Data driven estimation of soil and vegetation attributes using airborne remote sensing

Airborne remote sensing using imaging spectroscopy and LiDAR (Light Detection and Ranging) measurements enable us to quantify ecosystem and land surface attributes. In this study we use high resolution airborne remote sensing to characterize soil attributes and the structure of vegetation canopy. Soil texture, organic matter, and chemical constituents are critical to ecosystem functioning, plant growth, and food security. However, most of the soil data available globally are of coarse resolutions at scales of 1:5 million and lack quantitative information for modeling and land management decisions at field or catchment scales. Thus the need for a spatially contiguous quantitative soil information is of immense scientific merit which can be obtained using airborne and space-borne imaging spectroscopy. Towards this goal we systematically explore the feasibility of characterizing soil properties from imaging spectroscopy using data driven modeling approaches. We have developed a modeling framework for quantitative prediction of different soil attributes using airborne imaging spectroscopy and limited field soil grab sample datasets. The results of our analysis using fine resolution (7.6m) Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data collected over midwestern United States immediately after the large 2011 Mississippi River flood indicate the feasibility of using the developed models for quantitative spatial prediction of soil attributes over large areas (> 700 sq. km) of the landscape. The quantitative predictions reveal coherent spatial correlations of the difference in constituent concentrations with legacy landscape features, and immediate disturbances on the landscape due to extreme events. Further for model validation using independent test data, we demonstrate that the results are better represented as a probability density function compared to a single validation subset. We have simulated up-scaled datasets at multiple spatial resolutions ranging from 10m to 90m from the AVIRIS data, including future space based Hyperspectral Infrared Imager (HyspIRI) like observations. These datasets are used to investigate the applicability of the developed modeling framework over increasing spatial resolutions on the characterization of soil constituents. We have outlined an evaluation framework with a set of metrics that considers the point-scale model performance as well as the consistency of cross-scale spatial predictions. The results indicate that the ensemble quantification method is scalable over the entire range of airborne to space-borne spatial resolutions and establishes the feasibility of quantification of soil constituents from space- based observations. Further, we develop a retrieval framework from satellites, which combines the developed modeling framework and spectral similarity measures for global scale characterization of soils using a weighted constrained optimization framework. The retrieval algorithm takes advantage of the potential of repeat temporal satellite measurements to evolve a dynamic spectral library and improve soil characterization. Finally, we demonstrate that in addition to soil constituents, hyperspectral data can add value to characterizations of leaf area density (LAD) estimations for dense overlapping canopies. We develop a method for the estimation of the vertical distribution of foliage or LAD using a combination of airborne LiDAR and hyperspectral data using a feature based data fusion approach. Tree species classification from hyperspectral data is used to develop a novel ellipsoidal ‘tree shaped’ voxel approach for characterizing the LAD of individual trees in a riparian forest setting. We found that the tree shaped voxels represents a more realistic characterization of the upper and middle parts of the tree canopy in terms of higher LAD values, for trees of different heights in a forest stand

HIRIS (High-Resolution Imaging Spectrometer: Science opportunities for the 1990s. Earth observing system. Volume 2C: Instrument panel report

The high-resolution imaging spectrometer (HIRIS) is an Earth Observing System (EOS) sensor developed for high spatial and spectral resolution. It can acquire more information in the 0.4 to 2.5 micrometer spectral region than any other sensor yet envisioned. Its capability for critical sampling at high spatial resolution makes it an ideal complement to the MODIS (moderate-resolution imaging spectrometer) and HMMR (high-resolution multifrequency microwave radiometer), lower resolution sensors designed for repetitive coverage. With HIRIS it is possible to observe transient processes in a multistage remote sensing strategy for Earth observations on a global scale. The objectives, science requirements, and current sensor design of the HIRIS are discussed along with the synergism of the sensor with other EOS instruments and data handling and processing requirements

Artificial intelligence for geologic mapping with imaging spectrometers

This project was a three year study at the Center for the Study of Earth from Space (CSES) within the Cooperative Institute for Research in Environmental Science (CIRES) at the University of Colorado, Boulder. The goal of this research was to develop an expert system to allow automated identification of geologic materials based on their spectral characteristics in imaging spectrometer data such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This requirement was dictated by the volume of data produced by imaging spectrometers, which prohibits manual analysis. The research described is based on the development of automated techniques for analysis of imaging spectrometer data that emulate the analytical processes used by a human observer. The research tested the feasibility of such an approach, implemented an operational system, and tested the validity of the results for selected imaging spectrometer data sets

A review of geothermal mapping techniques using remotely sensed data

Exploiting geothermal (GT) resources requires first and foremost locating suitable areas for its development. Remote sensing offers a synoptic capability of covering large areas in real time and can cost effectively explore prospective geothermal sites not easily detectable using conventional survey methods, thus can aid in the prefeasibility stages of geothermal exploration. In this paper, we evaluate the techniques and approaches used in literature for the detection of prospective geothermal sites. Observations have indicated that, while thermal temperature anomalies detection have been applicable in areas of magmatic episodes and volcanic activity, poor resolution especially from space borne data is still a challenge. Consequently, thermal anomalies have been detected with some degree of success using airborne data, however, this is mostly in locations of known surface manifestations such as hot springs and fumaroles. The indirect identification of indicator minerals related to geothermal systems have been applied using multispectral and hyperspectral data in many studies. However, the effectiveness of the techniques relies on the sophistication and innovative digital image processing methods employed to sieve out relevant spectral information. The use of algorithms to estimate land surface temperature and heat fluxes are also applied to aid thermal anomaly detection, nevertheless, remote sensing techniques are still complementary to geologic, geophysical and geochemical survey methods. While not the first of its kind, this review is aimed at identifying new developments, with a focus on the trends and limitations intrinsic to the techniques and a look at current gaps and prospects for the future.Keywords: Geothermal, remote sensing, thermal anomalies, indicator minerals, multispectral, hyperspectra

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists

Summaries of the Third Annual JPL Airborne Geoscience Workshop. Volume 1: AVIRIS Workshop

This publication contains the preliminary agenda and summaries for the Third Annual JPL Airborne Geoscience Workshop, held at the Jet Propulsion Laboratory, Pasadena, California, on 1-5 June 1992. This main workshop is divided into three smaller workshops as follows: (1) the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on June 1 and 2; (2) the Thermal Infrared Multispectral Scanner (TIMS) workshop, on June 3; and (3) the Airborne Synthetic Aperture Radar (AIRSAR) workshop, on June 4 and 5. The summaries are contained in Volumes 1, 2, and 3, respectively

Evaluación de la cobertura de la vegetación después de un incendio mediante análisis de mezclas espectrales. Aplicación y comparación de diferentes métodos de caracterización de factores de referencia

The analysis of satellite images allows one to monitor the regeneration of vegetation after a fire. In this work, a methodology for quantifying post fire vegetation cover was developed. The proposed methodology is based on the examination of Landsat 7 ETM+ images by using Spectral Mixture Analysis (SMA) and involves the following steps: a) pre-processing, b) inherent dimensionality image determination c) endmember characterization following two methods that thus lead to different models: traditional method based on the knowledge of the area worked as well as Minimum Noise Fraction and Pixel Purity Index method, d) model inversion and combination, e) comparison between the vegetation cover estimated by each model and that measured in field, and f) selection of the most accurate model and mapping of the vegetation cover for the study area. The cover estimated provided by the different models exhibited a high correlation (Spearman’s correlation coefficient >0.89). The average absolute difference between the estimated and field-measured vegetation cover obtained with the most accurate model for each fire never exceeded 6%.El análisis de imágenes de satélite permite el seguimiento y evaluación de los procesos de restauración post-incendio. En este trabajo se presentan parte de los resultados de una metodología dirigida a la cuantificación de la cobertura de la vegetación después de un incendio. La metodología propuesta se basa en el empleo de imágenes Landsat 7 ETM+ mediante un Análisis de Mezclas Espectrales (SMA) y supone los siguientes procesos: a) pre-procesado de la imagen, b) determinación de la dimensión intrínseca (inherent dimensionality) de la imagen c) selección y caracterización de los factores de referencia (endmember) mediante dos métodos propuestos en la literatura: estudio previo de la zona de trabajo, Minimum Noise Fraction y pixles puros (Pixel Purity Index), d) inversión del modelo y combinación, e) comparación entre la cobertura estimada por cada modelos y las medidas realizadas en el campo, y f) selección del modelo de mayor precisión para la realización de una cartografía de la cobertura de la vegetación en el área de estudio. La cobertura estimada por los diferentes modelos muestran una alta correlación (Coeficiente de correlación de Spearman > 0.89), lo que ha permitido establecer una diferencia entre las medidas de cobertura a través de la imagen y la estimación de campo que en ningún caso han superado el 6% de la cobertura post-inendio

Spectroscopy-supported digital soil mapping

Global environmental changes have resulted in changes in key ecosystem services that soils provide. It is necessary to have up to date soil information on regional and global scales to ensure that these services continue to be provided. As a result, Digital Soil Mapping (DSM) research priorities are among others, advancing methods for data collection and analyses tailored towards large-scale mapping of soil properties. Scientifically, this thesis contributed to the development of methodologies, which aim to optimally use remote and proximal sensing (RS and PS) for DSM to facilitate regional soil mapping. The main contributions of this work with respect to the latter are (I) the critical evaluation of recent research achievements and identification of knowledge gaps for large-scale DSM using RS and PS data, (II) the development of a sparse RS-based sampling approach to represent major soil variability at regional scale, (III) the evaluation and development of different state-of-the-art methods to retrieve soil mineral information from PS, (IV) the improvement of spatially explicit soil prediction models and (V) the integration of RS and PS methods with geostatistical and DSM methods. A review on existing literature about the use of RS and PS for soil and terrain mapping was presented in Chapter 2. Recent work indicated the large potential of using RS and PS methods for DSM. However, for large-scale mapping, current methods will need to be extended beyond the plot. Improvements may be expected in the fields of developing more quantitative methods, enhanced geostatistical analysis and improved transferability to other areas. From these findings, three major research interests were selected: (I) soil sampling strategies, (II) retrieval of soil information from PS and (III) spatially continuous mapping of soil properties at larger scales using RS. Budgetary constraints, limited time and available soil legacy data restricted the soil data acquisition, presented in Chapter 3. A 15.000 km2 area located in Northern Morocco served as test case. Here, a sample was collected using constrained Latin Hypercube Sampling (cLHS) of RS and elevation data. The RS data served as proxy for soil variability, as alternative for the required soil legacy data supporting the sampling strategy. The sampling aim was to optimally sample the variability in the RS data while minimizing the acquisition efforts. This sample resulted in a dataset representing major soil variability. The cLHS sample failed to express spatial correlation; constraining the LHS by a distance criterion favoured large spatial variability over short distances. The absence of spatial correlation in the sampled soil variability precludes the use of additional geostatistical analyses to spatially predict soil properties. Predicting soil properties using the cLHS sample is thus restricted to a modelled statistical relation between the sample and exhaustive predictor variables. For this, the RS data provided the necessary spatial information because of the strong spatial correlation while the spectral information provided the variability of the environment (Chapter 3 and 6). Concluding, the RS-based cLHS approach is considered a time and cost efficient method for acquiring information on soil resources over extended areas. This sample was further used for developing methods to derive soil mineral information from PS, and to characterize regional soil mineralogy using RS. In Chapter 4, the influences of complex scattering within the mixture and overlapping absorption features were investigated. This was done by comparing the success of PRISM’s MICA in determining mineralogy of natural samples and modelled spectra. The modelled spectra were developed by a linearly forward model of reflectance spectra, using the fraction of known constituents within the sample. The modelled spectra accounted for the co-occurrence of absorption features but eluded the complex interaction between the components. It was found that more minerals could be determined with higher accuracy using modelled reflectance. The absorption features in the natural samples were less distinct or even absent, which hampered the classification routine. Nevertheless, grouping the individual minerals into mineral categories significantly improved the classification accuracy. These mineral categories are particularly useful for regional scale studies, as key soil property for parent material characterization and soil formation. Characterizing regional soil mineralogy by mineral categories was further described in Chapter 6. Retrieval of refined information from natural samples, such as mineral abundances, is more complex; estimating abundances requires a method that accounts for the interaction between minerals within the intimate mixture. This can be done by addressing the interaction with a non-linear model (Chapter 5). Chapter 5 showed that mineral abundances in complex mixtures could be estimated using absorption features in the 2.1–2.4 µm wavelength region. First, the absorption behaviour of mineral mixtures was parameterized by exponential Gaussian optimization (EGO). Next, mineral abundances were successfully predicted by regression tree analysis, using these parameters as inputs. Estimating mineral abundances using prepared mixes of calcite, kaolinite, montmorillonite and dioctahedral mica or field samples proved the validity of the proposed method. Estimating mineral abundances of field samples showed the necessity to deconvolve spectra by EGO. Due to the nature of the field samples, the simple representation of the complex scattering behaviour by a few Gaussian bands required the parameters asymmetry and saturation to accurately deconvolve the spectra. Also, asymmetry of the EGO profiles showed to be an important parameter for estimating the abundances of the field samples. The robustness of the method in handling the omission of minerals during the training phase was tested by replacing part of the quartz with chlorite. It was found that the accuracy of the predicted mineral content was hardly affected. Concluding, the proposed method allowed for estimating more than two minerals within a mixture. This approach advances existing PS methods and has the potential to quantify a wider set of soil properties. With this method the soil science community was provided an improved inference method to derive and quantify soil properties The final challenge of this thesis was to spatially explicit model regional soil mineralogy using the sparse sample from Chapter 3. Prediction models have especially difficulties relating predictor variables to sampled properties having high spatial correlation. Chapter 6 presented a methodology that improved prediction models by using scale-dependent spatial variability observed in RS data. Mineral predictions were made using the abundances from X-ray diffraction analysis and mineral categories determined by PRISM. The models indicated that using the original RS data resulted in lower model performance than those models using scaled RS data. Key to the improved predictions was representing the variability of the RS data at the same scale as the sampled soil variability. This was realized by considering the medium and long-range spatial variability in the RS data. Using Fixed Rank Kriging allowed smoothing the massive RS datasets to these ranges. The resulting images resembled more closely the regional spatial variability of soil and environmental properties. Further improvements resulted from using multi-scale soil-landscape relationships to predict mineralogy. The maps of predicted mineralogy showed agreement between the mineral categories and abundances. Using a geostatistical approach in combination with a small sample, substantially improves the feasibility to quantitatively map regional mineralogy. Moreover, the spectroscopic method appeared sufficiently detailed to map major mineral variability. Finally, this approach has the potential for modelling various natural resources and thereby enhances the perspective of a global system for inventorying and monitoring the earth’s soil resources. With this thesis it is demonstrated that RS and PS methods are an important but also an essential source for regional-scale DSM. Following the main findings from this thesis, it can be concluded that: Improvements in regional-scale DSM result from the integrated use of RS and PS with geostatistical methods. In every step of the soil mapping process, spectroscopy can play a key role and can deliver data in a time and cost efficient manner. Nevertheless, there are issues that need to be resolved in the near future. Research priorities involve the development of operational tools to quantify soil properties, sensor integration, spatiotemporal modelling and the use of geostatistical methods that allow working with massive RS datasets. This will allow us in the near future to deliver more accurate and comprehensive information about soils, soil resources and ecosystem services provided by soils at regional and, ultimately, global scale.</p