Imaging Spectrometry for Soil Applications

This study presents our findings on the fusion of Imaging Spectroscopy (IS) and LiDAR data for urban feature extraction. We carried out necessary preprocessing of the hyperspectral image. Minimum Noise Fraction (MNF) transforms was used for ordering hyperspectral bands according to their noise. Thereafter, we employed Optimum Index Factor (OIF) to statistically select the three most appropriate bands combination from MNF result. The composite image was classified using unsupervised classification (k-mean algorithm) and the accuracy of the classification assessed. Digital Surface Model (DSM) and LiDAR intensity were generated from the LiDAR point cloud. The LiDAR intensity was filtered to remove the noise. Hue Saturation Intensity (HSI) fusion algorithm was used to fuse the imaging spectroscopy and DSM as well as imaging spectroscopy and filtered intensity. The fusion of imaging spectroscopy and DSM was found to be better than that of imaging spectroscopy and LiDAR intensity quantitatively. The three datasets (imaging spectrocopy, DSM and Lidar intensity fused data) were classified into four classes: building, pavement, trees and grass using unsupervised classification and the accuracy of the classification assessed. The result of the study shows that fusion of imaging spectroscopy and LiDAR data improved the visual identification of surface features. Also, the classification accuracy improved from an overall accuracy of 84.6% for the imaging spectroscopy data to 90.2% for the DSM fused data. Similarly, the Kappa Coefficient increased from 0.71 to 0.82. on the other hand, classification of the fused LiDAR intensity and imaging spectroscopy data perform poorly quantitatively with overall accuracy of 27.8% and kappa coefficient of 0.0988.

Evolution of paddy soil fertility in a millennium chronosequence based on imaging spectroscopy

The influence of vegetation cover on the spectroscopic estimation of soil properties

The relevance of forming a distinct scientific identity for the Ukrainian soil science school is increasingly important. Ukrainian soil science must achieve autonomy, separating itself from soviet legacies. The aim of this study is a historical analysis of the development of the modern Ukrainian soil science school, based on the examination of historical-geographical descriptions, scientific works, reports of soil surveys, and cartographic materials. The article addresses the key issue of soil science development, its main stages of evolution, and the historical context of the formation of this science. Scientific views on the periodization of soil science from the perspectives of both Ukrainian and foreign researchers are analyzed. Based on the obtained data, a new periodization of the Ukrainian soil science school’s development is presented. Understanding the historical features of this periodization is a crucial part of soil science historiography. The analysis of scientific works, historical-geographical descriptions, archival and cartographic materials shows that the development of soil science is closely related to practical issues in agriculture and soil management. The modern Ukrainian soil science school has undergone a long and complex process of formation: from empiricism, the era of feudalism (where it developed in global trends), through the genetic paradigm that dominated in the 19th century to the present day. The genetic paradigm became the foundation of modern soil science and made a significant contribution to the development of the Ukrainian school, as it had substantial input from domestic scientists. The genetic period of development of the Ukrainian soil science school, which laid the foundation for further scientific thought, is not entirely dependent on the research of “imperial” scientists alone, but also includes significant contributions from domestic soil scientists, a fact that is often overlooked. The modern Ukrainian soil science school, represented by various regional institutions, is based on the principles of genetic soil science, focusing on theoretical directions that are relevant (ecological, informational, social soil science, etc.). The formation of the modern Ukrainian school of soil science went through a long and complex stage of formation: from empiricism, the era of feudalism (where it developed in global trends), through the genetic paradigm that dominated in the 19th century. to the present day. It was the genetic paradigm that became the basis for modern soil science and left a significant contribution to its further development. The Ukrainian school is no exception, because its future scientific foundations were also formed during this period: the development of fundamental concepts and approaches to the study of soils (genesis, properties, geography and classification of soils) took place. The genetic period of the development of the Ukrainian school of soil science became fundamental for the formation of further scientific thought. It is important to note that this stage does not depend entirely on the achievements of only "imperial" scientists, and domestic soil scientists also made a significant contribution to this period, which is not paid enough attention to in the modern world. The modern Ukrainian school of soil science in global trends has evolved from genetic to ecological and anthropogenic soil science. This is a natural development, because in the modern world the ecological functions of soils are gaining special importance due to the increased anthropogenic impact on the natural environment, as well as the need to develop sustainable approaches to managing soil resources, preserving their fertility and preventing soil erosion and degradation. According to the authors, the modern Ukrainian soil science school is represented by various regional institutions engaged in research in relevant areas (agronomic, ecological, information, social soil science, etc.). The limited material and technical base encourages modern science to deepen theoretical foundations and principles. That is why Ukrainian soil science is at a crossroads in its further development: theorization or development of practical areas, which is difficult in today's conditions. But historical analysis shows that, having a solid scientific foundation and connections with other European centers of soil science, the Ukrainian school of soil science has a chance to become a regional leader of scientific thought. Keywords: Ukrainian soil science school, genetic soil science, historical analysis, periodization, soil science evolution, scientific identity, soil science research.

Imaging spectroscopy is a tool that can be used to spectrally identify and spatially map materials based on their specific chemical bonds. Spectroscopic analysis requires significantly more sophistication than has been employed in conventional broadband remote sensing analysis. We describe a new system that is effective at material identification and mapping: a set of algorithms within an expert system decision‐making framework that we call Tetracorder. The expertise in the system has been derived from scientific knowledge of spectral identification. The expert system rules are implemented in a decision tree where multiple algorithms are applied to spectral analysis, additional expert rules and algorithms can be applied based on initial results, and more decisions are made until spectral analysis is complete. Because certain spectral features are indicative of specific chemical bonds in materials, the system can accurately identify and map those materials. In this paper we describe the framework of the decision making process used for spectral identification, describe specific spectral feature analysis algorithms, and give examples of what analyses and types of maps are possible with imaging spectroscopy data. We also present the expert system rules that describe which diagnostic spectral features are used in the decision making process for a set of spectra of minerals and other common materials. We demonstrate the applications of Tetracorder to identify and map surface minerals, to detect sources of acid rock drainage, and to map vegetation species, ice, melting snow, water, and water pollution, all with one set of expert system rules. Mineral mapping can aid in geologic mapping and fault detection and can provide a better understanding of weathering, mineralization, hydrothermal alteration, and other geologic processes. Environmental site assessment, such as mapping source areas of acid mine drainage, has resulted in the acceleration of site cleanup, saving millions of dollars and years in cleanup time. Imaging spectroscopy data and Tetracorder analysis can be used to study both terrestrial and planetary science problems. Imaging spectroscopy can be used to probe planetary systems, including their atmospheres, oceans, and land surfaces.

Impact of varying irradiance on vegetation indices and chlorophyll fluorescence derived from spectroscopy data

Nowadays, there is a general penetration of ecology in other related sciences. Soil science is not an exception. To the evidence of this, the works of soil scientists may serve, that have appeared recently. It is shown that the ecology of soil is a broader area of the genetic soil science, than ecological soil science. In addition to the doctrine of the ecological functions of soil, modern soil ecology also includes the factor ecology and the doctrine of biosphere soil conservation. In modern soil science there are 2 main areas – fundamental, which aims to study all the features of soil as a natural body, and applied that examines various aspects of soil use by man. At the same time it should be noted that most of soil scientists until recently isolated a genetic soil science in two main areas – the genesis and the geography of soils. Academician L. I. Prasolov (1978) was the first who proposed to allocate soil ecology in a separate section of soil science, along with the above directions. V. R. Volobuev (1963) hold on to the similar views. I. A. Sokolov (1993) showed that the section «Soil ecology» is equal to such sections of soil science as the «Genesis of the soil» and «Geography of the soil». N. A. Gorin (2005) hold on to the similar point of view. On this basis, we offer the following vision of the place of soil ecology in the structure of modern soil science. This scheme is based on the allocation of basic research in the areas of soil science by the team of authors under the leadership of the Moscow State University V. A. Kovda and B. G. Rozanov (Pochvovedenie, 1988). The classification of the historic area of soil science is identified with the genesis of soil by us, and pedography – with the geography of soil. The scientific achievements of other fundamental areas (pedognostika, dynamic soil science, regional soil science, history and methodology of science) are widely used to address key issues of historical soil science and pedography. The structure of the direction «Ecology of soil» is developed by academician G. V. Dobrovolsky and E. D. Nikitin (2012). This doctrine of the ecological functions of soil, classification by B. F. Aparin (2012) is a fundamental direction, the theoretical basis of ecological soil science, related to the applied directions. After L. O. Karpachevsky (2005), who considers the ecological functions of soil subject as ecological soil science, we identify the ecological soil science with the doctrine of the ecological functions of soil in some extent. This view is confirmed by the definition of ecological soil science, formulated G. V. Dobrovolsky and G. S. Kust (2012) – «This is a direction in modern soil science, studied the role of soil as a unique habitat of plants, animals, microorganisms, and especially – in human life, in the functioning of the biosphere and the individual ecosystems». From the above definition, it is clear that in this case, the authors believe that the core of ecological soil science is ecological functions of soil, which manifest themselves through their specific role in nature and human life. The subject of the study of ecological soil science, as indicated by L. O. Karpachevsky (2005), is the ecological functions of soil. Modern physics of soil – is the area of soil science that studies the physical properties of the soil and the place in which physical processes are flowing (Voronin, 1986). As you can see from the definition, the ecological functions of soil caused by the physical properties of soil, remain outside the field of soil physics research. For this reason, there is a need for the provision and the development of ecological soil physics, which is based on theoretical and practical achievements of classical physics of soil, and will also pay close attention to research the ecological functions of soil.

We examine chemical mapping of reaction phases in a Cu-Al multilayer system using low-loss electron energy loss spectroscopy spectrum imaging and image spectroscopy techniques. The sensitivity of the plasmon peak position and shape to various crystal structures and phases is exploited using postprocessing of spectra into second derivative plasmon maps and line scans. Analytical transmission electron microscopy is complemented by studies of the orientation relationship of the multilayer system using high-resolution electron microscopy of interfaces and selected area diffraction. The techniques have been applied to the Cu-Al multilayer sample and sharply bound epitaxial phases are found, before and after heat treatment.

Soil Taxonomy of Croatia

Fuzzy logic and differences between interpretive soil maps

To develop a robust and rapid imaging technique for hyperpolarized (13)C MR Spectroscopic Imaging and investigate its performance. A concentric rings readout trajectory with constant angular velocity is proposed for hyperpolarized (13)C spectroscopic imaging and its properties are analyzed. Quantitative analyses of design tradeoffs are presented for several imaging scenarios. The first application of concentric rings on (13)C phantoms and in vivo animal hyperpolarized (13)C MR Spectroscopic Imaging studies were performed to demonstrate the feasibility of the proposed method. Finally, a parallel imaging accelerated concentric rings study is presented. The concentric rings MR Spectroscopic Imaging trajectory has the advantages of acquisition timesaving compared to echo-planar spectroscopic imaging. It provides sufficient spectral bandwidth with relatively high efficiency compared to echo-planar spectroscopic imaging and spiral techniques. Phantom and in vivo animal studies showed good image quality with half the scan time and reduced pulsatile flow artifacts compared to echo-planar spectroscopic imaging. Parallel imaging accelerated concentric rings showed advantages over Cartesian sampling in g-factor simulations and demonstrated aliasing-free image quality in a hyperpolarized (13)C in vivo study. The concentric rings trajectory is a robust and rapid imaging technique that fits very well with the speed, bandwidth, and resolution requirements of hyperpolarized (13)C MR Spectroscopic Imaging.

Imaging spectroscopic technique has been used for the mineral and rock geological mapping and alteration information extraction successfully with many reasonable results, but it is mainly used in arid and semi-arid land with low vegetation covering. In the case of the high vegetation covering, the outcrop of the altered rocks is small and distributes sparsely, the altered rocks is difficult to be identified directly. The target detection technique using imaging spectroscopic data should be introduced to the extraction of small geological targets under high vegetation covering area. In the paper, we take Ding-Ma gold deposit as the study area which located in Zhenan country, Shanxi province, the spectral features of the targets and the backgrounds are studied and analyzed using the field reflectance spectra, in addition to the study of the principle of the algorithms, some target detection algorithms which is appropriate to the small geological target detection are introduced. At last, the small altered rock targets under the covering of vegetation in forest are detected and discriminated in imaging spectroscopy data with the methods of spectral angle mapper (SAM), Constrained Energy Minimization (CEM) and Adaptive Cosine Estimator (ACE). The detection results are reasonable and indicate the ability of target detection algorithms in geological target detection in the forest area.

The goal of this work was to evaluate the relationship between neuronal injury/loss in the hippocampus, thalamus, and putamen in temporal lobe epilepsy (TLE) patients using (1)H magnetic resonance spectroscopic imaging. (1)H spectroscopic images from the hippocampus and thalamus of controls and patients with TLE were acquired at 4 T. The spectroscopic imaging data were reconstructed using an automated voxel-shifting method based on anatomic landmarks providing four, six, and three loci for the hippocampus, thalamus, and putamen, respectively. For correlation analysis, the hippocampal and striatal loci were averaged to provide single estimates of the entire structure, whereas the thalamus was divided into two regions, an anterior and posterior measure, using the average of three loci each. The ratio of N-acetyl aspartate to creatine (NAA/Cr), a measure of neuronal injury/loss, was significantly reduced in both the ipsilateral and contralateral hippocampi and thalami. NAA/Cr in the ipsilateral hippocampus was significantly correlated with the ipsilateral and contralateral anterior and posterior thalami, putamen, and contralateral hippocampus. In control subjects, the hippocampi were only correlated with each other. The data demonstrate that there is significant neuronal injury/loss in both the ipsilateral and contralateral thalami in temporal lobe epilepsy patients, with greater impairment in the anterior portions of the ipsilateral thalamus. The degree of injury/loss in the ipsilateral and contralateral thalamus and putamen is directly correlated with that of the ipsilateral hippocampus. This is consistent with the hypothesis that the impairment and damage associated with recurrent seizures as measured by N-acetyl aspartate originating in the hippocampus results in injury and impairment in other subcortical structures.

Background. Biogeographers assess how species distributions and abundances affect the structure, function, and composition of ecosystems. Yet we face a major challenge: it is difficult to precisely map species across landscapes. Novel Earth observations could obviate this challenge. Airborne imaging spectrometers measure plant functional traits at high resolution, and these measurements can be used to identify tree species. Plant traits are often highly conserved within species, and highly variable between species, which provides the biophysical basis for species mapping. In this paper I describe a trait-based approach to species identification with imaging spectroscopy, CCB-ID, which was developed as part of a NIST-sponsored ecological data science evaluation (ECODSE). Methods. These methods were developed using NEON airborne imaging spectroscopy data. CCB-ID classifies tree species using trait-based reflectance variation and decision tree-based machine learning models, approximating a morphological trait and dichotomous key method traditionally used in botanical classification. First, outliers were removed using a spectral variance threshold. The remaining samples were transformed using principal components analysis and resampled by species to reduce common species biases. Gradient boosting and random forest classifiers were trained using the transformed and resampled feature data. Prediction probabilities were then calibrated using sigmoid regression, and sample-scale predictions were averaged to the crown scale. Results. This approach performed well according to the competition metrics, receiving a rank-1 accuracy score of 0.919, and a cross-entropy cost score of 0.447 on the test data. Accuracy and specificity scores were high for all species, but precision and recall scores were variable for rare species. PCA transformation improved accuracy scores compared to models trained using reflectance data, but outlier removal and data resampling exacerbated class imbalance problems. Discussion. CCB-ID accurately classified tree species using NEON imaging spectroscopy data, reporting the best classification scores among participants. However, it failed to overcome several well-known species mapping challenges, like precisely identifying rare species. Key takeaways include (1) training models to maximize metrics beyond accuracy (e.g. recall) could improve rare species predictions, (2) within-genus trait variation may drive spectral separability, precluding efforts to distinguish between functionally convergent species, (3) outlier removal and data resampling exacerbated class imbalance problems, and should be carefully implemented, (4) PCA transformation greatly improved model results, and (5) feature selection could further improve species classification models. CCB-ID is open source, designed for use with NEON data, and available to support future species mapping efforts.

Imaging spectroscopy provides the opportunity to incorporate leaf and canopy optical data into ecological studies, but the extent to which remote sensing of vegetation can enhance the study of belowground processes is not well understood. In terrestrial systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling. We hypothesized that ecosystem productivity, and the chemical, structural and phylogenetic‐functional composition of plant communities would be detectable with remote sensing and could be used to predict belowground plant and soil processes in two grassland biodiversity experiments: the BioDIV experiment at Cedar Creek Ecosystem Science Reserve in Minnesota and the Wood River Nature Conservancy experiment in Nebraska. We tested whether aboveground vegetation chemistry and productivity, as detected from airborne sensors, predict soil properties, microbial processes and community composition. Imaging spectroscopy data were used to map aboveground biomass, green vegetation cover, functional traits and phylogenetic‐functional community composition of vegetation. We examined the relationships between the image‐derived variables and soil carbon and nitrogen concentration, microbial community composition, biomass and extracellular enzyme activity, and soil processes, including net nitrogen mineralization. In the BioDIV experiment—which has low overall diversity and productivity despite high variation in each—belowground processes were driven mainly by variation in the amount of organic matter inputs to soils. As a consequence, soil respiration, microbial biomass and enzyme activity, and fungal and bacterial composition and diversity were significantly predicted by remotely sensed vegetation cover and biomass. In contrast, at Wood River—where plant diversity and productivity were consistently higher—belowground processes were driven mainly by variation in the quality of aboveground inputs to soils. Consequently, remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates but aboveground biomass (or cover) did not. The contrasting associations between the quantity (productivity) and quality (composition) of aboveground inputs with belowground soil attributes provide a basis for using imaging spectroscopy to understand belowground processes across productivity gradients in grassland systems. However, a mechanistic understanding of how above and belowground components interact among different ecosystems remains critical to extending these results broadly.