Spectral Super-Resolution of Satellite Imagery with Generative Adversarial Networks

Hyperspectral (HS) data is the most accurate interpretation of surface as it provides fine spectral information with hundreds of narrow contiguous bands as compared to multispectral (MS) data whose bands cover bigger wavelength portions of the electromagnetic spectrum. This difference is noticeable in applications such as agriculture, geosciences, astronomy, etc. However, HS sensors lack on earth observing spacecraft due to its high cost. In this study, we propose a novel loss function for generative adversarial networks as a spectral-oriented and general-purpose solution to spectral super-resolution of satellite imagery. The proposed architecture learns mapping from MS to HS data, generating nearly 20x more bands than the given input. We show that we outperform the state-of-the-art methods by visual interpretation and statistical metrics.Les dades hiperspectrals (HS) són la interpretació més precisa de la superfície, ja que proporciona informació espectral fina amb centenars de bandes contigües estretes en comparació amb les dades multiespectrals (MS) les bandes cobreixen parts de longitud d'ona més grans de l'espectre electromagnètic. Aquesta diferència és notable en àmbits com l'agricultura, les geociències, l'astronomia, etc. No obstant això, els sensors HS manquen als satèl·lits d'observació terrestre a causa del seu elevat cost. En aquest estudi proposem una nova funció de cost per a Generative Adversarial Networks com a solució orientada a l'espectre i de propòsit general per la superresolució espectral d'imatges de satèl·lit. L'arquitectura proposada aprèn el mapatge de dades MS a HS, generant gairebé 20x més bandes que l'entrada donada. Mostrem que superem els mètodes state-of-the-art mitjançant la interpretació visual i les mètriques estadístiques.Los datos hiperspectral (HS) son la interpretación más precisa de la superficie, ya que proporciona información espectral fina con cientos de bandas contiguas estrechas en comparación con los datos multiespectrales (MS) cuyas bandas cubren partes de longitud de onda más grandes del espectro electromagnético. Esta diferencia es notable en ámbitos como la agricultura, las geociencias, la astronomía, etc. Sin embargo, los sensores HS escasean en los satélites de observación terrestre debido a su elevado coste. En este estudio proponemos una nueva función de coste para Generative Adversarial Networks como solución orientada al espectro y de propósito general para la super-resolución espectral de imágenes de satélite. La arquitectura propuesta aprende el mapeo de datos MS a HS, generando casi 20x más bandas que la entrada dada. Mostramos que superamos los métodos state-of-the-art mediante la interpretación visual y las métricas estadísticas

Improved boreal vegetation mapping using imaging spectroscopy to aid wildfire management, Interior Alaska

Thesis (Ph.D.) University of Alaska Fairbanks, 2023Wildfires are a natural and essential part of Alaska ecosystems, but excessive wildfires pose a risk to the ecosystem's health and diversity, as well as to human life and property. To manage wildfires effectively, vegetation/fuel maps play a critical role in identifying high-risk areas and allocating resources for prevention, suppression, and recovery efforts. Furthermore, vegetation/fuel maps are an important input for fire behavior models, along with weather and topography data. By predicting fire behavior, such as spread rate, intensity, and direction, fuel models allow fire managers to make informed decisions about wildfire suppression, management, and prevention. Traditionally used vegetation/fuel maps in Alaska are inadequate due to a lack of detailed information since they are primarily generated using coarser resolution (30m) multispectral data. Hyperspectral remote sensing offers an efficient approach for better characterization of forest vegetation due to the narrow bandwidth and finer spatial resolution. However, the high cost associated with data acquisition remains a significant challenge to the widespread application of hyperspectral data. The aim of this research is to create accurate and detailed vegetation maps and upscale them for the boreal region of Alaska. The study involves hyperspectral data simulation using Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) data and publicly available Sentinel-2 multispectral data, ground spectra convolved to Sentinel-2 and AVIRIS-NG using the spectral response function of each sensor. Simulated data captured the minute details found in the real AVIRIS-NG data and were classified to map vegetation. Using the ground data from Bonanza Creek Long-Term Ecological Research sites, we compared the new maps with the two existing map products (the LANDFIRE's Existing Vegetation Type (EVT) and Alaska Vegetation and Wetland Composite). The maps generated using simulated data showed an improvement of 33% in accuracy and are more detailed than existing map products. In addition to fuel maps, we performed sub-pixel level mapping to generate a needleleaf fraction map, which serves fire management needs since needleleaf species are highly flammable. However, validating the sub-pixel product was challenging. To overcome this, we devised a novel validation method incorporating high-resolution airborne hyperspectral data (1m) and ground data. The study addresses the limitations of traditional fuel/vegetation maps by providing a more detailed and accurate representation of vegetation/fuel in Alaska. The methods and findings advance fuel and vegetation mapping research in Alaska and offer a novel pathway to generate detailed fuel maps for boreal Alaska to aid wildfire management.Alaska Established Program to Stimulate Competitive Research (EPSCoR), AmericaView, and the College of Natural Science and Mathematics, National Science Foundation award OIA-1757348, State of Alaska and the U.S. Geological Survey Grant/Cooperative Agreement No. G18AP0007

Remote sensing of water quality indicators associated with mining activities : the case study of Mooi River in Carletonville, Gauteng Province, South Africa

Abstract: The mining sector is an important source of revenue for the South African economy; however, mining can have a detrimental impact on water quality. Therefore, efficient assessment and monitoring are needed to protect water bodies in mining-related environments. While remote sensing has proven to be an effective monitoring tool in various sectors, efforts must be intensified to apply it in the mining sector in order to combat the impact of mining pollution on water resources. Remote sensing techniques have been successfully used to estimate water quality parameters of inland waters, however, applications focussing on mining environments are rare. There is, therefore, a need to test the capabilities of the technology in mining areas in order to design an efficient water quality monitoring system that will allow relevant authorities to implement mitigation plans and sustain ecosystem services derived from the water bodies. This dissertation has investigated the capabilities of remote sensing in detecting and monitoring water quality parameters in a mining environment along the Mooi River, South Africa. The first objective of the dissertation sought to investigate the performances of raw hyperspectral data and simulated multispectral datasets in quantifying various water quality parameters. Seventy-eight water samples were collected from the study area. Reflectance measurements were taken from each sample using a field-spectroradiometer. The all-subsets regression technique and a support vector machine (SVM) were used to explore the relationships between 17 water quality parameters and hyperspectral datasets, as well as four simulated multispectral datasets (i.e. Landsat Operational Land Imager, Sentinel-2 Multispectral Instrument, WorldView-3 and SPOT 6). The results revealed the usefulness of combining hyperspectral and simulated datasets with different algorithms for effective water quality monitoring. Water quality parameters were estimated with high accuracy using a support vector machine (SVM), compared to the all-subsets regression approach for both datasets (raw hyperspectral and simulated). The second objective explored the accuracy of actual multispectral datasets in detecting water quality in the same river and field data utilised in the first objective mentioned above. The all-subsets regression technique that lists all possible models was applied to estimate the laboratory-measured parameters using reflectance values derived from the individual bands of Landsat OLI, Sentinel-2 MSI, ASTER and SPOT 6 data as explanatory variables. The results demonstrated the potential of multispectral reflectance data in water quality measurements...M.Sc. (Environmental Management