1,721,497 research outputs found
Thermal Infrared Remote Sensing: Sensors, Methods, Applications
No full textThis book provides a comprehensive overview of the state of the art in the field of thermal infrared remote sensing. Temperature is one of the most important physical environmental variables monitored by earth observing remote sensing systems. Temperature ranges define the boundaries of habitats on our planet. Thermal hazards endanger our resources and well-being. In this book renowned international experts have contributed chapters on currently available thermal sensors as well as innovative plans for future missions. Further chapters discuss the underlying physics and image processing techniques for analyzing thermal data. Ground-breaking chapters on applications present a wide variety of case studies leading to a deepened understanding of land and sea surface temperature dynamics, urban heat island effects, forest fires, volcanic eruption precursors, underground coal fires, geothermal systems, soil moisture variability, and temperature-based mineral discrimination. ‘Thermal Infrared Remote Sensing: Sensors, Methods, Applications’ is unique because of the large field it spans, the potentials it reveals, and the detail it provides. This book is an indispensable volume for scientists, lecturers, and decision makers interested in thermal infrared technology, methods, and applications
Bündelung der Fernerkundungsaktivitäten im DLR - Das Cluster "Angewandte Fernerkundung"
No full text
Calibration and Pre-processing of a Multi-decadal AVHRR Time Series
No full textSince the early 1980s, the German Remote Sensing Data Centre (DFD) of the German Aerospace Centre (DLR) has received archived and processed Advanced Very High Resolution Radiometer (AVHRR) data from the Polar Orbiting Environmental Satellites (POES) of the National Oceanic and Atmospheric Administration (NOAA). By December 2013, over 237,000 paths over Europe have since been archived at DLR. Based on these High Resolution Picture Transmission (HRPT) raw datasets, an operational pre-processing and value-adding chain has been developed (Dech et al., Aerosp Sci Technol 2(5):335–346, 1998; Tungalagsaikhan et al., Proc. 23th DGPF (12), 2003). In this chapter, the series of AVHRR sensors is introduced, and information on calibration and system correction procedures is given. Next, the pre-processing part of DLR’s processing chain is described, where focus is set on the calibration aspects. Time series examples are provided to show the influence of changes in calibration over time, and to illustrate the need for consistent pre-processing and data harmonization. According to these requirements DLR’s multi-decadal archive of AVHRR data will be re-processed in the frame of the TIMELINE project, providing consistent and well-calibrated time series data
Analysis of Snow Cover Time Series – Opportunities and Techniques
No full textSnow cover is one of the most dynamic land cover parameters that can be monitored from space and plays an important role for the Earth’s climate system and hydrological circle.While the spatial extent can be limited to narrow mountain ridges during summer, the snow cover percentage on the Northern Hemisphere may exceed 50 % (Lemke et al., Observations: changes in snow, ice and frozen ground. In: Solomon S, Qin D, Manning M, Chen Z, Marquis MC, Averyt K, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contributions of Working Group 1 to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge and New York, pp 337– 383, 2007) of the total land surface (~45 million km 14 2) during winter seasons (Barry et al., Global outlook for ice & snow. United Nations Environment Programme, Hertfordshire, 2007). Remote sensing has been used since the early 1970s to map terrestrial snow cover (Brown, J Clim 13:2339–2355, 2000) and both – sensors as well as retrieval algorithms – have undergone a substantial development since that time. This chapter will give a short introduction on how snow cover can be monitored from space. Furthermore, techniques will be outlined that show how time series analyses can be applied to remotely sensed snow cover products to reduce the compromising effect of cloud cover and to investigate the fundamental characteristics of snow. Time series of snow cover data allow for various analyses covering the fields of hydrology, climate research, flood prediction and management, and economy. Short term variations and extreme events can be analysed as well as long term climatological trends, constituting time series of snow cover data a valuable tool for a large bandwidth of applications
Remote Sensing Time Series Revealing Land Surface Dynamics: Status Quo and the Pathway Ahead
No full textThe face of our planet is changing at an unprecedented rate. Forest ecosystems diminish at alarming speed, urban and agricultural areas expand into the surrounding natural space, aquaculture is spreading, sea level rise leads to changes in coastal ecosystems, and even without obvious land cover change, land use intensity may change and complex ecosystems may undergo transient changes in composition. Satellite based earth observation is a powerful means to monitor these changes, and especially time series analysis holds the potential to reveal long term land surface dynamics. Whereas in past decades time series analysis was an elaborate undertaking mostly performed by a limited number of experts using coarse resolution data, attention shifts nowadays to open source tools and novel techniques for analyzing time series and the utilization of the same for numerous environmental applications. The reasons are the pressing call for climate-relevant, long term data analyses and value added products revealing past land surface dynamics and trends, the growing demand for global data sets, and the opening up of multidecadal remote sensing data archives, all at a time of considerably- improved hardware power, computer literacy, and a general trend towards cloud solutions and available open source algorithms and programming languages. This chapter presents a comprehensive overview of time series analysis. We introduce currently orbiting optical, radar, and thermal infrared sensors and elucidate which of them are suitable for long term monitoring tasks based on remote sensing time series analysis. We briefly summarize the theoretical concept of time series components and important seasonal statistical features and list the types of variables usually analyzed as time series. Furthermore, we address data related, sensor related, location related, and processing related challenges of time series analysis. Lastly, we assess current developments and upcoming opportunities
- …
