Error Propagation Analysis for Remotely Sensed Aboveground Biomass

Edited version available. Full version will remain embargoed due to copyright. AS DCAbstract Above-Ground Biomass (AGB) assessment using remote sensing has been an active area of research since the 1970s. However, improvements in the reported accuracy of wide scale studies remain relatively small. Therefore, there is a need to improve error analysis to answer the question: Why is AGB assessment accuracy still under doubt? This project aimed to develop and implement a systematic quantitative methodology to analyse the uncertainty of remotely sensed AGB, including all perceptible error types and reducing the associated costs and computational effort required in comparison to conventional methods. An accuracy prediction tool was designed based on previous study inputs and their outcome accuracy. The methodology used included training a neural network tool to emulate human decision making for the optimal trade-off between cost and accuracy for forest biomass surveys. The training samples were based on outputs from a number of previous biomass surveys, including 64 optical data based studies, 62 Lidar data based studies, 100 Radar data based studies, and 50 combined data studies. The tool showed promising convergent results of medium production ability. However, it might take many years until enough studies will be published to provide sufficient samples for accurate predictions. To provide field data for the next steps, 38 plots within six sites were scanned with a Leica ScanStation P20 terrestrial laser scanner. The Terrestrial Laser Scanning (TLS) data analysis used existing techniques such as 3D voxels and applied allometric equations, alongside exploring new features such as non-plane voxel layers, parent-child relationships between layers and skeletonising tree branches to speed up the overall processing time. The results were two maps for each plot, a tree trunk map and branch map. An error analysis tool was designed to work on three stages. Stage 1 uses a Taylor method to propagate errors from remote sensing data for the products that were used as direct inputs to the biomass assessment process. Stage 2 applies a Monte Carlo method to propagate errors from the direct remote sensing and field inputs to the mathematical model. Stage 3 includes generating an error estimation model that is trained based on the error behaviour of the training samples. The tool was applied to four biomass assessment scenarios, and the results show that the relative error of AGB represented by the RMSE of the model fitting was high (20-35% of the AGB) in spite of the relatively high correlation coefficients. About 65% of the RMSE is due to the remote sensing and field data errors, with the remaining 35% due to the ill-defined relationship between the remote sensing data and AGB. The error component that has the largest influence was the remote sensing error (50-60% of the propagated error), with both the spatial and spectral error components having a clear influence on the total error. The influence of field data errors was close to the remote sensing data errors (40-50% of the propagated error) and its spatial and non-spatial Overall, the study successfully traced the errors and applied certainty-scenarios using the software tool designed for this purpose. The applied novel approach allowed for a relatively fast solution when mapping errors outside the fieldwork areas.HCED iraq, Middle Technical Universit

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

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

TECHNIQUES FOR STUDYING THE BIOGEOCHEMISTRY OF NUTRIENTS IN THE TAMAR CATCHMENT

Chapter One describes nitrogen and phosphorus species in the aquatic environment, their role in eutrophication, current legislation relevant to nutrient water quality and catchment management and the role of predictive modelling of nutrient export with respect to the management of river catchments. It also summarises analytical techniques for the determination of nitrogen and phosphorus species in natural waters and the use of generic ecotoxicological assays to link nutrient water quality and organism health. Data integrity is essential to biogeochemical studies that inform scientific research and environmental management. Reliable, accurate data permit valid conclusions to be drawn. The quality assurance and quality control measures undertaken to ensure good analytical data in this study, including participation in the certification of a seawater certified reference material for nutrients (MOOS-1), are discussed in Chapter Two. In Chapter Three, the nutrient biogeochemistry of the waters leaving the Tamar catchment and entering the Tamar estuary is discussed. Historical nutrient and physico-chemical data for the Tamar River at Gunnislake were used to identify long-term environmental trends (1974 to 2004) and nutrient and physico-chemical data collected in this study between May 2003 and May 2004 was used to identify short-term trends over the study period. The nutrient export coefficient modelling approach was used to model phosphorus and nitrogen export from the Tamar catchment (Chapter Four). A TP export model from the Tamar catchment was successfully constructed using historical land use data and catchment demographics, calibrated with hindcasted water quality data, and validated with TP field data (May 2003 and May 2004) collected in this study. Modelled P (43. 5 tonnes P yˉ¹) export agreed within 8 % with the measured P load (40.1 tonnes P yˉ¹). An annual TN model was also constructed and calibrated for the Tamar catchment using the May 2003 to May 2004 field data. The calibrated model agreed within I % of the measured TN export (2053 tonnes N yˉ¹). The development and deployment of a portable Fl analyser for continuous, real-time monitoring of FRP in the Tamar catchment is discussed in Chapter Five. The optimised method can be used for the determination of FRP in freshwater systems (4-150 µg Lˉ¹ P) and in coastal waters (10-150 µg Lˉ¹ P) and is capable of sampling with high temporal resolution (up to 15 samples hˉ¹) . The analyser was used in situ (bank-side and shipboard deployment) to provide real-time FRP data and in the laboratory to determine FRP in freshwater samples. All data were in good agreement with values obtained using a validated air-segmented, continuous flow laboratory reference method The acute toxicity of nitrate and nitrite on the freshwater swan mussel, Anodonta cygnea, was investigated (Chapter Six). A 96 h LC50 value of 222 mg Lˉ¹ N for the exposure of A. cygnea to nitrite was established in this study. Toxicity studies indicated that nitrate was not toxic to A. cygnea. Established indicators of physiological stress were used to determine the effect of environmentally high and extreme levels of nitrite on A. cygnea. There was no significant difference in cardiac activity, condition index or lysosomal stability between control organisms (0 mg Lˉ¹ N) and organisms exposed to sub-lethal nitrite concentrations (0.1, 1.0, 22.2 mg Lˉ¹ N). Therefore, nitrite concentrations encountered in typical freshwater catchments such as the Tamar catchment are unlikely to induce physiological stress in A.cygnea