Exploring the Reasons for the Seasons Using Google Earth, 3D Models, and Plots

Public understanding of climate and climate change is of broad societal importance. However, misconceptions regarding reasons for the seasons abound amongst students, teachers, and the public, many of whom believe that seasonality is caused by large variations in Earth\u27s distance from the Sun. Misconceptions may be reinforced by textbook illustrations that exaggerate eccentricity or show an inclined view of Earth\u27s near-circular orbit. Textbook explanations that omit multiple factors influencing seasons, that do not mesh with students\u27 experiences, or that are erroneous, hinder scientifically valid reasoning. Studies show that many teachers share their students\u27 misconceptions, and even when they understand basic concepts, teachers may fail to appreciate the range of factors contributing to seasonal change, or their relative importance. We have therefore developed a learning resource using Google Earth, a virtual globe with other useful, weather- and climate-related visualizations. A classroom test of 27 undergraduates in a public research university showed that 15 improved their test scores after the Google Earth-based laboratory class, whereas 5 disimproved. Mean correct answers rose from 4.7/10 to 6/10, giving a paired t-test value of 0.21. After using Google Earth, students are helped to segue to a heliocentric view

Discipline-Based Planetary Education Research and Computational Fluid Dynamics Analysis of Mars

This thesis originates from the testing and implementation of an IRB-approved interactive animation designed to help students understand what causes The Reasons For The Seasons (RFTS) on Earth. Results from the testing indicated a small improvement in student understanding after exposure to the animation. Next, using the 3-D mapping tool Google Earth, students explored seasons and other planetary features on Mercury, Venus, the Moon and Mars through IRB-approved interactive tours which were developed and tested for astronomy education. Results from the tests indicated that there were statistically significant learning gains (p-value \u3c 0.05) after students interacted with the tours compared to those who did not. The development of the tours inspired a geophysics study of the possibility of former plate motion (or plate tectonics) on Mars. A 2-D finite element convection model for the mantle of Mars was designed and solved using COMSOL Multiphysics 5.1, to investigate whether or not thermal gradients in a Mars-sized planet could cause vigorous upper mantle convection, consistent with plate tectonic processes. Results from this project indicated that stable convection could occur in the interior of a Mars-like planet assuming the presence of sufficiently high thermal gradients at about 0.8 times the mantle temperature of Earth. The convective patterns resembled hot upwelling and cool downwelling which may be similar to subduction-like features. Furthermore, increasing the temperature of the hot boundaries resulted in faster, more rigorous convective motions and a hotter average temperature

Google Under-the-Earth: Seeing Beneath Stonehenge using Google Earth - a Tool for Public Engagement and the Dissemination of Archaeological Data

This article focuses on the use of Google Earth as a tool to facilitate public engagement and dissemination of data. It examines a case study based around one of the largest archaeological investigations of the Stonehenge landscape, the Stonehenge Riverside Project. A bespoke layer for Google Earth was developed to communicate the discoveries of the research by creating an engaging, interactive and informative multimedia application that could be viewed by users across the world. The article describes the creation of the layer: Google Under-the-Earth: Seeing Beneath Stonehenge, and the public uptake and response to this. The project was supported by a Google Research Award, and working alongside Google enabled a 'free to download' platform for users to view the data within in the form of Google Earth, as well as the integration of a variety of applications including: Google SketchUp, YouTube, and Flickr. In addition, the integration of specialist software, such as Esri ArcGIS, was fundamental to the integration of the spatial data gathered by the project. Methodologies used to create the application are documented here, including how different outputs were integrated such as geophysical survey, 3D reconstructions and landscape tours. The future possibilities for utilising Google Earth for public engagement and understanding in the discipline are examined

Precipitation and Greenness in Pastoral Lands of East Turkana, Kenya

Pastoralism has long supported livelihoods and provided essential ecosystem services in landscapes of East Africa. Vegetation productivity is central to the functioning of pastoral systems but may be affected by changes in climate and landuse. Vegetation monitoring is important for understanding the effects of global change in pastoral lands; however, it can be time and resource intensive. Remote sensing provides opportunities for efficient multi-scale monitoring of vegetation and climatic drivers. In this thesis, I explore the utility of satellite and UAV remote sensing for monitoring vegetation and precipitation trends and relationships in the East of Lake Turkana Region of northern Kenya. In Chapter 1, I examine regional greenness and precipitation time series at monthly, seasonal, and annual temporal resolutions, as well as relationships between greenness and precipitation from 2000 to 2022. I found evidence of long-term precipitation–greenness coupling at monthly and annual temporal resolutions. There were no trends in monthly or annual regional precipitation, while NDVI significantly increased at monthly temporal resolution but did not exhibit a significant trend at annual temporal resolution. Traditional pastoral practices, such as use of livestock corrals (bomas), also influence local vegetation composition and abundance. In chapter two, I use satellite and unmanned aerial vehicle (UAV) remote sensing data to monitor greenness in and around abandoned boma settlements at seasonal and annual temporal resolutions. Results showed that mean NDVI from UAV and Sentinel-2 data varied based on seasons (dry or wet) and from boma to boma. NDVI significantly differed between bomas and non-boma sites and there was significant positive correlation between NDVI with precipitation across all bomas, with an optimum temporal lag response of one month. Collectively, my results add to the body of literature demonstrating the utility of satellite and UAV-based remote sensing data for monitoring vegetation in pastoral systems. Advisor: Daniel R. Ude

Direct Nitrous Oxide Emissions From Tropical And Sub-Tropical Agricultural Systems : A Review and Modelling of Emission Factors

We acknowledge the financial support from the CGIAR Research Programs on Climate Change, Agriculture and Food Security (CCAFS). Grant ref. n. P25.Peer reviewedPublisher PD

Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome

Development of a new non-invasive vineyard yield estimation method based on image analysis

Doutoramento em Engenharia Agronómica / Instituto Superior de Agronomia. Universidade de LisboaPredicting vineyard yield with accuracy can provide several advantages to the whole vine and wine industry. Today this is majorly done using manual and sometimes destructive methods, based on bunch samples. Yield estimation using computer vision and image analysis can potentially perform this task extensively, automatically, and non-invasively. In the present work this approach is explored in three main steps: image collection, occluded fruit estimation and image traits conversion to mass. On the first step, grapevine images were collected in field conditions along some of the main grapevine phenological stages. Visible yield components were identified in the image and compared to ground truth. When analyzing inflorescences and bunches, more than 50% were occluded by leaves or other plant organs, on three cultivars. No significant differences were observed on bunch visibility after fruit set. Visible bunch projected area explained an average of 49% of vine yield variation, between veraison and harvest. On the second step, vine images were collected, in field conditions, with different levels of defoliation intensity at bunch zone. A regression model was computed combining canopy porosity and visible bunch area, obtained via image analysis, which explained 70-84% of bunch exposure variation. This approach allowed for an estimation of the occluded fraction of bunches with average errors below |10|%. No significant differences were found between the model’s output at veraison and harvest. On the last step, the conversion of bunch image traits into mass was explored in laboratory and field conditions. In both cases, cultivar differences related to bunch architecture were found to affect weight estimation. A combination of derived variables which included visible bunch area, estimated total bunch area, visible bunch perimeter, visible berry number and bunch compactness was used to estimate yield on undisturbed grapevines. The final model achieved a R2 = 0.86 between actual and estimated yield (n = 213). If performed automatically, the final approach suggested in this work has the potential to provide a non-invasive method that can be performed accurately across whole vineyards.N/

3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function

Dear Colleagues, The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest’s compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest’s ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed

Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor. Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016–2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community.We gratefully acknowledge the financial support by the European Space Agency (ESA) for airborne data acquisition and data analysis in the frame of the FLEXSense campaign (ESA Contract No. 4000125402/18/NL/NA). The research was also supported by the Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu, accessed on: 8 January 2022). This publication is also the result of the project implementation: “Scientific support of climate change adaptation in agriculture and mitigation of soil degradation” (ITMS2014+313011W580) supported by the Integrated Infrastructure Operational Programme funded by the ERDF