Martin Wooster’s research while affiliated with Kingston College and other places

The ever-increasing food demand globally exerts a pressing need to develop efficient water resource management to meet crop water requirements. While satellite remote sensing has proven invaluable for assessing basin-scale crop water conditions (CWC), foreseeable challenges still exist to evaluate field-scale water status in crop-intensive regions. In this study, we present a systematic framework to assess the field-scale CWC using an unmanned aerial vehicle (UAV) based thermal and multispectral imageries at two (upstream and downstream) instrumented sites of an agro-intensive Ganga basin Critical Zone Observatory (CZO), India. We first estimated the land surface temperature (LST) by incorporating emissivity and atmospheric radiance components. We then used the LST and normalised difference vegetation index (NDVI) to compute the vegetation temperature condition index (VTCI) for each site as a proxy for field scale CWC. Our results reveal a significant correlation between aerial LST and in-situ radiometer observations with an absolute difference of ≤±1K. The aerial LST estimation also showed high accuracy and consistently low error (≤3K) in predicting synchronous ground-based surface temperature with a slightly warm bias of 1–2 K. While the variability in mean VTCI across both windows is minimal, the downstream window reveals a sharp increase in drier CWC from 22 % to 51 % in comparison to the upstream region (20–27 %). Further, the field-scale VTCI demonstrated a significant positive correlation (r = 0.6 to 0.81, Kendall’s τ = 0.5 to 0.82) to concurrent soil moisture conditions during all acquisitions. Our study highlights the potential of UAV remote sensing for assessing of high-resolution CWC and designing strategies for regulating agricultural water resources and improving water use efficiency.

We summarize here the current development of a simulation strategy aiming at creating a 3D virtual fire lab that can model radiative transfer in simulated fire scenes, and render images in the infrared spectrum. While the end objective is to simulate open landscape scale vegetation fires to help improving fire monitoring Earth Observation products, this work presents a validation exercise performed using a small scale fire scene focusing on flame emission. The fire scene is simulated using the Fire Dynamics Simulator (FDS) model to generate 3D distribution of temperature, soot volume fraction, and CO 2 , CO and H 2 O gases molar fractions. It is then passed to the 3D Discrete Anisotropic Radiative Transfer (DART) model to simulate radiative transfer in multiple infrared bands at a spectral resolution of 0.25 μ m, and render images or intensity spectrum in the infra red spectra that can be compared against direct measurements.

Landscape fires, predominantly found in the frequently burning global savannas, are a substantial source of greenhouse gases and aerosols. The impact of these fires on atmospheric composition is partially determined by the chemical breakup of the constituents of the fuel into individual emitted chemical species, which is described by emission factors (EFs). These EFs are known to be dependent on, amongst other things, the type of fuel consumed, the moisture content of the fuel, and the meteorological conditions during the fire, indicating that savanna EFs are temporally and spatially dynamic. Global emission inventories, however, rely on static biome-averaged EFs, which makes them ill-suited for the estimation of regional biomass burning (BB) emissions and for capturing the effects of shifts in fire regimes. In this study we explore the main drivers of EF variability within the savanna biome and assess which geospatial proxies can be used to estimate dynamic EFs for global emission inventories. We made over 4500 bag measurements of CO2, CO, CH4, and N2O EFs using a UAS and also measured fuel parameters and fire-severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems under different seasonal conditions sampled over the course of six fire seasons between 2017 and 2022. We complemented our own data with EFs from 85 fires with locations and dates provided in the literature. Based on the locations, dates, and times of the fires we retrieved a variety of fuel, weather, and fire-severity proxies (i.e. possible predictors) using globally available satellite and reanalysis data. We then trained random forest (RF) regressors to estimate EFs for CO2, CO, CH4, and N2O at a spatial resolution of 0.25∘ and a monthly time step. Using these modelled EFs, we calculated their spatiotemporal impact on BB emission estimates over the 2002–2016 period using the Global Fire Emissions Database version 4 with small fires (GFED4s). We found that the most important field indicators for the EFs of CO2, CO, and CH4 were tree cover density, fuel moisture content, and the grass-to-litter ratio. The grass-to-litter ratio and the nitrogen-to-carbon ratio were important indicators for N2O EFs. RF models using satellite observations performed well for the prediction of EF variability in the measured fires with out-of-sample correlation coefficients between 0.80 and 0.99, reducing the error between measured and modelled EFs by 60 %–85 % compared to using the static biome average. Using dynamic EFs, total global savanna emission estimates for 2002–2016 were 1.8 % higher for CO, while CO2, CH4, and N2O emissions were, respectively, 0.2 %, 5 %, and 18 % lower compared to GFED4s. On a regional scale we found a spatial redistribution compared to GFED4s with higher CO, CH4, and N2O EFs in mesic regions and lower ones in xeric regions. Over the course of the fire season, drying resulted in gradually lower EFs of these species. Relatively speaking, the trend was stronger in open savannas than in woodlands, where towards the end of the fire season they increased again. Contrary to the minor impact on annual average savanna fire emissions, the model predicts localized deviations from static averages of the EFs of CO, CH4, and N2O exceeding 60 % under seasonal conditions.

Landscape fire activity is changing in many regions because of climate change. Smoke emissions from landscape fires contain many harmful air pollutants, and beyond the potential hazard posed to human health, these also have ecological impacts. Insects play essential roles in most ecosystems worldwide, and some work suggests they may also be sensitive to smoke exposure. There is therefore a need for a comprehensive review of smoke impacts on insects. We systematically reviewed the scientific literature from 1930 to 2022 to synthesize the current state of knowledge of the impacts of smoke exposure from landscape fires on the development, behavior, and mortality of insects. We found: (1) 42 relevant studies that met our criteria, with 29% focused on the United States of America and 19% on Canada; (2) of these, 40 insect species were discussed, all of which were sensitive to smoke pollution; (3) most of the existing research focuses on how insect behavior responds to landscape fire smoke (LFS); (4) species react differently to smoke exposure, with for example some species being attracted to the smoke (e.g., some beetles) while others are repelled (e.g., some bees). This review consolidates the current state of knowledge on how smoke impacts insects and highlights areas that may need further investigation. This is particularly relevant since smoke impacts on insect communities will likely worsen in some areas due to increasing levels of biomass burning resulting from the joint pressures of climate change, land use change, and more intense land management involving fire.

We present the first top-down CO fire emissions inventory for Africa based on the direct relation between geostationary satellite-based Fire Radiative Power (FRP) measures and satellite observations of Total Column Carbon Monoxide (TCCO). This work extends significantly the previous Fire Radiative Energy Emissions (FREM) approach that derived Total Particulate Matter (TPM) emission coefficients from FRP measures and Aerosol Optical Depth (AOD) observations. The use of satellite-based CO observations to derive CO emission coefficients, CeCO, addresses key uncertainties in the use of AOD measures to estimate fire-generated CO emissions including; the requirement for a smoke mass extinction coefficient in the AOD to TPM conversion; and the large variation in TPM emission factors – which are used to convert TPM emissions to CO emissions. We use the FREM-derived CO emission coefficients to produce a Pan-African CO fire emission inventory spanning 16 years. Regional CO emissions are in close agreement with the most recent version of GFED(v4.1s), despite the two inventories using completely different satellite datasets and methodologies to derive CO emissions. Dry Matter Consumed (DMC) and DMC per unit area values are generated from our CO emission inventory – the latter using the 20 m resolution Sentinal-2 FireCCISFD burnt area (BA) product for 2019. We carry out an evaluation of our FREM-based CO emissions by using them as input in the WRF-CMAQ chemical transport model and comparing simulated TCCO fields to independent Sentinal-5P TROPOMI TCCO observations. The results of this validation show FREM CO emissions to generally be in good agreement with these independent measures – particularly in the case of individual fire-generated CO plumes where modelled in-plume CO was within 5 % of satellite observations with a coefficient of determination of 0.80. Modelled and observed CO, averaged over the full model domain, are within 4 % of each other, though localised regions show an overestimation of modelled CO by up to 50 %. However, when compared to other evaluations of current state-of-the-art fire emissions inventories, the FREM CO emission inventory derived in this work shows some of the best agreement with independent measures. Updates to the previously published FREM TPM emissions coefficients are also provided in the Appendix of this article, along with a satellite and ground-based validation of this FREM TPM emissions inventory. The methodology and resulting CO fire inventory described in this work will form the basis of an upcoming operational LSASAF CO fire emissions product for Africa.

Biomass burning (BB) emits large quantities of greenhouse gases (GHG) and aerosols that impact climate and adversely affect human health. Although much research has focused on quantifying BB emissions on regional to global scales, field measurements of BB emission factors (EFs) are sparse, clustered and indicate high spatio-temporal variability. EFs are generally calculated from ground- or aeroplane measurements with respective potential biases towards smouldering or flaming combustion products. Unmanned aerial systems (UAS) have the potential to measure BB EFs in fresh smoke, targeting different parts of the plume at relatively low cost. We propose a light-weight UAS-based method to measure EFs for carbon monoxide (CO), carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), as well as PM2.5 (TSI Sidepak AM520) and equivalent black carbon (eBC, microAeth AE51) using a combination of a sampling system with Tedlar bags which can be analysed on the ground and airborne aerosol sensors. In this study, we address the main uncertainties associated with this approach (1) the degree to which taking a limited number of samples is representative for the integral smoke plume and including (2) the reliability of the lightweight aerosol sensors. This was done for prescribed burning experiments in the Kruger national park, South Africa where we compared fire-averaged EF from UAS-sampled bags for savanna fires to integrated EFs from co-located mast measurements. Both measurements matched reasonably well with linear R2 ranging from 0.81 to 0.94. Both aerosol sensors are not factory calibrated for BB particles and therefore require additional calibration. In a series of smoke chamber experiments, we compared the lightweight sensors to high-fidelity equipment to empirically determine specific calibration factors (CF) for measuring BB particles. For the PM mass concentration from a TSI Sidepak AM520, we found an optimal CF of 0.27, using a scanning mobility particle sizer and gravimetric reference methods, albeit that the CF varied for different vegetation fuel types. Measurements of eBC from the Aethlabs AE51 aethalometer agreed well with the multi-wavelength aethalometer (AE33) (linear R2 of 0.95 at λ = 880 nm) and the wavelength corrected Multi-Angle Absorption Photometer (MAAP, R2 0.83 measuring at λ = 637 nm). However, the high variability in observed BB mass absorption cross-section (MAC) values (5.2 ± 5.1 m2 g-1) suggested re-calibration may be required for individual fires. Overall, our results indicate that the proposed UAS setup can obtain representative BB EFs for individual savanna fires if proper correction factors are applied and operating limitations are well understood.

Biomass burning (BB) emits large quantities of greenhouse gases (GHG) and aerosols that impact climate and adversely affect human health. Although much research has focused on quantifying BB emissions on regional to global scales, field measurements of BB emission factors (EFs) are sparse, clustered and indicate high spatio-temporal variability. EFs are generally calculated from ground- or aeroplane measurements with respective potential biases towards smouldering or flaming combustion products. Unmanned aerial systems (UAS) have the potential to measure BB EFs in fresh smoke, targeting different parts of the plume at relatively low cost. We propose a light-weight UAS-based method to measure EFs for carbon monoxide (CO), carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), as well as PM2.5 (TSI Sidepak AM520) and equivalent black carbon (eBC, microAeth AE51) using a combination of a sampling system with Tedlar bags which can be analysed on the ground and airborne aerosol sensors. In this study, we address the main uncertainties associated with this approach (1) the degree to which taking a limited number of samples is representative for the integral smoke plume and including (2) the reliability of the lightweight aerosol sensors. This was done for prescribed burning experiments in the Kruger national park, South Africa where we compared fire-averaged EF from UAS-sampled bags for savanna fires to integrated EFs from co-located mast measurements. Both measurements matched reasonably well with linear R2 ranging from 0.81 to 0.94. Both aerosol sensors are not factory calibrated for BB particles and therefore require additional calibration. In a series of smoke chamber experiments, we compared the lightweight sensors to high-fidelity equipment to empirically determine specific calibration factors (CF) for measuring BB particles. For the PM mass concentration from a TSI Sidepak AM520, we found an optimal CF of 0.27, using a scanning mobility particle sizer and gravimetric reference methods, albeit that the CF varied for different vegetation fuel types. Measurements of eBC from the Aethlabs AE51 aethalometer agreed well with the multi-wavelength aethalometer (AE33) (linear R2 of 0.95 at λ = 880 nm) and the wavelength corrected Multi-Angle Absorption Photometer (MAAP, R2 0.83 measuring at λ = 637 nm). However, the high variability in observed BB mass absorption cross-section (MAC) values (5.2 ± 5.1 m2 g-1) suggested re-calibration may be required for individual fires. Overall, our results indicate that the proposed UAS setup can obtain representative BB EFs for individual savanna fires if proper correction factors are applied and operating limitations are well understood.

To pursue the development and validation of coupled fire-atmosphere models, the wildland fire modeling community needs validation data sets with scenarios where fire-induced winds influence fire front behavior, and with high temporal and spatial resolution. Helicopter-borne infrared thermal cameras have the potential to monitor landscape-scale wildland fires at a high resolution during experimental burns. To extract valuable information from those observations, three-step image processing is required: (a) Orthorectification to warp raw images on a fixed coordinate system grid, (b) segmentation to delineate the fire front location out of the orthorectified images, and (c) computation of fire behavior metrics such as the rate of spread from the time-evolving fire front location. This work is dedicated to the first orthorectification step, and presents a series of algorithms that are designed to process handheld helicopter-borne thermal images collected during savannah experimental burns. The novelty in the approach lies on its recursive design, which does not require the presence of fixed ground control points, hence relaxing the constraint on field of view coverage and helping the acquisition of high-frequency observations. For four burns ranging from four to eight hectares, long-wave and mid infra red images were collected at 1 and 3 Hz, respectively, and orthorectified at a high spatial resolution (<1 m) with an absolute accuracy estimated to be lower than 4 m. Subsequent computation of fire radiative power is discussed with comparison to concurrent space-borne measurements.