MASSIVE-SCALE TREE MODELLING FROM TLS DATA

This paper presents a method for reconstructing automatically the quantitative structure model of every tree in a forest plot from terrestrial laser scanner data. A new feature is the automatic extraction of individual trees from the point cloud. The method is tested with a 30-m diameter English oak plot and a 80-m diameter Australian eucalyptus plot. For the oak plot the total biomass was overestimated by about 17 %, when compared to allometry (N = 15), and the modelling time was about 100 min with a laptop. For the eucalyptus plot the total biomass was overestimated by about 8.5 %, when compared to a destructive reference (N = 27), and the modelling time was about 160 min. The method provides accurate and fast tree modelling abilities for, e.g., biomass estimation and ground truth data for airborne measurements at a massive ground scale.

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ESTIMATING SINGLE TREE STEM AND BRANCH BIOMASS USING TERRESTRIAL LASER SCANNING

Jurnal Teknologi ◽

10.11113/jt.v77.6860 ◽

2015 ◽

Vol 77 (26) ◽

Cited By ~ 2

Author(s):

Nurliyana Izzati Ishak ◽

Md Afif Abu Bakar ◽

Muhammad Zulkarnain Abdul Rahman ◽

Abd Wahid Rasib ◽

Kasturi Devi Kanniah ◽

...

Keyword(s):

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Stem Volume ◽

Biomass Estimation ◽

Forest Plot ◽

Individual Tree ◽

Matching Process ◽

Branch Biomass ◽

Individual Trees ◽

Tree Stem

This paper presents a novel non-destructive approach for individual tree stem and branch biomass estimation using terrestrial laser scanning data. The study area is located at the Royal Belum Reserved Forest area, Gerik, Perak. Each forest plot was designed with a circular shape and contains several scanning locations to ensure good visibility of each tree. Unique tree signage was located on trees with diameter at breast height (DBH) of 10cm and above. Extractions of individual trees were done manually and the matching process with the field collected tree properties were relied on the tree signage and tree location as collected by total station. Individual tree stems were reconstructed based on cylinder models from which the total stem volume was calculated. Biomass of individual tree stems was calculated by multiplying stem volume with specific wood density. Biomass of individual was estimated using similar concept of tree stem with the volume estimated from alpha-hull shape. The root mean squared errors (RMSE) of estimated biomass are 50.22kg and 27.20kg for stem and branch respectively.

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LOW COST MULTI-SENSOR ROBOT LASER SCANNING SYSTEM AND ITS ACCURACY INVESTIGATIONS FOR INDOOR MAPPING APPLICATION

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

10.5194/isprs-archives-xlii-2-w8-83-2017 ◽

2017 ◽

Vol XLII-2/W8 ◽

pp. 83-85 ◽

Cited By ~ 3

Author(s):

C. Chen ◽

X. Zou ◽

M. Tian ◽

J. Li ◽

W. Wu ◽

...

Keyword(s):

Inertial Measurement Unit ◽

Laser Scanning ◽

Low Cost ◽

Laser Scanner ◽

Ground Truth ◽

Measurement Unit ◽

Scanning System ◽

Ground Truth Data ◽

Multiple Sensor ◽

Laser Scanning System

In order to solve the automation of 3D indoor mapping task, a low cost multi-sensor robot laser scanning system is proposed in this paper. The multiple-sensor robot laser scanning system includes a panorama camera, a laser scanner, and an inertial measurement unit and etc., which are calibrated and synchronized together to achieve simultaneously collection of 3D indoor data. Experiments are undertaken in a typical indoor scene and the data generated by the proposed system are compared with ground truth data collected by a TLS scanner showing an accuracy of 99.2% below 0.25 meter, which explains the applicability and precision of the system in indoor mapping applications.

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To 3D or Not 3D: Choosing a Photogrammetry Workflow for Cultural Heritage Groups

Heritage ◽

10.3390/heritage2030112 ◽

2019 ◽

Vol 2 (3) ◽

pp. 1835-1851 ◽

Cited By ~ 14

Author(s):

Hafizur Rahaman ◽

Erik Champion

Keyword(s):

Low Cost ◽

Laser Scanner ◽

Ground Truth ◽

Point Clouds ◽

3D Models ◽

3D Modelling ◽

Quality Data ◽

Average Deviation ◽

Ground Truth Data ◽

Software Application

The 3D reconstruction of real-world heritage objects using either a laser scanner or 3D modelling software is typically expensive and requires a high level of expertise. Image-based 3D modelling software, on the other hand, offers a cheaper alternative, which can handle this task with relative ease. There also exists free and open source (FOSS) software, with the potential to deliver quality data for heritage documentation purposes. However, contemporary academic discourse seldom presents survey-based feature lists or a critical inspection of potential production pipelines, nor typically provides direction and guidance for non-experts who are interested in learning, developing and sharing 3D content on a restricted budget. To address the above issues, a set of FOSS were studied based on their offered features, workflow, 3D processing time and accuracy. Two datasets have been used to compare and evaluate the FOSS applications based on the point clouds they produced. The average deviation to ground truth data produced by a commercial software application (Metashape, formerly called PhotoScan) was used and measured with CloudCompare software. 3D reconstructions generated from FOSS produce promising results, with significant accuracy, and are easy to use. We believe this investigation will help non-expert users to understand the photogrammetry and select the most suitable software for producing image-based 3D models at low cost for visualisation and presentation purposes.

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Calibration of Planar Reflectors Reshaping LiDAR’s Field of View

Sensors ◽

10.3390/s21196501 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6501

Author(s):

Michał Pełka ◽

Janusz Będkowski

Keyword(s):

Open Source ◽

Mechanical Design ◽

Laser Scanner ◽

Measurement Instrument ◽

Ground Truth ◽

Calibration Method ◽

Field Of View ◽

Ground Truth Data ◽

Calibration Process ◽

Data Registration

This paper describes the calibration method for calculating parameters (position and orientation) of planar reflectors reshaping LiDAR’s (light detection and ranging) field of view. The calibration method is based on the reflection equation used in the ICP (Iterative Closest Point) optimization. A novel calibration process as the multi-view data registration scheme is proposed; therefore, the poses of the measurement instrument and parameters of planar reflectors are calculated simultaneously. The final metric measurement is more accurate compared with parameters retrieved from the mechanical design. Therefore, it is evident that the calibration process is required for affordable solutions where the mechanical design can differ from the inaccurate assembly. It is shown that the accuracy is less than 20 cm for almost all measurements preserving long-range capabilities. The experiment is performed based on Livox Mid-40 LiDAR augmented with six planar reflectors. The ground-truth data were collected using Z + F IMAGER 5010 3D Terrestrial Laser Scanner. The calibration method is independent of mechanical design and does not require any fiducial markers on the mirrors. This work fulfils the gap between rotating and Solid-State LiDARs since the field of view can be reshaped by planar reflectors, and the proposed method can preserve the metric accuracy. Thus, such discussion concludes the findings. We prepared an open-source project and provided all the necessary data for reproducing the experiments. That includes: Complete open-source code, the mechanical design of reflector assembly and the dataset which was used in this paper.

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ESTIMATION METHODOLOGY FOR FOREST BIOMASS IN MONGOLIA USING REMOTE SENSING

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

10.5194/isprs-archives-xlii-5-w3-7-2019 ◽

2019 ◽

Vol XLII-5/W3 ◽

pp. 7-12

Author(s):

T. Altanchimeg ◽

T. Renchin ◽

P. De Maeyer ◽

E. Natsagdorj ◽

B. Tseveen ◽

...

Keyword(s):

Remote Sensing ◽

Forest Biomass ◽

Remote Sensing Data ◽

Ground Truth ◽

Co2 Concentration ◽

Global Perspective ◽

Biomass Estimation ◽

Area Index ◽

Ground Truth Data ◽

Concentration Changes

Abstract. The forest biomass is one of the most important parameters for the global carbon stock. Information on the forest volume, coverage and biomass are important to develop the global perspective on the CO2 concentration changes. Objective of this research is to estimate forest biomass in the study area. The study area is Hangal sum, Bulgan province, Mongolia. Backscatter coefficients for vertical transmit and vertical receive (VV), for vertical transmit and horizontal receive (VH) from Sentinel data and Leaf Area Index (LAI) from Landsat data were used in the study area. We developed biomass estimation approach using ground truth data which is DBH, height and soil moisture. The coefficient α, β, δ, γ were found from the approach. The output map from the approach was compared with VV and VH, LAI data. The relationship between output map and VH data shows a positive result R2 = 0.61. This study suggests that the biomass estimation using Remote sensing data can be applied in forest region in the North.

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Assessment of Forest above Ground Biomass Estimation Using Multi-Temporal C-band Sentinel-1 and Polarimetric L-band PALSAR-2 Data

Remote Sensing ◽

10.3390/rs10091424 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1424 ◽

Cited By ~ 16

Author(s):

Xiaodong Huang ◽

Beth Ziniti ◽

Nathan Torbick ◽

Mark Ducey

Keyword(s):

Optical Sensors ◽

Forest Canopy ◽

Alpha Angle ◽

Ground Truth ◽

Biomass Estimation ◽

Above Ground Biomass ◽

Ground Truth Data ◽

Ground Biomass ◽

Multi Temporal ◽

L Band

Synthetic Aperture Radar (SAR), as an active sensor transmitting long wavelengths, has the advantages of working day and night and without rain or cloud disturbance. It is further able to sense the geometric structure of forests more than passive optical sensors, making it a valuable tool for mapping forest Above Ground Biomass (AGB). This paper studies the ability of the single- and multi-temporal C-band Sentinel-1 and polarimetric L-band PALSAR-2 data to estimate live AGB based on ground truth data collected in New England, USA in 2017. Comparisons of results using the Simple Water Cloud Model (SWCM) on both VH and VV polarizations show that C-band reaches saturation much faster than the L-band due to its limited forest canopy penetration. The exhaustive search multiple linear regression model over the many polarimetric parameters from PALSAR-2 data shows that the combination of polarimetric parameters could slightly improve the AGB estimation, with an adjusted R2 as high as 0.43 and RMSE of around 70 Mg/ha when decomposed Pv component and Alpha angle are used. Additionally, the single- and multi-temporal C-band Sentinel-1 data are compared, which demonstrates that the multi-temporal Sentinel-1 significantly improves the AGB estimation, but still has a much lower adjusted R2 due to the limitations of the short wavelength. Finally, a site-level comparison between paired control and treatment sites shows that the L-band aligns better with the ground truth than the C-band, showing the high potential of the models to be applied to relative biomass change detection.

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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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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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