Virtual Globes for UAV-based data integration: Sputnik GIS and Google Earth™ applications

“This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Digital Earth on 03 May 2018, available online: https://www.tandfonline.com/doi/abs/10.1080/17538947.2018.1470205"The integration of local measurements and monitoring via global-scale Earth observations has become a new challenge in digital Earth science. The increasing accessibility and ease of use of virtual globes (VGs) represent primary advantages of this integration, and the digital Earth scientific community has adopted this technology as one of the main methods for disseminating the results of scientific studies. In this study, the best VG software for the dissemination and analysis of high-resolution UAV (Unmanned Aerial Vehicle) data is identified for global and continuous geographic scope support. The VGs Google Earth and Sputnik Geographic Information System (GIS) are selected and compared for this purpose. Google Earth is a free platform and one of the most widely used VGs, and one of its best features its ability to provide users with quality visual results. The proprietary software Sputnik GIS more closely approximates the analytical capacity of a traditional GIS and provides outstanding advantages, such as DEM overlapping and visualization for its disseminationThis work was supported by Xunta de Galicia under the Grant “Financial aid for the consolidation and structure of competitive units of investigation in the universities of the University Galician System (2016-18)” (Ref. ED431B 2016/030 and Ref. ED341D R2016/023). The authors also acknowledge support provided by “Realización de vuelos virtuales en las parcelas del proyecto Green deserts LIFE09 / ENV/ES / 000447”S

Degradation of a transgressive coastal Dunefield by pines plantation and strategies for recuperation (Lagoa Do Peixe National Park, Southern Brazil)

The transgressive dunefield of the Lagoa de Peixe Natural Park was modified drastically due to intensive pine plantation and the leeward development of the associated degraded areas. The present study analyzed the disturbances in the dunefield of the Lagoa do Peixe Natural Park due to pine tree plantation and the impact of the subsequent deforestation program conducted in the region. Aerial photographs, satellite images, and DGPS topographic data collected over a 70-year-long period were retrieved, analyzed, and compared, which allowed the observation of the geomorphological evolution of the dunefield. In addition, a profile of GPR on the inlet (during a period with the channel closed) was analyzed. In 1948, the surface of the sand barrier was occupied by high transverse dunes and low barchan dunes. Pine tree plantation on the inner side fixed the transgressive dunes and, consequently, avoided the filling of the shallow lagoon, although degraded areas were generated on the lee side of the pines. Simultaneous pine plantations in the backshore avoided the aeolian sediment input to the dunefield, generating a large interdune area along with the development of a few parabolic dunes, which resulted in cannibalization of the transgressive dunes. In 2001, pine trees occupied 15.03% of the total area analyzed in the present study, while the degraded area accounted for 10.81% of the total area. Progressive deforestation was performed (ring bound technique for tree gradual death), maintaining three lines of pines in contact with the dunes, to promote autochthonous vegetation growth, thereby preventing the filling of the adjacent lagoon with aeolian sediments. By the year 2018, the pine tree plantation area reduced to 3.25%, the dunefield area was 79.03%, and the extension of the degraded areas had increased and reached 17.71% of the total area. The pine tree plantation and the deforestation for conservation purposes are the main factors explaining the degradation of the dunefield during the period between 1948 and 2018, while regional climatic oscillations contributed as the secondary factor. Although internationally controversial, the present case study demonstrates that the removal of this exotic vegetation, through dune vegetation recovery programs, is often unsuccessful and may generate more degraded areas. However, in the case presented here, it was essential to remove the forest to ensure the dune field geodynamics and, therefore, the base (biotope) of the natural system (maintenance of the lagoon and the dunefield)

Response of land surface phenology to variation in tree cover during green-up and senescence periods in the semi-arid savannas in Southern Africa

Understanding the spatio-temporal dynamics of land surface phenology is important to understanding changes in landscape ecological processes of semi-arid savannas in Southern Africa. The aim of the study was to determine the influence of variation in tree cover percentage on land surface phenological response in the semi-arid savanna of Southern Africa. Various land surface phenological metrics for the green-up and senescing periods of the vegetation were retrieved from leaf index area (LAI) seasonal time series (2001 to 2015) maps for a study region in South Africa. Tree cover (%) data for 100 randomly selected polygons grouped into three tree cover classes, low (40%, n = 34), were used to determine the influence of varying tree cover (%) on the phenological metrics by means of the t-test. The differences in the means between tree cover classes were statistically significant (t-test p < 0.05) for the senescence period metrics but not for the green-up period metrics. The categorical data results were supported by regression results involving tree cover and the various phenological metrics, where tree cover (%) explained 40% of the variance in day of the year at end of growing season compared to 3% for the start of the growing season. An analysis of the impact of rainfall on the land surface phenological metrics showed that rainfall influences the green-up period metrics but not the senescence period metrics. Quantifying the contribution of tree cover to the day of the year at end of growing season could be important in the assessment of the spatial variability of a savanna ecological process such as the risk of fire spread with time.The Council for Industrial and Scientific Research (CSIR) Parliamentary Grant, Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) and ECOPOTENTIAL project which received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 641762.http://www.mdpi.com/journal/remotesensingam2017Plant Production and Soil Scienc

Advancements in the satellite sensing of the impacts of climate and variability on bush encroachment in savannah rangelands

An increase in shrubs or woody species is likely, directly or indirectly, to significantly affect rural livelihoods, wildlife/livestock productivity and conservation efforts. Poor and inappropriate land use management practices have resulted in rangeland degradation, particularly in semi-arid regions, and this has amplified the bush encroachment rate in many African countries, particularly in key savannah rangelands. The rate of encroachment is also perceived to be connected to other environmental factors, such as climate change, fire and rainfall variability, which may influence the structure and density of the shrubs (woody plants), when compared to uncontrolled grazing. Remote sensing has provided robust data for global studies on both bush encroachment and climate variability over multiple decades, and these data have complemented the local and regional evidence and process studies. This paper thus provides a detailed review of the advancements in the use of remote sensing for the monitoring of bush encroachment on the African continent, which is fuelled by climate variability in the rangeland areas

The effect of fire disturbances on woody plant encroachment at Loskop, Irene and Roodeplaat Farms, South Africa

Dissertation (MSc (Environmental Management))--University of Pretoria, 2022.The study on bush encroachment goes as far back as 1917, which is ranked among the top three rangeland problems in South Africa with expected increase in affected areas. Bush encroachment is considered one of the most substantial forms of land degradation because it occurs at the expense of beneficial herbaceous layer. Even with substantial number of studies on bush encroachment, the studies have not provided a broad comprehension of the problem, which complicates its management. Climate change, fire regimes herbivory and excessive increase of CO2 in the atmosphere are some of the key drivers of the current levels of bush encroachment. It is estimated that 20 million ha of South Africa’s agricultural productivity and biodiversity is under the threat of bush encroachment. As a result, the economic productivity of affected rangelands is negatively affected. This study investigated the effects of fire frequency/history (the rate of fire occurrence over an area in a given time period) on tree density and plant diversity. It further investigates the contribution of fire to the current extent of bush encroachment using remote sensing data over a nineteen year-period from the year 2000 to 2019. The study sites are based on three Agricultural Research Council (ARC) farms namely Loskop, Irene and Roodeplaat. Firstly, the in-situ and remotely sensed moderate resolution imaging spectroradiometer (MODIS) data were used to determine how fire influences the vegetation structure (tree density and plant diversity) using Analysis of Variance (ANOVA and Kruskal-Wallis (KW-H)). Secondly, in-situ, MODIS and Landsat data were used to build models needed for mapping areas of tree density change. The study investigated the indicators of bush encroachment namely, tree density within the study sites. The study found that there is a low to moderate correlation between burned areas and tree density in Loskop, Irene and Roodeplaat farms with the Pearson correlation coefficients of -0.06, 0.38 and 0.38 respectively. The significant tree density models had moderate to relatively high R-squares of 0.59, 0.49 and 0.82 for Loskop, Irene and Roodeplaat farms respectively. The findings of this study showed that fire frequency did not significantly influence the bush encroachment as measured by tree density and diversity in Loskop and Roodeplaat farms. However, there was evidence of fire frequency significantly influencing an increase in tree density in Irene farm. Due to lack of herbivores in some parts of Loskop and Roodeplaat farms because of water scarcity, fire alone may have not been a frequent enough disturbance to significantly influence tree density. The models calculated in this study serve as a foundation for understanding and calculating the tree density in response to fire. The findings of this study serve as a guide for resource managers to better manage fire regimes and their effect on vegetation cover at a local scale. Keywords: Fire, Plant diversity, Remote sensing, Tree densityAgricultural Research Council (ARC)Geography, Geoinformatics and MeteorologyMSc (Environmental Management)Unrestricte

Data-driven model development in environmental geography - Methodological advancements and scientific applications

Die Erfassung räumlich kontinuierlicher Daten und raum-zeitlicher Dynamiken ist ein Forschungsschwerpunkt der Umweltgeographie. Zu diesem Ziel sind Modellierungsmethoden erforderlich, die es ermöglichen, aus limitierten Felddaten raum-zeitliche Aussagen abzuleiten. Die Komplexität von Umweltsystemen erfordert dabei die Verwendung von Modellierungsstrategien, die es erlauben, beliebige Zusammenhänge zwischen einer Vielzahl potentieller Prädiktoren zu berücksichtigen. Diese Anforderung verlangt nach einem Paradigmenwechsel von der parametrischen hin zu einer nicht-parametrischen, datengetriebenen Modellentwicklung, was zusätzlich durch die zunehmende Verfügbarkeit von Geodaten verstärkt wird. In diesem Zusammenhang haben sich maschinelle Lernverfahren als ein wichtiges Werkzeug erwiesen, um Muster in nicht-linearen und komplexen Systemen zu erfassen. Durch die wachsende Popularität maschineller Lernverfahren in wissenschaftlichen Zeitschriften und die Entwicklung komfortabler Softwarepakete wird zunehmend der Fehleindruck einer einfachen Anwendbarkeit erzeugt. Dem gegenüber steht jedoch eine Komplexität, die im Detail nur durch eine umfassende Methodenkompetenz kontrolliert werden kann. Diese Problematik gilt insbesondere für Geodaten, die besondere Merkmale wie vor allem räumliche Abhängigkeit aufweisen, womit sie sich von "gewöhnlichen" Daten abheben, was jedoch in maschinellen Lernanwendungen bisher weitestgehend ignoriert wird. Die vorliegende Arbeit beschäftigt sich mit dem Potenzial und der Sensitivität des maschinellen Lernens in der Umweltgeographie. In diesem Zusammenhang wurde eine Reihe von maschinellen Lernanwendungen in einem breiten Spektrum der Umweltgeographie veröffentlicht. Die einzelnen Beiträge stehen unter der übergeordneten Hypothese, dass datengetriebene Modellierungsstrategien nur dann zu einem Informationsgewinn und zu robusten raum-zeitlichen Ergebnissen führen, wenn die Merkmale von geographischen Daten berücksichtigt werden. Neben diesem übergeordneten methodischen Fokus zielt jede Anwendung darauf ab, durch adäquat angewandte Methoden neue fachliche Erkenntnisse in ihrem jeweiligen Forschungsgebiet zu liefern. Im Rahmen der Arbeit wurde eine Vielzahl relevanter Umweltmonitoring-Produkte entwickelt. Die Ergebnisse verdeutlichen, dass sowohl hohe fachwissenschaftliche als auch methodische Kenntnisse unverzichtbar sind, um den Bereich der datengetriebenen Umweltgeographie voranzutreiben. Die Arbeit demonstriert erstmals die Relevanz räumlicher Überfittung in geographischen Lernanwendungen und legt ihre Auswirkungen auf die Modellergebnisse dar. Um diesem Problem entgegenzuwirken, wird eine neue, an Geodaten angepasste Methode zur Modellentwicklung entwickelt, wodurch deutlich verbesserte Ergebnisse erzielt werden können. Diese Arbeit ist abschließend als Appell zu verstehen, über die Standardanwendungen der maschinellen Lernverfahren hinauszudenken, da sie beweist, dass die Anwendung von Standardverfahren auf Geodaten zu starker Überfittung und Fehlinterpretation der Ergebnisse führt. Erst wenn Eigenschaften von geographischen Daten berücksichtigt werden, bietet das maschinelle Lernen ein leistungsstarkes Werkzeug, um wissenschaftlich verlässliche Ergebnisse für die Umweltgeographie zu liefern

Ecological infrastructure importance for drought mitigation in rural South African catchments: the Cacadu Catchment case example

Water scarcity is recognised as one of the significant challenges facing many countries, including South Africa. The threat of water scarcity is exacerbated by the coupled impacts of climate and anthropogenic drivers. Ongoing droughts and continued land cover change and degradation influence the ability of catchments to partition rainwater runoff, thereby affecting streamflow returns. However, quantifying land degradation accurately remains a challenge. This thesis used the theoretical lens of investing in ecological infrastructure to improve the drought mitigation function in rural catchments. This theoretical framework allows for a social-ecological systems approach to understand and facilitate science-based strategies for promoting ecosystem recovery. Specifically, this study aimed to explore the role and benefit of ecological infrastructure for improving drought mitigation, and consequently, water security for rural communities. Thus, this study sought to assess the consequences of human actions to catchment health status using the 15th Sustainable Development Goal indicator for the proportion of degraded land over the total land area as a surrogate. Secondly, hydrological modelling was used to describe how different land covers influence catchment hydrology, which related to how ecological infrastructure enables drought risk-reduction for mitigation regulation. Finally, this study developed a spatial prioritisation plan for restoration to improve drought mitigation for four focal ecological infrastructure (EI) categories (i.e. wetlands, riparian margins, abandoned agricultural fields and grasslands). The focal EI categories were selected for their importance in delivering water-related ecosystem services when sustainably managed. Chapter 1 sets the scene (i.e. provides the study background) and Chapter 2 provides a review of the literature. In Chapter 3, the recently released global GIS toolbox (TRENDS.EARTH) was used for tracking land change and for assessing the SDG 15.3.1 degradation indicator of i.e. Cacadu catchment over 15 years at a 300 m resolution. The results showed a declining trend in biomass productivity within the Cacadu catchment led to moderate degradation, with 16.79% of the total landscape degraded, which was determined by the pugin using the one-out, all-out rule. The incidence of degradation was detected in middle reaches of the catchment (i.e. S10F-J), while some improvement was detected in upper reaches (S10A-C) and lower reaches (S10J). In Chapter 4, a GIS-based Analytic Hierarchical Process (AHP) based on community stakeholder priorities, open-access spatial datasets and expert opinions, was used to identify EI focal areas that are best suitable for restoration to increase the drought mitigation capacity of the Cacadu catchment. The collected datasets provided three broad criteria (ecosystem health, water provision and social benefit) for establishing the AHP model using 12 spatial attributes. Prioritisation results show that up to 89% of the Cacadu catchment is suitable for restoration to improve drought mitigation. Catchments S10B-D, and S10F, S10G and S10J were highly prioritised while S10A, S10E and S10H received low priority, due to improving environmental conditions and low hydrological potential. Areas that were prioritised with consideration for local livelihoods overlap the areas for drought mitigation and form a network of villages from the middle to lower catchment reaches. Prioritised restoration areas with a consideration of societal benefit made up 0.56% of wetlands, 4.27% of riparian margins, 92.06% of abandoned croplands, and 51.86% of grasslands. Chapter 5 reports on use of the Pitman groundwater model to help understand the influence of land modification on catchment hydrology, and highlight the role of restoration interventions. The Cacadu catchment is ungauged, therefore the neighbouring Indwe catchment was used for parameter transfer through a spatial regionalisation technique. Results suggest that degradation increases surface runoff and aggravates recharge reduction, thereby reducing streamflow during low flow periods. In areas where there is natural land cover recovery, the Pitman Model simulated similar dry season streamflow to the natural land cover. Combining the outcomes from the three assessments allowed the study to highlight the role and benefits of ecological infrastructure in terms of drought mitigation. Study findings were interpreted to make recommendations for the role and benefit of ecological infrastructure for drought mitigation at a landscape scale and tertiary catchment level, within the context of available management options. The results support the notion that multiple science data sources can promote investments in ecological infrastructure. However, better spatial and temporal resolution datasets at a national level are still needed to improve the accuracy of studies such as the one outlined in this thesis. The study recommends adopting better ecosystem protection approaches and collaborative governance at multiple levels to reduce the vulnerability of rural communities to drought impacts.Thesis (MSc) -- Faculty of Science, Institute for Water Research, 202