Assessing the Perspectives of Ground Penetrating Radar for Precision Farming

The United Nations 2030 Agenda for Sustainable Development highlighted the importance of adopting sustainable agricultural practices to mitigate the threat posed by climate change to food systems around the world, to provide wise water management and to restore degraded lands. At the same time, it suggested the benefits and advantages brought by the use of near-surface geophysical measurements to assist precision farming, in particular providing information on soil variability at both vertical and horizontal scales. Among such survey methodologies, Ground Penetrating Radar has demonstrated its effectiveness in soil characterisation as a consequence of its sensitivity to variations in soil electrical properties and of its additional capability of investigating subsurface stratification. The aim of this contribution is to provide a comprehensive review of the current use of the GPR technique within the domain of precision irrigation, and specifically of its capacity to provide detailed information on the within-field spatial variability of the textural, structural and hydrological soil properties, which are needed to optimize irrigation management, adopting a variable-rate approach to preserve water resources while maintaining or improving crop yields and their quality. For each soil property, the review analyses the commonly adopted operational and data processing approaches, highlighting advantages and limitations

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

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

Remote Sensing of Plant Biodiversity

At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing

GPR Method for the Detection and Characterization of Fractures and Karst Features: Polarimetry, Attribute Extraction, Inverse Modeling and Data Mining Techniques

The presence of fractures, joints and karst features within rock strongly influence the hydraulic and mechanical behavior of a rock mass, and there is a strong desire to characterize these features in a noninvasive manner, such as by using ground penetrating radar (GPR). These features can alter the incident waveform and polarization of the GPR signal depending on the aperture, fill and orientation of the features. The GPR methods developed here focus on changes in waveform, polarization or texture that can improve the detection and discrimination of these features within rock bodies. These new methods are utilized to better understand the interaction of an invasive shrub, Juniperus ashei, with subsurface flow conduits at an ecohydrologic experimentation plot situated on the limestone of the Edwards Aquifer, central Texas. First, a coherency algorithm is developed for polarimetric GPR that uses the largest eigenvalue of a scattering matrix in the calculation of coherence. This coherency is sensitive to waveshape and unbiased by the polarization of the GPR antennas, and it shows improvement over scalar coherency in detection of possible conduits in the plot data. Second, a method is described for full-waveform inversion of transmission data to quantitatively determine fracture aperture and electromagnetic properties of the fill, based on a thin-layer model. This inversion method is validated on synthetic data, and the results from field data at the experimentation plot show consistency with the reflection data. Finally, growing hierarchical self-organizing maps (GHSOM) are applied to the GPR data to discover new patterns indicative of subsurface features, without representative examples. The GHSOMs are able to distinguish patterns indicating soil filled cavities within the limestone. Using these methods, locations of soil filled cavities and the dominant flow conduits were indentified. This information helps to reconcile previous hydrologic experiments conducted at the site. Additionally, the GPR and hydrologic experiments suggests that Juniperus ashei significantly impacts infiltration by redirecting flow towards its roots occupying conduits and soil bodies within the rock. This research demonstrates that GPR provides a noninvasive tool that can improve future subsurface experimentation

Acoustic sounding of snow water equivalent

An acoustic frequency-swept wave was investigated as a means for determining Snow Water Equivalent (SWE) in cold wind-swept prairie and sub-alpine environments. Building on previous research conducted by investigators who have examined the propagation of sound in snow, digital signal processing was used to determine acoustic pressure wave reflection coefficients at the interfaces between 'layers' indicative of changes in acoustic impedance. Using an iterative approach involving boundary conditions at the interfaces, the depth-integrated SWE was determined using the Berryman equation from porous media physics. Apparatuses used to send and receive sound waves were designed and deployed during the winter season at field sites situated near the city of Saskatoon, Saskatchewan, and in Yoho National Park, British Columbia. Data collected by gravimetric sampling was used as comparison for the SWE values determined by acoustic sounding. The results are encouraging and suggest that this procedure is similar in accuracy to SWE data collected using gravimetric sampling. Further research is required to determine the applicability of this technique for snow situated at other geographic locations

Understanding forest health with Remote sensing-Part II-A review of approaches and data models

Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time- and labour-inte

Review Article: Global Monitoring of Snow Water Equivalent Using High-Frequency Radar Remote Sensing

Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth\u27s surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth\u27s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world\u27s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth\u27s cold regions\u27 ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements

QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA

Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi-spectral Instrument (MSIs) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSIs have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat-2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management

Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS)