Modeling wildland fire radiance in synthetic remote sensing scenes

This thesis develops a framework for implementing radiometric modeling and visualization of wildland fire. The ability to accurately model physical and op- tical properties of wildfire and burn area in an infrared remote sensing system will assist efforts in phenomenology studies, algorithm development, and sensor evaluation. Synthetic scenes are also needed for a Wildland Fire Dynamic Data Driven Applications Systems (DDDAS) for model feedback and update. A fast approach is presented to predict 3D flame geometry based on real time measured heat flux, fuel loading, and wind speed. 3D flame geometry could realize more realistic radiometry simulation. A Coupled Atmosphere-Fire Model is used to de- rive the parameters of the motion field and simulate fire dynamics and evolution. Broad band target (fire, smoke, and burn scar) spectra are synthesized based on ground measurements and MODTRAN runs. Combining the temporal and spa- tial distribution of fire parameters, along with the target spectra, a physics based model is used to generate radiance scenes depicting what the target might look like as seen by the airborne sensor. Radiance scene rendering of the 3D flame includes 2D hot ground and burn scar cooling, 3D flame direct radiation, and 3D indirect reflected radiation. Fire Radiative Energy (FRE) is a parameter defined from infrared remote sensing data that is applied to determine the radiative energy released during a wildland fire. FRE derived with the Bi-spectral method and the MIR radiance method are applied to verify the fire radiance scene synthesized in this research. The results for the synthetic scenes agree well with published values derived from wildland fire images

Bidirectional recurrent imputation and abundance estimation of LULC classes with MODIS multispectral time series and geo-topographic and climatic data

Remotely sensed data are dominated by mixed Land Use and Land Cover (LULC) types. Spectral unmixing (SU) is a key technique that disentangles mixed pixels into constituent LULC types and their abundance fractions. While existing studies on Deep Learning (DL) for SU typically focus on single time-step hyperspectral (HS) or multispectral (MS) data, our work pioneers SU using MODIS MS time series, addressing missing data with end-to-end DL models. Our approach enhances a Long-Short Term Memory (LSTM)-based model by incorporating geographic, topographic (geo-topographic), and climatic ancillary information. Notably, our method eliminates the need for explicit endmember extraction, instead learning the input-output relationship between mixed spectra and LULC abundances through supervised learning. Experimental results demonstrate that integrating spectral-temporal input data with geo-topographic and climatic information significantly improves the estimation of LULC abundances in mixed pixels. To facilitate this study, we curated a novel labeled dataset for Andalusia (Spain) with monthly MODIS multispectral time series at 460m resolution for 2013. Named Andalusia MultiSpectral MultiTemporal Unmixing (Andalusia-MSMTU), this dataset provides pixel-level annotations of LULC abundances along with ancillary information. The dataset (https://zenodo.org/records/7752348) and code (https://github.com/jrodriguezortega/MSMTU) are available to the public

Multiscale Imaging of Evapotranspiration

Evapotranspiration (ET; evaporation + transpiration) is central to a wide range of biological, chemical, and physical processes in the Earth system. Accurate remote sensing of ET is challenging due to the interrelated and generally scale dependent nature of the physical factors which contribute to the process. The evaporation of water from porous media like sands and soils is an important subset of the complete ET problem. Chapter 1 presents a laboratory investigation into this question, examining the effects of grain size and composition on the evolution of drying sands. The effects of composition are found to be 2-5x greater than the effects of grain size, indicating that differences in heating caused by differences in reflectance may dominate hydrologic differences caused by grain size variation. In order to relate the results of Chapter 1 to the satellite image archive, however, the question of information loss between hyperspectral (measurements at 100s of wavelength intervals) laboratory measurements and multispectral (≤ 12 wavelength intervals) satellite images must be addressed. Chapter 2 focuses on this question as applied to substrate materials such as sediment, soil, rock, and non-photosynthetic vegetation. The results indicate that the continuum that is resolved by multispectral sensors is sufficient to resolve the gradient between sand-rich and clay-rich soils, and that this gradient is also a dominant feature in hyperspectral mixing spaces where the actual absorptions can be resolved. Multispectral measurements can be converted to biogeophysically relevant quantities using spectral mixture analysis (SMA). However, retrospective multitemporal analysis first requires cross-sensor calibration of the mixture model. Chapter 3 presents this calibration, allowing multispectral image data to be used interchangeably throughout the Landsat 4-8 archive. In addition, a theoretical explanation is advanced for the observed superior scaling properties of SMA-derived fraction images over spectral indices. The physical quantities estimated by the spectral mixture model are then compared to simultaneously imaged surface temperature, as well as to the derived parameters of ET Fraction and Moisture Availability. SMA-derived vegetation abundance is found to produce substantially more informative ET maps, and SMA-derived substrate fraction is found to yield a surprisingly strong linear relationship with surface temperature. These results provide context for agricultural applications. Chapter 5 investigates the question of mapping and monitoring rice agricultural using optical and thermal satellite image time series. Thermal image time series are found to produce more accurate maps of rice presence/absence, but optical image time series are found to produce more accurate maps of rice crop timing. Chapter 6 takes a more global approach, investigating the spatial structure of agricultural networks for a diverse set of landscapes. Surprisingly consistent scaling relations are found. These relations are assessed in the context of a network-based approach to land cover analysis, with potential implications for the scale dependence of ET estimates. In sum, this thesis present a novel approach to improving ET estimation based on a synthesis of complementary laboratory measurements, satellite image analysis, and field observations. Alone, each of these independent sources of information provides novel insights. Viewed together, these insights form the basis of a more accurate and complete geophysical understanding of the ET phenomenon

Uncertainty Assessment of Spectral Mixture Analysis in Remote Sensing Imagery

Spectral mixture analysis (SMA), a scheme of sub-pixel-based classifications, is one of the widely used models to map fractional land use and land cover information in remote sensing imagery. It assumes that: 1) a mixed pixel is composed by several pure land cover classes (endmembers) linearly or nonlinearly, and 2) the spectral signature of each endmember is a constant within the entire spatial extent of analysis. SMA has been commonly applied to impervious surface area extraction, vegetation fraction estimation, and land use and land cover change (LULC) mapping. Limitations of SMA, however, still exist. First, the existence of between- and within-class variability prevents the selection of accurate endmembers, which results in poor accuracy of fractional land cover estimates. Weighted spectral mixture analysis (WSMA) and transformed spectral mixture analysis (TSMA) are alternate means to address the within- and between- class variability. These methods, however, have not been analyzed systematically and comprehensively. The effectiveness of each WSMA and TSMA scheme is still unknown, in particular within different urban areas. Second, multiple endmember SMA (MESMA) is a better alternative to address spectral mixture model uncertainties. It, nonetheless, is time consuming and inefficient. Further, incorrect endmember selections may still limit model performance as the best-fit endmember model might not be the optimal model due to the existence of spectral variability. Therefore, this study aims 1) to explore endmember uncertainties by examining WSMA and TSMA modeling comprehensively, and 2) to develop an improved MESMA model in order to address the uncertainties of spectral mixture models. Results of the WSMA examination illustrated that some weighting schemes did reduce endmember uncertainties since they could improve the fractional estimates significantly. The results also indicated that spectral class variance played a key role in addressing the endmember uncertainties, as the better performing weighting schemes were constructed with spectral class variance. In addition, the results of TSMA examination demonstrated that some TSMAs, such as normalized spectral mixture analysis (NSMA), could effectively solve the endmember uncertainties because of their stable performance in different study areas. Results of Class-based MEMSA (C-MESMA) indicated that it could address spectral mixture model uncertainties by reducing a lot of the calculation burden and effectively improving accuracy. Assessment demonstrated that C-MEMSA significantly improving accuracy. Major contributions of this study can be summarized as follow. First, the effectiveness of addressing endmember uncertainties have been fully discussed by examining: 1) the effectiveness of ten weighted spectral mixture models in urban environments; and 2) the effectiveness of 26 transformed spectral mixture models in three locations. Constructive guidance regarding handling endmember uncertainties using WSMA and TSMA have been provided. Second, the uncertainties of spectral mixture model were reduced by developing an improved MESMA model, named C-MESMA. C-MESMA could restrict the distribution of endmembers and reduce the calculation burden of traditional MESMA, increasing SMA accuracy significantly

Evaluation of Disaggregation Methods for Downscaling MODIS Land Surface Temperature to Landsat Spatial Resolution in Barrax Test Site

Thermal infrared (TIR) data are usually acquired at a coarser spatial resolution (CR) than visible and near infrared (VNIR). Several disaggregation methods have been recently developed to enhance the TIR spatial resolution using VNIR data. These approaches are based on the retrieval of a relation between TIR and VNIR data at CR, or training of a neural network, to be applied at the fine resolution afterward. In this work, different disaggregation methods are applied to the combination of two different sensors in the experimental test site of Barrax, Spain. The main objective is to test the feasibility of these techniques when applied to satellites provided with no TIR bands. Landsat and moderate imaging spectroradiometer (MODIS) images were used for this work. Land surface temperature (LST) fromMODIS images was disaggregated to the Landsat spatial resolution using Landsat VNIR data. Landsat LST was used for the validation and comparison of the different techniques. Best results were obtained by the method based on a linear regression between normalized difference vegetation index (NDVI) and LST. An average RMSE = ±1.9 K was observed between disaggregated and Landsat LST fromfour different dates in a study area of 120 km

Hyperspectral Imaging for Landmine Detection

This PhD thesis aims at investigating the possibility to detect landmines using hyperspectral imaging. Using this technology, we are able to acquire at each pixel of the image spectral data in hundreds of wavelengths. So, at each pixel we obtain a reflectance spectrum that is used as fingerprint to identify the materials in each pixel, and mainly in our project help us to detect the presence of landmines. The proposed process works as follows: a preconfigured drone (hexarotor or octorotor) will carry the hyperspectral camera. This programmed drone is responsible of flying over the contaminated area in order to take images from a safe distance. Various image processing techniques will be used to treat the image in order to isolate the landmine from the surrounding. Once the presence of a mine or explosives is suspected, an alarm signal is sent to the base station giving information about the type of the mine, its location and the clear path that could be taken by the mine removal team in order to disarm the mine. This technology has advantages over the actually used techniques: • It is safer because it limits the need of humans in the searching process and gives the opportunity to the demining team to detect the mines while they are in a safe region. • It is faster. A larger area could be cleared in a single day by comparison with demining techniques • This technique can be used to detect at the same time objects other than mines such oil or minerals. First, a presentation of the problem of landmines that is expanding worldwide referring to some statistics from the UN organizations is provided. In addition, a brief presentation of different types of landmines is shown. Unfortunately, new landmines are well camouflaged and are mainly made of plastic in order to make their detection using metal detectors harder. A summary of all landmine detection techniques is shown to give an idea about the advantages and disadvantages of each technique. In this work, we give an overview of different projects that worked on the detection of landmines using hyperspectral imaging. We will show the main results achieved in this field and future work to be done in order to make this technology effective. Moreover, we worked on different target detection algorithms in order to achieve high probability of detection with low false alarm rate. We tested different statistical and linear unmixing based methods. In addition, we introduced the use of radial basis function neural networks in order to detect landmines at subpixel level. A comparative study between different detection methods will be shown in the thesis. A study of the effect of dimensionality reduction using principal component analysis prior to classification is also provided. The study shows the dependency between the two steps (feature extraction and target detection). The selection of target detection algorithm will define if feature extraction in previous phase is necessary. A field experiment has been done in order to study how the spectral signature of landmine will change depending on the environment in which the mine is planted. For this, we acquired the spectral signature of 6 types of landmines in different conditions: in Lab where specific source of light is used; in field where mines are covered by grass; and when mines are buried in soil. The results of this experiment are very interesting. The signature of two types of landmines are used in the simulations. They are a database necessary for supervised detection of landmines. Also we extracted some spectral characteristics of landmines that would help us to distinguish mines from background

LINKING MULTIVARIATE OBSERVATIONS OF THE LAND SURFACE TO VEGETATION PROPERTIES AND ECOSYSTEM PROCESSES

Remotely sensed images from satellites and aircrafts, as well as regional networks and monitoring stations such as eddy flux towers, are collecting large volumes of multivariate data that contain information about the land surface and ecosystem processes. To derive from these systems information and knowledge relevant to how the Earth system functions and how it is changing, we need tools that to filter and mine the large data streams currently being acquired at different spatial and temporal scales. A challenge for Earth System Science lies in accurately identifying and portraying the relationships between the measurements at the sensor and quantity o f interest (i.e. ecosystem process or land surface property)

Summaries of the Fifth Annual JPL Airborne Earth Science Workshop. Volume 1: AVIRIS Workshop

This publication is the first of three containing summaries for the Fifth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on January 23-26, 1995. The main workshop is divided into three smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on January 23-24. The summaries for this workshop appear in this volume; (2) The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on January 25-26. The summaries for this workshop appear in Volume 3; and (3) The Thermal Infrared Multispectral Scanner (TIMS) workshop, on January 26. The summaries for this workshop appear in Volume 2

The 2014-2015 lava flow field at Holuhraun: Deriving physical properties of the lava using multi remote sensing techniques and datasets

The purpose of this thesis is to employ remote sensing to study lava flow products during the 2014-2015 eruption at Holuhraun, Iceland. Multimodal remote sensing techniques and datasets were applied and developed for three study themes (1) deriving thermal properties from satellite infrared remote sensing, (2) differentiating lava surface using airborne hyperspectral remote sensing, and (3) quantifying lava surface roughness from elevation model acquired by airborne LiDAR. In the first study, we present a new approach based on infrared satellite images to derive thermal properties within the lava field during eruption and then compare the results with field measurement during the 2014-2015 eruption at Holuhraun. We develop a new spectral index for Landsat 8, named the thermal eruption index (TEI), based on the SWIR and TIR bands (bands 6 and 10). The purpose of the TEI consists mainly of two parts: (i) as a threshold for differentiating between different thermal domains; and (ii) to apply dualband technique to determine the maximum subpixel temperature (Th) of the lava. Lava surface roughness effects are accounted for by using the Hurst exponent (H), which is estimated from radar backscattering profiles. A higher H (smooth surface) generates thinner crust and high thermal flux meanwhile a lower H (rough surface) generates thicker crust and lower thermal flux. The total thermal flux peak is underestimated compared to other studies, although the trend shows good agreement with both field observation and other studies. In the second study, we focus on retrieving the lava surface types contributing to the signal recorded by airborne hyperspectral at the very top surface of the 2014-2015 lava flow field at Holuhraun. For this purpose, an airborne hyperspectral image acquired at Holuhraun with an AisaFENIX sensor onboard a NERC (Natural Environment Research Council Airborne Research Facility) campaign. For sub-pixel analysis, we used the sequential maximum angle convex cone (SMACC) algorithm to identify the spectral image endmembers and the linear spectral mixture analysis (LSMA) method was employed to retrieve the abundances. SMACC and LSMA methods offer a fast selection for volcanic product segregation. However, ground-truthing of spectra is recommended for future work. In the third study, we perform both the topographic position index (TPI) and onedimensional Hurst Exponent to derived lava flow unit roughness on the 2014-2015 lava flow field at Holuhraun using both airborne LiDAR and photogrammetry topography datasets. The roughness assessment was acquired from four lava flow features: (1) spiny pāhoehoe, (2) lava pond, (3) rubbly pāhoehoe lava, and (4) inflated channel. The TPI patterns on spiny lava and inflated channels show that the intermediate TPI values correspond to a small slope indicating a flat and smooth surface. Lava pond is characterized by low to high TPI values and forms a wave-like pattern. Meanwhile, irregular transitions patterns from low to high TPI values characterize lava with rough blocky surfaces, i.e. rubbly pāhoehoe to 'ā'a flows and lobes and their margins. These lobes and margins may give the impression of having similar roughness as the ”rough” surface on meters scale since this is an “apparent” roughness. On centimeters scale these multitudes of lobes feature coherent and smooth surfaces because they are pāhoehoe. The surface roughness of these lava features falls within the H range of 0.30 ± 0.05 to 0.76 ± 0.04. The rubbly pāhoehoe / 'ā'a has the roughest surface and the inflated lava channel along with pāhoehoe feature the smoothest surfaces among these four surface types. In general, the Hurst exponent values in the 2014-2015 lava field at Holuhraun has a strong tendency in 0.5, which is compatible with results from other study of geological surface roughness. Overall, this project provides an important insights into the application of remote sensing for monitoring and studying active lava flow fields and the techniques developed here will benefit such work in future events.Tilgangurinn með verkefninu var að rannsaka hraunrennsli og landform er urðu til í eldgosinu norðan Vatnajökuls 2014-2015 og kennt við Holuhraun. Fjölþátta fjarkönnunartækni og gögn úr gervitunglum og flugvélum voru nýtt við úrvinnslu verkefnisins. Rannsóknin sneri að þremur megin þáttum: (1) greiningu á eðli varmaútstreymis frá Holuhrauni, út frá innrauðri varmageislun sem mæld er með gervitunglagögnum (2) aðgreining á mismunandi hraunyfirborði, út frá ofur-fjölrófs mælingum úr lofti, og (3) greiningu og flokkun á yfirborðshrjúfleika Holuhrauns út frá hæðarlíkani er aflað var með LiDAR settur upp í flugvél. Fyrsti þáttur beindist að eðli varmaútstreymis á meðan á eldgosi stóð. Stuðst var við gervitunglagögn og mælingar með FLIR tækni á meðan eldgosið stóð yfir. Afraksturinn er nýr hitastuðull fyrir Landsat 8 og greiningu á eldgosum, (TEI). Hitastuðullinn TEI er unninn út frá SWIR og TIR böndum Landsat 8 (bönd 6 og 10). Með TEI næst fram tvennt: (i) að greina þröskuld milli tveggja hitasviða; og (ii) að beita tvíbanda tækni til að greina hitastig innan hverrar myndeiningar (Th) af hrauninu. Hrjúfleiki hraunsins hefur áhrif á varmaútstreymi, og er gert ráð fyrir honum með því að reikna Hurst veldisstuðulinn (H) og eru reiknuð út frá radar endurkasti hraunyfirborðs. Hátt H einkennir flatt og mjúkt yfirborð og þunna skorpu á hrauninu, á meðan að lágt H einkennir úfið yfirborð, þykka skorpu og lága varmaútgeislun. Heildar varmaútgeislun með þessari aðferð er heldur vanmetin en ofmetin í samanburði við aðrar aðferðir. Hinsvegar er góð fylgni með mælingum í mörkinni og samanburðar aðferðum. Annar hluti rannsóknarinnar sneri að túlkun ofur-fjölrófsgreininga á yfirborði Holuhrauns. Flogið var yfir Holuhraun sumarið 2015 með ofur-fjölrófsmæli (AisaFENIX) um borð í flugvél frá NERC (Natural Environment Research Council Airborne Research Facility). Við greiningu á yfirborði innan hverrar myndeiningar var, (i) notast við aðferð runubundins hámarkshorns kúptrar keilu (SMACC) til að finna útmörk ofurrófs mælinganna, (ii) blönduð línulega rófgreining (LSMA) var nýtt til að greina styrk eða gnægð innan myndeiningar. SMACC og LSMA aðferðirnar bjóða upp á mjög hraða greiningu á yfirborði og útfellingum efna á yfirborðið. Hins vegar þarf að gera fleiri rófmælingar á staðnum, til þess að auka notkunnargetu aðferðarinnar í hraungosum framtíðarinnar. Þriðji þáttur rannsóknarinnar sneri að því að greina landfræðilega stöðuvísitölu (TPI) og einvíðan Hurst veldisvísi til að meta hrjúfleika á hinu endanlega yfirborði Holuhrauns. Við þessa greiningu var notast við LiDAR mælingu af hrauninu og hæðagrunn unninn út frá ljósmyndum. Hrjúfleikinn var metinn fyrir fjögur yfirborð sem einkenna hraunið: (1) broddahraun „spiny pāhoehoe lava“, (2) hrauntjörn „lava pond“, (3) klumpahraun „rubbly pāhoehoe lava“ og (4) upptjakkaða hrauntröð „inflated lava channel“. TPI fyrir yfirborð (1) og (4) gefur meðalgildi sem einkennist af litlum halla og flötu yfirborði. Hrauntjörnin einkennist af lágum og háum TPI gildum sem endurspegla bylgjukennt mynstur. Hinsvegar einkennast hrjúfustu yfirborðin (3) og hraunjaðrar af óreglulegu mynstri lágra og hárra TPI gilda. Hrjúfleika stuðull þessara yfirborða H, er á bilinu 0.30 ± 0.05 til 0.76 ± 0.04. Mestur er hrjúfleiki kubbahrauna og minnstur er hrjúfleiki þandar hrauntraðar. Hurts veldisvísir Holuhrauns er nærri 0.5, en það er í mjög góðu samræmi við niðurstöður fyrri rannsókna á jarðfræðilegum yfirborðum. Í heild gefur verkefnið mikilvæga sýn á notagildi fjarkönnunaraðferða við rauntímaeftirlit með hraungosum, m.a. með þróun stuðla sem munu nýtast við atburði framtíðar. Þá voru tengsl hraunmyndana við ýmsa eiginleika eldgosa skýrð, sem aftur getur gefið vísbendingar um eðli fyrri atburða