Interrelations among leaf and canopy nitrogen, optical and structural traits

A correlation between canopy nitrogen and albedo has been observed across a wide range of forest types. Determining the nature and mechanisms behind the relationship would help to understand the role of nitrogen in the climate system and better understand forest-climate interactions. The purpose of this study was to examine sources of variation in leaf and canopy optical traits with respect to variation in nitrogen concentrations at both scales. We found that %N was significantly correlated with leaf and canopy albedo and that both %N and albedo were strongly correlated with forest composition. Many canopy structural traits were found to correlate with each other, as well as with canopy %N and albedo. We hypothesize that a combination of canopy structural attributes are responsible for the correlation between canopy %N and albedo, partially due to their effect on the photon recollision probability

Automated reconstruction of tree and canopy structure for modeling the internal canopy radiation regime

Understanding canopy radiation regimes is critical to successfully modeling vegetation growth and function. For instance, the vertical distribution of photosynthetically active radiation (PAR) affects vegetation growth, informative upon carbon and energy cycling. Availing upon advances in information capture and computing power, geometrically explicit modeling of forest structure becomes increasingly possible. A primary challenge however is acquiring the forest mensuration data required to parameterize these models and the related automation of modeling forest structure. In this research, to address these issues we employ a novel and automated approach that capitalizes upon the rich information afforded by ground-based laser scanning technology. The method is implemented in two steps: in the first step, geometric explicit models of canopy structure are created from the ground-based laser scanning data. These geometric explicit models are used to simulate the vertical range to first hit. In the second step, we derive canopy gap probability from full waveform laser scanning data which have been used in a number of studies for characterization of radiation transmission (Jupp et al., 2009; Yang et al., 2010) and do not require any geometric explicit modeling. The radiative consistency of the geometric explicit models from step 1 is validated against the gap probabilities of step 2. The results show a strong relationship between the radiative transmission properties of the geometric models and canopy gap probabilities at plot level (R = 0.91 to 0.97), while the geometric models suggest the additional benefit to serve as a bridge in scaling between shoot level and canopy level radiation.Keywords: Laser scanning, Explicit geometric, Ray tracing, Canopy structure, Modeling, Photosynthetically active radiatio

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

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

A neuro-genetic hybrid approach to automatic identification of plant leaves

Plants are essential for the existence of most living things on this planet. Plants are used for providing food, shelter, and medicine. The ability to identify plants is very important for several applications, including conservation of endangered plant species, rehabilitation of lands after mining activities and differentiating crop plants from weeds. In recent times, many researchers have made attempts to develop automated plant species recognition systems. However, the current computer-based plants recognition systems have limitations as some plants are naturally complex, thus it is difficult to extract and represent their features. Further, natural differences of features within the same plant and similarities between plants of different species cause problems in classification. This thesis developed a novel hybrid intelligent system based on a neuro-genetic model for automatic recognition of plants using leaf image analysis based on novel approach of combining several image descriptors with Cellular Neural Networks (CNN), Genetic Algorithm (GA), and Probabilistic Neural Networks (PNN) to address classification challenges in plant computer-based plant species identification using the images of plant leaves. A GA-based feature selection module was developed to select the best of these leaf features. Particle Swam Optimization (PSO) and Principal Component Analysis (PCA) were also used sideways for comparison and to provide rigorous feature selection and analysis. Statistical analysis using ANOVA and correlation techniques confirmed the effectiveness of the GA-based and PSO-based techniques as there were no redundant features, since the subset of features selected by both techniques correlated well. The number of principal components (PC) from the past were selected by conventional method associated with PCA. However, in this study, GA was used to select a minimum number of PC from the original PC space. This reduced computational cost with respect to time and increased the accuracy of the classifier used. The algebraic nature of the GA’s fitness function ensures good performance of the GA. Furthermore, GA was also used to optimize the parameters of a CNN (CNN for image segmentation) and then uniquely combined with PNN to improve and stabilize the performance of the classification system. The CNN (being an ordinary differential equation (ODE)) was solved using Runge-Kutta 4th order algorithm in order to minimize descritisation errors associated with edge detection. This study involved the extraction of 112 features from the images of plant species found in the Flavia dataset (publically available) using MATLAB programming environment. These features include Zernike Moments (20 ZMs), Fourier Descriptors (21 FDs), Legendre Moments (20 LMs), Hu 7 Moments (7 Hu7Ms), Texture Properties (22 TP) , Geometrical Properties (10 GP), and Colour features (12 CF). With the use of GA, only 14 features were finally selected for optimal accuracy. The PNN was genetically optimized to ensure optimal accuracy since it is not the best practise to fix the tunning parameters for the PNN arbitrarily. Two separate GA algorithms were implemented to optimize the PNN, that is, the GA provided by MATLAB Optimization Toolbox (GA1) and a separately implemented GA (GA2). The best chromosome (PNN spread) for GA1 was 0.035 with associated classification accuracy of 91.3740% while a spread value of 0.06 was obtained from GA2 giving rise to improved classification accuracy of 92.62%. The PNN-based classifier used in this study was benchmarked against other classifiers such as Multi-layer perceptron (MLP), K Nearest Neigbhour (kNN), Naive Bayes Classifier (NBC), Radial Basis Function (RBF), Ensemble classifiers (Adaboost). The best candidate among these classifiers was the genetically optimized PNN. Some computational theoretic properties on PNN are also presented

EXAMINING THE ROLE OF HOST USE ON DIVERGENCE IN THE REDHEADED PINE SAWFLY, \u3cem\u3eNEODIPRION LECONTEI\u3c/em\u3e, ACROSS MULTIPLE SPATIAL SCALES

Phytophagous insects make up over one quarter of described species on Earth, and this incredible diversity seems directly linked to feeding on plants. Comparative studies of sister groups have shown shifts to herbivory are consistently associated with increased species diversity in insects, but the reasons for this diversification remain unclear. While other explanations, such as decreased extinction rates or influences on population structure, exist, one prominent hypothesis suggests shifts and subsequent adaptation to novel host plants can lead to the evolution of reproductive barriers. Given their extreme specialization on host plants in the genus Pinus and intimate, life-long association with their host plants, divergent host use has been suspected to drive speciation in the conifer sawfly genus Neodiprion. Previous work showed host shifts coincide with speciation events in the genus; but could not determine if these host shifts initiated speciation or if they occurred after other reproductive barriers arose. Determining the contribution and timing of host shifts relative to speciation will require examination of populations at the earliest stages of divergence, before post-speciation changes amass. If host shifts frequently drive speciation in the genus, there will likely be evidence of host-driven divergence within species occurring on a wide range of host plants. The goal of this dissertation is to examine populations of the red-headed pine sawfly, Neodiprion lecontei, an abundant, well-studied pest species that occurs on multiple hosts throughout its range, for evidence of host-driven divergence. Using a combination of reduced representation genomic sequencing, population genomics, and ecological assays, I specifically look for evidence of 1) genetic differentiation between populations utilizing different host plants, 2) ecological divergence in female oviposition preference, larval performance, and ovipositor morphology between populations on different hosts, and 3) ecologically-driven reproductive isolation between genetically and ecologically divergent populations. Each chapter of this dissertation examines the role of host use in driving ecological, genetic, and/or reproductive divergence within N. lecontei at a different spatial scale. First, I surveyed range-wide patterns of diversity. I identified three genetic clusters, dated the divergence of these clusters to the late Pleistocene, and found evidence that both dispersal limitation (geography) and host use contribute to genetic differentiation within N. lecontei. Next, I looked within one of these genetic clusters for additional evidence of the role of host in driving divergence. Sawflies in this cluster primarily utilize two hosts which differ significantly in needle architecture. Although I found no evidence of neutral genetic differentiation between hosts exists, I did detect spatial and temporal differences in host use, and host-specific differences in ovipositor morphology, a performance-related trait. Finally, I examine a single site where N. lecontei utilizes three structurally divergent species of pine. Although there was little genetic structure, no sexual isolation, and no distinct host preferences, the host types were partially temporally isolated and varied in ovipositor morphology and larval performance across on the three hosts. Overall, although divergent host use consistently resulted in divergent ovipositor morphology, a reduction in gene flow via temporal or geographic isolation may be required before additional forms of ecological and genetic differentiation can develop. Together these results suggest host shifts alone may not be enough to drive population divergence and speciation in Neodiprion