Advances in Waveform and Photon Counting Lidar Processing for Forest Vegetation Applications

Full waveform (FW) and photon counting LiDAR (PCL) data have garnered greater attention due to increasing data availability, a wealth of information they contain and promising prospects for large scale vegetation mapping. However, many factors such as complex processing steps and scarce non-proprietary tools preclude extensive and practical uses of these data for vegetation characterization. Therefore, the overall goal of this study is to develop algorithms to process FW and PCL data and to explore their potential in real-world applications. Study I explored classical waveform decomposition methods such as the Gaussian decomposition, Richardson–Lucy (RL) deconvolution and a newly introduced optimized Gold deconvolution to process FW LiDAR data. Results demonstrated the advantages of the deconvolution and decomposition method, and the three approaches generated satisfactory results, while the best performances varied when different criteria were used. Built upon Study I, Study II applied the Bayesian non-linear modeling concepts for waveform decomposition and quantified the propagation of error and uncertainty along the processing steps. The performance evaluation and uncertainty analysis at the parameter, derived point cloud and surface model levels showed that the Bayesian decomposition could enhance the credibility of decomposition results in a probabilistic sense to capture the true error of estimates and trace the uncertainty propagation along the processing steps. In study III, we exploited FW LiDAR data to classify tree species through integrating machine learning methods (the Random forests (RF) and Conditional inference forests (CF)) and Bayesian inference method. Results of classification accuracy highlighted that the Bayesian method was a superior alternative to machine learning methods, and rendered users with more confidence for interpreting and applying classification results to real-world tasks such as forest inventory. Study IV focused on developing a framework to derive terrain elevation and vegetation canopy height from test-bed sensor data and to pre-validate the capacity of the upcoming Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission. The methodology developed in this study illustrates plausible ways of processing the data that are structurally similar to expected ICESat-2 data and holds the potential to be a benchmark for further method adjustment once genuine ICESat-2 are available

Mapping and Monitoring Forest Cover

This book is a compilation of six papers that provide some valuable information about mapping and monitoring forest cover using remotely sensed imagery. Examples include mapping large areas of forest, evaluating forest change over time, combining remotely sensed imagery with ground inventory information, and mapping forest characteristics from very high spatial resolution data. Together, these results demonstrate effective techniques for effectively learning more about our very important forest resources

3D Classification of Power Line Scene Using Airborne Lidar Data

Failure to adequately maintain vegetation within a power line corridor has been identified as a main cause of the August 14, 2003 electric power blackout. Such that, timely and accurate corridor mapping and monitoring are indispensible to mitigate such disaster. Moreover, airborne LiDAR (Light Detection And Ranging) has been recently introduced and widely utilized in industries and academies thanks to its potential to automate the data processing for scene analysis including power line corridor mapping. However, today’s corridor mapping practice using LiDAR in industries still remains an expensive manual process that is not suitable for the large-scale, rapid commercial compilation of corridor maps. Additionally, in academies only few studies have developed algorithms capable of recognizing corridor objects in the power line scene, which are mostly based on 2-dimensional classification. Thus, the objective of this dissertation is to develop a 3-dimensional classification system which is able to automatically identify key objects in the power line corridor from large-scale LiDAR data. This dissertation introduces new features for power structures, especially for the electric pylon, and existing features which are derived through diverse piecewise (i.e., point, line and plane) feature extraction, and then constructs a classification model pool by building individual models according to the piecewise feature sets and diverse voltage training samples using Random Forests. Finally, this dissertation proposes a Multiple Classifier System (MCS) which provides an optimal committee of models from the model pool for classification of new incoming power line scene. The proposed MCS has been tested on a power line corridor where medium voltage transmission lines (115 kV and 230 kV) pass. The classification results based on the MCS applied by optimally selecting the pre-built classification models according to the voltage type of the test corridor demonstrate a good accuracy (89.07%) and computationally effective time cost (approximately 4 hours/km) without additional training fees

A Learnable Model with Calibrated Uncertainty Quantification for Estimating Canopy Height from Spaceborne Sequential Imagery

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/.Global-scale canopy height mapping is an important tool for ecosystem monitoring and sustainable forest management. Various studies have demonstrated the ability to estimate canopy height from a single spaceborne multispectral image using end-to-end learning techniques. In addition to texture information of a single-shot image, our study exploits multi temporal information of image sequences to improve estimation accuracy. We adopt a convolutional variant of a long short-term memory (LSTM) model for canopy height estimation from multitemporal instances of Sentinel-2 products. Furthermore, we utilize the deep ensembles technique for meaningful uncertainty estimation on the predictions and postprocessing isotonic regression model for calibrating them. Our lightweight model (∼320k trainable parameters) achieves the mean absolute error (MAE) of 1.29 m in a European test area of 79 km2. It outperforms the state-of-the-art methods based on single-shot spaceborne images as well as costly airborne images while providing additional confidence maps that are shown to be well calibrated. Moreover, the trained model is shown to be transferable in a different country of Europe using a fine-tuning area of as low as ∼2 km2 with MAE = 1.94 m.publishedVersio

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

AUTOMATED TREE-LEVEL FOREST QUANTIFICATION USING AIRBORNE LIDAR

Traditional forest management relies on a small field sample and interpretation of aerial photography that not only are costly to execute but also yield inaccurate estimates of the entire forest in question. Airborne light detection and ranging (LiDAR) is a remote sensing technology that records point clouds representing the 3D structure of a forest canopy and the terrain underneath. We present a method for segmenting individual trees from the LiDAR point clouds without making prior assumptions about tree crown shapes and sizes. We then present a method that vertically stratifies the point cloud to an overstory and multiple understory tree canopy layers. Using the stratification method, we modeled the occlusion of higher canopy layers with respect to point density. We also present a distributed computing approach that enables processing the massive data of an arbitrarily large forest. Lastly, we investigated using deep learning for coniferous/deciduous classification of point cloud segments representing individual tree crowns. We applied the developed methods to the University of Kentucky Robinson Forest, a natural, majorly deciduous, closed-canopy forest. 90% of overstory and 47% of understory trees were detected with false positive rates of 14% and 2% respectively. Vertical stratification improved the detection rate of understory trees to 67% at the cost of increasing their false positive rate to 12%. According to our occlusion model, a point density of about 170 pt/m² is needed to segment understory trees located in the third layer as accurately as overstory trees. Using our distributed processing method, we segmented about two million trees within a 7400-ha forest in 2.5 hours using 192 processing cores, showing a speedup of ~170. Our deep learning experiments showed high classification accuracies (~82% coniferous and ~90% deciduous) without the need to manually assemble the features. In conclusion, the methods developed are steps forward to remote, accurate quantification of large natural forests at the individual tree level

Coupling UAV and satellite data for tree species identification to map the distribution of Caspian poplar

Context Mapping the distribution of species, especially those that are endemic and endangered like certain tree species, is a vital step in the effective planning and execution of conservation programs and monitoring efforts. This task gains even more significance as it directly contributes to forest conservation by highlighting the importance of species diversity. Objectives Our study objective was to assess the detection accuracy of a specific tree using different remote sensing sources and approaches. Methods Initially, individual trees were identified and classified using a canopy height model derived from UAV data. Next, we carried out the classification of satellite data within the Google Earth Engine. Lastly, we scaled the UAV-RGB dataset to match the spatial resolution of Sentinel-2, which was then employed to train random forest models using the multispectral data from Sentinel-2. Results For the UAV data, we achieved overall accuracies of 56% for automatically delineated tree crowns and 83% for manually delineated ones. Regarding the second approach using Sentinel-2 data, the classification in the Noor forest yielded an overall accuracy of 74% and a Kappa coefficient of 0.57, while in the Safrabasteh forest, the accuracy was 80% with a Kappa of 0.61. In the third approach, our findings indicate an improvement compared to the second approach, with the overall accuracy and Kappa coefficient of the classification rising to 82% and 0.68, respectively. Conclusions In this study, it was found that according to the purpose and available facilities, satellite and UAV data can be successfully used to identify a specific tree species

Application of machine learning techniques to weather forecasting

84 p.El pronóstico del tiempo es, incluso hoy en día, una actividad realizada principalmente por humanos. Si bien las simulaciones por computadora desempeñan un papel importante en el modelado del estado y la evolución de la atmósfera, faltan metodologías para automatizar la interpretación de la información generada por estos modelos. Esta tesis doctoral explora el uso de metodologías de aprendizaje automático para resolver problemas específicos en meteorología y haciendo especial énfasis en la exploración de metodologías para mejorar la precisión de los modelos numéricos de predicción del tiempo. El trabajo presentado en este manuscrito comprende dos enfoques diferentes a la aplicación de algoritmos de aprendizaje automático a problemas de predicción meteorológica. En la primera parte, las metodologías clásicas, como la regresión multivariada no paramétrica y los árboles binarios, se utilizan para realizar regresiones en datos meteorológicos. Esta primera parte, está centrada particularmente en el pronóstico del viento, cuya naturaleza circular crea desafíos interesantes para los algoritmos clásicos de aprendizaje automático. La segunda parte de esta tesis explora el análisis de los datos meteorológicos como un problema de predicción estructurado genérico utilizando redes neuronales profundas. Las redes neuronales, como las redes convolucionales y recurrentes, proporcionan un método para capturar la estructura espacial y temporal inherente en los modelos de predicción del tiempo. Esta parte explora el potencial de las redes neuronales convolucionales profundas para resolver problemas difíciles en meteorología, como el modelado de la precipitación a partir de campos de modelos numéricos básicos. La investigación que sustenta esta tesis sirve como un ejemplo de cómo la colaboración entre las comunidades de aprendizaje automático y meteorología puede resultar mutuamente beneficiosa y conducir a avances en ambas disciplinas. Los modelos de pronóstico del tiempo y los datos de observación representan ejemplos únicos de conjuntos de datos grandes (petabytes), estructurados y de alta calidad, que la comunidad de aprendizaje automático exige para desarrollar la próxima generación de algoritmos escalables