Seabed mapping in coastal shallow waters using high resolution multispectral and hyperspectral imagery

Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.Peer ReviewedPostprint (published version

Imaging ductal carcinoma using a hyperspectral imaging system

Hyperspectral Imaging (HSI) is a non-invasive optical imaging modality that shows the potential to aid pathologists in breast cancer diagnoses cases. In this study, breast cancer tissues from different patients were imaged by a hyperspectral system to detect spectral differences between normal and breast cancer tissues, as well as early and late stages of breast cancer. If the spectral differences in these tissue types can be measured, automated systems can be developed to help the pathologist identify suspect biopsy samples, which will improve sample throughput and assist in making critical treatment decisions. Tissue samples from ten different patients were provided by the WVU Pathology Department. The samples from each patient included both normal and ductal carcinoma tissue, both stained and unstained. These cells were imaged using a snapshot HSI system, and the spectral reflectances were evaluated to see if there was a measurable spectral difference between the various cell types. Analysis of the spectral reflectance values indicated that wavelengths near 550nm show the best differentiation between tissue types. This information was used to train image processing algorithms using supervised and unsupervised data. K-Means and Support Vector Machine (SVM) approaches were applied to the hyperspectral data cubes, and successfully detected spectral tissue differences with sensitivity of 85.45%, and specificity of 94.64% with TNR of 95.8%, and FPR of 4.2%. These results were verified by ground truth marking of the tissue samples by a pathologist. This interdisciplinary work will build a bridge between pathology and hyperspectral optical diagnostic imaging in order to reduce time and workload on the pathologist, which can lead to benefit of lead reducing time, and increasing the accuracy of diagnoses

Mapping Prosopis glandulosa (mesquite) invasion in the arid environment of South African using remote sensing techniques

A dissertation submitted to the School of Geography, Archaeology and Environmental Studies, Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science in Environmental Sciences. Johannesburg, March 2016.Mapping Prosopis glandulosa (mesquite) invasion in the arid environment of South Africa using remote sensing techniques Mureriwa, Nyasha Abstract Decades after the first introduction of the Prosopis spp. (mesquite) to South Africa in the late 1800s for its benefits, the invasive nature of the species became apparent as its spread in regions of South Africa resulting in devastating effects to biodiversity, ecosystems and the socio-economic wellbeing of affected regions. Various control and management practices that include biological, physical, chemical and integrated methods have been tested with minimal success as compared to the rapid spread of the species. From previous studies, it has been noted that one of the reasons for the low success rates in mesquite control and management is a lack of sufficient information on the species invasion dynamic in relation to its very similar co-existing species. In order to bridge this gap in knowledge, vegetation species mapping techniques that use remote sensing methods need to be tested for the monitoring, detection and mapping of the species spread. Unlike traditional field survey methods, remote sensing techniques are better at monitoring vegetation as they can cover very large areas and are time-effective and cost-effective. Thus, the aim of this research was to examine the possibility of mapping and spectrally discriminating Prosopis glandulosa from its native co-existing species in semi-arid parts of South Africa using remote sensing methods. The specific objectives of the study were to investigate the spectral separability between Prosopis glandulosa and its co-existing species using field spectral data as well as to upscale the results to different satellites resolutions. Two machine learning algorithms (Random Forest (RF) and Support Vector Machines (SVM)) were also tested in the mapping processes. The first chapter of the study evaluated the spectral discrimination of Prosopis glandulosa from three other species (Acacia karoo, Acacia mellifera and Ziziphus mucronata) in the study area using in-situ spectroscopy in conjunction with the newly developed guided regularized random forest (GRRF) algorithm in identifying key wavelengths for multiclass classification. The GRRF algorithm was used as a method of reducing the problem of high dimensionality associated with hyperspectral data. Results showed that there was an increase in the accuracy of discrimination between the four species when the full set of 1825 wavelengths was used in classification (79.19%) as compared to the classification used by the 11 key wavelengths identified by GRRF (88.59%). Results obtained from the second chapter showed that it is possible to spatially discriminate mesquite from its co-existing acacia species and other general land-cover types at a 2 m resolution with overall accuracies of 86.59% for RF classification and 85.98% for SVM classification. The last part of the study tested the use of the more cost effective SPOT-6 imagery and the RF and SVM algorithms in mapping Prosopis glandulosa invasion and its co-existing indigenous species. The 6 m resolution analysis obtained accuracies of 78.46% for RF and 77.62% for SVM. Overall it was concluded that spatial and spectral discrimination of Prosopis glandulosa from its native co-existing species in semi-arid South Africa was possible with high accuracies through the use of (i) two high resolution, new generation sensors namely, WorldView-2 and SPOT-6; (ii) two robust classification algorithms specifically, RF and SVM and (iii) the newly developed GRRF algorithm for variable selection and reducing the high dimensionality problem associated with hyperspectral data. Some recommendations for future studies include the replication of this study on a larger scale in different invaded areas across the country as well as testing the robustness of the RF and SVM classifiers by making use of other machine learning algorithms and classification methods in species discrimination. Keywords: Prosopis glandulosa, field spectroscopy, cost effectiveness, Guided Regularised Random Forest, Support Vector Machines, Worldview-2, Spot-

Terrain classification using machine learning algorithms in a multi-temporal approach A QGIS plug-in implementation

Land cover and land use (LCLU) maps are essential for the successful administration of a nation’s topography, however, conventional on-site data gathering methods are costly and time-consuming. By contrast, remote sensing data can be used to generate up-to-date maps regularly with the help of machine learning algorithms, in turn, allowing for the assessment of a region’s dynamics throughout time. The present dissertation will focus on the implementation of an automated land use and land cover classifier based on remote sensing imagery provided by the mod ern sentinel-2 satellite constellation. The project, with Portugal at its focus, will expand on previous approaches by utilizing temporal data as an input variable in order to harvest the contextual information contained in the vegetation cycles. The pursued solution investigated the implementation of a 9-class classifier plug-in for an industry standard, open-source geographic information system. In the course of the testing procedure, various processing techniques and machine learning algorithms were evaluated in a multi-temporal approach. Resulting in a final overall accuracy of 65,9% across the targeted classes.Mapas de uso e ocupação do solo são cruciais para o entendimento e administração da topografia de uma nação, no entanto, os métodos convencionais de aquisição local de dados são caros e demorados. Contrariamente, dados provenientes de métodos de senso riamento remoto podem ser utilizados para gerar regularmente mapas atualizados com a ajuda de algoritmos de aprendizagem automática. Permitindo, por sua vez, a avaliação da dinâmica de uma região ao longo do tempo. Utilizando como base imagens de sensoriamento remoto fornecidas pela recente cons telação de satélites Sentinel-2, a presente dissertação concentra-se na implementação de um classificador de mapas de uso e ocupação do solo automatizado. O projeto, com foco em Portugal, irá procurar expandir abordagens anteriores através do aproveitamento de informação contextual contida nos ciclos vegetativos pela utilização de dados temporais adicionais. A solução adotada investigou a produção e implementação de um classificador geral de 9 classes num plug-in de um sistema de informação geográfico de código aberto. Durante o processo de teste, diversas técnicas de processamento e múltiplos algoritmos de aprendizagem automática foram avaliados numa abordagem multi-temporal, culminando num resultado final de precisão geral de 65,9% nas classes avaliadas

Integration of stacked-autoencoders and convolutional neural networks for hyperspectral image classification

Orientador: Prof. Dr. Jorge Antônio Silva CentenoTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências da Terra, Programa de Pós-Graduação em Ciências Geodésicas. Defesa : Curitiba, 24/05/2021Inclui referências: p. 97-103Resumo: Deep Learning ou aprendizado profundo abriu novas possibilidades para o pré-processamento, processamento e análise de dados hiperespectrais usando várias camadas de redes neurais e pode ser usado como ferramenta de extração de atributos. Nesta pesquisa, é desenvolvido um modelo híbrido baseado em pixels que integra Stacked-Autoencoders (SAE) y Redes Neurais Convolucionais (CNN) para classificar dados hiperespectrais. O núcleo do modelo integrado (SAE-1DCNN) é um Autoencoder que é aprimorado usando camadas convolucionais nas etapas de codificação (encoding) e decodificação (decoding). Isso permite melhorar a discriminação de dados no treinamento não supervisionado e reduzir o tempo no processamento, pois permite uma descrição dos atributos baseada na assinatura hiperespectral do pixel e aproveita a eficácia da arquitetura profunda com base nas camadas convolucionais e pooling. Como filtros unidimensionais foram aplicados no modelo integrado, o tempo de processamento é consideravelmente menor do que ao usar filtros 2D-CNN. Em uma primeira etapa, o modelo SAE-1DCNN é usado para extração de atributos e, em seguida, esses resultados são usados em uma etapa final para uma classificação supervisionada. Assim, na primeira etapa os parâmetros da rede são ajustados usando amostras de treinamento e após na segunda etapa uma abordagem fine-tuning composta de regressão logística com base na função de ativação softmax foi aplicada para classificação. Três aspectos são analisados nesta pesquisa: a capacidade do modelo de excluir bandas ruidosas, sua capacidade de redução da dimensionalidade e seu potencial para realizar a classificação da cobertura da terra usando dados hiperespectrais. Os experimentos foram realizados com diferentes conjuntos de dados hiperespectrais: Indian Pines, Universidade de Pavia e Salinas, amplamente utilizados pela comunidade científica, e uma imagem hiperespectral capturada na Fazenda Canguiri da Universidade Federal do Paraná (UFPR) no Paraná-Brasil. Para validar a metodologia proposta, os resultados obtidos foram comparados aos métodos tradicionais de aprendizado de máquina para verificar o potencial da integração de autoencoders (AE) e redes convolucionais. Os resultados obtidos mostraram similaridade com os métodos tradicionais em termos de acurácia da classificação hiperespectral, porém demandaram menos tempo de processamento, portanto, a metodologia proposta (SAE-1DCNN) é considerada promissora, sólida e pode ser uma alternativa para o pré-processamento de dados hiperespectrais e processamento.Abstract: Deep learning opened new possibilities for hyperspectral data processing and analysis using multiple neural nets layers and can be used as a feature extraction tool. In this research, a pixel-based hybrid model is developed that integrates Stacked-Autoencoders (SAE) and Convolutional Neural Network (CNN) for hyperspectral image classification. The core of the integrated model (SAE-1DCNN) is an autoencoder that is improved by using convolutional layers in the encoding and decoding steps. This allows improving data discrimination in unsupervised training and reducing the processing time because it allows a feature-based description of the pixel's hyperspectral signature and takes advantage of the effectiveness of deep architecture based on the convolutional and pooling layers. As one-dimensional filters are applied, the processing time is considerably lower than when using 2D-CNN filters. In a first step, the SAE-1DCNN model is used for feature extraction and then these results are used in a final supervised classification step. Thus, in the first stage, the parameters of the net are adjusted using training samples and then, in the second stage, a fine-tuning approach followed by logistic regression based on the softmax activation function was applied for classification. Three aspects are analyzed in detail: the capacity of the model to exclude noisy bands, its ability to dimensionality reduction, and its potential to perform land cover classification based on hyperspectral data. Experiments were performed using different hyperspectral data sets: Indian Pines University of Pavia and Salinas, widely used by the scientific community, and a hyperspectral image captured at the Canguiri Farm of the Federal University of Paraná (UFPR) in Paraná-Brazil. To validate the proposed methodology, the obtained results were compared to traditional machine learning methods to verify the potential of the integration of autoencoders (AE) and convolutional nets. These obtained results showed similarity with traditional methods in terms of hyperspectral classification accuracy, however, they demanded less time for processing, therefore, the proposed methodology (SAE-1DCNN) is considered promising, solid, and can be an alternative for hyperspectral data pre-processing and processing.Resumen: Deep Learning o aprendizaje profundo abrió nuevos desafíos para el preprocesamiento, procesamiento y análisis de datos hiperespectrales usando varias capas de redes neuronales y puede ser usado como herramienta de extracción de atributos. En esta investigación, se desarrolla un modelo híbrido basado en pixeles que integra Stacked-Autoencoders (SAE) y redes Neuronales Convolucionales (CNN) para clasificar datos hiperespectrales. Este enfoque uso un modelo basado en pixeles que integra Convolutional Neural Networks (CNN) y Stacked-Autoencoders (SAE). El núcleo del modelo integrado (SAE-1DCNN) es un Autoencoder (AE) mejorado que usa capas convolucionales en las etapas de codificación y decodificación. Esto permite mejorar la discriminación de datos a través de un entrenamiento supervisado y además reducir el tiempo en el procesamiento, pues permite una descripción de los atributos basad en la respuesta hiperespectral del pixel y aprovecha la efectividad de la arquitectura profunda en las capas convolucionales (convolutional) y de agrupamiento (pooling). En este modelo integrado se aplican filtros unidimensionales lo que permite que el tiempo en el procesamiento sea menor si se compara con los filtros bidimensionales 2D-CNN. En una primera etapa, el modelo SAE-1DCNN es usado para la extracción de atributos y en seguida, esos resultados son usados para la etapa final basada en la clasificación supervisada. De esta forma, en la primera etapa los parámetros de la red son ajustados usando las muestras de entrenamiento y después en la segunda etapa el enfoque conocido como fine-tuning fue aplicado para la clasificación de cobertura terrestre basado en regresión logística y la función de activación softmax. Tres aspectos son analizados en esta investigación, la capacidad del modelo para excluir bandas ruidosas, la capacidad para seleccionar las bandas redundantes y así reducir la dimensionalidad y el potencial para realizar la clasificación de la cobertura terrestre usando datos hiperespectrales. Los experimentos fueron realizados con diferentes conjuntos de datos hiperespectrales: Indian Pines, Universidad de Pavia y Salinas, ampliamente usados en trabajos científicos, y una imagen hiperespectral capturada en la Hacienda Canguiri de la Universidad Federal de Paraná (UFPR) en Paraná-Brasil. Para validar la metodología propuesta, los resultados obtenidos se compararon con métodos tradicionales de aprendizaje de máquina (machine learning) para verificar el potencial de la integración de Autoencoders (AE) y redes convolucionales. Los resultados obtenidos mostraron similitud con los métodos tradicionales en cuanto a la precisión de clasificación hiperespectral, sin embargo, exigieron menos tiempo de procesamiento, por lo que, la metodología propuesta (SAE-1DCNN) se considera prometedora, sólida y puede ser una alternativa para el pré-procesamiento y procesamiento de datos hiperespectrales

Electronic sensor technologies in monitoring quality of tea: A review

Tea, after water, is the most frequently consumed beverage in the world. The fermentation of tea leaves has a pivotal role in its quality and is usually monitored using the laboratory analytical instruments and olfactory perception of tea tasters. Developing electronic sensing platforms (ESPs), in terms of an electronic nose (e-nose), electronic tongue (e-tongue), and electronic eye (e-eye) equipped with progressive data processing algorithms, not only can accurately accelerate the consumer-based sensory quality assessment of tea, but also can define new standards for this bioactive product, to meet worldwide market demand. Using the complex data sets from electronic signals integrated with multivariate statistics can, thus, contribute to quality prediction and discrimination. The latest achievements and available solutions, to solve future problems and for easy and accurate real-time analysis of the sensory-chemical properties of tea and its products, are reviewed using bio-mimicking ESPs. These advanced sensing technologies, which measure the aroma, taste, and color profiles and input the data into mathematical classification algorithms, can discriminate different teas based on their price, geographical origins, harvest, fermentation, storage times, quality grades, and adulteration ratio. Although voltammetric and fluorescent sensor arrays are emerging for designing e-tongue systems, potentiometric electrodes are more often employed to monitor the taste profiles of tea. The use of a feature-level fusion strategy can significantly improve the efficiency and accuracy of prediction models, accompanied by the pattern recognition associations between the sensory properties and biochemical profiles of tea

Non-Parametric Spatial Spectral Band Selection methods

© Cranfield University 2021. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerThis project is about the development of band selection (BS) techniques for better target detection and classification in remote sensing and hyperspectral imaging (HSI). Conventionally, this is achieved just by using the spectral features for guiding the band compression. However, this project develops a BS method which uses both spatial and spectral features to allow a handful of crucial spectral bands to be selected for enhancing the target detection and classification performances. This thesis firstly outlines the fundamental concepts and background of remote sensing and HSI, followed by the theories of different atmospheric correction algorithms — in order to assess the reflectance conversion for band selection — and BS techniques, with a detailed explanation of the Hughes principle, which postulates the fundamental drawback for having high-dimensional data in HSI. Subsequently, the thesis highlights the performances of some advanced BS techniques and to point out their deficiencies. Most of the existing BS work in the field have exhibited maximal classification accuracy when more spectral bands have been utilized for classification; this apparently disagrees with the theoretical model of the Hughes phenomenon. The thesis then presents a spatial spectral mutual information (SSMI) BS scheme which utilizes a spatial feature extraction technique as a pre-processing step, followed by the clustering of the mutual information (MI) of spectral bands for enhancing the BS efficiency. Through this BS scheme, a sharp ’bell’-shaped accuracy-dimensionality characteristic has been observed, peaking at about 20 bands. The performance of the proposed SSMI BS scheme has been validated through 6 HSI datasets, and its classification accuracy is shown to be ~10% better than 7 state-of-the-art BS algorithms. These results confirm that the high efficiency of the BS scheme is essentially important to observe, and to validate, the Hughes phenomenon at band selection through experiments for the first time.PH

Drone-based Integration of Hyperspectral Imaging and Magnetics for Mineral Exploration

The advent of unoccupied aerial systems (UAS) as disruptive technology has a lasting impact on remote sensing, geophysics and most geosciences. Small, lightweight, and low-cost UAS enable researchers and surveyors to acquire earth observation data in higher spatial and spectral resolution as compared to airborne and satellite data. UAS-based applications range from rapid topographic mapping using photogrammetric techniques to hyperspectral and geophysical measurements of surface and subsurface geology. UAS surveys contribute to identifying metal deposits, monitoring of mine sites and can reveal arising environmental issues associated with mining. Further, affordable UAS technology will boost exploration data availability and expertise in the global south. This thesis investigates the application of UAS-based multi-sensor data for mineral exploration, in particular the integration of hyperspectral imagers, magnetometers and digital cameras (covering the visible red, green, blue light spectrum). UAS-based research is maturing, however the aforementioned methods are not unified effectively. RGB-based photogrammetry is used to investigate topography and surface texture. Image spectrometers measure mineral-specific surface signatures. Magnetometers detect geomagnetic field changes caused by magnetic minerals at surface and depth. The integration of such UAS sensor-based methods in this thesis augments exploration potential with non-invasive, high-resolution, safe, rapid and practical survey methods. UAS-based surveying acquired, processed and integrated data from three distinct test sites. The sites are located in Finland (Fe-Ti-V at Otanmäki; apatite at Siilinjärvi) and Greenland (Ni-Cu-PGE at Qullissat, Disko Island) and were chosen as geologically diverse areas in subarctic to arctic environments. Restricted accessibility, unfavourable atmospheric conditions, dark rocks, debris and vegetation cover and low solar illumination were common features. While the topography in Finland was moderately flat, a steep landscape challenged the Greenland field work. These restraints meant that acquisitions varied from site to site and how data was integrated and interpreted is dependent on the commodity of interest. Iron-based spectral absorption and magnetic mineral response were detected using hyperspectral and magnetic surveying in Otanmäki. Multi-sensor-based image feature detection and classification combined with magnetic forward modelling enabled seamless geologic mapping in Siilinjärvi. Detailed magnetic inversion and multispectral photogrammetry led to the construction of a comprehensive 3D model of magmatic exploration targets in Greenland. Ground truth at different intensity was employed to verify UAS-based data interpretations during all case studies. Laboratory analysis was applied when deemed necessary to acquire geologic-mineralogic validation (e.g., X-ray diffraction and optical microscopy for mineral identification to establish lithologic domains, magnetic susceptibility measurements for subsurface modelling), for example for trace amounts of magnetite in carbonatite (Siilinjärvi) and native iron occurrence in basalt (Qullissat). Technical achievements were the integration of a multicopter-based prototype fluxgate-magnetometer data from different survey altitudes with ground truth, and a feasibility study with a high-speed multispectral image system for fixed-wing UAS. The employed case studies transfer the experiences made towards general recommendations for UAS application-based multi-sensor integration. This thesis highlights the feasibility of UAS-based surveying at target scale (1–50 km2) and solidifies versatile survey approaches for multi-sensor integration.Ziel dieser Arbeit war es, das Potenzial einer Drohnen-basierten Mineralexploration mit Multisensor-Datenintegration unter Verwendung optisch-spektroskopischer und magnetischer Methoden zu untersuchen, um u. a. übertragbare Arbeitsabläufe zu erstellen. Die untersuchte Literatur legt nahe, dass Drohnen-basierte Bildspektroskopie und magnetische Sensoren ein ausgereiftes technologisches Niveau erreichen und erhebliches Potenzial für die Anwendungsentwicklung bieten, aber es noch keine ausreichende Synergie von hyperspektralen und magnetischen Methoden gibt. Diese Arbeit umfasste drei Fallstudien, bei denen die Drohnengestützte Vermessung von geologischen Zielen in subarktischen bis arktischen Regionen angewendet wurde. Eine Kombination von Drohnen-Technologie mit RGB, Multi- und Hyperspektralkameras und Magnetometern ist vorteilhaft und schuf die Grundlage für eine integrierte Modellierung in den Fallstudien. Die Untersuchungen wurden in einem Gelände mit flacher und zerklüfteter Topografie, verdeckten Zielen und unter oft schlechten Lichtverhältnissen durchgeführt. Unter diesen Bedingungen war es das Ziel, die Anwendbarkeit von Drohnen-basierten Multisensordaten in verschiedenen Explorationsumgebungen zu bewerten. Hochauflösende Oberflächenbilder und Untergrundinformationen aus der Magnetik wurden fusioniert und gemeinsam interpretiert, dabei war eine selektive Gesteinsprobennahme und Analyse ein wesentlicher Bestandteil dieser Arbeit und für die Validierung notwendig. Für eine Eisenerzlagerstätte wurde eine einfache Ressourcenschätzung durchgeführt, indem Magnetik, bildspektroskopisch-basierte Indizes und 2D-Strukturinterpretation integriert wurden. Fotogrammetrische 3D-Modellierung, magnetisches forward-modelling und hyperspektrale Klassifizierungen wurden für eine Karbonatit-Intrusion angewendet, um einen kompletten Explorationsabschnitt zu erfassen. Eine Vektorinversion von magnetischen Daten von Disko Island, Grönland, wurden genutzt, um großräumige 3D-Modelle von undifferenzierten Erdrutschblöcken zu erstellen, sowie diese zu identifizieren und zu vermessen. Die integrierte spektrale und magnetische Kartierung in komplexen Gebieten verbesserte die Erkennungsrate und räumliche Auflösung von Erkundungszielen und reduzierte Zeit, Aufwand und benötigtes Probenmaterial für eine komplexe Interpretation. Der Prototyp einer Multispektralkamera, gebaut für eine Starrflügler-Drohne für die schnelle Vermessung, wurde entwickelt, erfolgreich getestet und zum Teil ausgewertet. Die vorgelegte Arbeit zeigt die Vorteile und Potenziale von Multisensor-Drohnen als praktisches, leichtes, sicheres, schnelles und komfortabel einsetzbares geowissenschaftliches Werkzeug, um digitale Modelle für präzise Rohstofferkundung und geologische Kartierung zu erstellen

CHARACTERIZING FOREST STANDS USING UNMANNED AERIAL SYSTEMS (UAS) DIGITAL PHOTOGRAMMETRY: ADVANCEMENTS AND CHALLENGES IN MONITORING LOCAL SCALE FOREST COMPOSITION, STRUCTURE, AND HEALTH

Present-day forests provide a wide variety of ecosystem services to the communities that rely on them. At the same time, these environments face routine and substantial disturbances that direct the need for site-specific, timely, and accurate monitoring/management (i.e., precision forestry). Unmanned Aerial Systems (UAS or UAV) and their associated technologies offer a promising tool for conducting such precision forestry. Now, even with only natural color, uncalibrated, UAS imagery, software workflows involving Structure from Motion (SfM) (i.e., digital photogrammetry) modelling and segmentation can be used to characterize the features of individual trees or forest communities. In this research, we tested the effectiveness of UAS-SfM for mapping local scale forest composition, structure, and health. Our first study showed that digital (automated) methods for classifying forest composition that utilized UAS imagery produced a higher overall accuracy than those involving other high-spatial-resolution imagery (7.44% - 16.04%). The second study demonstrated that natural color sensors could provide a highly efficient estimate of individual tree diameter at breast height (dbh) (± 13.15 cm) as well as forest stand basal area, tree density, and stand density. In the final study, we join a growing number of researchers examining precision applications in forest health monitoring. Here, we demonstrate that UAS, equipped with both natural color and multispectral sensors, are more capable of distinguishing forest health classes than freely available high-resolution airborne imagery. For five health classes, these UAS data produced a 14.93% higher overall accuracy in comparison to the airborne imagery. Together, these three chapters present a wholistic approach to enhancing and enriching precision forest management, which remains a critical requirement for effectively managing diverse forested landscapes