bluuebunny/crossref_metadata_2025 huggingface dataset:
Remote sensing of methane leakage from natural gas and petroleum systems revisited
Authors: Schneising Oliver , Buchwitz Michael , Reuter Maximilian , Vanselow Steffen , Bovensmann Heinrich , Burrows John P.
Published: August 2020

Abstract. The switch from the use of coal to natural gas or oil for energy generation potentially reduces
greenhouse gas emissions and thus the impact on global warming and climate change because of
the higher energy creation per CO2 molecule emitted. However, the climate benefit over coal
is offset by methane (CH4) leakage from natural gas and petroleum systems, which reverses
the climate impact mitigation if the rate of fugitive emissions exceeds the compensation point at
which the global warming resulting from the leakage and the benefit from the reduction of coal
combustion coincide. Consequently, an accurate quantification of CH4 emissions from the
oil and gas industry is essential to evaluate the suitability of natural gas and petroleum as
bridging fuels on the way to a carbon-neutral future. We show that regional CH4 release from large oil and gas fields can be monitored from space
by using dense daily recurrent measurements of the TROPOspheric Monitoring Instrument (TROPOMI)
onboard the Sentinel-5 Precursor satellite to quantify emissions and leakage rates. The average
emissions for the time period 2018/2019 from the five most productive basins in the United States,
the Permian, Appalachian, Eagle Ford, Bakken, and Anadarko, are estimated to be 3.18±1.13, 2.36±0.88, 1.37±0.63, 0.89±0.56, and 2.74±0.74 Mt yr−1, respectively. This corresponds to
CH4 leakage rates relative to the associated production between 1.2 % and 1.4 % for
the first four production regions, which are consistent with bottom-up estimates and likely fall
below the break-even leakage rate for immediate climate benefit. For the Anadarko Basin, the
fugitive emission rate is larger and amounts to 3.9±1.1 %, which likely exceeds the
break-even rate for immediate benefit and roughly corresponds to the break-even rate for a 20-year
time horizon. The determined values are smaller than previously derived satellite-based leakage
rates for the time period 2009–2011, which was an early phase of hydraulic fracturing, indicating
that it is possible to improve the climate footprint of the oil and gas industry by adopting new
technologies and that efforts to reduce methane emissions have been successful. For two of
the world's largest natural gas fields, Galkynysh and Dauletabad in Turkmenistan, we find collective
methane emissions of 3.26±1.17 Mt yr−1, which corresponds to a leakage rate of
4.1±1.5 %, suggesting that the Turkmen energy industry is not employing methane emission
avoidance strategies and technologies as successfully as those currently widely used in the United
States. The leakage rates in Turkmenistan and in the Anadarko Basin indicate that there is
potential to reduce fugitive methane emissions from natural gas and petroleum systems worldwide. In
particular, relatively newly developed oil and gas plays appear to have larger leakage rates
compared to more mature production areas.
An experimental study on light scattering matrices for Chinese loess dust with different particle size distributions
Authors: Liu Jia , Zhang Qixing , Huo Yinuo , Wang Jinjun , Zhang Yongming
Published: August 2020

Abstract. Mineral dust suspended in the atmosphere has significant effects on radiative balance and climate change. The Chinese Loess Plateau (CLP) is generally considered one of the main sources of Asian dust aerosol. After being lifted by wind, dust particles with various size distributions can be
transported over different distances. In this study, an original loess sample
was collected from Luochuan, which is centrally located on the CLP, and two
samples with different size distributions were obtained afterwards.
“Pristine loess” was used to represent dust that only affects source
regions, part of pristine loess was milled to finer “milled loess”
that can be transported over long distances. Light scattering matrices for
these two samples were measured at 532 nm wavelength from 5 to
175∘ angles. Particle size distribution, refractive index,
chemical component, and microscopic appearance were also characterized for
auxiliary analyses. Experimental results showed that there are obvious
discrepancies in angular behaviors of matrix elements for pristine
loess and milled loess, and these discrepancies are different from
those for other kinds of dust with distinct size distributions. Given that
the effective radii of these two loess samples differ by more than 20 times,
it is reasonable to conclude that the difference in size distributions plays
a major role in leading to different matrices, while differences in refractive index and microstructure have relatively small contributions.
Qualitative analyses of numerical simulation results of irregular particles
also validate this conclusion. Gaussian spheres may be promising
morphological models for simulating the scattering matrix of loess but need
further quantitative verification. Finally, synthetic scattering matrices
for both pristine loess and milled loess were constructed over
0–180∘, and the previous average scattering matrix for loess dust was updated. This study presents measurement results of Chinese loess dust and an updated average scattering matrix for loess, which are useful for validating existing models, developing more advanced models for optical simulations of loess dust, and helping to improve retrieval accuracy of dust aerosol properties over both source and downwind areas.
The composition of endolithic communities in gypcrete is determined by the specific microhabitat architecture
Authors: Casero María Cristina , Meslier Victoria , DiRuggiero Jocelyne , Quesada Antonio , Ascaso Carmen , Kowaluk Tomasz , Wierzchos Jacek
Published: August 2020

Abstract. Endolithic microhabitats have been described as the last refuge for life in arid and hyper-arid deserts where life has to deal with harsh environmental conditions. A number of rock substrates from the hyper-arid Atacama Desert, colonized by endolithic microbial communities, such as halite, gypsum crusts, gypcrete, calcite, granite and ignimbrite, have been characterized and compared using different approaches. In this work, three different endolithic microhabitats are described, each one with a particular origin and architecture, found within a lithic substrate known as gypcrete. Gypcrete, an evaporitic rock mainly composed of gypsum (CaSO4 ⋅ 2H2O) and collected in the Cordón de Lila area of the desert (Preandean Atacama Desert), was found to harbour cryptoendolithic (within pore spaces in the rock), chasmoendolithic (within cracks and fissures) and hypoendolithic (within microcave-like pores in rock-bottom layer) microhabitats. A combination of microscopy investigations strategies and high-throughput sequencing approaches were used to characterize the endolithic communities at the microscale in these microhabitats within the same piece of lithic substrate. Microscopy techniques revealed differences in the architecture of the endolithic microhabitats and in the distribution of the microorganisms within those microhabitats. Cyanobacteria and Proteobacteria were dominant in the endolithic communities, of which the hypoendolithic community was the least diverse and hosted unique taxa. These results show, for the first time, that the differences in the architecture of a microhabitat, even within the same piece of lithic substrate, might be an essential factor in shaping the diversity and composition of endolithic microbial communities.
Vertical mobility of pyrogenic organic matter in soils: A column experiment
Authors: Schiedung Marcus , Bellè Severin-Luca , Sigmund Gabriel , Kalbitz Karsten , Abiven Samuel
Published: August 2020

Abstract. Pyrogenic organic matter (PyOM) is a major and persistent component of soil organic matter but its mobility and cycling in soils is largely unknown. We conducted a column experiment with a topsoil and subsoil of a sand and a sandy loam to study the mobility of highly 13C labelled ryegrass PyOM (> 2.8 at%), applied as a layer on a 7 cm long soil column, under saturated conditions. Further, we used fresh and oxidized PyOM (accelerated aging with H2O2) to identify changes in its migration through the soil with aging and associated surface oxidation. Due to the isotopic signature, we were able to trace the PyOM carbon (PyOM-C) in the soil columns, including density fractions, its effect on native soil organic carbon (nSOC) and its total export in percolates sequentially sampled after 1000–18 000 l m−2. In total, 4–11 % of the added PyOM-C was mobilized and
A multi-year time series of observation-based 3D horizontal and vertical quasi-geostrophic global ocean currents
Authors: Buongiorno Nardelli Bruno
Published: August 2020

Abstract. Estimates of 3D ocean circulation are needed to improve our
understanding of ocean dynamics and to assess their impact on marine
ecosystems and Earth climate. Here we present the OMEGA3D product, an
observation-based time series of (quasi-)global 3D ocean currents covering
the 1993–2018 period, developed by the Italian Consiglio Nazionale delle
Ricerche within the European Copernicus Marine Environment Monitoring
Service (CMEMS). This dataset was obtained by applying a diabatic
quasi-geostrophic (QG) diagnostic model to the data-driven CMEMS-ARMOR3D weekly
reconstruction of temperature and salinity as well as ERA Interim fluxes. Outside
the equatorial band, vertical velocities were retrieved in the upper 1500 m at 1∕4∘ nominal resolution and successively used to
compute the horizontal ageostrophic components. Root mean square differences between OMEGA3D total horizontal velocities and totally independent drifter
observations at two different depths (15 and 1000 m) decrease with respect
to corresponding estimates obtained from zero-order geostrophic balance,
meaning that estimated vertical velocities can also be deemed reliable.
OMEGA3D horizontal velocities are also closer to drifter observations than
velocities provided by a set of reanalyses spanning a comparable time
period but based on data assimilation in ocean general circulation
numerical models. The full OMEGA3D product (released on 31 March 2020) is available
upon free registration at https://doi.org/10.25423/cmcc/multiobs_glo_phy_w_rep_015_007 (Buongiorno Nardelli, 2020a). The reduced subset used here for validation and review purposes is
openly available at https://doi.org/10.5281/zenodo.3696885
(Buongiorno Nardelli, 2020b).
Carbon Emissions and Removals by Forests: New Estimates 1990–2020
Authors: Tubiello Francesco N. , Pekkarinen Anssi , Marklund Lars , Wanner Nathan , Conchedda Giulia , Federici Sandro , Rossi Simone , Grassi Giacomo
Published: August 2020

Abstract. Trends in global, regional and national CO2 emissions and removals from forest for the period 1990–2020, are estimated for the first time using data from the Forest Resources Assessment (FRA) 2020, providing new information with respect to the previous FRA 2015. Estimates indicate significant reduction of deforestation emissions over the study period, albeit more slowly than previously assessed, from an average of 4.3 Gt CO2 yr−1 during 1991–2000, to an average of 2.9 Gt CO2 yr−1 during 2016–2020. Remaining forest land was a significant net carbon sink globally and over the entire period, albeit decreasing in strength, from −3.4 Gt CO2 yr−1 in 1991–2000 to −2.5 Gt CO2 yr−1 during 2016–2020. The overall net contribution of forests to atmospheric CO2(i.e., the combined effect of deforestation and forest emissions/removals) was an overall emission source of roughly 0.4 Gt CO2 yr−1 on average during 1991–2020, more than one-third less than previously estimated. Remarkably, the new data also suggest an overall net sink of about −0.7 Gt CO2 yr−1 during 2011–2015, never reported before. Forest emissions/removals data independently reported by countries to the United Nations Framework on Climate Change were in excellent agreement with the FAO estimates over the entire period 1990–2020, confirming a large sink on forest land estimated for 2011–2015. Data are made available as open access via the Zenodo portal (Tubiello, 2020), with DOI 10.5281/zenodo.3941973.
The role and value of distributed precipitation data for hydrological models
Authors: Loritz Ralf , Hrachowitz Markus , Neuper Malte , Zehe Erwin
Published: August 2020

Abstract. This study investigates the role and value of distributed rainfall for the runoff generation of a mesoscale catchment (20 km2). We compare the performance of three hydrological models at different periods and show that a distributed model driven by distributed rainfall yields only to improved performances during certain periods. These periods are dominated by convective storms that are typically characterized by higher spatial and temporal variabilities compared to stratiform precipitation events that dominate the rainfall generation in winter. Motivated by these findings we develop a spatially adaptive model that is capable to dynamically adjust its spatial structure during runtime to represent the varying importance of distributed rainfall within a hydrological model without losing predictive performance compared to a spatially distributed model. Our results highlight that adaptive modeling might be a promising way to better understand the varying relevance of distributed rainfall in hydrological models as well as reiterate that it might be one way to reduce computational times. They furthermore show that hydrological similarity concerning the runoff generation does not necessarily mean similarity for other dynamic variables such as the distribution of soil moisture.
CAMERA CALIBRATION MODELS AND METHODS FOR CORRIDOR MAPPING WITH UAVS
Authors: Cledat E. , Cucci D. A. , Skaloud J.
Published: August 2020

Abstract. Camera calibration refers to the modeling of the relationship between the coordinates of object points and their projections on the image plane. This is usually done by parametric models that describe the physical properties of the lens systems and camera assemblies, such as the camera principal distance, the principal point, and various types of optical distortions. In photogrammetry, accurate knowledge of the parameters of such models, often referred to as Interior Orientation (IO), is of ultimate importance. In this work, we target advanced corridor mapping applications with UAVs. In this scenario, the camera calibration is not completely observable due to the unfavorable geometry of the flight trajectory (e.g., no cross flight lines available and a single altitude) and needs to be determined beforehand. Further challenges are introduced by the limited mechanical stability of UAV-grade cameras. This may cause slight variations in the IO that need to be recovered while processing production flights. We review and compare two well known camera models, the Brown-Conrady and the Ebner’s self-calibration functions, in 36 calibration setups and provide a discussion of the results, where sub ground sampling distance accuracy in the checkpoints was achieved for some, but not all, configurations.
TARGETLESS PHOTOGRAMMETRY NETWORK SIMULATION FOR INSPECTION PLANNING IN OIL AND GAS INDUSTRY
Authors: Buschinelli P. , Salazar J. D. , Regner D. , Oliveira D. , Machado M. , Marcellino G. , Sales D. C. , Santos J. M. , Marinho C. A. , Stemmer M. R. , Pinto T. C.
Published: August 2020

Abstract. The oil and gas offshore industry demands regular inspections of components and structures that are subjected to extreme operational and environmental conditions. In this context, risers are pipelines that transport mainly oil, gas, water, and cables between submarine structures and the surface offshore platform, in the portion not touching the ocean floor. The emerged part of these risers is typically inspected by industrial climbing, which is a very time-consuming activity, has high operational costs, is dangerous and has a strong dependence on inspector skills. Remotely Piloted Aircraft Systems (RPAS) have been recently used for visual inspection of risers, however, no quantitative or geometrical evaluation has been conducted using this kind of image acquisition yet. An image-based measurement technique, such as close-range photogrammetry, can provide a 3D reconstruction using images, but a series of requisites is mandatory to achieve good results as image acquisition sequence, overlap, camera positioning network, spatial resolution and object texture in non-prepared and targetless scenes. The analysis of different image acquisition strategies using a real RPAS is too difficult because it demands a lot of time, good weather, daylight, and a scene similar to where risers are installed. An alternative is to use simulation. In this paper a ROS/Gazebo simulation is described and used to create a realistic textured 3D virtual environment of the platform, risers and RPA, providing a fast and low-cost solution to simulate different RPA trajectories for photogrammetry image acquisition in targetless scenes. These trajectories are evaluated by comparing the measured risers through photogrammetry to its CAD/simulated model. Since the scene is not prepared, the RPA position/orientation or a stereo vision setup can be used to set scale to the measurement result. The best trajectory found during simulations was also evaluated in a real experiment.
AUTOMATED CO-REGISTRATION OF INTRA-EPOCH AND INTER-EPOCH SERIES OF MULTISPECTRAL UAV IMAGES FOR CROP MONITORING
Authors: Mc’Okeyo P. O. , Nex F. , Persello C. , Vrieling A.
Published: August 2020

Abstract. The application of UAV-based aerial imagery has advanced exponentially in the past two decades. This can be attributed to UAV operational flexibility, ultra-high spatial resolution, inexpensiveness, and UAV-based sensors enhancement. Nonetheless, the application of multitemporal series of multispectral UAV imagery still suffers significant misregistration errors, and therefore becoming a concern for applications such as precision agriculture. Direct image georeferencing and co-registration is commonly done using ground control points; this is usually costly and time consuming. This research proposes a novel approach for automatic co-registration of multitemporal UAV imagery using intensity-based keypoints. The Speeded Up Robust Features (SURF), Binary Robust Invariant Scalable Keypoints (BRISK), Maximally Stable Extremal Regions (MSER) and KAZE algorithms, were tested and parameters optimized. Image matching performance of these algorithms informed the decision to pursue further experiments with only SURF and KAZE. Optimally parametrized SURF and KAZE algorithms obtained co-registration accuracies of 0.1 and 0.3 pixels for intra-epoch and inter-epoch images respectively. To obtain better intra-epoch co-registration accuracy, collective band processing is advised whereas one-to-one matching strategy is recommended for inter-epoch co-registration. The results were tested using a maize crop monitoring case and the; comparison of spectral response of vegetation between the UAV sensors, Parrot Sequoia and Micro MCA was performed. Due to the missing incidence sensor, spectral and radiometric calibration of Micro MCA imagery is observed to be key in achieving optimal response. Also, the cameras have different specifications and thus differ in the quality of their respective photogrammetric outputs.
WHAT IDENTIFIES A WHALE BY ITS FLUKE? ON THE BENEFIT OF INTERPRETABLE MACHINE LEARNING FOR WHALE IDENTIFICATION
Authors: Kierdorf J. , Garcke J. , Behley J. , Cheeseman T. , Roscher R.
Published: August 2020

Abstract. Interpretable and explainable machine learning have proven to be promising approaches to verify the quality of a data-driven model in general as well as to obtain more information about the quality of certain observations in practise. In this paper, we use these approaches for an application in the marine sciences to support the monitoring of whales. Whale population monitoring is an important element of whale conservation, where the identification of whales plays an important role in this process, for example to trace the migration of whales over time and space. Classical approaches use photographs and a manual mapping with special focus on the shape of the whale flukes and their unique pigmentation. However, this is not feasible for comprehensive monitoring. Machine learning methods, especially deep neural networks, have shown that they can efficiently solve the automatic observation of a large number of whales. Despite their success for many different tasks such as identification, further potentials such as interpretability and their benefits have not yet been exploited. Our main contribution is an analysis of interpretation tools, especially occlusion sensitivity maps, and the question of how the gained insights can help a whale researcher. For our analysis, we use images of humpback whale flukes provided by the Kaggle Challenge ”Humpback Whale Identification”. By means of spectral cluster analysis of heatmaps, which indicate which parts of the image are important for a decision, we can show that the they can be grouped in a meaningful way. Moreover, it appears that characteristics automatically determined by a neural network correspond to those that are considered important by a whale expert.
BETTER GENERIC OBJECTS COUNTING WHEN ASKING QUESTIONS TO IMAGES: A MULTITASK APPROACH FOR REMOTE SENSING VISUAL QUESTION ANSWERING
Authors: Lobry S. , Marcos D. , Kellenberger B. , Tuia D.
Published: August 2020

Abstract. Visual Question Answering for Remote Sensing (RSVQA) aims at extracting information from remote sensing images through queries formulated in natural language. Since the answer to the query is also provided in natural language, the system is accessible to non-experts, and therefore dramatically increases the value of remote sensing images as a source of information, for example for journalism purposes or interactive land planning. Ideally, an RSVQA system should be able to provide an answer to questions that vary both in terms of topic (presence, localization, counting) and image content. However, aiming at such flexibility generates problems related to the variability of the possible answers. A striking example is counting, where the number of objects present in a remote sensing image can vary by multiple orders of magnitude, depending on both the scene and type of objects. This represents a challenge for traditional Visual Question Answering (VQA) methods, which either become intractable or result in an accuracy loss, as the number of possible answers has to be limited. To this end, we introduce a new model that jointly solves a classification problem (which is the most common approach in VQA) and a regression problem (to answer numerical questions more precisely). An evaluation of this method on the RSVQA dataset shows that this finer numerical output comes at the cost of a small loss of performance on non-numerical questions.
PLANAR POLYGONS DETECTION IN LIDAR SCANS BASED ON SENSOR TOPOLOGY ENHANCED RANSAC
Authors: Guinard S. A. , Mallé Z. , Ennafii O. , Monasse P. , Vallet B.
Published: August 2020

Abstract. Detecting planar structures in point clouds is a very central step of the point cloud processing pipeline as many Lidar scans, in particular in anthropic environments, present such planar structures. Many improvements have been proposed to RANSAC and the Hough transform, the two major types of plane detection methods. An important limitation however is that these methods detect planes running across the whole scene instead of more localized planar patches. Moreover, they do not exploit the sensor information that often comes with Lidar point cloud (sensor topology and optical center position in particular). In this paper we address both issues: we aim at detecting planar polygons that have a limited spatial extent, and we exploit sensor topology. The latter is used to enhance a RANSAC framework on two aspects: to make seed points selection more local and to define more compact sets of inliers through sensor space region growing.
LR-CNN: LOCAL-AWARE REGION CNN FOR VEHICLE DETECTION IN AERIAL IMAGERY
Authors: Liao W. , Chen X. , Yang J. , Roth S. , Goesele M. , Yang M. Y. , Rosenhahn B.
Published: August 2020

Abstract. State-of-the-art object detection approaches such as Fast/Faster R-CNN, SSD, or YOLO have difficulties detecting dense, small targets with arbitrary orientation in large aerial images. The main reason is that using interpolation to align RoI features can result in a lack of accuracy or even loss of location information. We present the Local-aware Region Convolutional Neural Network (LR-CNN), a novel two-stage approach for vehicle detection in aerial imagery. We enhance translation invariance to detect dense vehicles and address the boundary quantization issue amongst dense vehicles by aggregating the high-precision RoIs’ features. Moreover, we resample high-level semantic pooled features, making them regain location information from the features of a shallower convolutional block. This strengthens the local feature invariance for the resampled features and enables detecting vehicles in an arbitrary orientation. The local feature invariance enhances the learning ability of the focal loss function, and the focal loss further helps to focus on the hard examples. Taken together, our method better addresses the challenges of aerial imagery. We evaluate our approach on several challenging datasets (VEDAI, DOTA), demonstrating a significant improvement over state-of-the-art methods. We demonstrate the good generalization ability of our approach on the DLR 3K dataset.
ROBUST VISUAL-INERTIAL ODOMETRY IN DYNAMIC ENVIRONMENTS USING SEMANTIC SEGMENTATION FOR FEATURE SELECTION
Authors: Irmisch P. , Baumbach D. , Ernst I.
Published: August 2020

Abstract. Camera based navigation in dynamic environments with high content of moving objects is challenging. Keypoint-based localization methods need to reliably reject features that do not belong to the static background. Here, traditional statistical methods for outlier rejection quickly reach their limits. A common approach is the combination with an inertial measurement unit for visual-inertial odometry. Also, deep learning based semantic segmentation was recently successfully applied in camera based localization to identify features on common objects. In this work, we study the application of mask-based feature selection based on semantic segmentation for robust localization in high dynamic environments. We focus on visual-inertial odometry, but similarly investigate a state-of-the-art pure vision-based method as baseline. For a versatile evaluation, we use challenging self-recorded datasets based on different sensor systems. This includes a combined dataset of a real world system and its synthetic clone with a large number of humans for in-depth analysis. We further deploy large-scale datasets from pedestrian navigation in a mall with escalator scenes and vehicle navigation during the day and at night. Our results show that visual-inertial odometry performs generally well in dynamic environments itself, but also shows significant failures in challenging scenes, which are prevented by using the segmentation aid.
URBAN CHANGE DETECTION BASED ON SEMANTIC SEGMENTATION AND FULLY CONVOLUTIONAL LSTM NETWORKS
Authors: Papadomanolaki M. , Vakalopoulou M. , Karantzalos K.
Published: August 2020

Abstract. Change detection is a very important problem for the remote sensing community. Among the several approaches proposed during recent years, deep learning provides methods and tools that achieve state of the art performances. In this paper, we tackle the problem of urban change detection by constructing a fully convolutional multi-task deep architecture. We present a framework based on the UNet model, with fully convolutional LSTM blocks integrated on top of every encoding level capturing in this way the temporal dynamics of spatial feature representations at different resolution levels. The proposed network is modular due to shared weights which allow the exploitation of multiple (more than two) dates simultaneously. Moreover, our framework provides building segmentation maps by employing a multi-task scheme which extracts additional feature attributes that can reduce the number of false positive pixels. We performed extensive experiments comparing our method with other state of the art approaches using very high resolution images of urban areas. Quantitative and qualitative results reveal the great potential of the proposed scheme, with F1 score outperforming the other compared methods by almost 2.2%.
INVESTIGATING FULLY CONVOLUTIONAL NETWORK TO SEMANTIC LABELLING OF BATHYMETRIC POINT CLOUD
Authors: Daniel S. , Dupont V.
Published: August 2020

Abstract. The benefit of autonomous vehicles in hydrography is largely based on the ability of these platforms to carry out survey campaigns in a fully autonomous manner. One solution is to have real-time processing onboard the survey vessel. To meet this real-time processing goal, deep learning based-models are favored. Although Artificial Intelligence (AI) is booming, the main studies have been devoted to optical images and more recently, to LIDAR point clouds. However, little attention has been paid to the underwater environment. In this paper, we present an investigation into the adaptation of deep neural network to multi-beam echo-sounder (MBES) point cloud in order to classify sea-bottom morphology. More precisely, the paper investigates whether fully convolutional network can be trained while using the native 3D structure of the point cloud. A preprocessing approach is provided in order to overcome the lack of adequate training data. The results reported from the test data sets show the level of complexity related to natural, underwater terrain features where a classification accuracy no better than 65% can be reached when 2 micro topographic classes are used. Point density and resolution have a strong impact on the seabed morphology thereby affecting the classification scheme.
PERFORMANCE EVALUATION OF A MOBILE MAPPING APPLICATION USING SMARTPHONES AND AUGMENTED REALITY FRAMEWORKS
Authors: Hasler O. , Blaser S. , Nebiker S.
Published: August 2020

Abstract. In this paper, we present a performance evaluation of our smartphone-based mobile mapping application based on an augmented reality (AR) framework in demanding outdoor environments. The implementation runs on Android and iOS devices and demonstrates the great potential of smartphone-based 3D mobile mapping. The application includes several functionalities such as device tracking, coordinate, and distance measuring as well as capturing georeferenced imagery. We evaluated our prototype system by comparing measured points from the tracked device with ground control points in an outdoor environment with four different campaigns. The campaigns consisted of open and closed-loop trajectories and different ground surfaces such as grass, concrete and gravel. Two campaigns passed a stairway in either direction. Our results show that the absolute 3D accuracy of device tracking with state-of-the-art AR framework on a standard smartphone is around 1% of the travelled distance and that the local 3D accuracy reaches sub-decimetre level.
MULTI-MODAL DEEP LEARNING WITH SENTINEL-3 OBSERVATIONS FOR THE DETECTION OF OCEANIC INTERNAL WAVES
Authors: Drees L. , Kusche J. , Roscher R.
Published: August 2020

Abstract. The observation of waves that propagate along density interfaces inside the ocean poses a significant challenge, as their visible surface signatures are much lower compared to their internal amplitudes. However, monitoring internal waves is important as they redistribute large amounts of energy, play a role in mixing and vertical heat transfer, and modify water and nutrient transports. Although satellite observations would allow global monitoring of internal waves at constant time intervals, their automatic detection is challenging: In optical images, internal waves are hardly visible and can be obscured by clouds, whereas radar data have limitations in coastal regions and their spatial coverage is not perfect. Furthermore, the occurrence of internal waves can be confused with other ocean phenomena. In this work, we present an automated detection framework for internal waves based on multiple data sources in order to compensate for the shortcoming of single data sources. In our application, we use Ocean and Land Color Imager and Synthetic Aperture Radar Altimeter data. Our contributions are (1) we develop a multi-modal deep neural network SONet with multi-streams and late fusion, which performs a classification on the basis of training with both modalities, and (2) we establish a method to deal with missing modalities. Experiments in the Amazon Shelf region show SONet achieves adequate results when both modalities are available, but also when only a single modality is available. By exploiting correlations between the modalities, SONet classifies OLCI images off the SRAL ground track better than uni-modal network ONet, which describes a great advantage of our multi-modal network.
AN UNSUPERVISED REGISTRATION OF 3D POINT CLOUDS TO 2D CAD MODEL: A CASE STUDY OF FLOOR PLAN
Authors: Alizadeh Naeini A. , Ahmad A. , Sheikholeslami M. M. , Claudio P. , Sohn G.
Published: August 2020

Abstract. Thanks to the proliferation of commodity 3D devices such as HoloLens, one can have easy access to the 3D model of indoor building objects. However, this model does not match 2D available computer-aided design (CAD) models as the as-built model. To address this problem, in this study, a 3-step registration method is proposed. First, binary images, including walls and background, are generated for the 3D point cloud (PC) and the 2D CAD model. Then, 2D-to-2D corresponding pixels (CPs) are extracted based on the intersection of walls in each binary image of PC (BIPC) and binary CAD (BCAD) model. Since the 3D PC space coordinates (XYZ) of all BIPC's pixels are known, BIPC part of the 2D-to-2D CPs can be considered 3D. Lastly, the parameters of the 8-parameter affine are estimated using the 2D-to-3D CPs, which are pixel coordinates in BCAD model as well as their correspondences in the 3D PC space. Experimental results indicate the efficiency of our proposed method compared to manual registration.
A COARSE-TO-FINE BAND REGISTRATION FRAMEWORK FOR MULTI/HYPERSPECTRAL REMOTE SENSING IMAGES CONSIDERING CLOUD INFLUENCE
Authors: Zhao X. , Gao Z. , Sun W. , Wen F.
Published: August 2020

Abstract. Band registration is one of the most critical steps in the production of multi/hyperspectral images and determines the accuracy of applications directly. Because of the characteristics of imaging devices in some multi/hyperspectral satellites, there may be a time difference between bands during push-broom imaging, which leads to displacements of moving clouds with respect to the ground. And a large number of feature points may gather around cloud contours due to the high contrast and rich texture, resulting in building a transformation more suitable for moving clouds and making ground objects ghosted and blurred. This brings a big challenge for registration methods based on feature extraction and matching. In this paper, we propose a novel coarse-to-fine band registration framework for multi/hyperspectral images containing moving clouds. In the coarse registration stage, a cloud mask is generated by grayscale stretching, morphology and other operations. Based on this mask, a coarse matching of cloud-free regions is performed to eliminate large misalignment between bands. In the refinement stage, low-rank analysis and RASL (Robust Alignment by Sparse and Low-rank decomposition) are used to optimize the rank of coarse results to achieve fine registration between bands. After experiments on a total of 102 images (83 cloudy images and 19 cloud-free images with all 32 bands) from Zhuhai-1 hyperspectral satellite, our method can achieve a registration accuracy of 0.6 pixels in cloudy images, 0.41 pixels in cloud-free images, which is enough for subsequent applications.
DERIVATION OF SUPRAGLACIAL DEBRIS COVER BY MACHINE LEARNING ALGORITHMS ON THE GEE PLATFORM: A CASE STUDY OF GLACIERS IN THE HUNZA VALLEY
Authors: Xie F. , Liu S. , Gao Y. , Zhu Y. , Wu K. , Qi M. , Duan S. , Tahir A. M.
Published: August 2020

Abstract. Calculating the spatial-temporal distribution of supraglacial debris cover on glaciers is essential for understanding mass balance processes, glacier lake outburst floods, hydrological predictions, and glacier fluctuations that have attracted attention in recent years. However, due to the reflectance of supraglacial debris is similar to that of non-glacier slopes, mapping supraglacial debris cover based on optical remote sensing remains challenging. In this paper, we used NDSI and machine learning algorithm to extract debris cover on glaciers in Hunza Valley, Pakistan. Our result showed that the RF model has the best classification accuracy with kappa coefficient of 0.94 and overall accuracy of 96%. The debris-covered area increased by 21.31% from 1990 to 2019 (394.76 km2 – 478.88 km2) in the study area. Results and the method are of significance in the assessment of meltwater modeling for glaciers with debris cover.
SEMANTIC SEGMENTATION OF BRAZILIAN SAVANNA VEGETATION USING HIGH SPATIAL RESOLUTION SATELLITE DATA AND U-NET
Authors: Neves A. K. , Körting T. S. , Fonseca L. M. G. , Girolamo Neto C. D. , Wittich D. , Costa G. A. O. P. , Heipke C.
Published: August 2020

Abstract. Large-scale mapping of the Brazilian Savanna (Cerrado) vegetation using remote sensing images is still a challenge due to the high spatial variability and spectral similarity of the different characteristic vegetation types (physiognomies). In this paper, we report on semantic segmentation of the three major groups of physiognomies in the Cerrado biome (Grasslands, Savannas and Forests) using a fully convolutional neural network approach. The study area, which covers a Brazilian conservation unit, was divided into three regions to enable testing the approach in regions that were not used in the training phase. A WorldView-2 image was used in cross validation experiments, in which the average overall accuracy achieved with the pixel-wise classifications was 87.0%. The F-1 score values obtained with the approach for the classes Grassland, Savanna and Forest were of 0.81, 0.90 and 0.88, respectively. Visual assessment of the semantic segmentation outcomes was also performed and confirmed the quality of the results. It was observed that the confusion among classes occurs mainly in transition areas, where there are adjacent physiognomies if a scale of increasing density is considered, which agrees with previous studies on natural vegetation mapping for the Cerrado biome.
LINKING PHOTOSYNTHETIC LIGHT USE EFFICIENCY AND OPTICAL VEGETATION ACTIVE INDICATORS: IMPLICATIONS FOR GROSS PRIMARY PRODUCTION ESTIMATION BY REMOTE SENSING
Authors: Wang S. , Li Z. , Zhang Y. , Yang D. , Ni C.
Published: August 2020

Abstract. Over the last 40 years, the light use efficiency (LUE) model has become a popular approach for gross primary productivity (GPP) estimation in the carbon and remote sensing communities. Despite the fact that the LUE model provides a simple but effective way to approximate GPP at ecosystem to global scales from remote sensing data, when implemented in real GPP modelling, however, the practical form of the model can vary. By reviewing different forms of LUE model and their performances at ecosystem to global scales, we conclude that the relationships between LUE and optical vegetation active indicators (OVAIs, including vegetation indices and sun-induced chlorophyll fluorescence-based products) across time and space are key for understanding and applying the LUE model. In this work, the relationships between LUE and OVAIs are investigated at flux-tower scale, using both remotely sensed and simulated datasets. We find that i) LUE-OVAI relationships during the season are highly site-dependent, which is complexed by seasonal changes of leaf pigment concentration, canopy structure, radiation and Vcmax; ii) LUE tends to converge during peak growing season, which enables applying pure OVAI-based LUE models without specifically parameterizing LUE and iii) Chlorophyll-sensitive OVAIs, especially machine-learning-based SIF-like signal, exhibits a potential to represent spatial variability of LUE during the peak growing season.We also show the power of time-series model simulations to improve the understanding of LUE-OVAI relationships at seasonal scale.
VERIFICATION OF MCD19A2 DATA AND STUDY OF AEROSOL CHARACTERISTICS IN BEIJING-TIANJIN-HEBEI REGION
Authors: Jia C. , Sun L. , Zhang X. , Wang Y.
Published: August 2020

Abstract. The atmospheric aerosol pollution in the Beijing-Tianjin-Hebei(BTH) region of China is particularly serious. To quickly and accurately evaluate the aerosol pollution in BTH region using the remote sensing method – this article verified the accuracy of the global 1km resolution MCD19A2 aerosol optical depth (AOD) product to clarify its reliability in BTH region, moreover, MCD19A2 was used to study the characteristics of aerosols and its correlation with air quality index (AQI). First, the AOD spatial distribution in BTH region from 2014 to 2018 was obtained through the processes of MCD19A2 such as format conversion, mosaic. Then the AOD results were verified with AERONET measured data through accurate space-time matching, proving the high accuracy and reliability of MCD19A2 (R = 0.9504, MAE = 0.0863, RMSE = 0.1243, EE = 82.26%). Besides, based on the AOD results at different scales, the spatiotemporal characteristics of aerosols in BTH region were analyzed from the aspects of spatial distribution, annual variation, and seasonal variation. Finally, the study shows that MCD19A2 AOD and AQI have a high correlation and similar spatiotemporal characteristics, proving the direct indication of MCD19A2 on air pollution. This article plays an important role in the research of aerosol characteristics, air pollution monitoring and so on.
THE RECONSTRUCTION OF NDVI TIME SERIES USING SPATIO-TEMPORAL INFORMATION
Authors: Xu L. , Yang J. , Li S. , Li X.
Published: August 2020

Abstract. Due to the influence of cloud cover, atmospheric disturbance and many other factors, normalized difference vegetation index (NDVI) corrupted by noises has a negative effect on the downstream applications. To this end, researchers have developed a large number of methods to reconstruct NDVI time series. The harmonic analysis of time series (HANTS) is one of the most widely used approaches of NDVI reconstruction. In this paper, HANTS algorithm was improved by the utilization of spatio-temporal information of NDVI time series with spatial filling and filtering, which makes up the deficiency of HANTS that only uses temporal information of NDVI time series. The simulation experiments on moderate resolution imaging spectroradiometer (MODIS) NDVI time series have proved that our method has effectively improved the quantitative and qualitative reconstruction performance of HANTS algorithm.
REGION-BASED FUZZY CLUSTERING IMAGE SEGMENTATION ALGORITHM WITH KULLBACK-LEIBLER DISTANCE
Authors: Li X. L. , Chen J. S.
Published: August 2020

Abstract. To effectively describe the uncertainty of remote sensing image segmentation, a novel region-based algorithm using fuzzy clustering and Kullback-Leibler (KL) distance is proposed. By regular tessellation, the image domain is completely divided into several sub-blocks to overcome the complex noise existed in high-resolution remote sensing images. Taking the blocks as the basic processing units, KL divergence is used to model the distance between blocks and clusters, which enables the model to describe the uncertainty of the non-similarity relationship. Besides, based on the theory of Markov Random Field (MRF), the regionalized KL entropy regularization term is established and added to the objective function to further consider the spatial constraints. Finally, the optimal segmentation results are obtained by estimating the parameters. The experiments carried out on different kinds of remote sensing images by comparing algorithms fully demonstrate the performance of the proposed algorithm.
CITY-SCALE HUMAN MOBILITY PREDICTION MODEL BY INTEGRATING GNSS TRAJECTORIES AND SNS DATA USING LONG SHORT-TERM MEMORY
Authors: Miyazawa S. , Song X. , Jiang R. , Fan Z. , Shibasaki R. , Sato T.
Published: August 2020

Abstract. Human mobility analysis on large-scale mobility data has contributed to multiple applications such as urban and transportation planning, disaster preparation and response, tourism, and public health. However, when some unusual events happen, every individual behaves differently depending on their personal routine and background information. To improve the accuracy of the crowd behavior prediction model, understanding supplemental spatiotemporal topics, such as when, where and what people observe and are interested in, is important. In this research, we develop a model integrating social network service (SNS) data into the human mobility prediction model as background information of the mobility. We employ multi-modal deep learning models using Long short-term memory (LSTM) architecture to incorporate SNS data to a human mobility prediction model based on Global Navigation Satellite System (GNSS) data. We process anonymized interpolated GNSS trajectories from mobile phones into mobility sequence with discretized grid IDs, and apply several topic modeling methods on geo-tagged data to extract spatiotemporal topic features in each spatiotemporal unit similar to the mobility data. Thereafter, we integrate the two datasets in the multi-modal deep learning prediction models to predict city-scale mobility. The experiment proves that the models with SNS topics performed better than baseline models.
RESCUE MANAGEMENT AND ASSESSMENT OF STRUCTURAL DAMAGE BY UAV IN POST-SEISMIC EMERGENCY
Authors: D’Urso M. G. , Manzari V. , Lucidi S. , Cuzzocrea F.
Published: August 2020

Abstract. The increasing frequency of emergencies urges the need for a detailed and thorough knowledge of the landscape. The first hours after a disaster are not only chaotic and problematic, but also decisive to successfully save lives and reduce damage to the building stock. One of the most important factors in any emergency response is to get an adequate awareness of the real situation, what is only possible after a thorough analysis of all the available information obtained through the Italian protocol Topography Applied to Rescue. To this purpose geomatic tools are perfectly suited to create, manage and dynamically enrich an organized archive of data to have a quick and functional access to information useful for several types of analysis, helping to develop solutions to manage the emergency and improving the success of rescue operations. Moreover, during an emergency like an earthquake, the conventional inspection to assess the damage status of buildings requires special tools and a lot of time. Therefore, given the large number of buildings requiring safety measures and rehabilitation, efficient use of limited resources such as time and equipment, as well as the safety of the involved personnel are important aspects. The applications shown in the paper are intended to underline how the above-mentioned objective, in particular the rehabilitation interventions of the built heritage, can be achieved through the use of data acquired from UAV platform integrated with geographic data stored in GIS platforms.