Stephen Parkes

Real-time field measurements of stable isotopes in water and CO2 by Fourier transform infrared spectrometry

Continuous records of isotope behaviour in the environment are invaluable to understanding mass and energy fluxes. Although techniques such as isotope ratio mass spectrometry provide high precision data, they are not well suited to the analysis of a large number of samples and are currently restricted to use in the laboratory. Fourier transform infrared spectrometers are relatively cheap and sufficiently portable and robust to be taken into the field to collect continuous records of gas-phase isotope behaviour. Several examples of the application of this technique will be presented. One data set provides half-hourly determinations of vertical profiles of D/H in water vapour above agricultural fields over a 3-week period; the same infrared spectra can also be used to determine 13C/12C in CO2. The technique has also been applied to the study of CO2 in ambient air and in a limestone cave system. Some of the features and complications associated with the method will also be considered.

Methane, nitrous oxide and ammonia emissions from pigs housed on litter and from stockpiling of spent litter

Mitigation of agricultural greenhouse gas emissions is a target area for the Australian Government and the pork industry. The present study measured methane (CH4), nitrous oxide (N2O) and ammonia (NH3) from a deep-litter piggery and litter stockpile over two trials in southern New South Wales, to compare emissions from housing pigs on deep litter with those of pigs from conventional housing with uncovered anaerobic effluent-treatment ponds. Emissions were measured using open-path Fourier transform infrared spectrometry, in conjunction with a backward Lagrangian stochastic model. Manure excretion was determined by mass balance and emission factors (EFs) were developed to report emissions relative to volatile solids and nitrogen (N) input. Nitrous oxide emissions per animal unit (1 AU = 500 kg liveweight) from deep-litter sheds were negligible in winter, and 8.4 g/AU.day in summer. Ammonia emissions were 39.1 in winter and 52.2 g/AU.day in summer, while CH4 emissions were 16.1 and 21.6 g/AU.day in winter and summer respectively. Emission factors averaged from summer and winter emissions showed a CH4 conversion factor of 3.6%, an NH3-N EF of 10% and a N2O-N EF of 0.01 kg N2O-N/kg N excreted. For the litter stockpile, the simple average of summer and winter showed an EF for NH3-N of 14%, and a N2O-N EF of 0.02 kg N2O-N/kg-N of spent litter added to the stockpile. We observed a 66% and 80% decrease in emissions from the manure excreted in litter-based housing with litter stockpiling or without litter stockpiling, compared with conventional housing with an uncovered anaerobic effluent-treatment pond. This provides a sound basis for mitigation strategies that utilise litter-based housing as an alternative to conventional housing with uncovered anaerobic effluent-treatment ponds.

Use of unmanned aerial vehicles for efficient beach litter monitoring

A global beach litter assessment is challenged by use of low-efficiency methodologies and incomparable protocols that impede data integration and acquisition at a national scale. The implementation of an objective, reproducible and efficient approach is therefore required. Here we show the application of a remote sensing based methodology using a test beach located on the Saudi Arabian Red Sea coastline. Litter was recorded via image acquisition from an Unmanned Aerial Vehicle, while an automatic processing of the high volume of imagery was developed through machine learning, employed for debris detection and classification in three categories. Application of the method resulted in an almost 40 times faster beach coverage when compared to a standard visual-census approach. While the machine learning tool faced some challenges in correctly detecting objects of interest, first classification results are promising and motivate efforts to further develop the technique and implement it at much larger scales.

Capturing the Diurnal Cycle of Land Surface Temperature Using an Unmanned Aerial Vehicle

Characterizing the land surface temperature (LST) and its diurnal cycle is important in understanding a range of surface properties, including soil moisture status, evaporative response, vegetation stress and ground heat flux. While remote-sensing platforms present a number of options to retrieve this variable, there are inevitable compromises between the resolvable spatial and temporal resolution. For instance, the spatial resolution of geostationary satellites, which can provide sub-hourly LST, is often too coarse (3 km) for many applications. On the other hand, higher-resolution polar orbiting satellites are generally infrequent in time, with return intervals on the order of weeks, limiting their capacity to capture surface dynamics. With recent developments in the application of unmanned aerial vehicles (UAVs), there is now the opportunity to collect LST measurements on demand and at ultra-high spatial resolution. Here, we detail the collection and analysis of a UAV-based LST dataset, with the purpose of examining the diurnal surface temperature response: something that has not been possible from traditional satellite platforms at these scales. Two separate campaigns were conducted over a bare desert surface in combination with either Rhodes grass or a recently harvested maize field. In both cases, thermal imagery was collected between 0800 and 1700 local solar time. The UAV-based diurnal cycle was consistent with ground-based measurements, with a mean correlation coefficient and root mean square error (RMSE) of 0.99 and 0.68 ◦C, respectively. LST retrieved over the grass surface presented the best results, with an RMSE of 0.45 ◦C compared to 0.67 ◦C for the single desert site and 1.28 ◦C for the recently harvested maize surface. Even considering the orders of magnitude difference in scale, an exploratory analysis comparing retrievals of the UAV-based diurnal cycle with METEOSAT geostationary data yielded pleasing results (R = 0.98; RMSE = 1.23 ◦C). Overall, our analysis revealed a diurnal range over the desert and maize surfaces of ~20 ◦C and ~17 ◦C respectively, while the grass showed a reduced amplitude of ~12 ◦C. Considerable heterogeneity was observed over the grass surface at the peak of the diurnal cycle, which was likely indicative of the varying crop water status. To our knowledge, this study presents the first spatially varying analysis of the diurnal LST captured at ultra-high resolution, from any remote platform. Our findings highlight the considerable potential to utilize UAV-based retrievals to enhance investigations across multi-disciplinary studies in agriculture, hydrology and land-atmosphere investigations.

Reproducibility of crop surface maps extracted from Unmanned Aerial Vehicle (UAV) derived digital surface maps

Crop height measured from UAVs fitted with commercially available RGB cameras provide an affordable alternative to retrieve field scale high resolution estimates. The study presents an assessment of between flight reproducibility of Crop Surface Maps (CSM) extracted from Digital Surface Maps (DSM) generated by Structure from Motion (SfM) algorithms. Flights were conducted over a centre pivot irrigation system covered with an alfalfa crop. An important step in calculating the absolute crop height from the UAV derived DSM is determining the height of the underlying terrain. Here we use automatic thresholding techniques applied to RGB vegetation index maps to classify vegetated and soil pixels. From interpolation of classified soil pixels, a terrain map is calculated and subtracted from the DSM. The influence of three different thresholding techniques on CSMs are investigated. Median Alfalfa crop heights determined with the different thresholding methods varied from 18cm for K means thresholding to 13cm for Otsu thresholding methods. Otsu thresholding also gave the smallest range of crop heights and K means thresholding the largest. Reproducibility of median crop heights between flight surveys was 4-6cm for all thresholding techniques. For the flight conducted later in the afternoon shadowing caused soil pixels to be classified as vegetation in key locations around the domain, leading to lower crop height estimates. The range of crop heights was similar for both flights using K means thresholding (35-36cm), local minimum thresholding depended on whether raw or normalised RGB intensities were used to calculate vegetation indices (30-35cm), while Otsu thresholding had a smaller range of heights and varied most between flights (26-30cm). This study showed that crop heights from multiple survey flights are comparable, however, they were dependent on the thresholding method applied to classify soil pixels and the time of day the flight was conducted.