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

Spectral Characteristics of the Dynamic World Land Cover Classification

The Dynamic World product is a discrete land cover classification of Sentinel 2 reflectance imagery that is global in extent, retrospective to 2015, and updated continuously in near real time. The classifier is trained on a stratified random sample of 20,000 hand-labeled 5 × 5 km Sentinel 2 tiles spanning 14 biomes globally. Since the training data are based on visual interpretation of image composites by both expert and non-expert annotators, without explicit spectral properties specified in the class definitions, the spectral characteristics of the classes are not obvious. The objective of this study is to quantify the physical distinctions among the land cover classes by characterizing the spectral properties of the range of reflectance present within each of the Dynamic World classes over a variety of landscapes. This is achieved by comparing both the eight-class probability feature space (excluding snow) and the maximum probability class assignment (label) distributions to continuous land cover fraction estimates derived from a globally standardized spectral mixture model. Standardized substrate, vegetation, and dark (SVD) endmembers are used to unmix nine Sentinel 2 reflectance tiles from nine spectral diversity hotspots for comparison between the SVD land cover fraction continua and the Dynamic World class probability continua and class assignments. The variance partition for the class probability feature spaces indicates that eight of these nine hotspots are effectively five-dimensional to 95% of variance. Class probability feature spaces of the hotspots all show a tetrahedral form with probability continua spanning multiple classes. Comparison of SVD land cover fraction distributions with maximum probability class assignments (labels) and probability feature space distributions reveal a clear distinction between (1) physically and spectrally heterogeneous biomes characterized by continuous gradations in vegetation density, substrate albedo, and structural shadow fractions, and (2) more homogeneous biomes characterized by closed canopy vegetation (forest) or negligible vegetation cover (e.g., desert, water). Due to the ubiquity of spectrally heterogeneous biomes worldwide, the class probability feature space adds considerable value to the Dynamic World maximum probability class labels by offering users the opportunity to depict inherently gradational heterogeneous landscapes otherwise not generally offered with other discrete thematic classifications

Spatial Patterns of Chemical Weathering at the Basal Tertiary Nonconformity in California from Multispectral and Hyperspectral Optical Remote Sensing

Visible through shortwave (VSWIR) spectral reflectance of the geologic units across the basal Tertiary nonconformity (BTN) is characterized at three spatially disparate locations in California. At two of these sites, location-specific spectral endmembers are obtained from AVIRIS imaging spectroscopy and linear spectral mixture models are used to visualize spatial patterns in chemical weathering associated with the BTN. Weathering patterns are found to match well with traditional geologic maps of the BTN at each site, but results show more spatially detailed quantitative geologic information about the spatial variability of chemical weathering near the nonconformity than is possible in a traditional geologic map. Spectral endmembers and unmixing results are also compared across locations. At the two locations with AVIRIS coverage, strong absorptions centered near 2200 nm are observed, consistent with previous geologic publications reporting intense chemical weathering at the BTN. Information loss associated with multispectral sampling of the reflectance continuum is also examined by resampling endmembers from the Maniobra location to mimic the spectral response functions of the WorldView 3, Sentinel-2 and Landsat 8 sensors. Simulated WorldView 3 data most closely approximate the full information content of the AVIRIS observations, resulting in nearly unbiased unmixing results for both endmembers. Mean fraction differences are −0.02 and +0.03 for weathered and unweathered endmembers, respectively. Sentinel-2 and Landsat 8 are unable to distinguish narrow, deep SWIR absorptions from changes in the overall amplitude of the SWIR spectral continuum, resulting in information loss and biased unmixing results. Finally, we characterize a third location using Sentinel-2 observations only. At this site we also find spectrally distinct features associated with several lithologies, providing new information relevant to the mapping of geologic contacts which is neither present in high spatial resolution visible imagery, nor in published geologic mapping. Despite these limitations, the spatial pattern of the Sentinel-2 and Landsat 8 fraction estimates is sufficiently similar to that of the WorldView 3 and AVIRIS fraction estimates to be useful for mapping purposes in cases where hyperspectral data are unavailable.Keywords: AVIRIS; Sentinel-2; WorldView 3; reflectance spectroscopy; basal Tertiary nonconformity; paleosols; southwestern U.S. cordillera; spectral mixture analysis; Eocene; Paleocen

An integrated study of earth resources in the state of California using remote sensing techniques Progress report

Integrated approach to water management by remote sensing in Californi

Which Vegetation Index? Benchmarking Multispectral Metrics to Hyperspectral Mixture Models in Diverse Cropland

The monitoring of agronomic parameters like biomass, water stress, and plant health can benefit from synergistic use of all available remotely sensed information. Multispectral imagery has been used for this purpose for decades, largely with vegetation indices (VIs). Many multispectral VIs exist, typically relying on a single feature—the spectral red edge—for information. Where hyperspectral imagery is available, spectral mixture models can use the full VSWIR spectrum to yield further insight, simultaneously estimating area fractions of multiple materials within mixed pixels. Here we investigate the relationships between VIs and mixture models by comparing hyperspectral endmember fractions to six common multispectral VIs in California’s diverse crops and soils. In so doing, we isolate spectral effects from sensor- and acquisition-specific variability associated with atmosphere, illumination, and view geometry. Specifically, we compare: (1) fractional area of photosynthetic vegetation (Fv) from 64,000,000 3–5 m resolution AVIRIS-ng reflectance spectra; and (2) six popular VIs (NDVI, NIRv, EVI, EVI2, SR, DVI) computed from simulated Planet SuperDove reflectance spectra derived from the AVIRIS-ng spectra. Hyperspectral Fv and multispectral VIs are compared using both parametric (Pearson correlation, ρ) and nonparametric (Mutual Information, MI) metrics. Four VIs (NIRv, DVI, EVI, EVI2) showed strong linear relationships with Fv (ρ > 0.94; MI > 1.2). NIRv and DVI showed strong interrelation (ρ > 0.99, MI > 2.4), but deviated from a 1:1 correspondence with Fv. EVI and EVI2 were strongly interrelated (ρ > 0.99, MI > 2.3) and more closely approximated a 1:1 relationship with Fv. In contrast, NDVI and SR showed a weaker, nonlinear, heteroskedastic relation to Fv (ρ < 0.84, MI = 0.69). NDVI exhibited both especially severe sensitivity to unvegetated background (–0.05 < NDVI < +0.6) and saturation (0.2 < Fv < 0.8 for NDVI = 0.7). The self-consistent atmospheric correction, radiometry, and sun-sensor geometry allows this simulation approach to be further applied to indices, sensors, and landscapes worldwide

Spectral Mixture Analysis as a Unified Framework for the Remote Sensing of Evapotranspiration

This study illustrates a unified, physically-based framework for mapping landscape parameters of evapotranspiration (ET) using spectral mixture analysis (SMA). The framework integrates two widely used approaches by relating radiometric surface temperature to subpixel fractions of substrate (S), vegetation (V), and dark (D) spectral endmembers (EMs). Spatial and temporal variations in these spectral endmember fractions reflect process-driven variations in soil moisture, vegetation phenology, and illumination. Using all available Landsat 8 scenes from the peak growing season in the agriculturally diverse Sacramento Valley of northern California, we characterize the spatiotemporal relationships between each of the S, V, D land cover fractions and apparent brightness temperature (T) using bivariate distributions in the ET parameter spaces. The dark fraction scales inversely with shortwave broadband albedo (ρ < −0.98), and show a multilinear relationship to T. Substrate fraction estimates show a consistent (ρ ≈ 0.7 to 0.9) linear relationship to T. The vegetation fraction showed the expected triangular relationship to T. However, the bivariate distribution of V and T shows more distinct clustering than the distributions of Normalized Difference Vegetation Index (NDVI)-based proxies and T. Following the Triangle Method, the V fraction is used with T to compute the spatial maps of the ET fraction (EF; the ratio of the actual total ET to the net radiation) and moisture availability (Mo; the ratio of the actual soil surface evaporation to potential ET at the soil surface). EF and Mo estimates derived from the V fraction distinguish among rice growth stages, and between rice and non-rice agriculture, more clearly than those derived from transformed NDVI proxies. Met station-based reference ET & soil temperatures also track vegetation fraction-based estimates of EF & Mo more closely than do NDVI-based estimates of EF & Mo. The proposed approach using S, V, D land cover fractions in conjunction with T (SVD+T) provides a physically-based conceptual framework that unifies two widely-used approaches by simultaneously mapping the effects of albedo and vegetation abundance on the surface temperature field. The additional information provided by the third (Substrate) fraction suggests a potential avenue for ET model improvement by providing an explicit observational constraint on the exposed soil fraction and its moisture-modulated brightness. The structures of the T, EF & Mo vs SVD feature spaces are complementary and that can be interpreted in the context of physical variables that scale linearly and that can be represented directly in process models. Using the structure of the feature spaces to represent the spatiotemporal trajectory of crop phenology is possible in agricultural settings, because variations in the timing of planting and irrigation result in continuous trajectories in the physical parameter spaces that are represented by the feature spaces. The linear scaling properties of the SMA fraction estimates from meter to kilometer scales also facilitate the vicarious validation of ET estimates using multiple resolutions of imagery

Mapping Prosopis glandulosa (mesquite) invasion in the arid environment of South African using remote sensing techniques

A dissertation submitted to the School of Geography, Archaeology and Environmental Studies, Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science in Environmental Sciences. Johannesburg, March 2016.Mapping Prosopis glandulosa (mesquite) invasion in the arid environment of South Africa using remote sensing techniques Mureriwa, Nyasha Abstract Decades after the first introduction of the Prosopis spp. (mesquite) to South Africa in the late 1800s for its benefits, the invasive nature of the species became apparent as its spread in regions of South Africa resulting in devastating effects to biodiversity, ecosystems and the socio-economic wellbeing of affected regions. Various control and management practices that include biological, physical, chemical and integrated methods have been tested with minimal success as compared to the rapid spread of the species. From previous studies, it has been noted that one of the reasons for the low success rates in mesquite control and management is a lack of sufficient information on the species invasion dynamic in relation to its very similar co-existing species. In order to bridge this gap in knowledge, vegetation species mapping techniques that use remote sensing methods need to be tested for the monitoring, detection and mapping of the species spread. Unlike traditional field survey methods, remote sensing techniques are better at monitoring vegetation as they can cover very large areas and are time-effective and cost-effective. Thus, the aim of this research was to examine the possibility of mapping and spectrally discriminating Prosopis glandulosa from its native co-existing species in semi-arid parts of South Africa using remote sensing methods. The specific objectives of the study were to investigate the spectral separability between Prosopis glandulosa and its co-existing species using field spectral data as well as to upscale the results to different satellites resolutions. Two machine learning algorithms (Random Forest (RF) and Support Vector Machines (SVM)) were also tested in the mapping processes. The first chapter of the study evaluated the spectral discrimination of Prosopis glandulosa from three other species (Acacia karoo, Acacia mellifera and Ziziphus mucronata) in the study area using in-situ spectroscopy in conjunction with the newly developed guided regularized random forest (GRRF) algorithm in identifying key wavelengths for multiclass classification. The GRRF algorithm was used as a method of reducing the problem of high dimensionality associated with hyperspectral data. Results showed that there was an increase in the accuracy of discrimination between the four species when the full set of 1825 wavelengths was used in classification (79.19%) as compared to the classification used by the 11 key wavelengths identified by GRRF (88.59%). Results obtained from the second chapter showed that it is possible to spatially discriminate mesquite from its co-existing acacia species and other general land-cover types at a 2 m resolution with overall accuracies of 86.59% for RF classification and 85.98% for SVM classification. The last part of the study tested the use of the more cost effective SPOT-6 imagery and the RF and SVM algorithms in mapping Prosopis glandulosa invasion and its co-existing indigenous species. The 6 m resolution analysis obtained accuracies of 78.46% for RF and 77.62% for SVM. Overall it was concluded that spatial and spectral discrimination of Prosopis glandulosa from its native co-existing species in semi-arid South Africa was possible with high accuracies through the use of (i) two high resolution, new generation sensors namely, WorldView-2 and SPOT-6; (ii) two robust classification algorithms specifically, RF and SVM and (iii) the newly developed GRRF algorithm for variable selection and reducing the high dimensionality problem associated with hyperspectral data. Some recommendations for future studies include the replication of this study on a larger scale in different invaded areas across the country as well as testing the robustness of the RF and SVM classifiers by making use of other machine learning algorithms and classification methods in species discrimination. Keywords: Prosopis glandulosa, field spectroscopy, cost effectiveness, Guided Regularised Random Forest, Support Vector Machines, Worldview-2, Spot-

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

Imperial Users onl

Ash Tree Identification Based on the Integration of Hyperspectral Imagery and High-density Lidar Data

Monitoring and management of ash trees has become particularly important in recent years due to the heightened risk of attack from the invasive pest, the emerald ash borer (EAB). However, distinguishing ash from other deciduous trees can be challenging. Both hyperspectral imagery and Light detection and ranging (LiDAR) data are two valuable data sources that are often used for tree species classification. Hyperspectral imagery measures detailed spectral reflectance related to the biochemical properties of vegetation, while LiDAR data measures the three-dimensional structure of tree crowns related to morphological characteristics. Thus, the accuracy of vegetation classification may be improved by combining both techniques. Therefore, the objective of this research is to integrate hyperspectral imagery and LiDAR data for improving ash tree identification. Specifically, the research aims include: 1) using LiDAR data for individual tree crowns segmentation; 2) using hyperspectral imagery for extraction of relative pure crown spectra; 3) fusing hyperspectral and LiDAR data for ash tree identification. It is expected that the classification accuracy of ash trees will be significantly improved with the integration of hyperspectral and LiDAR techniques. Analysis results suggest that, first, 3D crown structures of individual trees can be reconstructed using a set of generalized geometric models which optimally matched LiDAR-derived raster image, and crown widths can be further estimated using tree height and shape-related parameters as independent variables and ground measurement of crown widths as dependent variables. Second, with constrained linear spectral mixture analysis method, the fractions of all materials within a pixel can be extracted, and relative pure crown-scale spectra can be further calculated using illuminated-leaf fraction as weighting factors for tree species classification. Third, both crown shape index (SI) and coefficient of variation (CV) can be extracted from LiDAR data as invariant variables in tree’s life cycle, and improve ash tree identification by integrating with pixel-weighted crown spectra. Therefore, three major contributions of this research have been made in the field of tree species classification:1) the automatic estimation of individual tree crown width from LiDAR data by combining a generalized geometric model and a regression model, 2) the computation of relative pure crown-scale spectral reflectance using a pixel-weighting algorithm for tree species classification, 3) the fusion of shape-related structural features and pixel-weighted crown-scale spectral features for improving of ash tree identification