Uncertainty in parameterizing floodplain forest friction for natural flood management, using remote sensing

One potential Natural Flood Management (NFM) option is floodplain reforestation or manage existing riparian forests, with a view to increasing flow resistance and attenuate flood hydrographs. However, the effectiveness of floodplain forests as resistance agents, during different magnitude overbank floods, has yet to be appropriately parameterized for hydraulic models. Remote sensing offers high-resolution datasets capable of characterizing vegetation structure from a variety of platforms, but they contain uncertainty. For the first time, we demonstrate uncertainty propagation in remote sensing derivations of complex vegetation structure through roughness prediction and floodplain flow for extreme flows and different forest types (young and old Poplar plantations, young and old Pine plantations, and an unmanaged riparian forest). The lowest uncertainties resulted from terrestrial and airborne lidar, where airborne lidar is currently best at defining canopy leaf area, but more research is needed to determine wood area. Mean literature uncertainties in stem density, trunk diameter, wood, and leaf area indices (20, 10, 30, 20%, respectively) resulted in a combined Manning’s n uncertainty from 11–13% to 11–17% at 2 m to 8 m flow depths. This equates to 7–8% roughness uncertainty per 10% combined forest structure uncertainty. Individually, stem density and trunk diameter uncertainties resulted in the largest Manning’s n uncertainty at all flow depths, especially for flow though Pine plantations. For deeper flows, leaf and woody areas become much more important, especially for unmanaged riparian forests with low canopy morphology. Forest structure errors propagated to flow depth demonstrate that even small flows can change by a decimeter, while deeper flows can change by 40 cm or more. For flow depth, errors in canopy structure are deemed more severe in flows depths beyond 4–6 m. This study highlights the need for lower uncertainty in all forest structure components using remote sensing, to improve roughness parameterization and flood modeling for NFM

Analysis and Exploitation of Automatically Generated Scene Structure from Aerial Imagery

The recent advancements made in the field of computer vision, along with the ever increasing rate of computational power has opened up opportunities in the field of automated photogrammetry. Many researchers have focused on using these powerful computer vision algorithms to extract three-dimensional point clouds of scenes from multi-view imagery, with the ultimate goal of creating a photo-realistic scene model. However, geographically accurate three-dimensional scene models have the potential to be exploited for much more than just visualization. This work looks at utilizing automatically generated scene structure from near-nadir aerial imagery to identify and classify objects within the structure, through the analysis of spatial-spectral information. The limitation to this type of imagery is imposed due to the common availability of this type of aerial imagery. Popular third-party computer-vision algorithms are used to generate the scene structure. A voxel-based approach for surface estimation is developed using Manhattan-world assumptions. A surface estimation confidence metric is also presented. This approach provides the basis for further analysis of surface materials, incorporating spectral information. Two cases of spectral analysis are examined: when additional hyperspectral imagery of the reconstructed scene is available, and when only R,G,B spectral information can be obtained. A method for registering the surface estimation to hyperspectral imagery, through orthorectification, is developed. Atmospherically corrected hyperspectral imagery is used to assign reflectance values to estimated surface facets for physical simulation with DIRSIG. A spatial-spectral region growing-based segmentation algorithm is developed for the R,G,B limited case, in order to identify possible materials for user attribution. Finally, an analysis of the geographic accuracy of automatically generated three-dimensional structure is performed. An end-to-end, semi-automated, workflow is developed, described, and made available for use

Estimation de paramètres structuraux des arbres dans une savane à partir de mesures LiDAR terrestre et d'imagerie à très haute résolution spatiale

This thesis takes its place in a context where information on the biophysical state of forest ecosystems at spatial scales only remote sensing can retrieve is in demand more than ever. In order to provide reliable information using validated approaches, the remote sensing research community recognises the need for new and innovative methods, especially in heterogeneous environments like savannas. The recent emergence of terrestrial LiDAR scanners (TLS) and the increase in the computational capability of computers which allow running ray tracing model simulations with a high level of realism hold great potential to improve our understanding of the processes influencing the radiance measured by satellite sensors. This thesis makes use of these two cutting edge technologies for estimating the spatial distribution of tree leaf area, a key element of modeling radiative transfer processes. The first part of the thesis concerns the development of methods for estimating tridimensional leaf area distribution in a savanna environment from TLS measurements. The methods presented address certain issues related to TLs measures affecting the application of classical theories (the probability of light transmission and the contact frequency) to the estimation of leaf area through indirect means. These issues pertain to the cross-section of laser pulses emitted by a TLS and the occlusion effects caused by the interception of laser pulses by material inside the crown. The developed methods also exploit additional information provided by the active nature of the TLS sensor that is not available to passive sensors like hemispherical photography, i.e. the intensity of a pulse return offers the possibility to distinguish between energy interception by wood and foliage. A simplified approach of this method is presented to promote its use by other research groups. This approach consists of a series of parameterisations and represents a significant gain in terms of the required resources to produce the leaf area, estimates. The second part of the thesis explores the combination of the tree representations generated in the first part with a ray tracing model to simulate the interactions of light with tree crowns. This approach is highly innovative and our study showed its potential to improve our understanding of the factors influencing the radiative environment in a savanna. The methods presented offer a solution to map leaf area at the individual tree scale over large areas from very high spatial resolution imagery

Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

Utilising airborne scanning laser (LiDAR) to improve the assessment of Australian native forest structure

Enhanced understanding of forest stocks and dynamics can be gained through improved forest measurement, which is required to assist with sustainable forest management decisions, meet Australian and international reporting needs, and improve research efforts to better respond to a changing climate. Integrated sampling schemes that utilise a multi-scale approach, with a range of data sourced from both field and remote sensing, have been identified as a way to generate the required forest information. Given the multi-scale approach proposed by these schemes, it is important to understand how scale potentially affects the interpretation and reporting of forest from a range of data. ¶ To provide improved forest assessment at a range of scales, this research has developed a strategy for facilitating tree and stand level retrieval of structural attributes within an integrated multi-scale analysis framework. ..

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