A subpixel target detection algorithm for hyperspectral imagery

The goal of this research is to develop a new algorithm for the detection of subpixel scale target materials on the hyperspectral imagery. The signal decision theory is typically to decide the existence of a target signal embedded in the random noise. This implies that the detection problem can be mathematically formalized by signal decision theory based on the statistical hypothesis test. In particular, since any target signature provided by airborne/spaceborne sensors is embedded in a structured noise such as background or clutter signatures as well as broad band unstructured noise, the problem becomes more complicated, and particularly much more under the unknown noise structure. The approach is based on the statistical hypothesis method known as Generalized Likelihood Ratio Test (GLRT). The use of GLRT requires estimating the unknown parameters, and assumes the prior information of two subspaces describing target variation and background variation respectively. Therefore, this research consists of two parts, the implementation of GLRT and the characterization of two subspaces through new approaches. Results obtained from computer simulation, HYDICE image and AVI RIS image show that this approach is feasible

MDAS: a new multimodal benchmark dataset for remote sensing

In Earth observation, multimodal data fusion is an intuitive strategy to break the limitation of individual data. Complementary physical contents of data sources allow comprehensive and precise information retrieval. With current satellite missions, such as ESA Copernicus programme, various data will be accessible at an affordable cost. Future applications will have many options for data sources. Such a privilege can be beneficial only if algorithms are ready to work with various data sources. However, current data fusion studies mostly focus on the fusion of two data sources. There are two reasons; first, different combinations of data sources face different scientific challenges. For example, the fusion of synthetic aperture radar (SAR) data and optical images needs to handle the geometric difference, while the fusion of hyperspectral and multispectral images deals with different resolutions on spatial and spectral domains. Second, nowadays, it is still both financially and labour expensive to acquire multiple data sources for the same region at the same time. In this paper, we provide the community with a benchmark multimodal data set, MDAS, for the city of Augsburg, Germany. MDAS includes synthetic aperture radar data, multispectral image, hyperspectral image, digital surface model (DSM), and geographic information system (GIS) data. All these data are collected on the same date, 7 May 2018. MDAS is a new benchmark data set that provides researchers rich options on data selections. In this paper, we run experiments for three typical remote sensing applications, namely, resolution enhancement, spectral unmixing, and land cover classification, on MDAS data set. Our experiments demonstrate the performance of representative state-of-the-art algorithms whose outcomes can serve as baselines for further studies. The dataset is publicly available at https://doi.org/10.14459/2022mp1657312 (Hu et al., 2022a) and the code (including the pre-trained models) at https://doi.org/10.5281/zenodo.7428215 (Hu et al., 2022b)

Radiometrically-Accurate Hyperspectral Data Sharpening

Improving the spatial resolution of hyperpsectral image (HSI) has traditionally been an important topic in the field of remote sensing. Many approaches have been proposed based on various theories including component substitution, multiresolution analysis, spectral unmixing, Bayesian probability, and tensor representation. However, these methods have some common disadvantages, such as that they are not robust to different up-scale ratios and they have little concern for the per-pixel radiometric accuracy of the sharpened image. Moreover, many learning-based methods have been proposed through decades of innovations, but most of them require a large set of training pairs, which is unpractical for many real problems. To solve these problems, we firstly proposed an unsupervised Laplacian Pyramid Fusion Network (LPFNet) to generate a radiometrically-accurate high-resolution HSI. First, with the low-resolution hyperspectral image (LR-HSI) and the high-resolution multispectral image (HR-MSI), the preliminary high-resolution hyperspectral image (HR-HSI) is calculated via linear regression. Next, the high-frequency details of the preliminary HR-HSI are estimated via the subtraction between it and the CNN-generated-blurry version. By injecting the details to the output of the generative CNN with the low-resolution hyperspectral image (LR-HSI) as input, the final HR-HSI is obtained. LPFNet is designed for fusing the LR-HSI and HR-MSI covers the same Visible-Near-Infrared (VNIR) bands, while the short-wave infrared (SWIR) bands of HSI are ignored. SWIR bands are equally important to VNIR bands, but their spatial details are more challenging to be enhanced because the HR-MSI, used to provide the spatial details in the fusion process, usually has no SWIR coverage or lower-spatial-resolution SWIR. To this end, we designed an unsupervised cascade fusion network (UCFNet) to sharpen the Vis-NIR-SWIR LR-HSI. First, the preliminary high-resolution VNIR hyperspectral image (HR-VNIR-HSI) is obtained with a conventional hyperspectral algorithm. Then, the HR-MSI, the preliminary HR-VNIR-HSI, and the LR-SWIR-HSI are passed to the generative convolutional neural network to produce an HR-HSI. In the training process, the cascade sharpening method is employed to improve stability. Furthermore, the self-supervising loss is introduced based on the cascade strategy to further improve the spectral accuracy. Experiments are conducted on both LPFNet and UCFNet with different datasets and up-scale ratios. Also, state-of-the-art baseline methods are implemented and compared with the proposed methods with different quantitative metrics. Results demonstrate that proposed methods outperform the competitors in all cases in terms of spectral and spatial accuracy

A multiscale remote sensing assessment of subtropical indigenous forests along the wild coast, South Africa

The subtropical forests located along South Africa’s Wild Coast region, declared as one of the biodiversity hotspots, provide benefits to the local and national economy. However, there is evidence of increased pressure exerted on the forests by growing population and reduced income from activities not related to forest products. The ability of remote sensing to quantify subtropical forest changes over time, perform species discrimination (using field spectroscopy) and integrating field spectral and multispectral data were all assessed in this study. Investigations were conducted at pixel, leaf and sub-pixel levels. Both per-pixel and sub-pixel classification methods were used for improved forest characterisation. Using SPOT 6 imagery for 2013, the study determined the best classification algorithm for mapping sub-tropical forest and other land cover types to be the maximum likelihood classifier. Maximum likelihood outperformed minimum distance, spectral angle mapper and spectral information divergence algorithms, based on overall accuracy and Kappa coefficient values. Forest change analysis was made based on spectral measurements made at top of the atmosphere (TOC) level. When applied to the 2005 and 2009 SPOT 5 images, subtropical forest changes between 2005-2009 and 2009-2013 were quantified. A temporal analysis of forest cover trends in the periods 2005-2009 and 2009-2013 identified a decreasing trend of -3648.42 and -946.98 ha respectively, which translated to 7.81 percent and 2.20 percent decrease. Although there is evidence of a trend towards decreased rates of forest loss, more conservation efforts are required to protect the Wild Coast ecosystem. Using field spectral measurements data, the hierarchical method (comprising One-way ANOVA with Bonferroni correction, Classification and Regression Trees (CART) and Jeffries Matusita method) successfully selected optimal wavelengths for species discrimination at leaf level. Only 17 out of 2150 wavelengths were identified, thereby reducing the complexities related to data dimensionality. The optimal 17 wavelength bands were noted in the visible (438, 442, 512 and 695 nm), near infrared (724, 729, 750, 758, 856, 936, 1179, 1507 and 1673 nm) and mid-infrared (2220, 2465, 2469 and 2482 nm) portions of the electromagnetic spectrum. The Jeffries-Matusita (JM) distance method confirmed the separability of the selected wavelength bands. Using these 17 wavelengths, linear discriminant analysis (LDA) classified subtropical species at leaf level more accurately than partial least squares discriminant analysis (PLSDA) and random forest (RF). In addition, the study integrated field-collected canopy spectral and multispectral data to discriminate proportions of semi-deciduous and evergreen subtropical forests at sub-pixel level. By using the 2013 land cover (using MLC) to mask non-forested portions before sub-pixel classification (using MTMF), the proportional maps were a product of two classifiers. The proportional maps show higher proportions of evergreen forests along the coast while semi-deciduous subtropical forest species were mainly on inland parts of the Wild Coast. These maps had high accuracy, thereby proving the ability of an integration of field spectral and multispectral data in mapping semi-deciduous and evergreen forest species. Overall, the study has demonstrated the importance of the MLC and LDA and served to integrate field spectral and multispectral data in subtropical forest characterisation at both leaf and top-of-atmosphere levels. The success of both the MLC and LDA further highlighted how essential parametric classifiers are in remote sensing forestry applications. Main subtropical characteristics highlighted in this study were species discrimination at leaf level, quantifying forest change at pixel level and discriminating semi-deciduous and evergreen forests at sub-pixel level

Urban Growth and LULC Change Dynamics Using Landsat Record of Region of Waterloo from 1984 to 2013

Frequent human activities resulted by rapid urbanization lead to a variety of urban-related environmental and socio-economic issues. Therefore, for effective environmental management and urban planning, monitoring urban growth and detecting its resulting land use and land cover (LULC) change is very important. Most of the previous studies focused on bi-temporal or coarsely multi-temporal change detection to extract stationary change information over a time span. However, higher-order change information, for instance, acceleration or deceleration of urban growth, which would not be observed by bi-temporal method, is more meaningful information for policy makers to understand the urbanization process. With the free access to the USGS Landsat archive and development of remote sensing techniques, detecting urban growth pattern (intensification or sprawl) and LULC change dynamics with temporally high frequent datasets become possible. In this study, bi-temporal, multi-temporal and long-term annual change detection were applied to the Region of Waterloo, Ontario, Canada, to identify the urban growth pattern and LULC change dynamics. Classification was performed for each scene to extract LULC information from 1984 to 2013. This study demonstrates that machine learning classifiers, such as support vector machine (SVM), random forest (RF) and artificial neural network (ANN), perform better than classical maximum likelihood classifier (MLC), among which SVM performs the best. Total urban built-up area of the Region of Water increased from 30% in 1984 to 55% in 2013, replacing large area of vegetated area (agricultural lands and grassland. Outward (sprawl) and inward (intensification) growth patterns were detected both spatially and temporally. Within this time span, built-up area experienced a relatively accelerating growth in 1990s and in early 2000s. In terms of long-term record, Kitchener had the fastest growing rate of low-density built-up (residential) area. The coverage of high-density built-up (commercial and industrial) area in Cambridge increased most dramatically. And the built-up area of Waterloo experienced the lowest growth rate. These important findings indicate that using long-term temporal-dense Landsat datasets enables monitoring of urban growth and LULC change dynamics. Such valuable long-term results can be used for better analysis of urban growth and LULC change patterns for planners and policy makers to comprehensively understand the urbanization process in the past and make better planning in the future

고해상도 초분광영상을 활용한 하천 부유사농도 계측기법 개발

학위논문(박사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2022. 8. 서일원.기존의 하천 부유사 농도 계측은 샘플링 기반 직접계측 방식에 의존하여 시공간적 고해상도 자료 취득이 어려운 실정이다. 이러한 한계점을 극복하기 위해 최근 위성과 드론을 활용하여 촬영된 다분광 혹은 초분광 영상을 통해 고해상도의 부유사농도 시공간분포를 계측하는 기법에 대한 연구가 활발히 진행되고 있다. 하지만, 다른 하천 물리량 계측에 비해 부유사 계측 연구는 하천에 따라 부유사가 다양하게 분포하고 다른 부유물질 혹은 하상에 의한 바닥 반사의 영향 때문에 분광 자료를 통해 정확한 부유사농도 분포를 재현하기 어려운 실정이다. 특히, 부유사 분광 특성에 영향을 미치는 입도분포, 광물특성, 침강성 등이 하천에 따라 강한 지역성을 나타내기에 이러한 요인에서 야기되는 분광다양성으로 인해 특정 시기와 지역에만 적합한 원격탐사 기반 계측 모형들이 개발되어 왔다. 본 연구에서는 이러한 분광다양성을 반영하여 다양한 하천 및 유사 조건에서 적용 가능한 고해상도 초분광영상을 활용한 하천 부유사농도 계측방법을 개발하기 위해 초분광 군집화 기법과 다양한 파장대의 분광 밴드를 학습할 수 있는 기계학습 회귀 모형을 결합하여 CMR-OV라는 방법론을 제시하였다. CMR-OV 개발 및 검증은 1) 실험적 연구를 통한 하천 부유사 분광 특성의 주요 교란 요인 분석, 2) 최적 회귀모형 선정 및 초분광 클러스터링과의 결합, 3) 현장적용성 평가의 과정을 거쳐 수행되었다. 실험적 연구에서는 우선 실내 실험실에서 횡방향 혼합기를 활용하여 바닥 반사를 제거하고 완전 혼합된 상태에서 부유사의 고유 초분광 스펙트럼 자료를 수집하였다. 이를 바탕으로 실제 하천과 유사한 조건의 실규모 옥외 수로 실험에서 다양한 유사 특성(입도 및 광물)과 하상 특성(식생 및 모래)에 대한 초분광 자료를 수집하여 고유 초분광 스펙트럼과 비교하였다. 그 결과, 부유사의 분광 특성은 유사의 종류 및 입도에 따라 농도 증가에 따른 초분광 스펙트럼의 반사율 변화가 상이하게 나타났다. 또한, 1 m 이하의 얕은 수심 조건에서는 바닥 반사의 영향으로 하상 종류에 따라 초분광 스펙트럼의 개형이 크게 변화하였으며, 고농도의 부유사가 분포할 때도 바닥 반사가 크게 영향을 미치는 것을 확인하였다. 이러한 분광다양성이 반영된 부유사농도와 초분광 자료의 관계를 구축하기 위하여 기계학습 기반 랜덤포레스트 회귀 모형과 가우시안 혼합 모형 기반 초분광 군집 기법을 결합한 CMR-OV를 적용한 결과, 기존 연구들에서 주로 활용된 밴드비 기반의 모형과 단일 기계학습모형에 비해 정확도가 크게 향상하였다. 특히, 기존 최적 밴드비 분석 (OBRA) 방법은 비선형성을 고려해도 좁은 영역의 파장대만을 고려하는 한계점으로 인해 분광다양성을 반영하지 못하는 것으로 밝혀졌다. 하지만, CMR-OV는 폭 넓은 파장대 영역을 고려함과 동시에 높은 정확도를 산출하였다. 최종적으로 CMR-OV를 황강의 직선구간 및 사행구간과 낙동강과 황강의 합류부에 적용하여 현장검증을 수행한 결과, 기존 모형 대비 정확도와 부유사 농도 맵핑의 정밀성에서 큰 개선이 있었으며, 비학습지역에서도 높은 정확도를 산출하였다. 특히, 하천 합류부에서는 초분광 군집을 통해 두 하천 흐름의 경계층을 명확히 구별하였으며, 이를 바탕으로 지류와 본류에 대해 각각 분리된 회귀모형을 구축하여 복잡한 합류부 근역 경계층에서의 부유사 분포를 보다 정확하게 재현하였다. 또한, 나아가서 재현된 고해상도의 부유사 공간분포를 바탕으로 혼합도를 산정한 결과, 기존 점계측 대비 상세하게 부유사 혼합에 대한 정량적 평가가 가능한 것으로 나타났다. 따라서, 본 연구에서 개발한 초분광영상 기반 부유사 계측 기술을 통해 추후 하천 조사 및 관리 실무의 정확성 및 효율성을 크게 증진할 수 있을 것으로 기대된다.The conventional measurement method of suspended sediment concentration (SSC) in the riverine system is labor-intensive and time-consuming since it has been conducted using the sampling-based direct measurement method. For this reason, it is challenging to collect high-resolution datasets of SSC in rivers. In order to overcome this limitation, remote sensing-based techniques using multi- or hyper-spectral images from satellites or UAVs have been recently carried out to obtain high-resolution SSC distributions in water environments. However, suspended sediment in rivers is more dynamic and spatially heterogeneous than those in other fields. Moreover, the sediment and streambed properties have strong regional characteristics depending on the river type; thus, only models suitable for a specific period and region have been developed owing to the increased spectral variability of the water arising from various types of suspended matter in the water and the heterogeneous streambed properties. Therefore, to overcome the limitations of the existing monitoring system, this study proposed a robust hyperspectral imagery-based SSC measurement method, termed cluster-based machine learning regression with optical variability (CMR-OV). This method dealt with the spectral variability problem by combining hyperspectral clustering and machine learning regression with the Gaussian mixture model (GMM) and Random forest (RF) regression. The hyperspectral clustering separated the complex dataset into several homogeneous datasets according to spectral characteristics. Then, the machine learning regressors corresponding to clustered datasets were built to construct the relationship between the hyperspectral spectrum and SSC. The development and validation of the proposed method were carried out through the following processes: 1) analysis of confounding factors in the spectral variability through experimental studies, 2) selection of an optimal regression model and validation of hyperspectral clustering, and 3) evaluation of field applicability. In the experimental studies, the intrinsic hyperspectral spectra of suspended sediment were collected in a completely mixed state after removing the bottom reflection using a horizontal rotating cylinder. Then, hyperspectral data on various sediment properties (particle size and mineral contents) and river bed properties (sand and vegetation) were collected from sediment tracer experiments in field-scale open channels under different hydraulic conditions and compared with intrinsic hyperspectral spectra. Consequently, the change of the hyperspectral spectrum was different according to the sediment type and particle size distribution. In addition, under the shallow water depth condition of 1 m or less, the shape of the hyperspectral spectrum changed significantly depending on the bed type due to the bottom reflectance. The bottom reflectance substantially affected the hyperspectral spectrum even when the high SSC was distributed. As a result of combining the GMM and RF regression with building a relationship between the SSC and hyperspectral data reflecting the spectral variability, the accuracy was substantially improved compared to the other methods. In particular, even when nonlinearity is considered based on the existing optimal band ratio analysis (OBRA) method, spectral variability could not be reflected due to the limitation of considering only a narrow wavelength range. On the other hand, CMR-OV showed high accuracy while considering a wide range of wavelengths with clusters having distinct spectral characteristics. Finally, the CMR-OV was applied to the straight and meandering reaches of the Hwang River and the confluence of the Nakdong and Hwang Rivers in South Korea to assess field applicability. There was a remarkable improvement in the accuracy and precision of SSC mapping under various river conditions compared to the existing models, and CMR-OV showed robust performance even with non-calibrated datasets. At the river confluence, the mixing pattern between the main river and tributary was apparently retrieved from CMR-OV under optically complex conditions. Compared to the non-clustered model, hyperspectral clustering played a primary role in improving the performance by separating the water bodies originating from both rivers. It was also possible to quantitatively evaluate the complicated mixing pattern in detail compared to the existing point measurement. Therefore, it is expected that the accuracy and efficiency of river investigation will be significantly improved through the SSC measurement method presented in this study.Abstract of dissertation i List of figures ix List of tables xvii List of abbreviations xix List of symbols xxii 1. Introduction 1 2. Theoretical research 13 2.1.1 Pre-processing of hyperspectral image (HSI) 19 2.1.2 Optical characteristics of suspended sediment in rivers 28 2.1.2.1 Theory of solar radiation transfer in rivers 28 2.1.2.2 Heterogeneity of sediment properties 33 2.1.2.3 Effects of bottom reflectance 38 2.1.2.4 Vertical distribution of suspended sediment 41 2.1.3 Retrieval of suspended sediment from remote sensing data 46 2.1.3.1 Remote sensing-based regression approach 46 2.1.3.2 Clustering of remote sensing data 52 2.2 Mapping of suspended sediment concentration in rivers 56 2.2.1 Traditional method for spatial measurement 56 2.2.2 Spatial measurement at river confluences 57 2.2.2.1 Dynamics of flow and mixing at river confluences 57 2.2.2.2 Field experiments in river confluences 64 3. Experimental studies 68 3.1 Experimental cases 68 3.2 Laboratory experiment 74 3.2.1 Experimental setup 74 3.2.2 Experimental method 78 3.3 Field-scale experiments in River Experiment Center 83 3.3.1 Experiments in the straight channel 83 3.3.2 Experiments in the meandering channel 96 3.4 Field survey 116 3.4.1 Study area and field measurement 116 3.4.2 Hydraulic and sediment data in rivers with simple geometry 122 3.4.3 Hydraulic and sediment data in river confluences 126 3.5 Analysis of hyperspectral data of suspended sediment 141 3.5.1 Hyperspectral data of laboratory experiment 142 3.5.2 Hyperspectral data of field-scale experiments 146 3.5.2.1 Effect of bottom reflectance 146 3.5.2.2 Principal component analysis of the effect of suspended sediment properties 154 4. Development of suspended sediment concentration estimator using UAV-based hyperspectral imagery 164 4.1 Outline of Cluster-based Machine learning Regression with Optical Variability (CMR-OV) 164 4.2 Pre-processing of hyperspectral images 168 4.3 Regression models and clustering technique 173 4.3.1 Index-based regression models 173 4.3.2 Machine learning regression models 175 4.3.3 Relevant band selection 183 4.3.4 Gaussian mixture model for clustering 185 4.3.5 Performance criteria 188 4.4 Model development and evaluation 189 4.4.1 Comparison of regression models 189 4.4.1.1 OBRA-based explicit models 189 4.4.1.2 Machine learning-based implicit models 194 4.4.2 Assessment of hyperspectral clustering 200 4.4.3 Spatio-temporal SSCV mapping using CMR-OV 215 5. Evaluation of field applicability of CMR-OV 225 5.1 Outline of field applicability test 225 5.2 Cross-applicability validation of CMR-OV 227 5.3 Assessment of field applicability in rivers with simple geometry 234 5.4 Assessment of field applicability in river confluences 241 5.4.1 Classification of river regions using hyperspectral clustering 241 5.4.2 Retrievals of SSCV map 258 5.4.3 Mixing pattern extraction from SSCv map 271 6. Conclusions and future study 274 6.1 Conclusions 274 6.2 Future directions 278 Reference 280 Appendix 308 Appendix A. Breakthrough curve (BTC) analysis 308 Appendix B. Experimental data 310 Appendix B. 1. BTCs of in-situ measured SSC from field-scale experiments 310 Appendix B. 2. Dataset of spectra from hyperspectral images and corresponding SSC in rivers 330 Appendix C. CMR-OV code 331 국문초록 337박

The impact of spatial resolution on riparian leaf area index modelling using remote sensing

This thesis investigated the impact of differing sensor spatial resolutions on leaf area index (LAI) modelling. Airborne images along with ground measurements of LAI were acquired for riparian areas along the Oldman River in southern Alberta. Airborne images were spatially resampled to spatial resolutions between 18 cm and 500 m, and the Modified Simple Ratio (MSR) was calculated from the imagery. LAI regression models were created at each spatial resolution, and changes in the relationship between MSR and LAI were observed at each spatial resolution, as well as changes in the modelled LAI estimates. The relationship between MSR and LAI was scale invariant at spatial resolutions as low as 10 m, and only moderately changed until 30 m. MSR and predicted LAI gradually reduced as resolution coarsened further, with large changes occurred beyond 100 m. No relationship was evident between MSR and LAI at spatial resolutions coarser than 300 m

Multifractal analysis for multivariate data with application to remote sensing

Texture characterization is a central element in many image processing applications. Texture analysis can be embedded in the mathematical framework of multifractal analysis, enabling the study of the fluctuations in regularity of image intensity and providing practical tools for their assessment, the coefficients or wavelet leaders. Although successfully applied in various contexts, multi fractal analysis suffers at present from two major limitations. First, the accurate estimation of multifractal parameters for image texture remains a challenge, notably for small sample sizes. Second, multifractal analysis has so far been limited to the analysis of a single image, while the data available in applications are increasingly multivariate. The main goal of this thesis is to develop practical contributions to overcome these limitations. The first limitation is tackled by introducing a generic statistical model for the logarithm of wavelet leaders, parametrized by multifractal parameters of interest. This statistical model enables us to counterbalance the variability induced by small sample sizes and to embed the estimation in a Bayesian framework. This yields robust and accurate estimation procedures, effective both for small and large images. The multifractal analysis of multivariate images is then addressed by generalizing this Bayesian framework to hierarchical models able to account for the assumption that multifractal properties evolve smoothly in the dataset. This is achieved via the design of suitable priors relating the dynamical properties of the multifractal parameters of the different components composing the dataset. Different priors are investigated and compared in this thesis by means of numerical simulations conducted on synthetic multivariate multifractal images. This work is further completed by the investigation of the potential benefit of multifractal analysis and the proposed Bayesian methodology for remote sensing via the example of hyperspectral imaging

Fourth International Conference on Early Mars: Geologic, Hydrologic, and Climatic Evolution and the Implications for Life : October 2–6, 2017, Flagstaff, Arizona

The purpose of the conference is twofold: (1) to consider how astronomical variations in insolation, impacts, volcanism, and the presence of water affected the nature of the early terrestrial and martian climates and the physical and chemical environments that existed on both planets >3.7 Ga, (2) to discuss the investigations that might be conducted by present and future missions to test the hypotheses arising from (1).Universities Space Research AssociationNational Aeronautics and Space AdministrationUniversities Space Research AssociationNational Aeronautics and Space AdministrationLunar and Planetary InstituteConveners: Stephen Clifford, Lunar and Planetary Institute, Vic Baker, University of Arizona, David Beaty, NASA Mars Program Office, Alberto Fairen, Centro de Astrobiologia, Jack Farmer, Arizona State University, James Kasting, Pennsylvania State Universit