Hyperspectral imaging (HSI) is being widely applied in plant phenotyping platforms. Some new HSI devices such as LeafSpec was introduced recently which can provide a high signal-over-noise ratio along with higher spectral and spatial resolutions. However, most of the previous image processing algorithms only calculated the averaged spectrum from the leaf, but rarely include the spatially distributed information on the leaf level. Meanwhile, different nutrient stresses could result in different color patterns on the leaf which can be used to furtherly improve the quality of plant phenotyping. This study focused on the development of a new methodology that applies spatial distribution analysis on HSI soybean leaf images. Firstly, a novel way of encoding all the leaf pixels to a new coordinate system called Natural Leaf Coordinate System (NLCS) was developed. NLCS defined the coordinates of every pixel relative to the leaf venation so that the following spatial distribution analysis could be conducted more intuitively. Second, a new nitrogen index based on NLCS called NLCS-N was developed and able to outperform the whole leaf averaged NDVI by having a better correlation with the plants' nitrogen contents, and a more significant differentiation between the nitrogen-sufficient versus the nitrogen-deficient plants.

The United Nations Agenda 2030 and Sendai Framework, as well as African Union's Agenda 2063, are targeted at human peace and prosperity amidst environmental and economic sustainability. These frameworks contain goals for the earth's protection and human poverty/disaster risk reduction. The foremost priority of the Sendai Framework for Disaster Risk Reduction is the increased understanding of disaster risk and strengthening its governance and management. Three overarching questions warrant this study: what are the important predictors of disaster risk in the vulnerable continent of Africa? How does disaster risk relate to climate change literacy and people's beliefs in Africa? Do national action plans respond appropriately to key factors reflecting Africa's disaster risk? This study uses the climate change literacy and belief data from the Afrobarometer and disaster risk data from the Index for Risk Management (INFORM) of the European Commission. Using disaster risk index as the dependent variable and 30 independent variables, the important predictors contributing to disaster risk in all African countries were identified using random forest machine learning models. Essential variables in the model include projected conflict risk, current highly violent conflict intensity, uprooted people, other vulnerable groups, governance, physical infrastructure and access to health care, among others. Also, The higher the percentage of African countries' population that is climate literate, the lower the disaster risk. Conversely, the higher the climate change literacy of the population, the higher the percentage of people who believe that people can do little about climate change. Furthermore, 25 policies of countries with very high disaster risk were analysed. Within these selected policies, concepts related to violent conflicts were the least included, while those about vulnerability factors were the most included. Policies explored included more vulnerability concepts and much fewer hazard (violent conflict) concepts indicating the least responsiveness to hazards. The study provides a deeper understanding of disaster risk in Africa by showing essential factors and offers insight into disaster risk governance in line with the Sendai Framework.

Unmanned aerial vehicles (UAVs) provide growers and researchers with an efficient way to evaluate fields at high resolution. Flying UAVs and collecting imagery are made easily approachable through high-performance sensors that measure a wide spectrum of light and free flight software available on smartphones and tablets. In contrast to the efficiency of collecting imagery, extracting data from this imagery presents a major hurdle for researchers and growers. Current data analysis options require either an expensive subscription service or complex coding packages, effectively preventing many from utilizing remote sensing data. These solutions also are designed as a "black-box", where imagery goes in and data comes out, making customization and adaptability to the user's needs a challenge. To address these shortcomings, I developed an open-source analysis pipeline that is both approachable and robust. Starting with an orthomosaic and combining stock tools in the QGIS graphical user interface, this pipeline follows a simple step-by-step process to mask out soil and apply any user-defined index. From there the user can segment plots using a fast yet highly customizable gridding system, allowing for plot segmentation in unusual field layouts or planting regimes. This feature has been previously unsupported in many subscription and open-source programs alike. Plot-level data can then be exported for statistical analyses. Ultimately, this pipeline is aimed to attract more researchers and growers towards using remote sensing data in their research.

Mannat Khanna*1, Shaik Nasif Ahmed2,3, and Nimisha Vedanti 2,3

Recently, a tailored gravity field model was developed to fit local terrestrial gravity data by integrating Global Gravitational Models (GGMs), terrestrial gravity data, and Digital Elevation Models (DEMs). The numerical analysis of the newly developed tailored gravity model showed a substantial improvement by means of its possible application for geophysical exploration by exhibiting known geological features over the Southern Benue Trough of Nigeria. In this study, we apply a similar technique to develop a tailored gravity field model at the Limpopo Province in South Africa using a total of 8,603 terrestrial gravity measurements. Validation of results indicates that our tailored gravity model could reproduce the observed gravity data with the accuracy specified by a standard deviation of 8.9 mGal and with a systematic bias less than 0.1 mGal within the study area. We then inspected a possibility of using our tailored gravity field model to improve the accuracy of existing geoid/quasi geoid models at the study area. For this purpose, we compute a new (quasi)geoid model by applying the Remove-compute-restore numerical technique that treats separately the detailed gravity pattern that is closely correlated spatially with the topographic relief, the higher-to-medium gravity signal that is mostly captured by local/regional gravity data, and the long-wavelength gravity signal that is modelled by using GGMs. The accuracy of the new (quasi)geoid model was assessed by using the most recent South African gravimetric quasi-geoid model CDSM09A and the latest hybrid quasi-geoid model of South AfricaSAGEOID10. The comparison of our quasi-geoid model with the CDSM09A and SAGEOID10 quasi-geoid models was done at 7,225 quasi-geoid grid points. The comparison revealed that our new quasi-geoid model closely agrees with the CDSM09A and SAGEOID10 models. The differences between our and CDSM09A quasi-geoid models vary within-0.31 and 0.70 m, with a mean of 0.05 m and a standard deviation of 0.12 m. The corresponding differences between our and SAGEOID10 quasi-geoid models are between-0.35 and 0.70 m with a mean of 0.06 m and a standard deviation 0.12 m. The numerical analysis revealed that the new tailored gravity model could efficiently be used in various geophysical and geodetic applications.

Session Number and Title: GC41B: Connecting Cause and Effect in Analyses of Coupled Human and Geophysical Systems: The Early to Modern Anthropocene II Online Poster Discussion Abstract ID and Title: 1190904: Sustainability Analysis of the Indian Fashion Design Industry Final Paper Number: GC41B-02 Session Date and Time: Thursday, 15 December 2022; 08:00 - 09:00 VIEW POSTER

* The researcher, Mohamed Akl, is funded by a full scholarship from the Ministry of Higher Education of the Arab Republic of Egypt. Abstract: The Gravity Recovery and Climate Experiment (GRACE) satellite has proven to be an excellent tool for monitoring changes in total water storage (TWS), which vertically integrate water storage changes from the land surface to the deepest aquifers. The objective of many GRACE studies is to isolate groundwater storage changes from changes in TWS using independent in-situ, remotely sensed, simulated, or assimilated data to remove other water budget components. Using auxiliary datasets to account for water budget components have revealed large biases and uncertainties, especially over high latitude regions, leading to accumulating errors in GRACE-GW estimates. Comparisons with in-situ groundwater observations permit assessments to evaluate how accurately we can isolate groundwater storage signals from TWSA. Goodness-of-fit (GOF) indices e.g., spearman correlation, mean square error (MSE), Nash-Sutcliffe Efficiency (NSE), and the Kling-Gupta Efficiency (KGE), are commonly applied hydrologic fit metrics that express similarity of time series. Such metrics are used here to compare GRACE-GW estimations and in-situ groundwater observations. The use of GOF indices is constrained by their substantial sampling uncertainty, and controversial interpretation, which may lead to wrong judgement on GRACE-GW estimations. Bias, nonlinearity, and non-normality introduce challenges in our use and interpretation of GOF applied to GRACE-GW time series. The goal of this work is to improve interpretation and use of GOF metrics to validate GRACE-GW estimates, highlighting the importance of assessing multiple GOF criteria beyond simply correlation often applied in GRACE studies. Our results document that poor performance of GOF metrics do not simply translate to inaccurate extraction of GRACE-GW time series but may be attributed to the GOF metric applied. We show that a rigorous assessment of GOF enhances our ability to interpret GRACE-GW change.

A document by Jon Vandegriff. Click on the document to view its contents.

A document by Mohammad Reza Sadrian. Click on the document to view its contents.

Biomass burning (BB) is one of the largest sources of absorbing aerosols globally and accounts for about 40% of black carbon in the atmosphere. The Southern African region contributes approximately 35% of Earth’s BB aerosol emissions. During the Southern Hemisphere winter, smoke is transported over the southeast Atlantic Ocean, overlying and mixing with a semi-permanent stratocumulus cloud deck. Aerosol-cloud interactions contribute the largest uncertainty to anthropogenic forcing, and the southeast Atlantic region exhibits a large model-to-model divergence of climate forcing. This makes the region particularly valuable for understanding these interactions and was one of the factors motivating the three-year NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) mission. Previous studies using ORACLES datasets have explored the distribution of aerosol and cloud particles, however, changes in some aerosol properties during transport are not well documented. This study investigates the evolution of biomass burning aerosol properties from emission within Southern Africa, transport over land, and then over the Atlantic. Measurements from a collection of airborne in situ and remote-sensing instruments including 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) along with ground-based AERONET (Aerosol Robotic Network) are combined with results from two regional models, the WRF-AAM and WRF-CAM5 to explore the changes in the optical properties of these smoke plumes as they age. The aerosol age is determined using tracers from the WRF-AAM configured with 12 km resolution over the region’s spatial domain (41 ºS – 14 ºN, 34 ºW – 51 ºE). Changes in extinction, single scattering Albedo (SSA) and angstrom exponent (AE) with age as well as a comparative analysis between observations and model results were carried out using datasets from airborne PSAP (Particle Soot Absorption Photometer) and nephelometers, 4STAR, AERONET, and WRF-CAM5. 

Bioluminescence is used as marker e.g., in genetic or plant pathological studies. We developed a method to monitor bioluminescence at the whole plant level combined with phenotypic analysis of the plant. Using a CCD camera mounted in a cabinet shielding all external light we can image weak luminescence emissions from samples and map these to RGB images. Image processing delivers temporal and spatial data on the distribution of the luminescence together with phenotypic features of the plants. With this technology, microbial colonization of plants can be monitored. Arabidopsis plants were inoculated with Pseudomonas and Xanthomonas plant-pathogenic bacteria labelled with gene cassette for autonomous luminescence and disease progression was monitored over time. Luminescence imaging revealed accumulation of the bacteria in different plant tissues while the RGB images served to monitor plant growth and occurrence of disease symptoms. Applying this method, resistant plants could be selected from a mutant population. Disease responses of susceptible plants were compared to the responses of resistant plants. In the case of Pseudomonas, bacterial abundance reached its maximum during two to four days after inoculation, at a time when water soaking of the leaves could be observed as well with the RGB camera. At later stages-five to seven days after inoculation, disease symptoms in terms of leaf yellowing and tissue collapse occurred while bacterial populations appeared to decrease. With this method it was possible to monitor pathogen development and disease progression non-invasively at whole-plant level over time.

High-throughput measurements of photosynthesis of plants grown under various conditions may provide important insights into the plasticity of the photosynthetic performance of plants. Remote sensing of photosynthetic activity [i.e., solar-induced chlorophyll fluorescence (SIF)] and its derivatives are the next generation of remote techniques, enabling high-throughput photosynthesis measurements under field conditions. We hypothesized that by measuring SIF simultaneously with measurements of whole-plant water relations in a standardized controlled drought experiment, we would be able to quantify photosynthetic activity and to detect water stress at an early stage. A functional-phenotyping platform was used to apply the controlled drought treatment and to monitor the growth and water balance of tomato introgression lines (ILs). A new SIF-derived index, electron transport rate (RS-ETRi), was found to be negatively correlated with whole-plant stomatal conductance (Gsc) under non-stressed conditions; whereas a positive correlation was observed between those factors under drought stress. No significant relationships were found between SIF and plant biomass or Gsc. SIF 687 responded to drought earlier than any of the other measured vegetation indices (VIs). SIF parameters could not differentiate between IL lines; whereas differences between ILs were clearly identified by the gravimetric water-relations measurements. We concluded that SIF did not provide any advantage over commonly used methods for detecting physiological differences between the ILs. Overall, although SIF plays a significant role in photosynthesis, the relationship between SIF and photosynthesis is complex and we believe it would be an oversimplification to use SIF to quantify photosynthetic activity.

Animal behaviour such as dispersal and migration ensure their survival in the landscape. It has been established in the past few decades that wildlife conservation and study of their movement in the wilderness is vital for sustainable ecosystem. Thus, identification of regions having high movement permeability for planning and maintenance of functional wildlife corridors has turn out to be a fundamental requirement for habitat management. This study emphases on movement of big cats-Bengal Tiger (Panthera tigris tigris) and Leopard (Panthera pardus fusca) in the protected area of Rajaji National Park situated in Uttarakhand State of India. The National park is a designated tiger reserve with large amount of tigers and leopards at its disposal. Here, Circuitscape was used to generate connectivity map of the study area. The results were validated using occurrence points downloaded from GBIF. The habitat suitability and resistance of the landscape was estimated based on literature review and expert opinion survey. Since, both the species have comparable ecological niche, similar habitat parameters were used for generation of resistance map of the species. Occurrence points for the species were downloaded from GBIF. 60% of the points were used as nodes or focal points where species presence is recorded whereas 40% of the points were used in validation of the connectivity paths. Results depicts the current density map of the study area highlighting areas with high connectivity for the species.

A document by Rachel Lamb. Click on the document to view its contents.

Imperfect models are often used for forecasting and state estimation of complex dynamical systems, typically by mapping a reference initial state into model phase space, making a forecast, and then mapping back to the reference space. In many cases these mappings are implicit, and forecast errors thus reflect a combination of model forecast errors and mapping errors. Techniques to infer parameterizations and parameters to reduce model bias have been the subject of intense scrutiny; however, we lack a general framework for discovering optimal mappings between system and model attractors. Here we propose a novel Machine Learning paradigm for inferring cross-attractor transformations (CATs) that minimize forecast error. CATs are pairs of transformations from the phase space of a reference system to the phase space of a model and vice versa that serve as a bridge between the attractors of a true system and an imperfect model. A computationally efficient analog approximation to tangent linear and adjoint models is developed to enable efficient stochastic gradient descent algorithms to train CAT parameters. Neural networks constructed with a custom analog-adjoint layer permit specification of affine transformations as well as more general nonlinear transformations.

Recently, Zhou (2022) reported temporal change of seismic velocity in the Earth’s outer core based on relative travel time differences of SKS phase between some “doublets”. The study further suggested existence of a possible 2-3% density deficit in the outer core and a localized transient flow with a speed of ~40 km/year. We examine the seismic data of the best-quality “doublet” (event pair 19970503-20180910) reported in the study. We relocate the “doublet” based on a master-event relocation method (Wen , 2006) using the seismic data of the compressional waves that travel outside the outer core, including P or Pdiff , pP or pPdiff , pPn, PP or Pdiff Pdiff , and PcP waves recorded at the global seismographic network. The later event (20180910) is found to be located 14.20 km away, 204.33°NW, of the earlier event (19970503) with a source depth 1.45 km deeper. After correction for the effects of relative source location and origin time, SKS signals exhibit no discernable relative travel time differences between the two events at the frequency band ≥0.2 Hz at all the four most anomalous stations (COLA, INK, ULN, YAK) reported in Zhou (2022). However, SPdKS-SKPdS phases, which start bifurcating from the SKS phases at the distance range of those four reported anomalous stations, exhibit evident changes of waveform and travel time between the events. The “SKS signals” used inZhou (2022), which had a 50-s time window and were filtered from 0.01-0.05 Hz, contain signals of SKS and SPdKS-SKPdS phases. It is the changes of SPdKS-SKPdS phases, not that of SKS phases, that generate the apparent time shift in the low-frequency filtered “SKS signals” reported in Zhou (2022). The SPdKS-SKPdS phases of those reported anomalous stations sample a lowermost mantle region populated with ultra-low velocity zones (ULVZs). The separation of the two events is large and the SPdKS-SKPdS phases would sample ULVZs with slightly different paths between the two events, yielding different waveform and travel time (Wen & Helmberger , 1998). We conclude that there is no observable temporal change of seismic properties in the Earth’s outer core in the seismic data used in Zhou (2022) and the reported relative travel time difference in the “SKS signals” in Zhou(2022) is caused by waveform and relative travel time changes in SPdKS-SKPdS phases due to slightly different sampling paths to the ULVZs at the bottom of the mantle between the events.

Identifying areas of the land surface and surface water features (e.g., rivers, lakes, etc.) likely to contribute groundwater recharge to a public groundwater supply well is typically a first step toward source water protection. Identifying the contributions from these areas is important for assessing contamination sources, developing land use management strategies, and mitigating groundwater risk for drinking water supply. Simple analytical solutions that employ Darcy’s Law are unable to account for surface water boundary conditions within the flow system. Therefore, capture zone delineation is typically performed using three-dimensional, fully distributed numerical models that require considerable numbers of parameters, stratigraphic data, and user expertise. However, advanced analytical solutions exist that can provide approximations to such solutions using few parameters.In this work, the R Shiny web platform is developed to create an open-source application to allow Internet users to visualize potential flow systems near wells in the vicinity of surface water features. Assumptions include homogeneous stratigraphy and aquifer thickness, a steady state flow field, and relatively simple aquifer geometry. The web tool is currently being developed with a focus on Yukon Territory in northern Canada, where most of the population relies on groundwater, but less work has been done on the analysis of well vulnerability and source water protection than in southern Canada. The results are intended for estimation and education purposes and will be compared with numerical model results for some sites with pre-existing investigations. Abstract ID: 1168805

India predominately depends on rainfall for its agricultural water requirements. Effective rainfall (ER) is the amount of water needed for crop cultivation without any external water supply support. Hence, ER calculation is necessary for irrigation management. Besides crop water management, ER can also aid in understanding extreme hydrological analysis. This study aims to find the suitability of employing ER as an alternative for meteorological drought severity identification. In this study, one of the most widely used ER calculation methods, the USDA-SC (Smith) method, was employed to calculate ER from 2011 to 2020 throughout India. The Standard Precipitation Index (SPI-3) at 3 months intervals was used to validate the suitability of ER for assessing the drought. Four test sites were selected that cover different climate conditions of India to validate the using ER in alternative to drought. This study reveals that ER values accord with SPI-3 values in all most all 118 months. The ER was classified into 5 classes: extreme drought, moderate to severe drought, normal rainfall, severe wet, and extreme wet to assess the severity of drought. Out of 118 months of the study period, a total of 4, 2, 6 and 5 months fall into extreme drought class in Kundala (Odisha), Mallapur (Telangana), Morbi (Gujarath), and Kishangarh Urf Pharwahi (Punjab), respectively. There are no extreme wet months in Mallapur and moderate to severe drought months in Morbi. This signifies that Mallapur might be experiencing normal rainfall, and Morbi not having drought-prone months during the study period. In the overall analysis, 2011, 2012, 2013, and 2018 were dry years with low ER values. Further, ER can even identify the wet places apart from only severe to extreme dry places. The advantage of using ER as a drought indicator is that it not only classifies the drought character but also gives the magnitude of the available portion of rainfall.