2 lidar were used in place of the in-situ auxiliary bathymetric points to train the classical empirical models (i.e., the linear model and the band ratio model). The bathymetric points were firstly extracted from noisy ICESat-2 raw data photons by an improved point cloud processing algorithm, and then were corrected for bathymetric errors (which were caused by the refraction effect in the water column, the refraction effect on the water surface, and the fluctuation effect on the water surface). With the trained empirical models and Sentinel-2 multispectral images, the bathymetric maps were produced for Yongle Atoll, in the South China Sea and the lagoon near Acklins Island and Long Cay, to the southeast of Bahama with four-date Sentinel-2 images. The bathymetry performance (including the accuracy and consistency of multi-date data) was evaluated and compared with the in-situ measurements. The results indicate that the bathymetric accuracy is well, and the RMSE is lower or close to 10% of the maximum depth for the two models with four-date images in two study areas. The consistency of multi-date data is well with the mean R2 of 0.97. The main novelties of this study are that the accuracy bathymetric points can be obtained from the ICESat-2 raw data using the proposed signal processing and error correction method, and using the ICESat-2 bathymetric points, the satellite multispectral imagery based on empirical models is no longer limited by local priori measurements, which were essential in previous studies. Hence, In the future, with the help of free and open-access satellite data (i.e., ICESat-2 data and Sentinel-2 imagery), this approach can be extended to a larger scale to obtain bathymetric maps in the shallow water of coastal areas, surroundings of islands and reefs, and inland waters.
•Estimating bathymetric topography with only satellite remotely sensed data.•Using new ICESat-2 lidar points and Sentinel-2 multispectral imageries.•Proposing signal detection and bathymetric error correction method for ICESat-2.•Training empirical models by ICESat-2 bathymetric points to estimate water depths.•Drawing and validating bathymetry in two study areas with multi-date datasets.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•We present lidar system types and some considerations with regards to applications.•Current usage of lidar in forest ecology and productivity research is briefly reviewed.•Key issues for effective ...use of lidar in forest ecosystem science are identified.•Pathways for further promoting the role of lidar in advancing knowledge are suggested.
Forest structure is an important driver of ecosystem dynamics, including the exchange of carbon, water and energy between canopies and the atmosphere. Structural descriptors are also used in numerous studies of ecological processes and ecosystem services. Over the last 20+ years, lidar technology has fundamentally changed the way we observe and describe forest structure, and it will continue to impact the ways in which we investigate and monitor the relations between forest structure and functions. Here we present the currently available lidar system types (ground, air, and space-based), we highlight opportunities and challenges associated with each system, as well as challenges associated with a wider use of lidar technology and wider availability of lidar derived products. We also suggest pathways for lidar to further contribute to addressing questions in forest ecosystem science and increase benefits to a wider community of researchers.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We present the demonstration of an integrated frequency modulated continuous wave LiDAR on a silicon platform. The waveform calibration, the scanning system, and the balanced detectors are ...implemented on a chip. Detection and ranging of a moving hard target at upto 60 m with less than 5 mW of output power is demonstrated in this paper.
Este trabalho tem como objetivo avaliar o potencial de ferramentas tais como a fotografia histórica aérea, a prospecção aérea, as modernas fotografias por satélite, o LiDAR aéreo ou software GIS para ...o estudo de assentamentos militares romanos temporais. Cinco sítios localizados dentro dos limites da comunidade autónoma de Castela e Leão (Espanha) foram selecionados como amostra ilustrativa: La Chana, Valdemeda, Villalazán, El Burgo de Osma e Huerga de Frailes
Im Zusammenhang mit dem Erreichen der Klimaziele durch den Einsatz erneuerbarer Energien wurde in der Physikalisch-Technischen Bundesanstalt (PTB) in den letzten Jahren ein bistatisches Wind-Lidar ...(Light detection and ranging) entwickelt und aufgebaut. Dieses Wind-Lidar ermöglicht die für Windenergieanlagen notwendige Rückführung von Windgeschwindigkeiten in Höhen von 5 m bis 250 m mit kleinsten Messunsicherheiten in beliebigem Gelände, und stellt damit erstmals eine unabhängige Alternative zu bisher eingesetzten, kostspieligen Windmessmasten dar. Der geplante Einsatz des PTB-Lidars als rückgeführtes Transfernormal im Bereich der Windfernmessung erfordert neben vorherigen Vergleichsmessungen mit anderen Windfernmesssystemen in freiem Gelände auch eine detaillierte Untersuchung des PTB-Lidars unter kontrollierbaren Strömungsbedingungen zur Validierung seiner Messunsicherheit und Überwachung seiner Langzeitstabilität. Aus diesem Grund wurde im Kompetenzzentrum für Windenergie (CCW) der PTB ein neuer, speziell konstruierter Windkanal mit einem Laser-Doppler-Anemometer (LDA) als Strömungsgeschwindigkeitsnormal auf einer Plattform in 8 m Höhe errichtet. Dies ermöglicht es, das PTB-Lidar unterhalb der Messstrecke des Windkanals zu positionieren und auf die SI-Einheiten-rückgeführte Strömungsgeschwindigkeitsmessungen mit dem Wind-Lidar durchzuführen. Der Windkanal nach Göttinger Bauart hat eine offene Messstrecke mit einer Länge von 75 cm und einer Querschnittsfläche von 50 × 50 cm
. In der Messstrecke wird eine hohe Strömungshomogenität und ein niedriger Turbulenzgrad von <0,35 % im Geschwindigkeitsbereich von 1 m/s bis 30 m/s erreicht. Die erweiterte Messunsicherheit des LDA-Referenznormals beträgt 0,16 %. Sämtliche im Windkanal durchgeführten Vergleichsmessungen zeigen eine Geschwindigkeitsabweichung zwischen dem Lidar-System und dem LDA-Referenznormal von unter ±0,5 %. Eine Optimierung der Methode zur Positionierung und damit Lokalisierung des Lidar-Messvolumens in der Windkanalmessstrecke über einen dünnen Partikelfilm führte zu einer reproduzierten Verringerung der Geschwindigkeitsabweichung auf circa −0,10 %.
Deriving land cover from remotely sensed data is fundamental to many operational mapping and reporting programs as well as providing core information to support science activities. The ability to ...generate land cover maps has benefited from free and open access to imagery, as well as increased storage and computational power. The accuracy of the land cover maps is directly linked to the calibration (or training) data used, the predictors and ancillary data included in the classification model, and the implementation of the classification, among other factors (e.g., classification algorithm, land cover heterogeneity). Various means for improving calibration data can be implemented, including using independent datasets to further refine training data prior to mapping. Opportunities also arise from a profusion of possible calibration datasets from pre-existing land cover products (static and time series) and forest inventory maps through to observation from airborne and spaceborne lidar observations. In this research, for the 650 Mha forested ecosystems of Canada, we explored approaches to refine calibration data, integrate novel predictors, and optimize classifier implementation. We refined calibration data using measures of forest vertical structure, integrated novel spatial (via distance-to metrics) model predictors, and implemented a regionalized approach for optimizing training data selection and model-building to ensure local relevance of calibration data and capture of regional variability in land cover conditions. We found that additional vetting of training data involved the removal of 44.7% of erroneous samples (e.g. treed vegetation without vertical structure) from the training pool. Nationally, distance to ephemeral waterbodies was a key predictor of land cover, while the importance of distance to permanent water bodies varied on a regional basis. Regionalization of model implementation ensured that classification models used locally relevant descriptors and resulted in improved classification outcomes (overall accuracy: 77.9% ± 1.4%) compared to a generalized, national model (70.3% ± 2.5%). The methodological developments presented herein are portable to other land cover projects, monitoring programs, and remotely sensed data sources. The increasing availability of remotely sensed data for land cover mapping, as well as non-image data for aiding with model development (from calibration data to complementary spatial data layers) provide new opportunities to improve and further automate land cover mapping procedures.
•Methodological framework to produce annual land cover from Landsat time series•Training data derived from existing land cover products and refined using lidar data•Novel distance-to surfaces to inform models with spatial-ecological information used•A regionalized approach to training data selection and model development implemented•Open datasets with roles in training and modeling serve to improve land cover maps
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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