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Ultrafine particles (UFPs) are distributed highly unevenly in the lower troposphere. Although these UFPs are positively detectable and have been studied for more than a century, their three-dimensional distribution, formation, and budget in the atmosphere remain largely uncertain, despite their obvious climate relevance. This is due to their short lifetime and the fact that they are invisible to the human eye and to remote sensing techniques. From the moment of their emission or generation, their spatial distribution is a result of meteorological processes, regional-scale transport, local thermal convection, and rapid loss by interaction with clouds as cloud condensation nuclei. Here, we report about three-dimensional airborne in situ studies aimed at investigating UFP sources, distribution, and behavior on different spatial and temporal scales. We identified fossil fuel–burning power stations, refineries, and smelters as major anthropogenic UFP sources. On a regional scale, their emissions are significantly higher than urban emissions. Particle emissions from such power stations are released typically at altitudes between 200 and 300 m AGL. Detailed in situ measurements of particle concentration and related parameters, together with meteorological measurements and analyses, enable reliable source attribution even over several hundred kilometers downwind from the emitter. Comprehensive meteorological analysis is required to understand the highly variable 3D concentration patterns generated by advective transport and thermal convection. Knowledge of primary emission strength, together with size distributions and atmospheric 3D transport of UFPs derived from airborne measurements, makes it possible to estimate the aerosols' impact on meteorology, hydrological cycles, and climate. [ABSTRACT FROM AUTHOR]

A novel airborne approach using the latest technology in concentration measurements of methane (CH4) and ammonia (NH3), with quantum cascade laser gas analysers (QCLAs) and high-resolution wind, turbulence and other atmospheric parameters integrated into a low-and slow-flying modern airborne platform, was tested at a 17 000 head feedlot near Charlton, Victoria, Australia, in early 2015. Aircraft flights on 7 days aimed to define the lateral and vertical dimensions of the gas plume above and downwind of the feedlot and the gas concentrations within the plume, allowing emission rates of the target gases to be calculated. The airborne methodology, in the first instance, allowed the emissions to be qualitatively apportioned to individual rows of cattle pens, effluent ponds and manure piles. During each flight, independent measurements of emissions were conducted by ground-based inverse-dispersion and eddy covariance techniques, simultaneously. The aircraft measurements showed good agreement with earlier studies using more traditional approaches and the concurrent ground-based measurements. It is envisaged to use the aircraft technology for determining emissions from large-scale open grazing farms with low cattle densities. Our results suggested that this technique is able to quantify emissions from various sources within a feedlot (pens, manure piles and ponds), as well as the whole feedlot. Furthermore, the airborne technique enables tracing emissions for considerable distances downwind. In the current case, it was possible to detect elevated CH4 to at least 25 km and NH3 at least 7 km downwind of the feedlot. [ABSTRACT FROM AUTHOR]

Coal seam gas (CSG) accounts for about one-quarter of natural gas production in Australia and rapidly increasing amounts globally. This is the first study worldwide using airborne measurement techniques to quantify methane (CH4) emissions from a producing CSG field: the Surat Basin, Queensland, Australia. Spatially resolved CH4 emissions were quantified from all major sources based on top-down (TD) and bottom-up (BU) approaches, the latter using Australia's UNFCCC reporting workflow. Based on our TD-validated BU inventory, CSG sources emit about 0.4% of the produced gas, comparable to onshore dry gas fields in the USA and The Netherlands, but substantially smaller than in other onshore regions, especially those where oil is co-produced (wet gas). The CSG CH4 emission per unit of gas production determined in this study is two to three times higher than existing inventories for the region. Our results indicate that the BU emission factors for feedlots and grazing cattle need review, possibly requiring an increase for Queensland's conditions. In some subregions, the BU estimate for gathering and boosting stations is potentially too high. The results from our iterative BU inventory process, which feeds into TD data, illustrate how global characterization of CH4 emissions could be improved by incorporating empirical TD verification surveys into national reporting. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'. [ABSTRACT FROM AUTHOR]

The Surat Basin, Queensland, Australia, is a hot-spot of methane emissions in Australia. The different sources and the overall project will be presented in a separate overview (Kelly et al., 2019).This presentation will discuss the methods applied for the flight planning in order to achieve a quasi-Lagrangian observation of the air mass in which the various sources are emitting methane and carbon dioxide. Over this dry area in sunny Queensland in springtime, the emissions were quickly mixed into a rather deep convective boundary layer. Although this is an advantage for regional mass balances (as long as the boundary layer growth is known), it is a challenge for source attribution. Furthermore, as already discussed in Hiller et al. (2014), the data is suggesting, that dry deposition and photochemical reactions during the transport cannot be neglected. In Yacovitch et al. (2018) we have also shown that the uncertainty of a regional emission estimate is large when the conditions are not ideal. All of these experiences allowed to improve the methods and flight strategies now applied in the Surat Basin.In contrast to point measurements and mobile surveys on the ground, airborne measurements are covering a wide range of scales, i.e. from 5 m for the fast CO2 measurements (50 m for CH4) up to 100 km. The collected data along the tracks flown are therefore both useful for the identification of individual sources (as discussed in detail in Hacker et al., 2016), and for the regional mass balance. The vertical extend of the mixing was documented by ascents to the top of the actual convection, and the evolution of this mixing was also reproduced by a convection model.The high-resolution measurements allow to include turbulent vertical fluxes in the overall budget which will be an important aspect of our discussion.In summary, the data set and the diverse methods applied should allow to provide an overall emission estimate for the Surat Basin between Dalby and Miles with acceptable error bars. The 88 bag samples taken during the flights will support the source attributions as discussed by Kelly et al. (2019)ReferencesHacker, J.M., D. Chen, M. Bai, C. Ewenz, W. Junkermann, W. Lieff, B. McManus, B. Neininger, J. Sun, T. Coates, T. Denmead, T. Flesch, S. McGinn and J. Hill, 2016: Using airborne technology to quantify and apportion emissions of CH4 and NH3 from feedlots. Animal Production Science, 2016, 56, 190-203.Hiller R.V., B. Neininger, D. Brunner, C. Gerbig, D. Bretscher, T. Künzle, N. Buchmann, W. Eugster, 2014: Aircraft based CH4 flux estimates for validation of emissions from an agriculturally dominated area in Switzerland. Journal of Geophysical Research: Atmospheres 03/2014; DOI:10.1002/2013JD020918.Kelly et al.: Direct Measurement of Coal Seam Gas and Agricultural Methane Emissions in the Surat Basin, Australia. Submitted for EGU 2019.Yacovitch T.I., B. Neininger, S.C. Herndon, H.D. van der Gon, S. Jonkers, J. Hulskotte, J.R. Roscioli, D. Zavala-Araiza: Methane Emissions in the Netherlands, 2018: The Groningen Field. Elem Sci Anth, 6: 57. DOI: https://doi.org/10.1525/elementa.308. [ABSTRACT FROM AUTHOR]

Ultrafine particles (UFP) in the atmosphere may have significant impacts on the regional water and radiation budgets through secondary effects on cloud microphysics. Yet, as these particles are invisible for current remote sensing techniques, knowledge about their three-dimensional distribution, source strengths and budgets is limited. Building on a 40-yr-old Australia-wide airborne survey which provides a reference case study of aerosol sources and budgets, this study presents results from a new airborne survey over Eastern Australia, northern New South Wales and Queensland. Observations identified apparent changes in the number and distribution of major anthropogenic aerosol sources since the early 1970s, which might relate to the simultaneously observed changes in rainfall patterns over eastern Queensland. Coal-fired power stations in the inland areas between Brisbane and Rockhampton were clearly identified as the major sources for ultrafine particulate matter. Sugar mills, smelters and shipping along the coast close to the Ports of Townsville and Rockhampton were comparable minor sources. Airborne Lagrangian plume studies were applied to investigate source strength and ageing properties within power station plumes. Significant changes observed, compared to the measurements in the 1970s, included a significant increase in the number concentration of UFP related to coal-fired power station emissions in the sparsely populated Queensland hinterland coincident with the area with the most pronounced reduction in rainfall. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed. [ABSTRACT FROM AUTHOR]

Abstract: City lights present one of humankind's most unique footprints on Earth as seen from space. Resulting light pollution from artificial lights obscures the night sky for astronomy and has negative impacts on biodiversity as well as on human health. However, remote sensing studies of night lights to date have been mostly limited to coarse spatial resolution sensors such as the DMSP-OLS. Here we present a new source for high spatial resolution mapping of night lights from space, derived from a commercial satellite. We tasked the Israeli EROS-B satellite to acquire two night-time light images (at a spatial resolution of 1m) of Brisbane, Australia, and analyzed their radiometric quality and content with respect to land cover and land use. The spatial distribution of night lights as imaged by EROS-B corresponded with night-time images acquired by an airborne camera, although EROS-B was not as sensitive to low light levels. Using land cover and land use data at the statistical local area level, we could statistically explain 89% of the variability in night-time lights. Arterial roads and commercial and service areas were found to be some of the brightest land use types. Overall, we found that EROS-B imagery provides fine spatial resolution images of night lights, opening new avenues for studying light pollution in cities worldwide. [Copyright &y& Elsevier]

Abstract: Savannas comprise a large proportion of the terrestrial land surface and are highly varied in their composition, structure and function. These characteristics alter land–atmosphere exchanges of heat, water, carbon dioxide and other trace gases, which feed back to the climate at multiple scales. Australian savannas provide significant ecosystem services and further systematic scientific study is needed to sustainably manage these ecosystems. We undertook an interdisciplinary research effort to understand the patterns and processes of carbon, water and energy cycles across northern Australian landscapes across scales from point to region. We quantified the land surface–atmosphere exchanges across the vast region of Australian savannas using a hierarchical, integrated measurement and modelling approach to determine regional greenhouse gas and water budgets. The research team comprised groups from seven institutions and four countries. The research effort comprised of a multi- year measurement and modelling endeavour and culminated in an intensive field program held in September 2008 (late dry season). The program aimed to improve our knowledge of the spatial and temporal variability of land–atmosphere exchanges and the processes driving them as well as our ability to remotely sense them and simulate them using land surface models, which are crucial components of Global Climate Models. We attempted to robustly integrate small scale processes, such as leaf photosynthesis to larger scales applicable to modelling and remote sensing for the savanna region. In this paper we provide the scientific background and our overall approach to the program with reference to the papers in this “Savanna Patterns of Energy and Carbon Integrated Across the Landscape” (SPECIAL) issue. [Copyright &y& Elsevier]

High-resolution measurements obtained from NOAA “best” atmospheric turbulence (BAT) probes mounted on an EGRETT high-altitude research aircraft were used to characterize turbulence in the upper troposphere and lower stratosphere at scales from 2 m to 20 km, focusing on three-dimensional behavior in the sub-kilometer-scale range. Data were analyzed for 129 separate level flight segments representing 41 h of flight time and 12 600 km of wind-relative flight distances. The majority of flights occurred near the tropopause layer of the winter subtropical jet stream in the Southern Hemisphere. Second-order structure functions for velocity and temperature were analyzed for the separate level-flight segments, individually and in various ensembles. A 3D scaling range was observed at scales less than about 100 m, with power-law exponents for the structure functions of the velocity component in the flight direction varying mostly between 0.4 and 0.75 for the separate flight segments, but close to 2/3 for the ensemble-averaged curves for all levels and for various subensembles. Structure functions in the 3D scaling range were decoupled from those at scales greater than 10 km, with the large-scale structure functions showing less variation than those at smaller scales. Weakly anisotropic behavior was observed in the 3D range, with structure parameters for the lateral and vertical velocities on the same order as those in the flight direction but deviating from the expected isotropic value. Anisotropy was correlated with turbulence intensity, with greater anisotropy associated with weaker turbulence. [ABSTRACT FROM AUTHOR]

Cliff–ramp patterns (CR) are a common feature of scalar turbulence, characterized by a sharp temperature increase (cliff) followed by a more gradual temperature decrease (ramp). Aircraft measurements obtained from NOAA best aircraft turbulence probes (BAT) were used to characterize and compare CR patterns observed under stably stratified conditions in the upper troposphere, a region for which there are few such studies. Experimental data were analyzed for three locations, one over Wales and two over southern Australia, the latter in correspondence with the Southern Hemisphere winter subtropical jet stream. Comparison of observed CR patterns with published direct numerical simulations (DNS) revealed that they were likely signatures of Kelvin–Helmholtz (KH) billows, with the ramps associated with the well-mixed billows and the cliffs marking the highly stretched braids. Strong correlation between potential temperature and horizontal velocity supported the KH link, though expected correlations with vertical velocity were not observed. The temperature fronts associated with the cliffs were oriented in a direction approximately normal to the mean wind direction. Locally high values of temperature structure constant near these fronts were associated with steep temperature gradients across the fronts; this may be misleading in the context of electromagnetic propagation, suggesting a false positive indication of high levels of small-scale turbulence that would not correspond to scintillation effects. Billow aspect ratios, braid angles, and length scales were estimated from the data and comparisons with published DNS provided a means for assessing the stage of evolution of the KH billows and the initial Richardson number of the layer. [ABSTRACT FROM AUTHOR]

This paper presents an open-source canopy height profile (CHP) toolkit designed for processing small-footprint full-waveform LiDAR data to obtain the estimates of effective leaf area index (LAIe) and CHPs. The use of the toolkit is presented with a case study of LAIe estimation in discontinuous-canopy fruit plantations. The experiments are carried out in two study areas, namely, orange and almond plantations, with different percentages of canopy cover (48% and 40%, respectively). For comparison, two commonly used discrete-point LAIe estimation methods are also tested. The LiDAR LAIe values are first computed for each of the sites and each method as a whole, providing “apparent” site-level LAIe, which disregards the discontinuity of the plantations' canopies. Since the toolkit allows for the calculation of the study area LAIe at different spatial scales, between-tree-level clumping can be easily accounted for and is then used to illustrate the impact of the discontinuity of canopy cover on LAIe retrieval. The LiDAR LAIe estimates are therefore computed at smaller scales as a mean of LAIe in various grid-cell sizes, providing estimates of “actual” site-level LAIe. Subsequently, the LiDAR LAIe results are compared with theoretical models of “apparent” LAIe versus “actual” LAIe, based on known percent canopy cover in each site. The comparison of those models to LiDAR LAIe derived from the smallest grid-cell sizes against the estimates of LAIe for the whole site has shown that the LAIe estimates obtained from the CHP toolkit provided values that are closest to those of theoretical models. [ABSTRACT FROM AUTHOR]

A Canopy Height Profile (CHP) procedure presented in Harding et al. (2001) for large footprint LiDAR data was tested in a closed canopy environment as a way of extracting vertical foliage profiles from LiDAR raw-waveform. In this study, an adaptation of this method to small-footprint data has been shown, tested and validated in an Australian sparse canopy forest at plot- and site-level. Further, the methodology itself has been enhanced by implementing a dataset-adjusted reflectance ratio calculation according to Armston et al. (2013) in the processing chain, and tested against a fixed ratio of 0.5 estimated for the laser wavelength of 1550 nm. As a by-product of the methodology, effective leaf area index (LAIe) estimates were derived and compared to hemispherical photography values. To assess the influence of LiDAR aggregation area size on the estimates in a sparse canopy environment, LiDAR CHPs and LAIes were generated by aggregating waveforms to plot- and site-level footprints (plot/site-aggregated) as well as in 5 m grids (grid-processed). LiDAR profiles were then compared to field biomass profiles generated based on field tree measurements. The correlation between field and LiDAR profiles was very high, with a mean R 2 of 0.75 at plot-level and 0.86 at site-level for 55 plots and the corresponding 11 sites. Gridding had almost no impact on the correlation between LiDAR and field profiles (only marginally improvement), nor did the dataset-adjusted reflectance ratio. However, gridding and the dataset-adjusted reflectance ratio were found to improve the correlation between raw-waveform LiDAR and hemispherical photography LAIe estimates, yielding the highest correlations of 0.61 at plot-level and of 0.83 at site-level. This proved the validity of the approach and superiority of dataset-adjusted reflectance ratio of Armston et al. (2013) over a fixed ratio of 0.5 for LAIe estimation, as well as showed the adequacy of small-footprint LiDAR data for LAIe estimation in discontinuous canopy forests. [ABSTRACT FROM AUTHOR]

The spread of an alien invasive grass (gamba grass-Andropogon gayanus) in the tropical savannas of Northern Australia is a major threat to habitat quality and biodiversity in the region, primarily through its influence on fire intensity. Effective control and eradication of this invader requires better insight into its spatial distribution and rate of spread to inform management actions. We used full-waveform airborne LiDAR to map areas of known A. gayanus invasion in the Batchelor region of the Northern Territory, Australia. Our stratified sampling campaign included wooded savanna areas with differing degrees of A. gayanus invasion and adjacent areas of native grass and woody tree mixtures. We used height and spatial contiguity based metrics to classify returns from A. gayanus and developed spatial representations of A. gayanus occurrence (1 m resolution) and canopy cover (10 m resolution). The cover classification proved robust against two independent field-based investigations at 500 m2 (R2 = 0.87, RMSE = 12.53) and 100 m2 (R2 = 0.79, RMSE = 14.13) scale. Our mapping results provide a solid benchmark for evaluating the rate and pattern of A. gayanus spread from future LiDAR campaigns. In addition, this high-resolution mapping can be used to inform satellite image analysis for the evaluation of A. gayanus invasion over broader regional scales. Our research highlights the huge potential that airborne LiDAR holds for facilitating the monitoring and management of savanna habitat condition. [ABSTRACT FROM AUTHOR]

Abstract: Using the airborne Polarimetric L-band Imaging Synthetic aperture radar (PLIS) the impact of high revisit cycle and full polarimetric acquisitions on biomass retrieval was investigated by means of backscatter-based multi-temporal methods. Parametric and non-parametric models were used to relate reference biomass levels obtained from field plot measurements and high point density lidar data to backscatter intensities or polarimetric target decomposition components. Single-date retrieval using multiple independent variables provided lower estimation errors when compared to models using one independent variable with errors decreasing by 2% to 15%. The multi-temporal aggregation of daily biomass estimates did not improve the overall retrieval accuracy but provided more reliable estimates with respect to single-date methods. Backscatter intensities improved estimation accuracies up to 10% compared to polarimetric target decomposition components. Using all four polarizations increased the estimation accuracy marginally (2%) when compared to a dual-polarized system. The biomass estimation error was considerably reduced (up to 30%) only by decreasing the spatial resolution and was related to decreasing forest variability with increasing pixel size. These results indicate that, at least in semi-arid areas, future L-band missions would not significantly improve biomass estimation accuracy using backscatter-based modeling approaches despite their better spatial resolution, higher revisit cycles and the availability of fully polarimetric information. [Copyright &y& Elsevier]

The Soil Moisture and Ocean Salinity (SMOS) satellite marks the commencement of dedicated global surface soil moisture missions, and the first mission to make passive microwave observations at L-band. On-orbit calibration is an essential part of the instrument calibration strategy, but on-board beam-filling targets are not practical for such large apertures. Therefore, areas to serve as vicarious calibration targets need to be identified. Such sites can only be identified through field experiments including both in situ and airborne measurements. For this purpose, two field experiments were performed in central Australia. Three areas are studied as follows: 1) Lake Eyre, a typically dry salt lake; 2) Wirrangula Hill, with sparse vegetation and a dense cover of surface rock; and 3) Simpson Desert, characterized by dry sand dunes. Of those sites, only Wirrangula Hill and the Simpson Desert are found to be potentially suitable targets, as they have a spatial variation in brightness temperatures of under normal conditions. However, some limitations are observed for the Simpson Desert, where a bias of 15 K in vertical and 20 K in horizontal polarization exists between model predictions and observations, suggesting a lack of understanding of the underlying physics in this environment. Subsequent comparison with model predictions indicates a SMOS bias of 5 K in vertical and 11 K in horizontal polarization, and an unbiased root mean square difference of 10 K in both polarizations for Wirrangula Hill. Most importantly, the SMOS observations show that the brightness temperature evolution is dominated by regular seasonal patterns and that precipitation events have only little impact. [ABSTRACT FROM PUBLISHER]

Abstract: Soil Surface Roughness (SR) provides a representation of surface variability which can be an important factor in a range of modelling applications such as surface water flow and sediment/nutrient transport. Moreover, it is a crucial parameter for interpreting backscatter characteristics of Synthetic Aperture Radar (SAR) for agricultural application such as near-surface soil moisture retrieval. SR is typically estimated using manual profiles of height variation along short transects (1 to 3m). However, this approach can be very time consuming and often only a small number of transects can be measured in this way, which may not be adequate to characterize the spatial variability of SR across the landscape. This study investigated the feasibility of utilising airborne Light Detection and Ranging (LiDAR) observations as an alternative for mapping SR attributes across an agricultural environment in New South Wales, Australia. To that end, SR attributes were extracted from airborne LiDAR observations and compared with those extracted from an extensive ground survey of SR making use of manual pin-profilers. Results show that LiDAR-estimates of soil profile surface heights Root Mean Square (RMS) are both accurate (compared to manual profiles) and precise (repeatable stable estimates) for fields presenting bare or fallow conditions and either presenting no row structure or as long as the orientation of the LiDAR scan line is perpendicular to the row structure. In such cases results indicated a strong correlation between LiDAR-estimated and ground-measured RMS estimates (R2 >0.68, p<0.05), with an RMSE better than 0.81cm and bias smaller than 0.48cm from a 400m flight altitude. Moreover, estimates produced from repeat pass LiDAR datasets were consistent and highly correlated (R2 0.98) suggesting that the approach is precise and robust, provided that key tillage parameters (i.e. presence of vegetative material and row direction) can be pre-classified. LiDAR estimates of surface height RMS were shown to be accurate enough to allow the tracking of temporal changes in surface roughness due to farming activities. In contrast, LiDAR-derived surface Correlation Length (CL) estimates were not found to be a reliable proxy of the ground-measured CL. [Copyright &y& Elsevier]

Abstract: Full-waveform laser scanning data acquired with a Riegl LMS-Q560 instrument were used to classify an orange orchard into orange trees, grass and ground using waveform parameters alone. Gaussian decomposition was performed on this data capture from the National Airborne Field Experiment in November 2006 using a custom peak-detection procedure and a trust-region-reflective algorithm for fitting Gauss functions. Calibration was carried out using waveforms returned from a road surface, and the backscattering coefficient γ was derived for every waveform peak. The processed data were then analysed according to the number of returns detected within each waveform and classified into three classes based on pulse width and γ. For single-peak waveforms the scatterplot of γ versus pulse width was used to distinguish between ground, grass and orange trees. In the case of multiple returns, the relationship between first (or first plus middle) and last return γ values was used to separate ground from other targets. Refinement of this classification, and further sub-classification into grass and orange trees was performed using the γ versus pulse width scatterplots of last returns. In all cases the separation was carried out using a decision tree with empirical relationships between the waveform parameters. Ground points were successfully separated from orange tree points. The most difficult class to separate and verify was grass, but those points in general corresponded well with the grass areas identified in the aerial photography. The overall accuracy reached 91%, using photography and relative elevation as ground truth. The overall accuracy for two classes, orange tree and combined class of grass and ground, yielded 95%. Finally, the backscattering coefficient γ of single-peak waveforms was also used to derive reflectance values of the three classes. The reflectance of the orange tree class (0.31) and ground class (0.60) are consistent with published values at the wavelength of the Riegl scanner (1550nm). The grass class reflectance (0.46) falls in between the other two classes as might be expected, as this class has a mixture of the contributions of both vegetation and ground reflectance properties. [Copyright &y& Elsevier]

Abstract: Savanna landscapes across north Australia are characterised by limited topographic variation, and in the Northern Territory, by a relatively constant decline in rainfall with distance inland. The North Australian Tropical Transect (NATT) traverses this 1000km gradient of largely intact vegetation which provides an ideal ‘living laboratory’ and framework to investigate the influence of vegetation structural and floristic change and climate drivers on land–atmosphere exchange at a regional scale. We conducted a multidisciplinary program examining carbon, water and energy fluxes as a function of climate and vegetation change along a sub-continental environmental gradient. Initial findings are reported in this Special Issue. During the program, an intensive field campaign was undertaken during the dry season to characterise vegetation and soil properties of eight flux tower sites used to describe spatial and temporal dynamics of fluxes across this gradient. This paper provides an overview of the savanna landscapes of north Australia detailing vegetation structural and physiological change along this gradient. Above-ground woody biomass, stem density, overstorey LAI and canopy height declined along sites that spanned an 1100mm annual rainfall gradient. Biomass ranged from 35 to 5t C ha−1 with dry season LAI ranging from ∼1 to 0.05 across savanna sites both intact and cleared for grazing. Across open-forest and woodland savanna, basal area ranged from 9.7 to 5.3m2 ha−1. While structural change was significant and correlated with rainfall, leaf scale physiological properties (maximal photosynthesis, V cmax, c i /c a , light use efficiency) of the dominant woody species showed little variation, despite the significant environmental gradient. It is likely that changes in structural properties dominate spatial patterns of flux as opposed to physiological plasticity or species differences along this gradient. [Copyright &y& Elsevier]

The National Airborne Field Experiment 2005 (NAFE'05) and the Campaign for validating the Operation of Soil Moisture and Ocean Salinity (CoSMOS) were undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. The objective of the joint campaign was to provide simulated Soil Moisture and Ocean Salinity (SMOS) observations using airborne L-band radiometers supported by soil moisture and other relevant ground data for the following: 1) the development of SMOS soil moisture retrieval algorithms; 2) developing approaches for downscaling the low-resolution data from SMOS; and 3) testing its assimilation into land surface models for root zone soil moisture retrieval. This paper describes the NAFE'05 and CoSMOS airborne data sets together with the ground data collected in support of both aircraft campaigns. The airborne L-band acquisitions included 40 km x 40 km coverage flights at 500-rn and 1-km resolution for the simulation of a SMOS pixel, multiresolution flights with ground resolution ranging from 1 km to 62.5 m, multiangle observations, and specific flights that targeted the vegetation dew and sun glint effect on L-band soil moisture retrieval. The L-band data were accompanied by airborne thermal infrared and optical measurements. The ground data consisted of continuous soil moisture profile measurements at 18 monitoring sites throughout the 40 km x 40 km study area and extensive spatial near-surface soil moisture measurements concurrent with airborne monitoring. Additionally, data were collected on rock coverage and temperature, surface roughness, skin and soil temperatures, dew amount, and vegetation water content and biomass. These data are available at www.nafe.unimelb. edu.au. [ABSTRACT FROM AUTHOR]

Over the last ten years, some operational airborne remote sensing systems have become available for mapping surface salinity over large areas in near real time. A new dual-polarized Polarimetric L-band Multibeam Radiometer (PLMR) has been developed to improve accuracy and precision when compared with previous instrument generations. This paper reports on the first field evaluation of the performance of the PLMR by measuring salinity gradients in the central Great Barrier Reef. Before calibration, the raw salinity values of the PLMR and conductivity-temperature-.depth (CTD) differed by 3–6 psu. The calibration, which uses in situ salinity data to remove long-term drifts in the PLMR as well as environmental effects such as surface roughness and radiation from the sky and atmosphere, was carried out by equating the means of the PLMR and CTD salinity data over a subsection of the transect, after which 85% of the salinity values between the PLMR and CTD are within 0.1 psu along the complete transect. From offshore to inshore across the shelf, the PLMR shows an average cross-shelf salinity increase of about 0.4 psu and a decrease of 2 psu over the inshore 20 km at - 19° S (around Townsville) and - 18° S (around Lucinda), respectively. The average cross-shelf salinity increase was 0.3 psu for the offshore 100 km over all transects. These results are consistent with the in situ CTD results. This survey shows that PLMR provided an effective method of rapidly measuring the surface salinity in near real time when a calibration could be made. [ABSTRACT FROM AUTHOR]

In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits. [ABSTRACT FROM AUTHOR]