Vergleich der Möglichkeiten zur Erstellung einer Reliefschummerung mit kommerziellen Produkten und Open-Source Software

Schummerungen sind heutzutage die anschaulichste und am weitesten verbreite Methode zur Darstellung des Reliefs in der Kartenebene. Analytische Verfahren zur Herstellung einer Schummerung finden sich meist im Kontext von Geoinformationssystemen, von denen eine große Vielzahl auf dem Markt erhältlich ist - darunter auch zunehmend Open-Source-Programme. Vor diesem Hintergrund ist es das Ziel der Arbeit sowohl den Funktionsumfang als auch die Möglichkeiten und Grenzen der Reliefdarstellung ausgewählter kommerzieller und freier Produkte zu untersuchen und zu vergleichen. Speziell wurden die Möglichkeiten zur Erstellung einer Reliefschummerung, farbiger Höhenschichten und Maskierung flacher Gebiete untersucht. Dafür wurden verschiedene Vergleichskriterien eingeführt nach denen zwei Beispielregionen, sowohl auf Meso- als auch auf Mikroebene, bearbeitet und bewertet wurden. Die Untersuchungen haben gezeigt, dass kommerzielle Produkte generell die anspruchsvolleren Schummerungen generieren und Open-Source Produkte keine Alternative für qualitativ hochwertige, kartographische Anliegen darstellen. Allerdings müssen die Einstellungen in den meisten Fällen manuell sowohl an das Programm als auch an den Kartenausschnitt angepasst werden. Die Ergebnisse und Schlussfolgerungen sind anschaulich in Tabellen mit abschließendem Fazit am Ende der Arbeit zusammengefasst.:1. Motivation 2. Grundlagen 2.1. Reliefdarstellungsmethoden 2.2. Schummerungsarten 2.3. Herstellung einer Schummerung 3. Möglichkeiten der analytischen Schummerung 3.1. Datengrundlage 3.2. Kommerzielle Produkte 3.3. Open-Source Software 4. Ergebnis 4.1. Zusammenfassung der Ergebnisse 4.2. Tabellarischer Vergleich 5. Schlussfolgerun

Vergleich der Möglichkeiten zur Erstellung einer Reliefschummerung mit kommerziellen Produkten und Open-Source Software

Schummerungen sind heutzutage die anschaulichste und am weitesten verbreite Methode zur Darstellung des Reliefs in der Kartenebene. Analytische Verfahren zur Herstellung einer Schummerung finden sich meist im Kontext von Geoinformationssystemen, von denen eine große Vielzahl auf dem Markt erhältlich ist - darunter auch zunehmend Open-Source-Programme. Vor diesem Hintergrund ist es das Ziel der Arbeit sowohl den Funktionsumfang als auch die Möglichkeiten und Grenzen der Reliefdarstellung ausgewählter kommerzieller und freier Produkte zu untersuchen und zu vergleichen. Speziell wurden die Möglichkeiten zur Erstellung einer Reliefschummerung, farbiger Höhenschichten und Maskierung flacher Gebiete untersucht. Dafür wurden verschiedene Vergleichskriterien eingeführt nach denen zwei Beispielregionen, sowohl auf Meso- als auch auf Mikroebene, bearbeitet und bewertet wurden. Die Untersuchungen haben gezeigt, dass kommerzielle Produkte generell die anspruchsvolleren Schummerungen generieren und Open-Source Produkte keine Alternative für qualitativ hochwertige, kartographische Anliegen darstellen. Allerdings müssen die Einstellungen in den meisten Fällen manuell sowohl an das Programm als auch an den Kartenausschnitt angepasst werden. Die Ergebnisse und Schlussfolgerungen sind anschaulich in Tabellen mit abschließendem Fazit am Ende der Arbeit zusammengefasst.:1. Motivation 2. Grundlagen 2.1. Reliefdarstellungsmethoden 2.2. Schummerungsarten 2.3. Herstellung einer Schummerung 3. Möglichkeiten der analytischen Schummerung 3.1. Datengrundlage 3.2. Kommerzielle Produkte 3.3. Open-Source Software 4. Ergebnis 4.1. Zusammenfassung der Ergebnisse 4.2. Tabellarischer Vergleich 5. Schlussfolgerun

Improved characterization of dryland degradation using trends in vegetation/ rainfall sequential linear regression (SERGS-TREND)

Land degradation in drylands has been investigated extensively over recent decades and several remote sensing based techniques attempt to decouple the human influence from the natural climate variability, but are contested in literature. We introduce a novel approach termed SeRGS-TREND that is designed to monitor land degradation by suppressing the impact from climate variability and highlight vegetation disturbances may it be human or climate-induced. SeRGS-TREND is based on the interpretation of the slope of a linear regression analysis within a sequentially moving window along the temporal axis of the time series of remote sensing data. The use of a moving window increases the probability of a statistically significant linear vegetation-rainfall relationship (VRR), which in turn provides an improved statistical basis for the results produced and thereby confidence in the assessment of degradation. We test and compare SeRGS-TREND and the commonly used RESTREND by simulating different degradation scenarios and find that SeRGS reveals both, more significant and more exact information about degradation events (e.g. starting and end point) while keeping the VRR correlation coefficients high, thus rendering results more reliable

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

Savannas cover a wide climatic gradient across large portions of the Earth’s land surface and are an important component of the terrestrial biosphere. Savannas have been undergoing changes that alter the composition and structure of their vegetation such as the encroachment of woody vegetation and increasing land-use intensity. Monitoring the spatial and temporal dynamics of savanna ecosystem structure (e.g., partitioning woody and herbaceous vegetation) and function (e.g., aboveground biomass) is of high importance. Major challenges include misclassification of savannas as forests at the mesic end of their range, disentangling the contribution of woody and herbaceous vegetation to aboveground biomass, and quantifying and mapping fuel loads. Here, we review current (2010–present) research in the application of satellite remote sensing in savannas at regional and global scales. We identify emerging opportunities in satellite remote sensing that can help overcome existing challenges. We provide recommendations on how these opportunities can be leveraged, specifically (1) the development of a conceptual framework that leads to a consistent definition of savannas in remote sensing; (2) improving mapping of savannas to include ecologically relevant information such as soil properties and fire activity; (3) exploiting high-resolution imagery provided by nanosatellites to better understand the role of landscape structure in ecosystem functioning; and (4) using novel approaches from artificial intelligence and machine learning in combination with multisource satellite observations, e.g., multi-/hyperspectral, synthetic aperture radar (SAR), and light detection and ranging (lidar), and data on plant traits to infer potentially new relationships between biotic and abiotic components of savannas that can be either proven or disproven with targeted field experiments

Towards improved remote sensing based monitoring of dryland ecosystem functioning using sequential linear regression slopes (SeRGS)

We present a method for remote sensing based monitoring of changes in dryland ecosystem functioning based on the assumption that an altered vegetation rainfall relationship (VRR) indicates changes in vegetation biophysical processes, potentially leading to changes in ecosystem functioning. We describe the VRR through a linear regression between integrated rainfall and vegetation productivity (using NDVI as a proxy) within a combined spatio-temporal window, sequentially moved over the study area and along the temporal axis of a time series. The trend in the slope values derived from such a sequential linear regression, termed SeRGS, thus represents a measure of change in the VRR. Scenarios of land degradation, defined here as a reduction in biological productivity, which may be caused by either climatic or anthropogenic factors are simulated for the period 1970–2016 from CRU rainfall and modelled NDVI data to test and evaluate the performance of the SeRGS method in detecting degradation, and compare it against the well-known RESTREND method. We found that SeRGS showed (1) overall more pronounced trends and higher significance levels (p ≤ 0.01) in detecting degradation events and (2) an improved statistical basis for the calculation of trends in the VRR (expressed by high coefficients of determination throughout the period of analysis), which was found to increase the validity of the results produced. Through the implementation of the temporal moving window the effect of inter-annual rainfall variability on vegetation productivity was effectively reduced, thereby enabling a more exact and reliable identification of the timing of degradation events (e.g. start, maximum and end of degradation) by using a time series breakpoint analysis (BFAST). Finally, the SeRGS method was applied using real data for Senegal (seasonally integrated MODIS NDVI and CHIRPS rainfall data 2000–2016) and we discuss patterns and trends. This study provides the theoretical basis for an improved assessment of changes in dryland ecosystem functioning, which is of relevance to land degradation monitoring targeting loss of vegetation productivity

Contrasting ecosystem vegetation response in global drylands under drying and wetting conditions

Increasing aridity is one major consequence of ongoing global climate change and is expected to cause widespread changes in key ecosystem attributes, functions, and dynamics. This is especially the case in naturally vulnerable ecosystems, such as drylands. While we have an overall understanding of past aridity trends, the linkage between temporal dynamics in aridity and dryland ecosystem responses remain largely unknown. Here, we examined recent trends in aridity over the past two decades within global drylands as a basis for exploring the response of ecosystem state variables associated with land and atmosphere processes (e.g., vegetation cover, vegetation functioning, soil water availability, land cover, burned area, and vapor-pressure deficit) to these trends. We identified five clusters, characterizing spatiotemporal patterns in aridity between 2000 and 2020. Overall, we observe that 44.5% of all areas are getting dryer, 31.6% getting wetter, and 23.8% have no trends in aridity. Our results show strongest correlations between trends in ecosystem state variables and aridity in clusters with increasing aridity, which matches expectations of systemic acclimatization of the ecosystem to a reduction in water availability/water stress. Trends in vegetation (expressed by leaf area index [LAI]) are affected differently by potential driving factors (e.g., environmental, and climatic factors, soil properties, and population density) in areas experiencing water-related stress as compared to areas not exposed to water-related stress. Canopy height for example, has a positive impact on trends in LAI when the system is stressed but does not impact the trends in non-stressed systems. Conversely, opposite relationships were found for soil parameters such as root-zone water storage capacity and organic carbon density. How potential driving factors impact dryland vegetation differently depending on water-related stress (or no stress) is important, for example within management strategies to maintain and restore dryland vegetation