Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects

Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Remote Sensing of the Aquatic Environments

The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet

Assessing sustainable development in industrial regions towards smart built environment management using Earth observation big data

This thesis investigates the sustainability of nationwide industrial regions using Earth observation big data, from environmental and socio-economic perspectives. The research contributes to spatial methodology design and decision-making support. New spatial methods, including the robust geographical detector and the concept of geocomplexity, are proposed to demonstrate the spatial properties of industrial sustainability. The study delivers scientific decision-making advice to industry stakeholders and policymakers for the post-construction assessment and future planning phases. The research has been published in prestigious geography journals, demonstrating its success

A novel approach to estimate glacier mass balance in the Tien Shan and Pamir based on transient snowline observations

Glaciers are recognised as an excellent proxy for climate change and their centennial massloss has accelerated during the past decades. The Central Asian mountain ranges Tien Shan and Pamir host over 25,000 glaciers that have been observed to respond heterogeneous to climate change. Glacier changes in the region have very important consequences on the water availability for the densely populated lowlands. Despite the significance and severity that climate change exerts on the Central Asian water towers, the glacier response is still poorly understood, hampering sound interpretations and predictions of future threats and opportunities. A significant data gap in the field measurement series from the mid-1990s to around 2010, limits the analysis of long-term trends. Despite the recent efforts to re-established the historical cryospheric monitoring network, continuous long-term glacier mass balance time series remain sparse for Central Asia. Thus, improved temporal and spatial coverage of glacier monitoring is essential. Remote sensing techniques are a powerful tool to study a large number of remotely located and unmeasured glaciers and provide a possibility to partly bridge the aforementioned deficit in data availability. However, the coarse temporal resolution of geodetic mass balance assessments is not suitable to improve the understanding of ongoing processes. This accentuates the indispensable need for improved and extended annual to seasonal observations of mass change of inaccessible and remote glaciers on a cost and labour effective basis as well as for a more elaborated and enhanced, process-orientated methodology. This work provides a combination of detailed in situ measurements and remote sensing based glacier mass change observation from local to regional scale. A multi-level strategy is applied to complement data from long-term glaciological surveys and remote sensing (snowline observations and geodetic mass balance measurements) with numerical modelling to obtain information at high temporal and spatial resolution for individual glaciers. Through modelling constrained with transient snowlines, annual mass balance time series for a large amount of glaciers located in the Tien Shan and Pamir were made available. Such mass balance estimates provide valuable baseline data for climate change assessments, runoff projection, hazard evaluation and enhance process understanding. A better understanding of the regional annual variability of glacier response to climate change in the Pamir and Tien Shan became possible based on the outcome of this thesis. In the presented thesis the results are discussed in detail, the weaknesses and strengths of the developed methodology are unfolded and the relevant perspective and future research outlined.Gletscher sind ausgezeichnete Indikatoren für den Klimawandel. Ihr langjähriger Massen- verlust hat sich in den letzten Jahrzehnten weltweit akzentuiert. Die zentralasiatischen Bergketten Tien Shan und Pamir beherbergen u¨ber 25’000 Gletscher. Studien zeigen, dass diese Gletscher heterogen auf den Klimawandel reagieren. Gletscherver¨anderungen in der Region haben wichtige Auswirkungen auf die Wasserverfügbarkeit für das dicht besiedelte Flachland. Trotz den bedeutenden Konsequenzen welche durch den Klimawandel auf diese regionalen Wasserspeicher ausgeübt wird, ist die Veränderung der Gletscher im Tien Shan und Pamir immer noch relativ unbekannt, was fundierte Interpretationen und Vorhersagen zukünftiger Gefahren und Chancen erschwert. Eine prägnante Datenlücke in den existierenden Messreihen von Mitte der 1990er Jahren bis ca. 2010 schränkt eine detaillierte Analyse langfristiger Entwicklungen weiter ein. Trotz der jüngsten Bemühungen, das historische Kryosphäremessnetz wieder herzustellen, bleiben kontinuierliche Langzeitmessungen für die Gletscher in Zentralasien limitiert. Eine verbesserte zeitliche und räumliche Abdeckung der Gletscherbeobachtungen ist daher unerlässlich. Fernerkundungstechniken sind gängige Methoden, um eine große Anzahl abgelegener und unerforschter Gletscher zu untersuchen. Mit solchen Methoden kann das Defizit an Datenverfügbarkeit der Region teilweise kompensiert werden. Die grobe zeitliche Auflösung der geodätischen Massenbilanzberechnungen und das somit limitierte Prozessverständnis unterstreichen jedoch den unabdingbaren Bedarf nach verbesserten und erweiterten jährlichen bis saisonalen Massenbilanzbeobachtungen. Ab- schätzungen auf ausgedehnter räumlicher Skala, sowie eine stärkere Prozess orientierte Forschung sind nötig. Die vorliegende Arbeit beschreibt eine Kombination aus detaillierten Feldmessungen und Fernerkundungsbeobachtungen der Gletschermassenänderung im Tien Shan und Pamir. Die angewandte Strategie basiert auf mehreren Ebenen aus lokalen bis regionalen Studien. Mit dieser Strategie werden Daten aus langzeit-glaziologischen Feldmessungen und aus der Fernerkundung (Schneelinienbeobachtungen, geodätische Massenbilanzmessungen) mit numerischen Modellierungen komplementieren. Dabei werden Informationen für ausgewählte Gletscher mit hoher zeitlicher und räumlicher Auflösung extrahiert. Durch das Modellieren mit wiederholten Schneelinienbeobachtungen, welche zur Kalibrierung verwendet werden, konnten jährliche Massenbilanzzeitreihen für eine große Anzahl von Gletschern im Studiengebiet berechnet werden. Solche grossräumigen und zeitlich hochaufgelösten Abschätzungen liefern wertvolle Grundlagen für detaillierte Studien über die Auswirkungen des Klimawandels, ermöglichen fundierte Abflussprojektionen und erlauben verbesserte Gefahrenanalysen. Basierend auf den Ergebnissen dieser Arbeit, wird ein besseres Verständnis der regionalen jährlichen Variabilität der Gletscherreaktionen auf den Klimawandel im Pamir und Tien Shan ermöglicht. In der hier vorgelegten Arbeit werden die Resultate im Detail diskutiert, die Schwächen und Stärken der entwickelten Methodik offengelegt und die relevanten Perspektiven abgefasst

A Two-Stage Fusion Framework to Generate a Spatio–Temporally Continuous MODIS NDSI Product over the Tibetan Plateau

The Tibetan Plateau (TP) is an important component of the global environmental system, on which the snow cover greatly affects the regional climate and ecology. Moderate resolution imaging spectroradiometer (MODIS) snow cover products have been demonstrated to be appropriate for investigating the snow cover over the TP. However, they are subject to cloud obscuration, and the TP’s extremely complex terrain makes the snow monitoring difficult. Therefore, in this paper, we propose a two-stage spatio–temporal fusion framework for the cloud removal of MODIS C6 snow products, including an adjusted Terra and Aqua combination (TAC) and a spatio–temporal fusion based on Gaussian kernel function and error correction (STF-GKF-EC). To the best of our knowledge, this is the first time that a spatio–temporally continuous daily 500-m MODIS normalized difference snow index (NDSI) product has been generated for the TP, which greatly improves the spatial and temporal resolutions of the current snow cover products. The main stage, STF-GKF-EC, adaptively weights the spatial and temporal correlations by the Gaussian kernel function, and further takes the rapid changes of snow cover into consideration through the error correction. The experiments indicated that STF-GKF-EC removes clouds completely, achieving an overall accuracy (OA) and mean absolute error (MAE) of 91.48% and 3.88, respectively. Based on the cloud-removed results, during 2001–2017, as far as the intra-annual variation is concerned, a large proportion of the snow cover appears between October and May, with a peak in February/March, and the variation is mainly controlled by temperature. For the inter-annual variation, an obvious increasing trend of 0.68/year for NDSI is observed before 2005, followed by a slight decreasing trend of 0.16/year, in which precipitation is a better explanation factor than temperature

Spatio-temporal trends for long-lasting contemporary snow in Lesotho : implications for human and livestock vulnerability

M.Sc., Faculty of Science, University of the Witwatersrand, 2011Prolonged snow cover in Lesotho frequently results in human and livestock deaths, due to isolation and exposure. MODIS Rapid Response imagery is emerging as an important source of near real-time data for global hazard mapping and emergency response. A dataset of daily MODIS snow cover images for the period 2003 – 2010 was acquired for Lesotho. Combined with high-resolution SPOT satellite images for two study areas, MODIS snow cover images were used to establish the frequency, extent and timing of snowfalls. A digital elevation model was used in conjunction with mean air temperature data to investigate the effects of altitude, aspect and temperature on the mean rate of daily snowmelt. A strong correlation exists between mean day-time temperatures and the mean rate of daily snowmelt throughout the winter season. The mean rate of snow dissipation is most rapid after late season (September – November) snowfalls and least rapid after mid season (July – August) snowfalls. Snow cover persisting for 1 – 5 days dissipates at a higher mean rate than snow cover that has persisted for 6 – 10 days. Snow lasts longest on south-facing slopes above 2500m a.s.l, with evidence of increased ablation due to wind deflation and higher insolation levels in the highlands above 3400m a.s.l. The southern Drakensberg highlands in the district of Quthing have the highest mean duration of snow cover (21 – 25 days per annum). The seasonal extent and duration of snow cover was related to the spatial location of villages and roads in Lesotho, in order to determine individual vulnerability to negative impacts associated with prolonged snow cover. A ranking system was applied to each village according to the seasonal duration of snow cover, and the accessibility and proximity to the nearest road. Snowfalls occur between 1 and 8 times per annum on average. Therefore, village vulnerability is generally low, as most settlements are situated on predominantly north-facing slopes in the western lowlands and Senqu River Valley, which remain largely snow-free throughout the winter season. Few villages experience prolonged snow cover, which is limited to predominantly south-facing slopes above 2500m a.s.l along the escarpment and interior mountain ranges. Village vulnerability increases during the mid season period as a result of the increased frequency and duration of snow cover in July and August. The villages of Thoteng (Butha-Buthe), Letseng-la-Terae (Mokhotlong) and Mabalane (Butha-Buthe) have the highest vulnerability for the 2003 – 2010 period