Novel approaches in GPR data processing for health monitoring of trees

The aggressive fungal attack is seriously threatening tree species in forests and woodlands in the UK and beyond. A lot has been said about the spread of disease and fungal attack on ash and oak trees in the United Kingdom and European countries. Within this context, Ground Penetrating Radar (GPR) has emerged as one of the most promising non-destructive testing (NDT) methods for acquisition of information about the internal structure of trees in terms of defect and their root system architecture. Nevertheless, current research has shown that there exists limited information and in depth studies within this important area of endeavour. This review paper reports on the current advances made within the context of GPR applications in health monitoring and assessment of trees and tree roots. This paper also discusses and reports on new areas of development including, the reverse-time migration, the microwave tomography and the pattern-recognition approaches within the signal processing and image analysis (interpretation) contexts

Accurate Tree Roots Positioning and Sizing over Undulated Ground Surfaces by Common Offset GPR Measurements

Tree roots detection is a popular application of the Ground-penetrating radar (GPR). Normally, the ground surface above the tree roots is assumed to be flat, and standard processing methods based on hyperbolic fitting are applied to the hyperbolae reflection patterns of tree roots for detection purposes. When the surface of the land is undulating (not flat), these typical hyperbolic fitting methods becomes inaccurate. This is because, the reflection patterns change with the uneven ground surfaces. When the soil surface is not flat, it is inaccurate to use the peak point of an asymmetric reflection pattern to identify the depth and horizontal position of the underground target. The reflection patterns of the complex shapes due to extreme surface variations results in analysis difficulties. Furthermore, when multiple objects are buried under an undulating ground, it is hard to judge their relative positions based on a B-scan that assumes a flat ground. In this paper, a roots fitting method based on electromagnetic waves (EM) travel time analysis is proposed to take into consideration the realistic undulating ground surface. A wheel-based (WB) GPR and an antenna-height-fixed (AHF) GPR System are presented, and their corresponding fitting models are proposed. The effectiveness of the proposed method is demonstrated and validated through numerical examples and field experiments.Comment: 11 pages, 6 figures, accepted by IEEE TI

Ground-penetrating radar as phenotyping tool for characterizing intraspecific variability in root traits of a widespread conifer

Drought is the main abiotic stress affecting Mediterranean forests. Root systems are responsible for water uptake, but intraspecific variability in tree root morphology is poorly understood mainly owing to sampling difficulties. The aim of this study was to gain knowledge on the adaptive relevance of rooting traits for a widespread pine using a non-invasive, high-throughput phenotyping technique.This work was partly funded by the Spanish Government, grant numbers AGL2015-68274-C3-3-R (MINECO/FEDER) and RTI2018-094691-B-C31 (MCIU/AEI/FEDER, EU). E. Lombardi was supported by a AGAUR FI-2020 pre-doctoral fellowship (with the support from the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund). J. P. Ferrio was supported by Reference Group H09_20R (Gobierno de Aragón). U. Rodríguez-Robles acknowledges the National Council for Science and Technology of Mexico (CONACyT), grant number 33235

A gans-based deep learning framework for automatic subsurface object recognition from ground penetrating radar data

Ground penetrating radar (GPR) is a well-known useful tool for subsurface exploration. GPR data can be recorded at a relatively high speed in a continuous way with hyperbolas being artifacts and evidence of disturbances in the soil. Automatic and accurate detection and interpretation of hyperbolas in GPR remains an open challenge. Recently deep learning techniques have achieved remarkable success in image recognition tasks and this has potential for interpretation of GPR data. However, training reliable deep learning models requires massive labeled data, which is challenging. To address the challenges, this work proposes a Generative Adversarial Nets (GANs)-based deep learning framework, which generates new training data to address the scarcity of GPR data, automatically learns features and detects subsurface objects (via hyperbola) through an end-to-end solution. We have evaluated our proposed approach using real GPR B-scan images from rail infrastructure monitoring applications and compared this with the state-of-the-art methods for object detection (i.e. Faster-RCNN, Cascade R-CNN, SSD and YOLO V2). The proposed approach outperforms the existing methods with high accuracy of 97% being the mean Average Precision (mAP). Moreover, the proposed approach also demonstrates the good generalizability through cross-validation on independent datasets

Mapping and assessment of tree roots using ground penetrating radar with low-cost GPS

In this paper, we have presented a methodology combining ground penetrating radar (GPR) and a low-cost GPS receiver for three-dimensional detection of tree roots. This research aims to provide an effective and affordable testing tool to assess the root system of a number of trees. For this purpose, a low-cost GPS receiver was used, which recorded the approximate position of each GPR track, collected with a 500 MHz RAMAC shielded antenna. A dedicated post-processing methodology based on the precise position of the satellite data, satellite clock offsets data, and a local reference Global Navigation Satellite System (GNSS) Earth Observation Network System (GEONET) Station close to the survey site was developed. Firstly, the positioning information of local GEONET stations was used to filter out the errors caused by satellite position error, satellite clock offset, and ionosphere. In addition, the advanced Kalman filter was designed to minimise receiver offset and the multipath error, in order to obtain a high precision position of each GPR track. Kirchhoff migration considering near-field effect was used to identify the three-dimensional distribution of the root. In a later stage, a novel processing scheme was used to detect and clearly map the coarse roots of the investigated tree. A successful case study is proposed, which supports the following premise: the current scheme is an affordable and accurate mapping method of the root system architecture

On the introduction of canny operator in an advanced imaging algorithm for real-time detection of hyperbolas in ground-penetrating radar data

This paper focuses on the use of the Canny edge detector as the first step of an advanced imaging algorithm for automated detection of hyperbolic reflections in ground-penetrating radar (GPR) data. Since the imaging algorithm aims to work in real time; particular attention is paid to its computational efficiency. Various alternative criteria are designed and examined, to fasten the procedure by eliminating unnecessary edge pixels from Canny-processed data, before such data go through the subsequent steps of the detection algorithm. The effectiveness and reliability of the proposed methodology are tested on a wide set of synthetic and experimental radargrams with promising results. The finite-difference time-domain simulator gprMax is used to generate synthetic radargrams for the tests, while the real radargrams come from GPR surveys carried out by the authors in urban areas. The imaging algorithm is implemented in MATLAB

Variabilita zásob uhlíku v půdě a možnost využití GPR radaru k jejich zjišťování

In the context of ongoing climate change, more attention is being given to soil and its organic carbon pool. This is because soil could partially compensate for the increasing amount of carbon dioxide in the atmosphere or, on the other hand, be a vast pool of carbon dioxide if organic matter stored in soil mineralizes. Therefore, the precision of soil organic carbon pool estimation, development of monitoring methods, and revelation of factors controlling the pool have been more and more focused on by soil scientists. Conventional soil sampling for soil organic carbon pool estimation and modelling includes manual sampling, measuring forest floor depth and bulk density, and taking soil samples for carbon concentration analysis. These are time and labour demanding. Therefore, there is an effort to develop precise models predicting the carbon pool based on its driving factors that would limit the amount of fieldwork. The models often use remote sensing data, and, in addition, there is an effort to estimate soil organic carbon concentration from soil spectral characteristics. Nevertheless, another variable needed to estimate the organic carbon pool is the thickness of the soil profile or individual soil horizons. The thickness can hardly be determined from remote sensing data, so it has to be measured...V souvislosti s probíhající klimatickou změnou zapříčiněnou zejména růstem oxidu uhličitého v atmosféře, je stále více pozornosti věnováno výpočtu organického uhlíku v půdě a možnostem jeho sekvestrace. Půda je největším terestrickým zásobníkem uhlíku a může zpomalovat stoupající množství oxidu uhličitého v atmosféře jeho sekvestrací nebo v opačném případě být významným zdroje oxidu uhličitého, pokud by došlo k mineralizaci organického uhlíku uloženého v půdě. Proto se pedologie stale více zabývá zpřesňováním odhadů uhlíkových zásob, vývojem metod jejich monitorování a hledáním faktorů, které sekvestraci a stabilizaci uhlíku v půdě ovlivňují. Konvenční sběr dat za účelem odhadů zásob uhlíku v půdě sestává z manuálního terénního průzkumu pomocí půdních sond, měření mocností horizontů a odběru vzorků pro stanovení obsahu organického uhlíku. Tyto práce jsou však časově i finančně značně náročné. Proto je snahou nalézt faktory, které zásobu organického uhlíku ovlivňují a na jejich základě predikovat množství uhlíku v místech, kde půdní průzkum nebyl proveden. Významný posun přinesl i dálkový průzkum země, který umožňuje odhadovat koncentraci půdního organického uhlíku na základě spektrální odrazivosti půdy. Nicméně, jedním z klíčových parametrů potřebných pro odhad zásob uhlíku v půdě je mocnost...Department of Physical Geography and GeoecologyKatedra fyzické geografie a geoekologiePřírodovědecká fakultaFaculty of Scienc

The SIMCA algorithm for processing ground penetrating radar data and its practical applications

The main objective of this thesis is to present a new image processing technique to improve the detectability of buried objects such as landmines using Ground Penetrating Radar (GPR). The main challenge of GPR based landmine detection is to have an accurate image analysis method that is capable of reducing false alarms. However an accurate image relies on having sufficient spatial resolution in the received signal. An Antipersonnel mine (APM) can have a diameter as little as 2cm, whereas many soils have very high attenuation at frequencies above 450 MHz. In order to solve the detection problem, a system level analysis of the issues involved with the recognition of landmines using image reconstruction is required. The thesis illustrates the development of a novel technique called the SIMCA (“SIMulated Correlation Algorithm”) based on area or volume correlation between the trace that would be returned by an ideal point reflector in the soil conditions at the site (obtained using the realistic simulation of Maxwell’s equations) and the actual trace. During an initialization phase, SIMCA carries out radar simulation using the system parameters of the radar and the soil properties. Then SIMCA takes the raw data as the radar is scanned over the ground and uses a clutter removal technique to remove various unwanted signals of clutter such as cross talk, initial ground reflection and antenna ringing. The trace which would be returned by a target under these conditions is then used to form a correlation kernel using a GPR simulator. The 2D GPR scan (B scan), formed by abutting successive time-amplitude plots taken from different spatial positions as column vectors,is then correlated with the kernel using the Pearson correlation coefficient resulting in a correlated image which is brightest at points most similar to the canonical target. This image is then raised to an odd power >2 to enhance the target/background separation. The first part of the thesis presents a 2-dimensional technique using the B scans which have been produced as a result of correlating the clutter removed radargram (’B scan’) with the kernel produced from the simulation. In order to validate the SIMCA 2D algorithm, qualitative evidence was used where comparison was made between the B scans produced by the SIMCA algorithm with B scans from some other techniques which are the best alternative systems reported in the open literature. It was found from this that the SIMCA algorithm clearly produces clearer B scans in comparison to the other techniques. Next quantitative evidence was used to validate the SIMCA algorithm and demonstrate that it produced clear images. Two methods are used to obtain this quantitative evidence. In the first method an expert GPR user and 4 other general users are used to predict the location of landmines from the correlated B scans and validate the SIMCA 2D algorithm. Here human users are asked to indicate the location of targets from a printed sheet of paper which shows the correlated B scans produced by the SIMCA algorithm after some training, bearing in mind that it is a blind test. For the second quantitative evidence method, the AMIRA software is used to obtain values of the burial depth and position of the target in the x direction and hence validate the SIMCA 2D algorithm. Then the absolute error values for the burial depth along with the absolute error values for the position in the x direction obtained from the SIMCA algorithm and the Scheers et al’s algorithm when compared to the corresponding ground truth values were calculated. Two-dimensional techniques that use B scans do not give accurate information on the shape and dimensions of the buried target, in comparison to 3D techniques that use 3D data (’C scans’). As a result the next part of the thesis presents a 3-dimensional technique. The equivalent 3D kernel is formed by rotating the 2D kernel produced by the simulation along the polar co-ordinates, whilst the 3D data is the clutter removed C scan. Then volume correlation is performed between the intersecting parts of the kernel and the data. This data is used to create iso-surfaces of the slices raised to an odd power > 2. To validate the algorithm an objective validation process which compares the actual target volume to that produced by the re-construction process is used. The SIMCA 3D technique and the Scheers et al’s (the best alternative system reported in the open literature) technique are used to image a variety of landmines using GPR scans. The types of mines included plastic, wooden and glass ones. In all cases clear images were obtained with SIMCA. In contrast Scheers’ algorithm, the present state-of-the-art, failed to provide clear images of non metallic landmines. For this thesis, the above algorithms have been tested for landmine data and for locating foundations in demolished buildings and to validate and demonstrate that the SIMCA algorithms are better than existing technologies such as the Scheers et al’s method and the REFLEXW commercial software

3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function

Dear Colleagues, The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest’s compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest’s ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed

Automated extraction of hyperbolic reflections and data processing from radargrams acquired by GPR scanning technology

Докторска дисертација посвећена је области аутоматизоване обраде радарграма формираних применом георадара. Развијени су и имплементирани нови алгоритми за аутоматизовану детекцију и одређивање координата темена хиперболичних рефлексија као и издвајање координата тачака на њиховим крацима. Све анализе и верификацијe су извршене над реалним и синтетичким подацима.Doktorska disertacija posvećena je oblasti automatizovane obrade radargrama formiranih primenom georadara. Razvijeni su i implementirani novi algoritmi za automatizovanu detekciju i određivanje koordinata temena hiperboličnih refleksija kao i izdvajanje koordinata tačaka na njihovim kracima. Sve analize i verifikacije su izvršene nad realnim i sintetičkim podacima.PhD thesis is dedicated to the field of automated processing of radargrams formed by the application of GPR. New algorithms for automated detection and determination of the coordinates of the apexes of hyperbolic reflections as well as the extraction of the coordinates of points on their prongs have been developed and implemented. All analyzes and verifications were performed on real and synthetic data