Assessment of magnetic data for landfill characterization by means of a probabilistic approach

The sustainable vision of the Dynamic Landfill Management (DLM) deals not only with present but also with long-term waste management. In this context, DLM enhances the environmental assessment of landfills after closure as well as the recovery of materials and energy resources, for which, a proper characterization is required. To this end, geophysical methods have demonstrated their suitability for landfill exploration, characterization and monitoring. Due to the complexity of these sites and challenges in data acquisition and/or processing, the use of multiple methods is the best approach for landfill investigations. In this work, we used multiple geophysical methods, co-located with several trial pits and boreholes, to estimate the structure of a waste disposal site located in a quarry, and to better delineate the underlying geology composed of limestone. We applied electrical resistivity tomography (ERT), time-domain induced polarization (IP), H/V spectral ratio from microtremor records and magnetometry. We made a structural joint interpretation using the different datasets and the ground truth data. First, the ERT and IP data were individually inverted, and a first structural model was derived. Afterwards, we followed a parametric analysis of the H/V data to corroborate the thickness of some layers at the position of the seismic stations. Then, this model was used to compute synthetic magnetic data and by comparing them with the observed total field magnetic anomalies, a refined model was produced. We evaluated the improvement of including magnetic modelling by using a probabilistic approach previously reported. This approach is based on the computation of conditional probabilities by comparing the inverted models with the co-located data from trial pits and boreholes. Overall, we delineated the lateral and vertical extension of the waste body, the distribution of ash and lime deposits and estimated the upper limit structure of the bedrock.

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Landfill characterization by multi-method geophysical investigation: the case study of Leppe (Germany)

10.5194/egusphere-egu2020-18151 ◽

2020 ◽

Author(s):

Tom Debouny ◽

David Caterina ◽

Itzel Isunza Manrique ◽

Pascal Beese-Vasbender ◽

Frédéric Nguyen

Keyword(s):

Seismic Velocity ◽

Biogas Production ◽

Geophysical Methods ◽

Ground Truth ◽

Spectral Ratio ◽

Added Value ◽

Geophysical Investigation ◽

Major Drawback ◽

Ground Truth Data ◽

Spatial Coverage

Whether environmental or economic interests are at stake, characterization of landfills is becoming a key operation. Characterization not only concerns old landfills, but also modern engineered landfills where the assessment and monitoring of internal processes such as leachate and biogas generation is of a primary importance. Nowadays, characterization is mostly carried out by conventional invasive methods based on drilling/trenching, sampling and laboratory analyses. Although they provide direct and analytical information, their spatial coverage, or representability, remains a major drawback. In addition, they can be expensive and increase the risk of damaging contamination barriers. Therefore, non- to minimally- invasive characterization geophysical techniques emerge as a complementary option. They allow to better capture the spatial heterogeneity across a site and are more cost-effective than punctual measurements alone. Furthermore, when compared with limited ground truth data, they may provide insights into waste composition, water content or temperature. The present study highlights the added value of a multiple geophysical approach to characterize a landfill located in Engelskirchen in Germany. Leppe landfill was used as a municipal solid waste (MSW) deposit site from 1982 until the end of 2004. Since then, only ash coming from the MSW incineration is discarded, mostly on top of the previous MSW deposit. The combination of geophysical methods used in this study included electrical resistivity tomography (ERT), &#160;induced polarization (IP), multichannel analysis of surface waves (MASW) and horizontal to vertical noise spectral ratio (HVSNR). The 3D ERT and IP model allowed to identify dry zones within the waste (which may have a direct impact on biogas production) and to roughly discriminate the layer of ash from the MSW layer. Seismic velocity model provided by MASW permitted to significantly improve the delineation between the two layers. HVNSR results combined with the information provided by MASW were used to estimate the thickness of the top layer on a larger area using a bilayer hypothesis. These geophysical characterization results were validated with available ground truth data. Overall, in the present case seismic methods showed to be more suited than geoelectrical techniques for the distinction between the ash and MSW layers.

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Disambiguating Energy Disaggregation: A Collective Probabilistic Approach

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/398 ◽

2017 ◽

Cited By ~ 4

Author(s):

Sabina Tomkins ◽

Jay Pujara ◽

Lise Getoor

Keyword(s):

Energy Consumption ◽

Energy Demand ◽

Negative Impact ◽

Probabilistic Approach ◽

Ground Truth ◽

Solution Quality ◽

Ground Truth Data ◽

Hinge Loss ◽

Energy Disaggregation ◽

Real World Datasets

Reducing household energy usage is a priority for improving the resiliency and stability of the power grid and decreasing the negative impact of energy consumption on the environment and public health.Relevant and timely feedback about the power consumption of specific appliances can help household residents to reduce their energy demand. Given only a total energy reading, such as that collected from a residential meter, energy disaggregation strives to discover the consumption of individual appliances. Existing disaggregation algorithms are computationally inefficient and rely heavily on high-resolution ground truth data. We introduce a probabilistic framework which infers the energy consumption of individual appliances using a hinge-loss Markov random field (HL-MRF), which admits highly scalable inference. To further enhance efficiency, we introduce a temporal representation which leverages state duration. We also explore how contextual information impacts solution quality with low-resolution data. Our framework is flexible in its ability to incorporate additional constraints; by constraining appliance usage with context and duration we can better disambiguate appliances with similar energy consumption profiles. We demonstrate the effectiveness of our framework on two public real-world datasets, reducing the error relative to a previous state-of-the-art method by as much as 50%.

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AUTOMATED STRUCTURAL FOREST CHANGES USING LIDAR POINT CLOUDS AND GIS ANALYSES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2021-603-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 603-608

Author(s):

A. Novo ◽

H. González-Jorge ◽

J. Martínez-Sánchez ◽

J. M. Fernández-Alonso ◽

H. Lorenzo

Keyword(s):

Spatial Data ◽

Forest Structure ◽

Laser Scanning ◽

Structural Model ◽

Canopy Cover ◽

Ground Truth ◽

Point Clouds ◽

Forest Community ◽

Structural Behaviour ◽

Ground Truth Data

Abstract. Forest spatial structure describes the relationships among different species in the same forest community. Automation in the monitoring of the structural forest changes and forest mapping is one of the main utilities of applications of modern geoinformatics methods. The obtaining objective information requires the use of spatial data derived from photogrammetry and remote sensing. This paper investigates the possibility of applying light detection and ranging (LiDAR) point clouds and geographic information system (GIS) analyses for automated mapping and detection changes in vegetation structure during a year of study. The research was conducted in an area of the Ourense Province (NWSpain). The airborne laser scanning (ALS) data, acquired in August 2019 and June of 2020, reveal detailed changes in forest structure. Based on ALS data the vegetation parameters will be analysed.To study the structural behaviour of the tree vegetation, the following parameters are used in each one of the sampling areas: (1) Relationship between the tree species present and their stratification; (2) Vegetation classification in fuel types; (3) Biomass (Gi); (4) Number of individuals per area; and (5) Canopy cover fraction (CCF). Besides, the results were compared with the ground truth data recollected in the study area.The development of a quantitative structural model based on Aerial Laser Scanning (ALS) point clouds was proposed to accurately estimate tree attributes automatically and to detect changes in forest structure. Results of statistical analysis of point cloud show the possibility to use UAV LiDAR data to characterize changes in the structure of vegetation.

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Quantification of ground ice through petrophysical joint inversion of seismic and electrical data applied to alpine permafrost

10.5194/egusphere-egu2020-7489 ◽

2020 ◽

Author(s):

Coline Mollaret ◽

Florian M. Wagner ◽

Christin Hilbich ◽

Christian Hauck

Keyword(s):

Electrical Resistivity ◽

Melting Point ◽

Joint Inversion ◽

Geophysical Methods ◽

Ground Truth ◽

Ground Ice ◽

Ground Truth Data ◽

Water Ice ◽

Refraction Seismic ◽

Ice Content

Quantification of ground ice is particularly crucial for understanding permafrost systems. The volumetric ice content is however rarely estimated in permafrost studies, as it is particularly difficult to retrieve. Geophysical methods have become more and more popular for permafrost investigations due to their capacity to distinguish between frozen and unfrozen regions and their complementarity to standard ground temperature data. Geophysical methods offer both a second (or third) spatial dimension and the possibility to gain insights on processes happening near the melting point (ground ice gain or loss at the melting point). Geophysical methods, however, may suffer from potential inversion imperfections and ambiguities (no unique solution). To reduce uncertainties and improve the interpretability, geophysical methods are standardly combined with ground truth data or other independent geophysical methods. We developed an approach of joint inversion to fully exploit the sensitivity of seismic and electrical methods to the phase change of water. We choose apparent resistivities and seismic travel times as input data of a petrophysical joint inversion to directly estimate the volumetric fractions of the pores (liquid water, ice and air) and the rock matrix. This approach was successfully validated with synthetic datasets (Wagner et al., 2019). This joint inversion scheme warrants physically-plausible solutions and provides a porosity estimation in addition to the ground ice estimation of interest. Different petrophysical models are applied to several alpine sites (ice-poor to ice-rich) and their advantages and limitations are discussed. The good correlation of the results with the available ground truth data (thaw depth and ice content data) demonstrates the high potential of the joint inversion approach for the typical landforms of alpine permafrost (Mollaret et al., 2020). The ice content is found to be 5 to 15 % at bedrock sites, 20 to 40 % at talus slopes, and up to 95 % at rock glaciers (in good agreement to the ground truth data from boreholes). Moreover, lateral variations of bedrock depth are correctly identified according to outcrops and borehole data (as the porosity is also an output of the petrophysical joint inversion). A time-lapse version of this petrophysical joint inversion may further reduce the uncertainties and will be beneficial for monitoring and modelling studies upon climate-induced degradation.&#160;References:Mollaret, C., Wagner, F. M. Hilbich, C., Scapozza, C., and Hauck, C. Petrophysical joint inversion of electrical resistivity and refraction seismic applied to alpine permafrost to image subsurface ice, water, air, and rock contents. Frontiers in Earth Science, 2020, submitted.Wagner, F. M., Mollaret, C., G&#252;nther, T., Kemna, A., and Hauck, C. Quantitative imaging of water, ice, and air in permafrost systems through petrophysical joint inversion of seismic refraction and electrical resistivity data. Geophysical Journal International, 219 (3):1866&#8211;1875, 2019. doi:10.1093/gji/ggz402.

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Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

Remote Sensing ◽

10.3390/rs13101966 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1966

Author(s):

Christopher W Smith ◽

Santosh K Panda ◽

Uma S Bhatt ◽

Franz J Meyer ◽

Anushree Badola ◽

...

Keyword(s):

Boreal Forest ◽

Ground Truth ◽

Burn Severity ◽

Classification Methods ◽

Spectral Indices ◽

Ground Truth Data ◽

Burn Scar ◽

Interior Alaska ◽

Remote Sensing Methods ◽

The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

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Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

Remote Sensing ◽

10.3390/rs13010026 ◽

2020 ◽

Vol 13 (1) ◽

pp. 26

Author(s):

Wen-Hao Su ◽

Jiajing Zhang ◽

Ce Yang ◽

Rae Page ◽

Tamas Szinyei ◽

...

Keyword(s):

Fusarium Head Blight ◽

Ground Truth ◽

Wheat Breeding ◽

Head Blight ◽

Detection Rates ◽

Ground Truth Data ◽

Resistant Cultivars ◽

Feature Pyramid ◽

Rater Error ◽

Wheat Lines

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

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Compact Quantum Magnetometer System on an Agile Underwater Glider

Sensors ◽

10.3390/s21041092 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1092

Author(s):

Brian R. Page ◽

Reeve Lambert ◽

Nina Mahmoudian ◽

David H. Newby ◽

Elizabeth L. Foley ◽

...

Keyword(s):

Test Procedure ◽

Reduction Process ◽

Ground Truth ◽

Sensor Technology ◽

Magnetic Data ◽

Magnetic Survey ◽

Underwater Glider ◽

Combined System ◽

Extended Operation ◽

Quantum Magnetometer

This paper presents results from the integration of a compact quantum magnetometer system and an agile underwater glider for magnetic survey. A highly maneuverable underwater glider, ROUGHIE, was customized to carry an increased payload and reduce the vehicle’s magnetic signature. A sensor suite composed of a vector and scalar magnetometer was mounted in an external boom at the rear of the vehicle. The combined system was deployed in a constrained pool environment to detect seeded magnetic targets and create a magnetic map of the test area. Presented is a systematic magnetic disturbance reduction process, test procedure for anomaly mapping, and results from constrained operation featuring underwater motion capture system for ground truth localization. Validation in the noisy and constrained pool environment creates a trajectory towards affordable littoral magnetic anomaly mapping infrastructure. Such a marine sensor technology will be capable of extended operation in challenging areas while providing high-resolution, timely magnetic data to operators for automated detection and classification of marine objects.

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Classification of Cattle Behaviours Using Neck-Mounted Accelerometer-Equipped Collars and Convolutional Neural Networks

Sensors ◽

10.3390/s21124050 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4050

Author(s):

Dejan Pavlovic ◽

Christopher Davison ◽

Andrew Hamilton ◽

Oskar Marko ◽

Robert Atkinson ◽

...

Keyword(s):

Neural Network ◽

Model Performance ◽

Ground Truth ◽

Practical Implementation ◽

Ground Truth Data ◽

Battery Lifetime ◽

Implementation Challenges ◽

Memory Footprint ◽

Commercial Farms ◽

Using Data

Monitoring cattle behaviour is core to the early detection of health and welfare issues and to optimise the fertility of large herds. Accelerometer-based sensor systems that provide activity profiles are now used extensively on commercial farms and have evolved to identify behaviours such as the time spent ruminating and eating at an individual animal level. Acquiring this information at scale is central to informing on-farm management decisions. The paper presents the development of a Convolutional Neural Network (CNN) that classifies cattle behavioural states (`rumination’, `eating’ and `other’) using data generated from neck-mounted accelerometer collars. During three farm trials in the United Kingdom (Easter Howgate Farm, Edinburgh, UK), 18 steers were monitored to provide raw acceleration measurements, with ground truth data provided by muzzle-mounted pressure sensor halters. A range of neural network architectures are explored and rigorous hyper-parameter searches are performed to optimise the network. The computational complexity and memory footprint of CNN models are not readily compatible with deployment on low-power processors which are both memory and energy constrained. Thus, progressive reductions of the CNN were executed with minimal loss of performance in order to address the practical implementation challenges, defining the trade-off between model performance versus computation complexity and memory footprint to permit deployment on micro-controller architectures. The proposed methodology achieves a compression of 14.30 compared to the unpruned architecture but is nevertheless able to accurately classify cattle behaviours with an overall F1 score of 0.82 for both FP32 and FP16 precision while achieving a reasonable battery lifetime in excess of 5.7 years.

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A machine learning approach to estimate the strain energy absorption in expanded polystyrene foams

Journal of Cellular Plastics ◽

10.1177/0021955x211021014 ◽

2021 ◽

pp. 0021955X2110210

Author(s):

Alejandro E Rodríguez-Sánchez ◽

Héctor Plascencia-Mora

Keyword(s):

Neural Network ◽

Energy Absorption ◽

Mechanical Energy ◽

Compressive Loading ◽

Ground Truth ◽

Expanded Polystyrene ◽

Polystyrene Foam ◽

Stress Strain ◽

Ground Truth Data ◽

Expanded Polystyrene Foam

Traditional modeling of mechanical energy absorption due to compressive loadings in expanded polystyrene foams involves mathematical descriptions that are derived from stress/strain continuum mechanics models. Nevertheless, most of those models are either constrained using the strain as the only variable to work at large deformation regimes and usually neglect important parameters for energy absorption properties such as the material density or the rate of the applying load. This work presents a neural-network-based approach that produces models that are capable to map the compressive stress response and energy absorption parameters of an expanded polystyrene foam by considering its deformation, compressive loading rates, and different densities. The models are trained with ground-truth data obtained in compressive tests. Two methods to select neural network architectures are also presented, one of which is based on a Design of Experiments strategy. The results show that it is possible to obtain a single artificial neural networks model that can abstract stress and energy absorption solution spaces for the conditions studied in the material. Additionally, such a model is compared with a phenomenological model, and the results show than the neural network model outperforms it in terms of prediction capabilities, since errors around 2% of experimental data were obtained. In this sense, it is demonstrated that by following the presented approach is possible to obtain a model capable to reproduce compressive polystyrene foam stress/strain data, and consequently, to simulate its energy absorption parameters.

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Multi-Temporal Arable Land Monitoring in Arid Region of Northwest China Using a New Extraction Index

Sustainability ◽

10.3390/su13095274 ◽

2021 ◽

Vol 13 (9) ◽

pp. 5274

Author(s):

Xinyang Yu ◽

Younggu Her ◽

Xicun Zhu ◽

Changhe Lu ◽

Xuefei Li

Keyword(s):

Arable Land ◽

Ground Truth ◽

Northwest China ◽

Hexi Corridor ◽

Ground Truth Data ◽

Land Protection ◽

Promising Tool ◽

Study Results ◽

Multi Temporal ◽

The Mean

Development of a high-accuracy method to extract arable land using effective data sources is crucial to detect and monitor arable land dynamics, servicing land protection and sustainable development. In this study, a new arable land extraction index (ALEI) based on spectral analysis was proposed, examined by ground truth data, and then applied to the Hexi Corridor in northwest China. The arable land and its change patterns during 1990–2020 were extracted and identified using 40 Landsat TM/OLI images acquired in 1990, 2000, 2010, and 2020. The results demonstrated that the proposed method can distinguish arable land areas accurately, with the User’s (Producer’s) accuracy and overall accuracy (kappa coefficient) exceeding 0.90 (0.88) and 0.89 (0.87), respectively. The mean relative error calculated using field survey data obtained in 2012 and 2020 was 0.169 and 0.191, respectively, indicating the feasibility of the ALEI method in arable land extracting. The study found that arable land area in the Hexi Corridor was 13217.58 km2 in 2020, significantly increased by 25.33% compared to that in 1990. At 10-year intervals, the arable land experienced different change patterns. The study results indicate that ALEI index is a promising tool used to effectively extract arable land in the arid area.

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