Monitoring Global Forest Land-Use and Change

Earth’s forests contain nearly three-fourths of the World’s floral and faunal diversity, function as a large carbon sink capable of mitigating the effects of global climate change, affect local and regional physical and chemical cycles and provide wood and non-wood products. However, humans are now capable of modifying their environment in ways more impactful and at rates faster than at any other time in history. Consistent and comparable estimates of global forest land-use and change are critical for monitoring human impacts on the Earth system. International treaties and reporting requirements aimed at safeguarding the delivery of forest-related ecosystem services depend on such estimates for measuring progress against their stated goals. Many existing studies have estimated tree cover and change at a variety of spatial scales from local to global. However, this existing research focuses largely on land cover classification, but generally lacks ecological context for estimating true human land use. The objective of this dissertation is to address this gap by exploring how forest land use can be mapped and monitored using medium spatial resolution optical satellite imagery in order to estimate forest land use change over time for large geographic areas. First, the effects of clouds, cloud shadows and missing data were analyzed to determine the amount of moderate spatial resolution, optical satellite data needed to detect and map land cover changes over large, spatially continuous areas on frequent time intervals. Second, an alternative method to spatially exhaustive mapping was developed and tested for estimating land cover and land use change globally employing object-based image analysis and a sample-based estimation approach. The method facilitated expert human intervention to identify true land use change in an operational way. Finally, these methods were applied to a globally distributed sample of remotely sensed data for the time periods 1990, 2000 and 2005. The results of this research produced the first consistent and comparable global time-series dataset of forest land-use estimates

Estimation of Pan-Tropical Deforestation and Implications for Conservation

Reducing tropical deforestation has been a primary focus for the implementation of policies that are aimed at biodiversity conservation, and reducing greenhouse gas emissions, as tropical forests have, biologically, the richest ecosystem on Earth, tropical deforestation is one of the largest sources of anthropogenic carbon emission into the atmosphere, and preventing it is the most inexpensive option, in order to reduce carbon emissions and conserve biodiversity. To set the effective policies and conservation plans to reduce emission from tropical deforestation, the evaluation of effectiveness of both the current and previous efforts for conservation is critical. The three studies in this dissertation describe the development of the methods to accurately monitor pan-tropical forest cover change, using satellite remote sensing data, and their integration with the econometrics approach, to evaluate the effectiveness of the tropical forest conservation practices. The dissertation contributes a method for long-term, global forest cover change estimation from Landsat, and the methods are applied to report the first, pan-tropical forest cover change trends, between the 1990s and the 2000s. The global forest cover change product from 1990 to 2000, which was produced, based on the developed methods which are evaluated to have an overall accuracy of 88%. The results demonstrate that tropical deforestation has accelerated between the 1990s and the 2000s by 62%, which contradicts the assertions of it being decelerating. The results further show that the increased deforestation rate between the 1990s and the 2000s is significantly correlated with the increases in Gross Domestic Product (GDP) growth rate, agricultural production growth, and urban population growth between the two decades. Protected Areas (PA), throughout the tropics, avoided 83,000 ± 22,000 km2 of the deforestation during the 2000s. The effectiveness of international aid can be suppressed by weak governance and the lack of forest change monitoring capacity of each country. The conclusions of this dissertation provide a historical baseline for the estimates of tropical forest cover change, and for the evaluation of effectiveness of such conservation efforts

Spatiotemporal anomaly detection: streaming architecture and algorithms

Includes bibliographical references.2020 Summer.Anomaly detection is the science of identifying one or more rare or unexplainable samples or events in a dataset or data stream. The field of anomaly detection has been extensively studied by mathematicians, statisticians, economists, engineers, and computer scientists. One open research question remains the design of distributed cloud-based architectures and algorithms that can accurately identify anomalies in previously unseen, unlabeled streaming, multivariate spatiotemporal data. With streaming data, time is of the essence, and insights are perishable. Real-world streaming spatiotemporal data originate from many sources, including mobile phones, supervisory control and data acquisition enabled (SCADA) devices, the internet-of-things (IoT), distributed sensor networks, and social media. Baseline experiments are performed on four (4) non-streaming, static anomaly detection multivariate datasets using unsupervised offline traditional machine learning (TML), and unsupervised neural network techniques. Multiple architectures, including autoencoders, generative adversarial networks, convolutional networks, and recurrent networks, are adapted for experimentation. Extensive experimentation demonstrates that neural networks produce superior detection accuracy over TML techniques. These same neural network architectures can be extended to process unlabeled spatiotemporal streaming using online learning. Space and time relationships are further exploited to provide additional insights and increased anomaly detection accuracy. A novel domain-independent architecture and set of algorithms called the Spatiotemporal Anomaly Detection Environment (STADE) is formulated. STADE is based on federated learning architecture. STADE streaming algorithms are based on a geographically unique, persistently executing neural networks using online stochastic gradient descent (SGD). STADE is designed to be pluggable, meaning that alternative algorithms may be substituted or combined to form an ensemble. STADE incorporates a Stream Anomaly Detector (SAD) and a Federated Anomaly Detector (FAD). The SAD executes at multiple locations on streaming data, while the FAD executes at a single server and identifies global patterns and relationships among the site anomalies. Each STADE site streams anomaly scores to the centralized FAD server for further spatiotemporal dependency analysis and logging. The FAD is based on recent advances in DNN-based federated learning. A STADE testbed is implemented to facilitate globally distributed experimentation using low-cost, commercial cloud infrastructure provided by Microsoft™. STADE testbed sites are situated in the cloud within each continent: Africa, Asia, Australia, Europe, North America, and South America. Communication occurs over the commercial internet. Three STADE case studies are investigated. The first case study processes commercial air traffic flows, the second case study processes global earthquake measurements, and the third case study processes social media (i.e., Twitter™) feeds. These case studies confirm that STADE is a viable architecture for the near real-time identification of anomalies in streaming data originating from (possibly) computationally disadvantaged, geographically dispersed sites. Moreover, the addition of the FAD provides enhanced anomaly detection capability. Since STADE is domain-independent, these findings can be easily extended to additional application domains and use cases

New Remote Sensing Methods for Detecting and Quantifying Forest Disturbance and Regeneration in the Eastern United States

Forest disturbances, such as wildfires, the southern pine beetle, and the hemlock woolly adelgid, affect millions of hectares of forest in North America with significant implications for forest health and management. This dissertation presents new methods to quantify and monitor disturbance through time in the forests of the eastern United States using remotely sensed imagery from the Landsat family of satellites, detect clouds and cloud-shadow in imagery, generate composite images from the clear-sky regions of multiple images acquired at different times, delineate the extents of disturbance events, identify the years in which they occur, and label those events with an agent and severity. These methods operate at a 30x30 m spatial resolution and a yearly temporal resolution. Overall accuracy for cloud and cloud-shadow detection is 98.7% and is significantly better than a leading method. Overall accuracy for designating a specific space and time as disturbed, stable, or regenerating is 85%, and accuracy for labeling disturbance events with a causal agent ranges from 42% to 90%, depending on agent, with overall accuracy, excluding samples marked as `uncertain\u27, of 81%. Due to the high spatial resolution of the imagery and resulting output, these methods are valuable for managers interested in monitoring specific forested areas. Additionally, these methods enable the discovery and quantification of forest dynamics at larger spatial scales in a way other datasets cannot. Applying these methods over the entire extent of the eastern United States highlands reveals significant differences in disturbance frequency by ecoregion, from less than 1% of forested area per year in the Central Appalachians, to over 5% in the Piedmont. Yearly variations from these means are substantial, with disturbance frequency being twice as high as the mean in some years. Additionally, these analyses reveal that some disturbance agents, such as the southern pine beetle, exhibit periodic dynamics. Finally, although these methods are applied here to the problem of forest disturbance in the eastern United States, the core innovations are easily extended to other locations or even to other applications of landscape change, such as vegetation succession, shifting coastlines, or urbanization

Holistic Multi-View Building Analysis in the Wild with Projection Pooling

We address six different classification tasks related to fine-grained building attributes: construction type, number of floors, pitch and geometry of the roof, facade material, and occupancy class. Tackling such a remote building analysis problem became possible only recently due to growing large-scale datasets of urban scenes. To this end, we introduce a new benchmarking dataset, consisting of 49426 images (top-view and street-view) of 9674 buildings. These photos are further assembled, together with the geometric metadata. The dataset showcases various real-world challenges, such as occlusions, blur, partially visible objects, and a broad spectrum of buildings. We propose a new projection pooling layer, creating a unified, top-view representation of the top-view and the side views in a high-dimensional space. It allows us to utilize the building and imagery metadata seamlessly. Introducing this layer improves classification accuracy -- compared to highly tuned baseline models -- indicating its suitability for building analysis.Comment: Accepted for publication at the 35th AAAI Conference on Artificial Intelligence (AAAI 2021

Development of Mining Sector Applications for Emerging Remote Sensing and Deep Learning Technologies

This thesis uses neural networks and deep learning to address practical, real-world problems in the mining sector. The main focus is on developing novel applications in the area of object detection from remotely sensed data. This area has many potential mining applications and is an important part of moving towards data driven strategic decision making across the mining sector. The scientific contributions of this research are twofold; firstly, each of the three case studies demonstrate new applications which couple remote sensing and neural network based technologies for improved data driven decision making. Secondly, the thesis presents a framework to guide implementation of these technologies in the mining sector, providing a guide for researchers and professionals undertaking further studies of this type. The first case study builds a fully connected neural network method to locate supporting rock bolts from 3D laser scan data. This method combines input features from the remote sensing and mobile robotics research communities, generating accuracy scores up to 22% higher than those found using either feature set in isolation. The neural network approach also is compared to the widely used random forest classifier and is shown to outperform this classifier on the test datasets. Additionally, the algorithms’ performance is enhanced by adding a confusion class to the training data and by grouping the output predictions using density based spatial clustering. The method is tested on two datasets, gathered using different laser scanners, in different types of underground mines which have different rock bolting patterns. In both cases the method is found to be highly capable of detecting the rock bolts with recall scores of 0.87-0.96. The second case study investigates modern deep learning for LiDAR data. Here, multiple transfer learning strategies and LiDAR data representations are examined for the task of identifying historic mining remains. A transfer learning approach based on a Lunar crater detection model is used, due to the task similarities between both the underlying data structures and the geometries of the objects to be detected. The relationship between dataset resolution and detection accuracy is also examined, with the results showing that the approach is capable of detecting pits and shafts to a high degree of accuracy with precision and recall scores between 0.80-0.92, provided the input data is of sufficient quality and resolution. Alongside resolution, different LiDAR data representations are explored, showing that the precision-recall balance varies depending on the input LiDAR data representation. The third case study creates a deep convolutional neural network model to detect artisanal scale mining from multispectral satellite data. This model is trained from initialisation without transfer learning and demonstrates that accurate multispectral models can be built from a smaller training dataset when appropriate design and data augmentation strategies are adopted. Alongside the deep learning model, novel mosaicing algorithms are developed both to improve cloud cover penetration and to decrease noise in the final prediction maps. When applied to the study area, the results from this model provide valuable information about the expansion, migration and forest encroachment of artisanal scale mining in southwestern Ghana over the last four years. Finally, this thesis presents an implementation framework for these neural network based object detection models, to generalise the findings from this research to new mining sector deep learning tasks. This framework can be used to identify applications which would benefit from neural network approaches; to build the models; and to apply these algorithms in a real world environment. The case study chapters confirm that the neural network models are capable of interpreting remotely sensed data to a high degree of accuracy on real world mining problems, while the framework guides the development of new models to solve a wide range of related challenges

Clearing the Clouds: Extracting 3D information from amongst the noise

Advancements permitting the rapid extraction of 3D point clouds from a variety of imaging modalities across the global landscape have provided a vast collection of high fidelity digital surface models. This has created a situation with unprecedented overabundance of 3D observations which greatly outstrips our current capacity to manage and infer actionable information. While years of research have removed some of the manual analysis burden for many tasks, human analysis is still a cornerstone of 3D scene exploitation. This is especially true for complex tasks which necessitate comprehension of scale, texture and contextual learning. In order to ameliorate the interpretation burden and enable scientific discovery from this volume of data, new processing paradigms are necessary to keep pace. With this context, this dissertation advances fundamental and applied research in 3D point cloud data pre-processing and deep learning from a variety of platforms. We show that the representation of 3D point data is often not ideal and sacrifices fidelity, context or scalability. First ground scanning terrestrial LIght Detection And Ranging (LiDAR) models are shown to have an inherent statistical bias, and present a state of the art method for correcting this, while preserving data fidelity and maintaining semantic structure. This technique is assessed in the dense canopy of Micronesia, with our technique being the best at retaining high levels of detail under extreme down-sampling (\u3c 1%). Airborne systems are then explored with a method which is presented to pre-process data to preserve a global contrast and semantic content in deep learners. This approach is validated with a building footprint detection task from airborne imagery captured in Eastern TN from the 3D Elevation Program (3DEP), our approach was found to achieve significant accuracy improvements over traditional techniques. Finally, topography data spanning the globe is used to assess past and previous global land cover change. Utilizing Shuttle Radar Topography Mission (SRTM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, paired with the airborne preprocessing technique described previously, a model for predicting land-cover change from topography observations is described. The culmination of these efforts have the potential to enhance the capabilities of automated 3D geospatial processing, substantially lightening the burden of analysts, with implications improving our responses to global security, disaster response, climate change, structural design and extraplanetary exploration

Combustion Feature Characterization using Computer Vision Diagnostics within Rotating Detonation Combustors

In recent years, the possibilities of higher thermodynamic efficiency and power output have led to increasing interest in the field of pressure gain combustion (PGC). Currently, a majority of PGC research is concerned with rotating detonation engines (RDEs), devices which may theoretically achieve pressure gain across the combustor. Within the RDE, detonation waves propagate continuously around a cylindrical annulus, consuming fresh fuel mixtures supplied from the base of the RDE annulus. Through constant-volume heat addition, pressure gain combustion devices theoretically achieve lower entropy generation compared to Brayton cycle combustors. RDEs are being studied for future implementation in gas turbines, where they would offer efficiency gains in both propulsion and power generation turbines. Much diagnostic work has been done to investigate the detonative behaviors within RDEs, including point measurements, optical diagnostics, thrust stands and other methods. However, to date, these analysis methods have been limited in either diagnostic sophistication or to post-processing due to the computationally expensive treatment of large data volumes. This is a result of the substantial data acquisition rates needed to study behavior on the incredibly short time scale of detonation interactions and propagation. As laboratory RDE operations become more reliable, industrial applications become more plausible. Real-time monitoring of combustion behavior within the RDE is a crucial step towards actively controlled RDE operation in the laboratory environment and eventual turbine integration. For these reasons, this study seeks to advance the efficiency of RDE diagnostic techniques from conventional post-processing efforts to lab-deployed real-time methods, achieving highly efficient detonation characterization through the application of convolutional neural networks (CNNs) to experimental RDE data. This goal is accomplished through the training of various CNNs, being image classification, object detection, and time series classification. Specifically, image classification aims to classify the number and direction of waves using a single image; object detection detects and classifies each detonation wave according to location and direction within individual images; and time series classification determines wave number and direction using a short window of sensor data. Each of these network outputs are used to develop unique RDE diagnostics, which are evaluated alongside conventional techniques with respect to real-time capabilities. Those real-time capable diagnostics are deployed and evaluated in the laboratory environment using an altered experimental setup via a live data acquisition environment. Completion of the research tasks results in overarching diagnostic capability developments of conventional methods, image classification, object detection, and timeseries classification applied to experimental RDE data. Each diagnostic is employed with varying strengths with respect to feasibility, long-term application, and performance, all of which are surveyed and compared extensively. Conventional methods, specifically detonation surface matrices, and object detection are found to offer diagnostic feedback rates of 0.017 and 9.50 Hz limited to post-processing, respectively. Image classification using high-speed chemiluminescence images, and timeseries classification using high-speed flame ionization and pressure measurements, achieve classification speeds enabling real-time diagnostic capabilities, averaging diagnostic feedback rates of 4 and 5 Hz when deployed in the laboratory environment, respectively. Among the CNN-based methods, object detection, while limited to post-processing usage, achieves the most refined diagnostic time-step resolution of 20 µsec compared to real-time-capable image and timeseries classification, which require the additional correlation of a sensor data window, extending their time-step resolutions to 80 msec. Through the application of machine learning to RDE data, methods and results presented offer beneficial advancement of diagnostic techniques from post-processing to real-time speeds. These methods are uniquely developed for various RDE data types commonly used in the PGC community and are successfully deployed in an altered laboratory environment. Feedback rates reported are expected to be satisfactory to the future development of an RDE active-control framework. This portfolio of diagnostics will bring valuable insight and direction throughout RDE technological maturation as a collective early application of machine learning to PGC technology