Using multi-spectral UAV imagery to extract tree crop structural properties and assess pruning effects

Unmanned aerial vehicles (UAV) provide an unprecedented capacity to monitor the development and dynamics of tree growth and structure through time. It is generally thought that the pruning of tree crops encourages new growth, has a positive effect on fruiting, makes fruit-picking easier, and may increase yield, as it increases light interception and tree crown surface area. To establish the response of pruning in an orchard of lychee trees, an assessment of changes in tree structure, i.e., tree crown perimeter, width, height, area and Plant Projective Cover (PPC), was undertaken using multi-spectral UAV imagery collected before and after a pruning event. While tree crown perimeter, width and area could be derived directly from the delineated tree crowns, height was estimated from a produced canopy height model and PPC was most accurately predicted based on the NIR band. Pre- and post-pruning results showed significant differences in all measured tree structural parameters, including an average decrease in tree crown perimeter of 1.94 m, tree crown width of 0.57 m, tree crown height of 0.62 m, tree crown area of 3.5 m(2), and PPC of 14.8%. In order to provide guidance on data collection protocols for orchard management, the impact of flying height variations was also examined, offering some insight into the influence of scale and the scalability of this UAV-based approach for larger orchards. The different flying heights (i.e., 30, 50 and 70 m) produced similar measurements of tree crown width and PPC, while tree crown perimeter, area and height measurements decreased with increasing flying height. Overall, these results illustrate that routine collection of multi-spectral UAV imagery can provide a means of assessing pruning effects on changes in tree structure in commercial orchards, and highlight the importance of collecting imagery with consistent flight configurations, as varying flying heights may cause changes to tree structural measurements

Elevated CO2 as a driver of global dryland greening.

While recent findings based on satellite records indicate a positive trend in vegetation greenness over global drylands, the reasons remain elusive. We hypothesize that enhanced levels of atmospheric CO2 play an important role in the observed greening through the CO2 effect on plant water savings and consequent available soil water increases. Meta-analytic techniques were used to compare soil water content under ambient and elevated CO2 treatments across a range of climate regimes, vegetation types, soil textures and land management practices. Based on 1705 field measurements from 21 distinct sites, a consistent and statistically significant increase in the availability of soil water (11%) was observed under elevated CO2 treatments in both drylands and non-drylands, with a statistically stronger response over drylands (17% vs. 9%). Given the inherent water limitation in drylands, it is suggested that the additional soil water availability is a likely driver of observed increases in vegetation greenness

Using Unmanned Aerial Vehicles to assess the rehabilitation performance of open cut coal mines

Mine sites are routinely required to rehabilitate their post-mining landforms with a safe, stable and sustainable land-cover. To assess these post-mining landforms, traditional on-ground field monitoring is generally undertaken. However, these labour intensive and time-consuming measurements are generally insufficient to catalogue land rehabilitation efforts across the large scales typical of mining sites (>100 ha). As an alternative, information derived from Unmanned Aerial Vehicles (UAV) can be used to map rehabilitation success and provide evidence of achieving rehabilitation site requirements across a range of scales. UAV based sensors have the capacity to collect information on rehabilitation sites with extensive spatial coverage in a repeatable, flexible and cost-effective manner. Here, we present an approach to automatically map indicators of safety, stability and sustainability of rehabilitation efforts, and demonstrate this framework across three coalmine sites. Using multi-spectral UAV imagery together with geographic object-based image analysis, an empirical classification system is proposed to convert these indicators into a status category based on a number of criteria related to land-cover, landform, erosion, and vegetation structure. For this study, these criteria include: mapping tall trees (Eucalyptus species); vegetation extent; senescent vegetation; extent of bare ground; and steep slopes. Converting these land-cover indicators into appropriate mapping categories on a polygon basis indicated the level of rehabilitation success and how these varied across sites and age of the rehabilitation activity. This work presents a framework and workflow for undertaking a UAV based assessment of safety, stability and sustainability of mine rehabilitation and also provides a set of recommendations for future rehabilitation assessment efforts

Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future

Satellite based remote sensing offers one of the few approaches able to monitor the spatial and temporal development of regional to continental scale droughts. A unique element of remote sensing platforms is their multi-sensor capability, which enhances the capacity for characterizing drought from a variety of perspectives. Such aspects include monitoring drought influences on vegetation and hydrological responses, as well as assessing sectoral impacts (e.g., agriculture). With advances in remote sensing systems along with an increasing range of platforms available for analysis, this contribution provides a timely and systematic review of multi-sensor remote sensing drought studies, with a particular focus on drought related datasets, drought related phenomena and mechanisms, and drought modeling. To explore this topic, we first present a comprehensive summary of large-scale remote sensing datasets that can be used for multi-sensor drought studies. We then review the role of multi-sensor remote sensing for exploring key drought related phenomena and mechanisms, including vegetation responses to drought, land-atmospheric feedbacks during drought, drought-induced tree mortality, drought-related ecosystem fires, post-drought recovery and legacy effects, flash drought, as well as drought trends under climate change. A summary of recent modeling advances towards developing integrated multi-sensor remote sensing drought indices is also provided. We conclude that leveraging multi-sensor remote sensing provides unique benefits for regional to global drought studies, particularly in: 1) revealing the complex drought impact mechanisms on ecosystem components; 2) providing continuous long-term drought related information at large scales; 3) presenting real-time drought information with high spatiotemporal resolution; 4) providing multiple lines of evidence of drought monitoring to improve modeling and prediction robustness; and 5) improving the accuracy of drought monitoring and assessment efforts. We specifically highlight that more mechanism-oriented drought studies that leverage a combination of sensors and techniques (e.g., optical, microwave, hyperspectral, LiDAR, and constellations) across a range of spatiotemporal scales are needed in order to progress and advance our understanding, characterization and description of drought in the future

CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals

Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precisionlevel attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multisatellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications

Partitioning of evapotranspiration using a stable isotope technique in an arid and high temperature agricultural production system

Agricultural production in the hot and arid low desert systems of southern California relies heavily on irrigation. A better understanding of how much and to what extent irrigated water is transpired by crops relative to being lost through evaporation would improve the management of increasingly limited water resources. In this study, we examined the partitioning of evapotranspiration (ET) over a field of forage sorghum (Sorghum bicolor), which was under evaluation as a potential biofuel feedstock, based on isotope measurements of three irrigation cycles at the vegetative stage. This study employed customized transparent chambers coupled with a laser-based isotope analyzer to continuously measure near-surface variations in the stable isotopic composition of evaporation (E, δE), transpiration (T, δT) and ET (δET) to partition the total water flux. Due to the extreme heat and aridity, δE and δT were very similar, which makes this system highly unusual. Contrary to an expectation that the isotopic signatures of T, E, and ET would become increasingly enriched as soils became drier, our results showed an interesting pattern that δE, δT, and δET increased initially as soil water was depleted following irrigation, but decreased with further soil drying in mid to late irrigation cycle. These changes are likely caused by root water transport from deeper to shallower soil layers. Results indicate that about 46% of the irrigated water delivered to the crop was used as transpiration, with 54% lost as direct evaporation. This implies that 28 − 39% of the total source water was used by the crop, considering the typical 60 − 85% efficiency of flood irrigation. The stable isotope technique provided an effective means of determining surface partitioning of irrigation water in this unusually harsh production environment. The results suggest the potential to further minimize unproductive water losses in these production systems

Stable water isotope and surface heat flux simulation using ISOLSM: Evaluation against in-situ measurements

Author's manuscript made available in accordance with the publisher's policy.The stable isotopes of water are useful tracers of water sources and hydrological processes. Stable water isotope-enabled land surface modeling is a relatively new approach for characterizing the hydrological cycle, providing spatial and temporal variability for a number of hydrological processes. At the land surface, the integration of stable water isotopes with other meteorological measurements can assist in constraining surface heat flux estimates and discriminate between evaporation (E) and transpiration (T). However, research in this area has traditionally been limited by a lack of continuous in-situ isotopic observations. Here, the National Centre for Atmospheric Research stable isotope-enabled Land Surface Model (ISOLSM) is used to simulate the water and energy fluxes and stable water isotope variations. The model was run for a period of one month with meteorological data collected from a coastal sub-tropical site near Sydney, Australia. The modeled energy fluxes (latent heat and sensible heat) agreed reasonably well with eddy covariance observations, indicating that ISOLSM has the capacity to reproduce observed flux behavior. Comparison of modeled isotopic compositions of evapotranspiration (ET) against in-situ Fourier Transform Infrared spectroscopy (FTIR) measured bulk water vapor isotopic data (10 m above the ground), however, showed differences in magnitude and temporal patterns. The disparity is due to a small contribution from local ET fluxes to atmospheric boundary layer water vapor (∼1% based on calculations using ideal gas law) relative to that advected from the ocean for this particular site. Using ISOLSM simulation, the ET was partitioned into E and T with 70% being T. We also identified that soil water from different soil layers affected T and E differently based on the simulated soil isotopic patterns, which reflects the internal working of ISOLSM. These results highlighted the capacity of using the isotope-enabled models to discriminate between different hydrological components and add insight into expected hydrological behavior

The effect of warming on grassland evapotranspiration partitioning using laser-based isotope monitoring techniques

Author's manuscript made available in accordance with the publisher's policy.The proportion of transpiration (T) in total evapotranspiration (ET) is an important parameter that provides insight into the degree of biological influence on the hydrological cycles. Studies addressing the effects of climatic warming on the ecosystem total water balance are scarce, and measured warming effects on the T/ET ratio in field experiments have not been seen in the literature. In this study, we quantified T/ET ratios under ambient and warming treatments in a grassland ecosystem using a stable isotope approach. The measurements were made at a long-term grassland warming site in Oklahoma during the May–June peak growing season of 2011. Chamber-based methods were used to estimate the δ2H isotopic composition of evaporation (δE), transpiration (δT) and the aggregated evapotranspiration (δET). A modified commercial conifer leaf chamber was used for δT, a modified commercial soil chamber was used for δE and a custom built chamber was used for δET. The δE, δET and δT were quantified using both the Keeling plot approach and a mass balance method, with the Craig–Gordon model approach also used to calculate δE. Multiple methods demonstrated no significant difference between control and warming plots for both δET and δT. Though the chamber-based estimates and the Craig–Gordon results diverged by about 12‰, all methods showed that δE was more depleted in the warming plots. This decrease in δE indicates that the evaporation flux as a percentage of total water flux necessarily decreased for δET to remain constant, which was confirmed by field observations. The T/ET ratio in the control treatment was 0.65 or 0.77 and the ratio found in the warming treatment was 0.83 or 0.86, based on the chamber method and the Craig–Gordon approach. Sensitivity analysis of the Craig–Gordon model demonstrates that the warming-induced decrease in soil liquid water isotopic composition is the major factor responsible for the observed δE depletion and the temperature dependent equilibrium effects are minor. Multiple lines of evidence indicate that the increased T/ET ratio under warming is caused mainly by reduced evaporation