Proximal sensing in soil profiles

Objective and quantitative soil information is crucial for pedological investigations and to inform diverse decision making processes. New techniques are required so that soil information can be ascertained in a timely manner to support sampling at finer spatial and temporal resolutions. Currently, no single technique can provide information on all of the properties of interest. This research investigated the conjoint use of visible near-infrared diffuse reflectance spectroscopy (VisNIR) and portable X-ray fluorescence spectroscopy (pXRF) for the in situ investigation of soil properties, profile variability and description. Fifteen soil pits across New South Wales, Australia, were selected for their diverse representation of soil properties. Sampling at these sites involved scanning three vertical with sensor readings taken at 2.5 cm intervals to a depth of 1 m within each transect. Soils were described by traditional pit description techniques and horizon based sampling was conducted to characterise the soil in terms of mineral composition, OC, TC, TN, CEC, EC, pH and PSA. A data fusion approach involving model averaging, and a mass balance was implemented to characterise the mineral composition of soils, including phyllosilicates sesquioxides, carbonate, gypsum, quartz and feldspars. Results were validated against X-ray diffraction analysis. To explore the predictive capability of scans taken in situ, existing spectral libraries were used to calibrate VisNIR and pXRF models and identify the best use of proximal sensor data to maximise soil information gain. As not all properties of interest have detectable spectral activity by either VisNIR or pXRF, a spectral soil inference system (SPEC-SINFERS) to augment the number of predicted properties. This system involved the propagation of sensor and model uncertainties through one hundred independent simulations for each calculation, and allowed the integration of both regression models and machine learning techniques

Proximal sensing for soil carbon accounting

Maintaining or increasing soil organic carbon (C) is vital for securing food production and for mitigating greenhouse gas (GHG) emissions, climate change, and land degradation. Some land management practices in cropping, grazing, horticultural, and mixed farming systems can be used to increase organic C in soil, but to assess their effectiveness, we need accurate and cost-efficient methods for measuring and monitoring the change. To determine the stock of organic C in soil, one requires measurements of soil organic C concentration, bulk density, and gravel content, but using conventional laboratory-based analytical methods is expensive. Our aim here is to review the current state of proximal sensing for the development of new soil C accounting methods for emissions reporting and in emissions reduction schemes. We evaluated sensing techniques in terms of their rapidity, cost, accuracy, safety, readiness, and their state of development. The most suitable method for measuring soil organic C concentrations appears to be visible-near-infrared (vis-NIR) spectroscopy and, for bulk density, active gamma-ray attenuation. Sensors for measuring gravel have not been developed, but an interim solution with rapid wet sieving and automated measurement appears useful. Field-deployable, multi-sensor systems are needed for cost-efficient soil C accounting. Proximal sensing can be used for soil organic C accounting, but the methods need to be standardized and procedural guidelines need to be developed to ensure proficient measurement and accurate reporting and verification. These are particularly important if the schemes use financial incentives for landholders to adopt management practices to sequester soil organic C. We list and discuss requirements for developing new soil C accounting methods based on proximal sensing, including requirements for recording, verification, and auditing

Optical devices evaluation for diagnosis of Plasmopara viticola

Remote sensing (RS) is the most widely adopted technique for crop monitoring in precision viticulture (PV).  Recent research looks at the development of proximal sensing technologies alternative to RS.  The present work considers the possible use of proximal sensing optical devices for diagnosis in vineyard; in particular, we evaluated the GreenSeeker RT100 and the Crop Circle (two commercial optical sensors) in detecting different levels of grapevine downy mildew symptoms.  The analysis was conducted on vine leaves that had been picked from plants of cv.  Cabernet Franc infected by Plasmopara viticola.  Leaves were divided into homogeneous infection classes and then analyzed through the optical devices and a portable Vis/NIR (visible/near infrared) spectrophotometer used as tester.  Data showed a linear relation between the percentage of symptomatic leaf area and normalized difference vegetation index NDVI calculated through the two optical sensors (R2 = 0.708 for GreenSeeker; R2 = 0.599 for Crop Circle; R2 = 0.950 for the spectrophotometer).  The regression obtained for GreenSeeker is more significant than the regression obtained for Crop Circle.  This fact suggests a greater capability of GreenSeeker than Crop Circle in detecting different disease levels and its possible use in diagnosis application in vineyard.Keywords: precision viticulture, diagnosis, NDVI, proximal sensing, optical devices 

Proximal hyperspectral imaging detects diurnal and drought-induced changes in maize physiology

Hyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits, which has been transferred from remote to proximal sensing applications, and from manual laboratory setups to automated plant phenotyping platforms. Due to the higher resolution in proximal sensing, illumination variation and plant geometry result in increased non-biological variation in plant spectra that may mask subtle biological differences. Here, a better understanding of spectral measurements for proximal sensing and their application to study drought, developmental and diurnal responses was acquired in a drought case study of maize grown in a greenhouse phenotyping platform with a hyperspectral imaging setup. The use of brightness classification to reduce the illumination-induced non-biological variation is demonstrated, and allowed the detection of diurnal, developmental and early drought-induced changes in maize reflectance and physiology. Diurnal changes in transpiration rate and vapor pressure deficit were significantly correlated with red and red-edge reflectance. Drought-induced changes in effective quantum yield and water potential were accurately predicted using partial least squares regression and the newly developed Water Potential Index 2, respectively. The prediction accuracy of hyperspectral indices and partial least squares regression were similar, as long as a strong relationship between the physiological trait and reflectance was present. This demonstrates that current hyperspectral processing approaches can be used in automated plant phenotyping platforms to monitor physiological traits with a high temporal resolution

Quantifying Forest Ground Flora Biomass Using Proximal Sensing

Current focus on forest conservation and forest sustainability has increased the level of attention given to measures of ground flora in forest ecosystems. Traditionally, such data are collected via time- and resource-intensive methods of field identification, clipping, and weighing. With increased focus on community composition and structure measures of forest ground flora, the manner in which these data are collected must change. This project uses color and color infrared digital cameras to proximally sense forest ground flora and to develop regression models to predict green and dry biomass (g/m^) from the proximally sensed data. Traditional vegetative indices such as the Normalized Difference Vegetative Index (NDVI) and the Average Visible Reflectance Index (AVR) explained 35-45% of the variation in forest ground flora biomass. Adding individual color band variables, especially the red and near infrared bands, to the regression model allowed the model to explain 66% and 58% of the variation in green and dry biomass, respectively, present

Unmanned aerial vehicle and proximal sensing of vegetation indices in olive tree (Olea europaea)

Remote and proximal sensing platforms at the service of precision olive growing are bringing new development possibilities to the sector. A proximal sensing platform is close to the vegetation, while a remote sensing platform, such as unmanned aerial vehicle (UAV), is more distant but has the advantage of rapidity to investigate plots. The study aims to compare multispectral and hyperspectral data acquired with remote and proximal sensing platforms. The comparison between the two sensors aims at understanding the different responses their use can provide on a crop, such as olive trees having a complex canopy. The multispectral data were acquired with a DJI multispectral camera mounted on the UAV Phantom 4. Hyperspectral acquisitions were carried out with a FieldSpec® HandHeld 2™ Spectroradiometer in the canopy portions exposed to South, East, West, and North. The multispectral images were processed with Geographic Information System software to extrapolate spectral information for each cardinal direction’s exposure. The three main Vegetation indices were used: normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), and modified soil adjusted vegetation index (MSAVI). Multispectral data e could describe the total variability of the whole plot differentiating each single plant status. Hyperspectral data were able to describe vegetation conditions more accurately; they appeared to be related to the cardinal exposure. MSAVI, NDVI, and NDRE showed correlation r =0.63**, 0.69**, and 0.74**, respectively, between multispectral and hyperspectral data. South and West exposures showed the best correlations with both platforms

Coastal Eye: Monitoring Coastal Environments Using Lightweight Drones

Monitoring coastal environments is a challenging task. This is because of both the logistical demands involved with in-situ data collection and the dynamic nature of the coastal zone, where multiple processes operate over varying spatial and temporal scales. Remote sensing products derived from spaceborne and airborne platforms have proven highly useful in the monitoring of coastal ecosystems, but often they fail to capture fine scale processes and there remains a lack of cost-effective and flexible methods for coastal monitoring at these scales. Proximal sensing technology such as lightweight drones and kites has greatly improved the ability to capture fine spatial resolution data at user-dictated visit times. These approaches are democratising, allowing researchers and managers to collect data in locations and at defined times themselves. In this thesis I develop our scientific understanding of the application of proximal sensing within coastal environments. The two critical review pieces consolidate disparate information on the application of kites as a proximal sensing platform, and the often overlooked hurdles of conducting drone operations in challenging environments. The empirical work presented then tests the use of this technology in three different coastal environments spanning the land-sea interface. Firstly, I use kite aerial photography and uncertainty-assessed structure-from-motion multi-view stereo (SfM-MVS) processing to track changes in coastal dunes over time. I report that sub-decimetre changes (both erosion and accretion) can be detected with this methodology. Secondly, I used lightweight drones to capture fine spatial resolution optical data of intertidal seagrass meadows. I found that estimations of plant cover were more similar to in-situ measures in sparsely populated than densely populated meadows. Lastly, I developed a novel technique utilising lightweight drones and SfM-MVS to measure benthic structural complexity in tropical coral reefs. I found that structural complexity measures were obtainable from SfM-MVS derived point clouds, but that the technique was influenced by glint type artefacts in the image data. Collectively, this work advances the knowledge of proximal sensing in the coastal zone, identifying both the strengths and weaknesses of its application across several ecosystems.Natural Environment Research Council (NERC