The Impact of Sensor Characteristics and Data Availability on Remote Sensing Based Change Detection

Land cover and land use change are among the major drivers of global change. In a time of mounting challenges for sustainable living on our planet any research benefits from interdisciplinary collaborations to gain an improved understanding of the human-environment system and to develop suitable and improve existing measures of natural resource management. This includes comprehensive understanding of land cover and land use changes, which is fundamental to mitigate global change. Remote sensing technology is essential for the analyses of the land surface (and hence related changes) because it offers cost-effective ways of collecting data simultaneously over large areas. With increasing variety of sensors and better data availability, the application of remote sensing as a means to assist in modeling, to support monitoring, and to detect changes at various spatial and temporal scales becomes more and more feasible. The relationship between the nature of the changes on the land surface, the sensor properties, and the conditions at the time of acquisition influences the potential and quality of land cover and land use change detection. Despite the wealth of existing change detection research, there is a need for new methodologies in order to efficiently explore the huge amount of data acquired by remote sensing systems with different sensor characteristics. The research of this thesis provides solutions to two main challenges of remote sensing based change detection. First, geometric effects and distortions occur when using data taken under different sun-target-sensor geometries. These effects mainly occur if sun position and/or viewing angles differ between images. This challenge was met by developing a theoretical framework of bi-temporal change detection scenarios. The concept includes the quantification of distortions that can occur in unfavorable situations. The invention and application of a new method – the Robust Change Vector Analysis (RCVA) – reduced the detection of false changes due to these distortions. The quality and robustness of the RCVA were demonstrated in an example of bi-temporal cross-sensor change detection in an urban environment in Cologne, Germany. Comparison with a state-of-the-art method showed better performance of RCVA and robustness against thresholding. Second, this thesis provides new insights into how to optimize the use of dense time series for forest cover change detection. A collection of spectral indices was reviewed for their suitability to display forest structure, development, and condition at a study site on Vancouver Island, British Columbia, Canada. The spatio-temporal variability of the indices was analyzed to identify those indices, which are considered most suitable for forest monitoring based on dense time series. Amongst the indices, the Disturbance Index (DI) was found to be sensitive to the state of the forest (i.e., forest structure). The Normalized Difference Moisture Index (NDMI) was found to be spatio-temporally stable and to be the most sensitive index for changes in forest condition. Both indices were successfully applied to detect abrupt forest cover changes. Further, this thesis demonstrated that relative radiometric normalization can obscure actual seasonal variation and long-term trends of spectral signals and is therefore not recommended to be incorporated in the time series pre-processing of remotely-sensed data. The main outcome of this part of the presented research is a new method for detecting discontinuities in time series of spectral indices. The method takes advantage of all available information in terms of cloud-free pixels and hence increases the number of observations compared to most existing methods. Also, the first derivative of the time series was identified (together with the discontinuity measure) as a suitable variable to display and quantify the dynamic of dense Landsat time series that cannot be observed with less dense time series. Given that these discontinuities are predominantly related to abrupt changes, the presented method was successfully applied to clearcut harvest detection. The presented method detected major events of forest change at unprecedented temporal resolution and with high accuracy (93% overall accuracy). This thesis contributes to improved understanding of bi-temporal change detection, addressing image artifacts that result from flexible acquisition features of modern satellites (e.g., off-nadir capabilities). The demonstrated ability to efficiently analyze cross-sensor data and data taken under unfavorable conditions is increasingly important for the detection of many rapid changes, e.g., to assist in emergency response. This thesis further contributes to the optimized use of remotely sensed time series for improving the understanding, accuracy, and reliability of forest cover change detection. Additionally, the thesis demonstrates the usability of and also the necessity for continuity in medium spatial resolution satellite imagery, such as the Landsat data, for forest management. Constellations of recently launched (e.g., Landsat 8 OLI) and upcoming sensors (e.g., Sentinel-2) will deliver new opportunities to apply and extend the presented methodologies.Der Einfluss von Sensorcharakteristik und Datenverfügbarkeit auf die fernerkundungsbasierte Veränderungsdetektion Landbedeckungs- und Landnutzungswandel gehören zu den Haupttriebkräften des Globalen Wandels. In einer Zeit, in der ein nachhaltiges Leben auf unserem Planeten zu einer wachsenden Herausforderung wird, profitiert die Wissenschaft von interdisziplinärer Zusammenarbeit, um ein besseres Verständnis der Mensch-Umwelt-Beziehungen zu erlangen und um verbesserte Maßnahmen des Ressourcenmanagements zu entwickeln. Dazu gehört auch ein erweitertes Verständnis von Landbedeckungs- und Landnutzungswandel, das elementar ist, um dem Globalen Wandel zu begegnen. Die Fernerkundungstechnologie ist grundlegend für die Analyse der Landoberfläche und damit verknüpften Veränderungen, weil sie in der Lage ist, große Flächen gleichzeitig zu erfassen. Mit zunehmender Sensorenvielfalt und besserer Datenverfügbarkeit gewinnt Fernerkundung bei der Modellierung, beim Monitoring sowie als Mittel zur Erkennung von Veränderungen in verschiedenen räumlichen und zeitlichen Skalen zunehmend an Bedeutung. Das Wirkungsgeflecht zwischen der Art von Veränderungen der Landoberfläche, Sensoreigenschaften und Aufnahmebedingungen beeinflusst das Potenzial und die Qualität fernerkundungsbasierter Landbedeckungs- und Landnutzungsveränderungs-detektion. Trotz der Fülle an bestehenden Forschungsleistungen zur Veränderungsdetektion besteht ein dringender Bedarf an neuen Methoden, die geeignet sind, das große Aufkommen von Daten unterschiedlicher Sensoren effizient zu nutzen. Die in dieser Abschlussarbeit durchgeführte Forschung befasst sich mit zwei aktuellen Problemfeldern der fernerkundungsbasierten Veränderungsdetektion. Das erste sind die geometrischen Effekte und Verzerrungen, die auftreten, wenn Daten genutzt werden, die unter verschiedenen Sonne-Zielobjekt-Sensor-Geometrien aufgenommen wurden. Diese Effekte treten vor allem dann auf, wenn unterschiedliche Sonnenstände und/oder unterschiedliche Einfallswinkel der Satelliten genutzt werden. Der Herausforderung wurde begegnet, indem ein theoretisches Konzept von Szenarien dargelegt wurde, die bei der bi-temporalen Veränderungsdetektion auftreten können. Das Konzept beinhaltet die Quantifizierung der Verzerrungen, die in ungünstigen Fällen auftreten können. Um die Falscherkennung von Veränderungen in Folge der resultierenden Verzerrungen zu reduzieren, wurde eine neue Methode entwickelt – die Robust Change Vector Analysis (RCVA). Die Qualität der Methode wird an einem Beispiel der Veränderungsdetektion im urbanen Raum (Köln, Deutschland) aufgezeigt. Ein Vergleich mit einer anderen gängigen Methode zeigt bessere Ergebnisse für die neue RCVA und untermauert deren Robustheit gegenüber der Schwellenwertbestimmung. Die zweite Herausforderung, mit der sich die vorliegende Arbeit befasst, betrifft die optimierte Nutzung von dichten Zeitreihen zur Veränderungsdetektion von Wäldern. Eine Auswahl spektraler Indizes wurde hinsichtlich ihrer Tauglichkeit zur Erfassung von Waldstruktur, Waldentwicklung und Waldzustand in einem Untersuchungsgebiet auf Vancouver Island, British Columbia, Kanada, bewertet. Um die Einsatzmöglichkeiten der Indizes für dichte Zeitreihen bewerten zu können, wurde ihre raum-zeitliche Variabilität untersucht. Der Disturbance Index (DI) ist ein Index, der sensitiv für das Stadium eines Waldes ist (d. h. seine Struktur). DerNormalized Difference Moisture Index (NDMI) ist raum-zeitlich stabil und zudem am sensitivsten für Veränderungen des Waldzustands. Beide Indizes wurden erfolgreich zur Erkennung von abrupten Veränderungen getestet. In der vorliegenden Arbeit wird aufgezeigt, dass die relative radiometrische Normierung saisonale Variabilität und Langzeittrends von Zeitreihen spektraler Signale verzerrt. Die relative radiometrische Normierung wird daher nicht zur Vorprozessierung von Fernerkundungszeitreihen empfohlen. Das wichtigste Ergebnis dieser Studie ist eine neue Methode zur Erkennung von Diskontinuitäten in Zeitreihen spektraler Indizes. Die Methode nutzt alle wolkenfreien, ungestörten Beobachtungen (d. h. unabhängig von der Gesamtbewölkung in einem Bild) in einer Zeitreihe und erhöht dadurch die Anzahl an Beobachtungen im Vergleich zu anderen Methoden. Die erste Ableitung und die Messgröße zur Erfassung der Diskontinuitäten sind gut geeignet, um die Dynamik dichter Zeitreihen zu beschreiben und zu quantifizieren. Dies ist mit weniger dichten Zeitreihen nicht möglich. Da diese Diskontinuitäten im Untersuchungsgebiet üblicherweise abrupter Natur sind, ist die Methode gut geeignet, um Kahlschläge zu erfassen. Die hier dargelegte neue Methode detektiert Waldbedeckungsveränderungen mit einzigartiger zeitlicher Auflösung und hoher Genauigkeit (93% Gesamtgenauigkeit). Die vorliegende Arbeit trägt zu einem verbesserten Verständnis bi-temporaler Veränderungsdetektion bei, indem Bildartefakte berücksichtigt werden, die infolge der Flexibilität moderner Sensoren entstehen können. Die dargestellte Möglichkeit, Daten zu analysieren, die von unterschiedlichen Sensoren stammen und die unter ungünstigen Bedingungen aufgenommen wurden, wird zukünftig bei der Erfassung von schnellen Veränderungen an Bedeutung gewinnen, z. B. bei Katastropheneinsätzen. Ein weiterer Beitrag der vorliegenden Arbeit liegt in der optimierten Anwendung von Fernerkundungszeitreihen zur Verbesserung von Verständnis, Genauigkeit und Verlässlichkeit der Waldveränderungsdetektion. Des Weiteren zeigt die Arbeit den Nutzen und die Notwendigkeit der Fortführung von Satellitendaten mit mittlerer Auflösung (z. B. Landsat) für das Waldmanagement. Konstellationen kürzlich gestarteter (z. B. Landsat 8 OLI) und zukünftiger Sensoren (z. B. Sentinel-2) werden neue Möglichkeiten zur Anwendung und Optimierung der hier vorgestellten Methoden bieten

Quantification and Prediction of Land Consumption and Its Climate Effects in the Rhineland Metropolitan Area Based on Multispectral Satellite Data and Land-Use Modelling 1975–2030

Land use and soil sealing are particularly high in metropolitan regions. They bring about conflicts of use: the demand for housing, business and economy is enormous, but at the same time, quality of life depends on a network of green spaces. With the aid of remote sensing, the change of urban areas can be observed and quantified over time. This study investigates the change dynamics of land cover and land use in North Rhine-Westphalia (NRW) with multispectral satellite data, focussing on imperviousness. Landsat data is used to monitor and analyse half a century of landscape development. In addition, recent trends in land surface temperature (LST) are estimated from MODIS data. Changes to the LST are caused by land cover and land use changes amongst other factors. Accordingly, a link can be shown between the medium-term LST changes and the hotspots of landscape transformation in NRW. Due to global climate change, land consumption is increasingly affecting the densely populated urban areas, which calls for measures to increase their resilience. The results of the study can be used by decision makers to assess the environmental impact of land use, the loss of agricultural land or the resulting effects of climate change

Multi-scale time series of biophysical parameters and vegetation structure in heterogeneous landscapes of West Africa

The aim of the BMBF-funded research project CONCERT is to identify emission mitigation options for the major greenhouse gases (GHG), in parallel with improving food security in West Africa. This will be achieved – among others – through the estimation and projection of GHG emission budgets for the region using a fully-coupled regional Earth System Model (ESM), specifically adapted to the WASCAL region. Science-based information for adaptive land management requires quantification of vegetation parameters at stand-scales, and updated high-resolution land cover and vegetation maps to upscale measured GHG fluxes to country-scales. For reliable ESM predictions of future GHG budgets and crop productivity, we need to improve our understanding of the spatial pattern and temporal dynamics of land use and land cover (LULC) in West Africa. Although various LULC datasets exist at global and continental scales, they are often coarse in spatial and temporal resolution and poorly describe the thematic, temporal, and spatial patterns in the heterogeneous savanna landscapes. Here, we assess time series of the leaf area index (LAI) based on earth observation data at different spatial and temporal resolutions. Machine Learning methods allow to fill gaps in the spatial and temporal domains in order to compute dense time series and assess vegetation dynamics. Time series of Sentinel-2-based LAI allow to detect multiple growing cycles with specific magnitudes and provides structural information of vegetation as an important input of ESM

Forest Structure Characterization in Germany: Novel Products and Analysis Based on GEDI, Sentinel-1 and Sentinel-2 Data

Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-towall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience

SAR phenology across major West-African land cover types

West Africa is an important hotspot of global change facing huge environmental and societal challenges. These include climate and land use change, migration, and conflicts, all of which are a major threat to food security. Food security – and a number of other envisaged achievements of the sustainable development goals (SDG) – depends largely on wise natural resource management. For several reasons, the West African environment and its changes are still poorly understood, although a large number of scientific studies have been conducted over the past years thanks to the establishment of the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL) and other initiatives. However, most of the studies are focused on only few study sites, only very few aim at large-scale assessments of climate change impacts or land use. Little is known about vegetation structure, which plays a crucial role in the estimation of the greenhouse gas budget and the carbon sequestration potential of complex ecosystems such as agroforestry systems. Agroforestry systems are a mixture of different land uses, characterized by a certain tree cover and crops in between. Ideally, the trees do not only shade the fields but also provide fruits that can be used as food or to feed animals. Among the consistent datasets that provide more detailed information about vegetation properties throughout the region is the Copernicus Global Land Cover product (Buchhorn et al. 2020). It is available for multiple years (2015-2019) and provides rich information with regard to vegetation, particularly forests. The spatial resolution is 100 m. However, West African ecosystems are diverse and complex. This complexity is also true for agroforestry systems, which are important agricultural production zones and at the same time fulfill numerous ecosystem services. Unfortunately, none of the well-established nor the recent land cover and land use products such as the beforementioned Copernicus product are able to adequately resolve agroforestry systems. Even the WorldCover 2020 product (Zanaga et al. 2021) with 10 m spatial resolution is not suitable to differentiate between cropland areas, forest cover, shrubland and agroforestry systems. While our hypothesis is that the spatial resolution of the Copernicus Sentinel satellites is limiting the classification of single trees, we expect differences in the phenology within agroforestry systems that can be mapped by means of remote sensing. Phenology, the characteristic, often seasonal life cycle of plants, is an important plant species trait and hence one of the essential biodiversity variables. Many methods exist to retrieve phenology from optical remote sensing data. While the resulting information aids in differentiating plant species or plant functional types, satellite-derived products are usually different from what can be observed in the field. West Africa experiences a strong climate gradient from the hot and dry Sahara Desert region to the moist Guinean forest ecozone. In terms of optical remote sensing, capabilities to retrieve dense time series is limited by frequent cloud cover, particularly in the southern part of the region. Therefore, we propose to use Sentinel-1 Synthetic Aperture Radar (SAR) data to retrieve phenology at pixel level (10 m spatial resolution). In recent years, Sentinel-1 SAR data is increasingly used to characterize phenology of field crops. Little is known about phenology of West African vegetation, particularly non-crops. Consequently, we sampled all classes of the Copernicus Land Cover product covering the ECOWAS region in West Africa and explored Sentinel-1 time series. Our pre-processing includes radiometric terrain correction, speckle filtering and time series smoothing using a Savitzky-Golay filter. For West Africa, only data in ascending orbit is available, resulting in a reduced temporal resolution compared to other regions. From the two polarizations, VV and VH, we computed several well-established indices (e.g. VH/VV ratio, radar vegetation index). We sampled the whole region, resulting in 250 samples per land cover class. For each sampling point we extracted the time series of the backscatter as well as the indices and tested their similarity. As the Copernicus product also provides fractions of each class, we were able to explore the relationship between fractional tree cover (and others) and the SAR backscatter and indices, respectively. Our results show that some of the classes are no longer separable at high spatial resolution (e.g. open evergreen forest vs. closed evergreen forest). After adequate join of similar classes, we were able to use the backscatter information as well as the uncorrelated indices to map Copernicus land cover classes at high spatial resolution (10 m) with acceptable accuracy. From the smoothed time series, we derived phenological parameters such as start of season, end of season and length of season, greenup and senescence. While a direct link to ground phenology is challenging, we are able to map groups of similar phenological behavior, which is important for a more comprehensive characterization of vegetation