Estimation of local training data point densities to support the assessment of spatial prediction uncertainty

Machine learning is frequently used in environmental and earth sciences to produce spatial or spatio-temporal predictions of environmental variables based on limited field samples – increasingly even on a global scale and far beyond the location of available training data. Since new geographic space often goes along with new environmental properties represented by the model's predictors, and since machine learning models do not perform well in extrapolation, this raises questions regarding the applicability of the trained models at the prediction locations. Methods to assess the area of applicability of spatial prediction models have been recently suggested and applied. These are typically based on distances in the predictor space between the prediction data and the nearest reference data point to represent the similarity to the training data. However, we assume that the density of the training data in the predictor space, i.e. how well an environment is represented in a model, is highly decisive for the prediction quality and complements the consideration of distances. We therefore suggest a local training data point density (LPD) approach. The LPD is a quantitative measure that indicates, for a new prediction location, how many similar reference data points have been included in the model training. Similarity here is defined by the dissimilarity threshold introduced by Meyer and Pebesma (2021) which is the maximum distance to a nearest training data point in the predictor space as observed during cross-validation. We assess the suitability of the approach in a simulation study and illustrate how the method can be used in real-world applications. The simulation study indicated a positive relationship between LPD and prediction performance and highlights the value of the approach compared to the consideration of the distance to a nearest data point only. We therefore suggest the calculation of the LPD to support the assessment of prediction uncertainties

Analysis of Unmanned Aerial System-Based CIR Images in Forestry—A New Perspective to Monitor Pest Infestation Levels

The detection of pest infestation is an important aspect of forest management. In the case of the oak splendour beetle (Agrilus biguttatus) infestation, the affected oaks (Quercus sp.) show high levels of defoliation and altered canopy reflection signature. These critical features can be identified in high-resolution colour infrared (CIR) images of the tree crown and branches level captured by Unmanned Aerial Systems (UAS). In this study, we used a small UAS equipped with a compact digital camera which has been calibrated and modified to record not only the visual but also the near infrared reflection (NIR) of possibly infested oaks. The flight campaigns were realized in August 2013, covering two study sites which are located in a rural area in western Germany. Both locations represent small-scale, privately managed commercial forests in which oaks are economically valuable species. Our workflow includes the CIR/NIR image acquisition, mosaicking, georeferencing and pixel-based image enhancement followed by object-based image classification techniques. A modified Normalized Difference Vegetation Index (NDVImod) derived classification was used to distinguish between five vegetation health classes, i.e., infested, healthy or dead branches, other vegetation and canopy gaps. We achieved an overall Kappa Index of Agreement (KIA) of 0.81 and 0.77 for each study site, respectively. This approach offers a low-cost alternative to private forest owners who pursue a sustainable management strategy

High-resolution classification of south patagonian peat bog microforms reveals potential gaps in up-scaled CH4 fluxes by use of Unmanned Aerial System (UAS) and CIR imagery

South Patagonian peat bogs are little studied sources of methane (CH4). Since CH4 fluxes can vary greatly on a small scale of meters, high-quality maps are needed to accurately quantify CH4 fluxes from bogs. We used high-resolution color infrared (CIR) images captured by an Unmanned Aerial System (UAS) to investigate potential uncertainties in total ecosystem CH4 fluxes introduced by the classification of the surface area. An object-based approach was used to classify vegetation both on species and microform level. We achieved an overall Kappa Index of Agreement (KIA) of 0.90 for the species- and 0.83 for the microform-level classification, respectively. CH4 fluxes were determined by closed chamber measurements on four predominant microforms of the studied bog. Both classification approaches were employed to up-scale CH4 closed chamber measurements in a total area of around 1.8 hectares. Including proportions of the surface area where no chamber measurements were conducted, we estimated a potential uncertainty in ecosystem CH4 fluxes introduced by the classification of the surface area. This potential uncertainty ranged from 14.2 mg·m-2· day-1 to 26.8 mg·m-2· day-1. Our results show that a simple classification with only few classes potentially leads to pronounced bias in total ecosystem CH4 fluxes when plot-scale fluxes are up-scaled.Fil: Lehmann, Jan R. K.. Westfalische Wilhelms Universitat; AlemaniaFil: Münchberger, Wiebke. Westfalische Wilhelms Universitat; AlemaniaFil: Knoth, Christian. Westfalische Wilhelms Universitat; AlemaniaFil: Blodau, Christian. Westfalische Wilhelms Universitat; AlemaniaFil: Nieberding, Felix. Westfalische Wilhelms Universitat; AlemaniaFil: Prinz, Torsten. Westfalische Wilhelms Universitat; AlemaniaFil: Pancotto, Veronica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Kleinebecker, Till. Westfalische Wilhelms Universitat; Alemani

Patterns in Mongolian nomadic household movement derived from GPS trajectories

This paper presents an approach for a quantitative analysis of movement patterns of nomadic households based on GPS trajectories. We distributed GPS loggers to 400 Mongolian herder households who carried them over a 9-month period, continuously recording position data every 30min. A total of 142of the resulting trajectories fulfilled our data quality criteria and were considered during the analysis. Based on this data, we derive summary indicators describing key parameters of the households’ mobility including measures of distance and number of movements as well as shape characteristics of the trajectories. We conduct an explorative statistical analysis of these summary indicators to investigate patterns in the nomadic mobility. We identify three movement strategies based on the number of different campsite locations and the distances traveled between campsites. We also compare the results to the existing literature on the mobility of Mongolian herders. Our findings show that GPS-based studies present a suitable framework to quantitatively analyze different movement strategies of nomadic herders

Numerical and Theoretical Considerations for the Design of the AVT-183 Diamond-Wing Experimental Investigations

A diamond-wing configuration has been developed to isolate and study blunt-leading edge vortex separation with both computations and experiments. The wing has been designed so that the results are relevant to a more complex Uninhabited Combat Air Vehicle concept known as SACCON. The numerical and theoretical development process for this diamond wing is presented, including a view toward planned wind tunnel experiments. This work was conducted under the NATO Science and Technology Organization, Applied Vehicle Technology panel. All information is in the public domain

EviGUIDE - a tool for evidence-based decision making in image-guided adaptive brachytherapy for cervical cancer

PURPOSE: To develop a novel decision-support system for radiation oncology that incorporates clinical, treatment and outcome data, as well as outcome models from a large clinical trial on magnetic resonance image-guided adaptive brachytherapy (MR-IGABT) for locally advanced cervical cancer (LACC). METHODS: A system, called EviGUIDE, was developed that combines dosimetric information from the treatment planning system, patient and treatment characteristics, and established tumor control probability (TCP), and normal tissue complication probability (NTCP) models, to predict clinical outcome of radiotherapy treatment of LACC. Six Cox Proportional Hazards models based on data from 1341 patients of the EMBRACE-I study have been integrated. One TCP model for local tumor control, and five NTCP models for OAR morbidities. RESULTS: EviGUIDE incorporates TCP-NTCP graphs to help users visualize the clinical impact of different treatment plans and provides feedback on achievable doses based on a large reference population. It enables holistic assessment of the interplay between multiple clinical endpoints and tumour and treatment variables. Retrospective analysis of 45 patients treated with MR-IGABT showed that there exists a sub-cohort of patients (20%) with increased risk factors, that could greatly benefit from the quantitative and visual feedback. CONCLUSION: A novel digital concept was developed that can enhance clinical decision- making and facilitate personalized treatment. It serves as a proof of concept for a new generation of decision support systems in radiation oncology, which incorporate outcome models and high-quality reference data, and aids the dissemination of evidence-based knowledge about optimal treatment and serve as a blueprint for other sites in radiation oncology

Tools for large-scale data analytics of an international multi-center study in radiation oncology for cervical cancer

PURPOSE: To develop and implement a software that enables centers, treating patients with state-of-the-art radiation oncology, to compare their patient, treatment, and outcome data to a reference cohort, and to assess the quality of their treatment approach. MATERIALS AND METHODS: A comprehensive data dashboard was designed, which al- lowed holistic assessment of institutional treatment approaches. The software was tested in the ongoing EMBRACE-II study for locally advanced cervical cancer. The tool created individualized dashboards and automatic analysis scripts, verified pro- tocol compliance and checked data for inconsistencies. Identified quality assurance (QA) events were analysed. A survey among users was conducted to assess usability. RESULTS: The survey indicated favourable feedback to the prototype and highlighted its value for internal monitoring. Overall, 2302 QA events were identified (0.4% of all collected data). 54% were due to missing or incomplete data, and 46% originated from other causes. At least one QA event was found in 519/1001 (52%) of patients. QA events related to primary study endpoints were found in 16% of patients. Sta- tistical methods demonstrated good performance in detecting anomalies, with precisions ranging from 71% to 100%. Most frequent QA event categories were Treatment Technique (27%), Patient Characteristics (22%), Dose Reporting (17%), Outcome 156 (15%), Outliers (12%), and RT Structures (8%). CONCLUSION: A software tool was developed and tested within a clinical trial in radia- tion oncology. It enabled the quantitative and qualitative comparison of institutional patient and treatment parameters with a large multi-center reference cohort. We demonstrated the value of using statistical methods to automatically detect implau- sible data points and highlighted common pitfalls and uncertainties in radiotherapy for cervical cancer

The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology

Background Chemical genetics provides a powerful alternative to conventional genetics for understanding gene function. However, its application to plants has been limited by the lack of a technology that allows detailed phenotyping of whole-seedling development in the context of a high-throughput chemical screen. We have therefore sought to develop an automated micro-phenotyping platform that would allow both root and shoot development to be monitored under conditions where the phenotypic effects of large numbers of small molecules can be assessed. Results The ‘Microphenotron’ platform uses 96-well microtitre plates to deliver chemical treatments to seedlings of Arabidopsis thaliana L. and is based around four components: (a) the ‘Phytostrip’, a novel seedling growth device that enables chemical treatments to be combined with the automated capture of images of developing roots and shoots; (b) an illuminated robotic platform that uses a commercially available robotic manipulator to capture images of developing shoots and roots; (c) software to control the sequence of robotic movements and integrate these with the image capture process; (d) purpose-made image analysis software for automated extraction of quantitative phenotypic data. Imaging of each plate (representing 80 separate assays) takes 4 min and can easily be performed daily for time-course studies. As currently configured, the Microphenotron has a capacity of 54 microtitre plates in a growth room footprint of 2.1 m², giving a potential throughput of up to 4320 chemical treatments in a typical 10 days experiment. The Microphenotron has been validated by using it to screen a collection of 800 natural compounds for qualitative effects on root development and to perform a quantitative analysis of the effects of a range of concentrations of nitrate and ammonium on seedling development. Conclusions The Microphenotron is an automated screening platform that for the first time is able to combine large numbers of individual chemical treatments with a detailed analysis of whole-seedling development, and particularly root system development. The Microphenotron should provide a powerful new tool for chemical genetics and for wider chemical biology applications, including the development of natural and synthetic chemical products for improved agricultural sustainability