11 research outputs found
High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)
Currently large uncertainties exist associated with the attribution and quantification of fugitive emissions of criteria pollutants and greenhouse gases such as methane across large regions and key economic sectors. In this study, data from the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) have been used to develop robust and reliable techniques for the detection and wide-area mapping of emission plumes of methane and other atmospheric trace gas species over challenging and diverse environmental conditions with high spatial resolution that permits direct attribution to sources. HyTES is a pushbroom imaging spectrometer with high spectral resolution (256 bands from 7.5 to 12 µm), wide swath (1–2 km), and high spatial resolution (∼ 2 m at 1 km altitude) that incorporates new thermal infrared (TIR) remote sensing technologies. In this study we introduce a hybrid clutter matched filter (CMF) and plume dilation algorithm applied to HyTES observations to efficiently detect and characterize the spatial structures of individual plumes of CH_4, H_2S, NH_3, NO_2, and SO_2 emitters. The sensitivity and field of regard of HyTES allows rapid and frequent airborne surveys of large areas including facilities not readily accessible from the surface. The HyTES CMF algorithm produces plume intensity images of methane and other gases from strong emission sources. The combination of high spatial resolution and multi-species imaging capability provides source attribution in complex environments. The CMF-based detection of strong emission sources over large areas is a fast and powerful tool needed to focus on more computationally intensive retrieval algorithms to quantify emissions with error estimates, and is useful for expediting mitigation efforts and addressing critical science questions
High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)
Currently large uncertainties exist associated with the attribution and quantification of fugitive emissions of criteria pollutants and greenhouse gases such as methane across large regions and key economic sectors. In this study, data from the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) have been used to develop robust and reliable techniques for the detection and wide-area mapping of emission plumes of methane and other atmospheric trace gas species over challenging and diverse environmental conditions with high spatial resolution that permits direct attribution to sources. HyTES is a pushbroom imaging spectrometer with high spectral resolution (256 bands from 7.5 to 12 µm), wide swath (1–2 km), and high spatial resolution (∼ 2 m at 1 km altitude) that incorporates new thermal infrared (TIR) remote sensing technologies. In this study we introduce a hybrid clutter matched filter (CMF) and plume dilation algorithm applied to HyTES observations to efficiently detect and characterize the spatial structures of individual plumes of CH_4, H_2S, NH_3, NO_2, and SO_2 emitters. The sensitivity and field of regard of HyTES allows rapid and frequent airborne surveys of large areas including facilities not readily accessible from the surface. The HyTES CMF algorithm produces plume intensity images of methane and other gases from strong emission sources. The combination of high spatial resolution and multi-species imaging capability provides source attribution in complex environments. The CMF-based detection of strong emission sources over large areas is a fast and powerful tool needed to focus on more computationally intensive retrieval algorithms to quantify emissions with error estimates, and is useful for expediting mitigation efforts and addressing critical science questions
A Compact, High Resolution Hyperspectral Imager for Remote Sensing of Soil Moisture
Measurement of soil moisture content is a key challenge across a variety of fields, ranging from civil engineering through to defence and agriculture. While dedicated satellite platforms like SMAP and SMOS provide high spatial coverage, their low spatial resolution limits their application to larger regional studies. The advent of compact, high lift capacity UAVs has enabled small scale surveys of specific farmland cites.
This thesis presents work on the development of a compact, high spatial and spectral resolution hyperspectral imager, designed for remote measurement of soil moisture content. The optical design of the system incorporates a bespoke freeform blazed diffraction grating, providing higher optical performance at a similar aperture to conventional Offner-Chrisp designs.
The key challenges of UAV-borne hyperspectral imaging relate to using only solar illumination, with both intermittent cloud cover and atmospheric water absorption creating challenges in obtaining accurate reflectance measurements. A hardware based calibration channel for mitigating cloud cover effects is introduced, along with a comparison of methods for recovering soil moisture content from reflectance data under varying illumination conditions. The data processing pipeline required to process the raw pushbroom data into georectified images is also discussed.
Finally, preliminary work on applying soil moisture techniques to leaf imaging are presented
Automatic class labeling of classified imagery using a hyperspectral library
vii, 93 leaves : ill., maps (some col.) ; 29 cmImage classification is a fundamental information extraction procedure in remote sensing that is used in land-cover and land-use mapping. Despite being considered as a replacement for manual mapping, it still requires some degree of analyst intervention. This makes the process of image classification time consuming, subjective, and error prone. For example, in unsupervised classification, pixels are automatically grouped into classes, but the user has to manually label the classes as one land-cover type or another. As a general rule, the larger the number of classes, the more difficult it is to assign meaningful class labels. A fully automated post-classification procedure for class labeling was developed in an attempt to alleviate this problem. It labels spectral classes by matching their spectral characteristics with reference spectra. A Landsat TM image of an agricultural area was used for performance assessment. The algorithm was used to label a 20- and 100-class image generated by the ISODATA classifier. The 20-class image was used to compare the technique with the traditional manual labeling of classes, and the 100-class image was used to compare it with the Spectral Angle Mapper and Maximum Likelihood classifiers. The proposed technique produced a map that had an overall accuracy of 51%, outperforming the manual labeling (40% to 45% accuracy, depending on the analyst performing the labeling) and the Spectral Angle Mapper classifier (39%), but underperformed compared to the Maximum Likelihood technique (53% to 63%). The newly developed class-labeling algorithm provided better results for alfalfa, beans, corn, grass and sugar beet, whereas canola, corn, fallow, flax, potato, and wheat were identified with similar or lower accuracy, depending on the classifier it was compared with
Multi-scalar remote sensing of the northern mixed prairie vegetation
Optimal scale of study and scaling are fundamental to ecological research, and have been made easier with remotely sensed (RS) data. With access to RS data at multiple scales, it is important to identify how they compare and how effectively information at a specific scale will potentially transfer between scales. Therefore, my research compared the spatial, spectral, and temporal aspects of scale of RS data to study biophysical properties and spatio-temporal dynamics of the northern mixed prairie vegetation.
I collected ground cover, dominant species, aboveground biomass, and leaf area index (LAI) from 41 sites and along 3 transects in the West Block of Grasslands National Park of Canada (GNPC; +49°, -107°) between June-July of 2006 and 2007. Narrowband (VIn) and broadband vegetation indices (VIb) were derived from RS data at multiple scales acquired through field spectroradiometry (1 m) and satellite imagery (10, 20, 30 m). VIs were upscaled from their native scales to coarser scales for spatial comparison, and time-series imagery at ~5-year intervals was used for temporal comparison.
Results showed VIn, VIb, and LAI captured the spatial variation of plant biophysical properties along topographical gradients and their spatial scales ranged from 35-200 m. Among the scales compared, RS data at finer scales showed stronger ability than coarser scales to estimate ground vegetation. VIn were found to be better predictors than VIb in estimating LAI. Upscaling at all spatial scales showed similar weakening trends for LAI prediction using VIb, however spatial regression methods were necessary to minimize spatial effects in the RS data sets and to improve the prediction results. Multiple endmember spectral mixture analysis (MESMA) successfully captured the spatial heterogeneity of vegetation and effective modeling of sub-pixel spectral variability to produce improved vegetation maps. However, the efficiency of spectral unmixing was found to be highly dependent on the identification of optimal type and number of region-specific endmembers, and comparison of spectral unmixing on imagery at different scales showed spectral resolution to be important over spatial resolution. With the development of a comprehensive endmember library, MESMA may be used as a standard tool for identifying spatio-temporal changes in time-series imagery. Climatic variables were found to affect the success of unmixing, with lower success for years of climatic extremes. Change-detection analysis showed the success of biodiversity conservation practices of GNPC since establishment of the park and suggests that its management strategies are effective in maintaining vegetation heterogeneity in the region.
Overall, my research has advanced the understanding of RS of the northern mixed prairie vegetation, especially in the context of effects of scale and scaling. From an eco-management perspective, this research has provided cost- and time-effective methods for vegetation mapping and monitoring. Data and techniques tested in this study will be even more useful with hyperspectral imagery should they become available for the northern mixed prairie
Research trends in the use of remote sensing for inland water quality science: Moving towards multidisciplinary applications
Remote sensing approaches to measuring inland water quality date back nearly 50 years to the beginning of the satellite era. Over this time span, hundreds of peer-reviewed publications have demonstrated promising remote sensing models to estimate biological, chemical, and physical properties of inland waterbodies. Until recently, most of these publications focused largely on algorithm development as opposed to implementation of those algorithms to address specific science questions. This slow evolution contrasts with terrestrial and oceanic remote sensing, where methods development in the 1970s led to publications focused on understanding spatially expansive, complex processes as early as the mid-1980s. This review explores the progression of inland water quality remote sensing from methodological development to scientific applications. We use bibliometric analysis to assess overall patterns in the field and subsequently examine 236 key papers to identify trends in research focus and scale. The results highlight an initial 30 year period where the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early-2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Recent and emerging resources, including improved data availability and enhanced processing platforms, are enabling researchers to address challenging science questions and model spatiotemporally explicit patterns in water quality. Examination of the literature shows that the past 10-15 years has brought about a focal shift within the field, where researchers are using improved computing resources, datasets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters
UAV and field spectrometer based remote sensing for maize phenotyping, varietal discrimination and yield forecasting.
Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Maize is the major staple food crop in the majority of Sub-Saharan African (SSA) countries.
However, production statistics (croplands and yields) are rarely measured, and where they are
recorded, accuracy is poor because the statistics are updated through the farm survey method,
which is error-prone and is time-consuming, and expensive. There is an urgent need to use
affordable, accurate, timely, and readily accessible data collection and spatial analysis tools,
including robust data extraction and processing techniques for precise yield forecasting for
decision support and early warning systems. Meeting Africa’s rising food demand, which is
driven by population growth and low productivity requires doubling the current production of
major grain crops like maize by 2050. This requires innovative approaches and mechanisms that
support accurate yield forecasting for early warning systems coupled with accelerated crop
genetic improvement.
Recent advances in remote sensing and geographical information system (GIS) have enabled
detailed cropland mapping, spatial analysis of land suitability, crop type, and varietal
discrimination, and ultimately grain yield forecasting in the developed world. However,
although remote sensing and spatial analysis afforded us unprecedented opportunities for
detailed data collection, their application in maize in Africa is still limited. In Africa, the challenge
of crop yield forecasting using remote sensing is a daunting task because agriculture is highly
fragmented, cropland is spatially heterogeneous, and cropping systems are highly diverse and
mosaic. The dearth of data on the application of remote sensing and GIS in crop yield forecasting
and land suitability analysis is not only worrying but catastrophic to food security monitoring
and early warning systems in a continent burdened with chronic food shortages. Furthermore,
accelerated crop genetic improvement to increase yield and achieve better adaptation to climate
change is an issue of increasing urgency in order to satisfy the ever-increasing food demand.
Recently, crop improvement programs are exploring the use of remotely sensed data that can be
used cost-effectively for varietal evaluation and analysis in crop phenotyping, which currently
remains a major bottleneck in crop genetic improvement. Yet studies on evaluation of maize varietal response to abiotic and biotic stresses found in the target production environments are limited.
Therefore, the aim of this study was to model spatial land suitability for maize production using
GIS and explore the potential use of field spectrometer and unmanned aerial vehicles (UAV)
based remotely sensed data in maize varietal discrimination, high-throughput phenotyping, and
yield prediction. Firstly, an overview of major remote-sensing platforms and their applicability
to estimating maize grain yield in the African agricultural context, including research challenges
was provided. Secondly, maize land suitability analysis using GIS and analytical hierarchical
process (AHP) was performed in Zimbabwe. Finally, the utility of proximal and UAV-based
remotely sensed data for maize phenotyping, varietal discrimination, and yield forecasting were
explored.
The results showed that the use of remote sensing data in estimating maize yield in the African
agricultural systems is still limited and obtaining accurate and reliable maize yield estimates
using remotely sensed data remains a challenge due to the highly fragmented and spatially
heterogeneous nature of the cropping systems. Our results underscored the urgent need to use
sensors with high spatial, temporal and spectral resolution, coupled with appropriate
classification techniques and accurate ground truth data in estimating maize yield and its spatiotemporal
dynamics in heterogeneous African agricultural landscapes for designing appropriate
food security interventions. In addition, using modern spatial analysis tools is effective in
assessing land suitability for targeting location-specific interventions and can serve as a decision
support tool for policymakers and land-use planners regarding maize production and varietal
placement.
Discriminating maize varieties using remotely sensed data is crucial for crop monitoring, high throughput
phenotyping, and yield forecasting. Using proximal sensing, our study showed that
maize varietal discrimination is possible at certain phenological growth stages at the field level,
which is crucial for yield forecasting and varietal phenotyping in crop improvement. In addition,
the use of proximal remote sensing data with appropriate pre-processing algorithms such as auto scaling and generalized least squares weighting significantly improved the discrimination ability
of partial least square discriminant analysis, and identify optimal spectral bands for maize
varietal discrimination. Using proximal sensing was not only able to discriminate maize varieties
but also identified the ideal phenological stage for varietal discrimination. Flowering and onset
of senescence appeared to be the most ideal stages for accurate varietal discrimination using our
data.
In this study, we also demonstrated the potential use of UAV-based remotely sensed data in
maize varietal phenotyping in crop improvement. Using multi-temporal UAV-derived
multispectral data and Random Forest (RF) algorithm, our study identified not only the optimal
bands and indices but also the ideal growth stage for accurate varietal phenotyping under maize
streak virus (MSV) infection. The RF classifier selected green normalized difference vegetation
index (GNDVI), green Chlorophyll Index (CIgreen), Red-edge Chlorophyll Index (CIred-edge),
and the Red band as the most important variables for classification. The results demonstrated
that spectral bands and vegetation indices measured at the vegetative stage are the most
important for the classification of maize varietal response to MSV. Further analysis to predict
MSV disease and grain yield using UAV-derived multispectral imaging data using multiple
models showed that Red and NIR bands were frequently selected in most of the models that gave
the highest prediction precision for grain yield. Combining the NIR band with Red band
improved the explanatory power of the prediction models. This was also true with the selected
indices. Thus, not all indices or bands measure the same aspect of biophysical parameters or crop
productivity, and combining them increased the joint predictive power, consequently increased
complementarity.
Overall, the study has demonstrated the potential use of spatial analysis tools in land suitability
analysis for maize production and the utility of remotely sensed data in maize varietal
discrimination, phenotyping, and yield prediction. These results are useful for targeting location-specific
interventions for varietal placement and integrating UAV-based high-throughput
phenotyping systems in crop genetic improvement to address continental food security,
especially as climate change accelerates
Multidecadal Remote Sensing of Inland Water Dynamics
Remote sensing approaches to measuring inland water dynamics date back more than 50 years. These approaches rely on the unique spectral properties of different waterbodies to delineate surface extents and estimate optically active water quality parameters. Until recently, inland water remote sensing focused largely on localized study domains due to limitations in modelling methods, computing power, and data access. Recent advances in these areas have created novel opportunities for data-driven-multidecadal remote sensing of inland waters at the landscape scale. Here, I highlight the history of inland water remote sensing along with the dominant methodologies, water quality constituents, and limitations involved. I then use this background to contextualize three macroscale inland water remote sensing studies of increasing complexity. The first combines field measurements with remotely sensed surface water extents to identify the impacts of small-scale gold mining in Peru. Our results suggest that mining is leading to synergistic increases in lake area and mercury loading that are significantly heightening exposure risk for people and wildlife. I move from measuring lake extents in Peru to measuring lake color in over 26,000 lakes across the United States. This analysis shows that lake color seasonality can be generalized into five distinct phenology groups that follow well-known patterns of algae growth and succession. The stability of a given lake (i.e. the likelihood it will move from one phenology group to another) is tied to lake and landscape level characteristics including climate and population density. Finally, I move from simple parameters such as quantity and color to estimating multidecadal changes in water clarity in U.S. lakes. I show that lake water clarity in the U.S. has increased by an average of 0.52 cm yr-1 since 1984, largely as a result of extensive U.S. freshwater pollution abatement measures. In combination, these three studies highlight that data intensive remote sensing approaches are expanding the capabilities of inland water remote sensing from local to global scales, and that macroscale remote sensing of inland waters reveals trends and processes that are unobservable using field data alone.Doctor of Philosoph
Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems
14 p.Mediterranean ecosystems are adapted to recurrent forest fires by having regeneration mechanisms that overcome the
immediate effects of fire. However, the increasing frequency of fires in most European Mediterranean countries is challenging
the natural regrowth capability of these ecosystems. In this context, monitoring post-fire vegetation recovery is a
priority for forest management and soil erosion control. In this work, a 13-year series (1999–2011) of Landsat 5 Thematic
Mapper (TM)/Landsat 7 Enhanced Thematic Mapper (ETM +) data was used to model post-fire vegetation recovery as a
function of burn severity and to quantify post-fire resilience as a measure of vegetation cover regrowth. We evaluated a
large forest fire located in Spain that burned approximately 30 km2 of Pinus pinaster Ait. in August 1998. 88 field plots of
four burn severity levels (unburned, low, moderate and high) were measured in the field a year after the fire. As a variable
representative of vegetation, we chose the shade normalized green vegetation fraction image (SGV) obtained by applying
Multiple Endmember Spectral Mixture Analysis (MESMA) to the original Landsat TM/ETM + images. The SGV values
were extracted for the 88 field plots and, after performing a one-way analysis of variance (ANOVA), a Fisher's Least
Significant Difference (LSD) test allowed us to estimate resilience of vegetation cover as the number of post-fire years
exhibiting a statistically significant difference between burned and unburned areas. Next, SGV values were referenced
to unburned control plots values and the vegetation recovery index (VRI) was defined. The evolution in time curve of
VRI for low, moderate and highly fire affected vegetation was fit using trend models (specifically, an exponential trend
for VRI in high and moderate burn severity levels; a linear trend for low burn severity level, Root Mean Square Error,
RMSE = 0.18, 0.13, and 0.09, respectively). We observed that vegetation cover affected by low severity fire recovered
to its original state after 7 years, and vegetation cover affected by moderate severity recovered after 13 years. Vegetation
affected by high severity fire was estimated to recover after 20 years. We conclude that VRI time series based on multitemporal
MESMA fractions from Landsat data can be considered a valuable indicator of the post-fire vegetation cover
recovery. Its temporal evolution represented post-fire vegetation cover regrowth adequately and facilitated the estimate
of vegetation cover resilience in Mediterranean forestsS