Mapping and monitoring heterogeneous landscapes: spatial, spectral and temporal unmixing of MERIS data

Research output: ThesisPhD Thesis - Research external, graduation external

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Abstract

Our environment is continuously undergoing change. This change takes place at several spatial and temporal scales and it is largely driven by anthropogenic activities. In order to protect our environment and to ensure a sustainable use of natural resources, a wide variety of national and international initiatives have been established. In this context, Earth observation sensors can provide a substantial amount of information about the biotic and abiotic conditions of our planet. For instance, high spatial resolution sensors, like Landsat TM, deliver data that can be used to produce maps of canopy properties and of land cover types. However, the use of this kind of sensors is not feasible for obtaining full coverage of large areas. Furthermore, high spatial resolution sensors generally do not provide sufficient temporal resolution for monitoring vegetation development during the year. This is especially true for areas having severe cloud coverage throughout the year. In this respect, coarse spatial resolution sensors, which deliver nearly daily data, have a higher chance of encountering cloud free areas. This facilitates large scale monitoring studies but at the expense of a lower spatial resolution providing images with potentially many mixed pixels.
Recent developments in imaging devices resulted into a new kind of sensor that works at a medium spatial resolution while providing high temporal and spectral resolutions. The MEdium Resolution Imaging Spectrometer (MERIS) aboard the European Space Agency’s ENVISAT platform belongs to this category. MERIS measures the solar radiation reflected from the Earth’s surface in 15 narrow spectral bands and it has a revisit time of 2-3 days. This unprecedented spectral and temporal resolution has resulted in several land, water and atmospheric products. In addition, two vegetation indices have been specifically designed to monitor vegetated canopies using this sensor: the MERIS Terrestrial Chlorophyll index (MTCI) and the MERIS Global Vegetation Index (MGVI). However, the spatial resolution provided by this sensor – 300 m in full resolution (FR mode) – is not sufficient to accurately map and monitor heterogeneous and fragmented landscapes. This is why the synergic use of high spatial resolution and MERIS data is investigated in this thesis. More precisely, the objective of this thesis is to develop a multi-sensor and multi-resolution data fusion approach that allows mapping and monitoring of heterogeneous and highly fragmented landscapes using MERIS data. The Netherlands is selected as study area because of its mixed landscapes where patches of arable land, natural vegetation, forests, and water bodies can be found next to each other. Besides this, The Netherlands also suffers from frequent cloud coverage, which severely hampers operational mapping and monitoring with both high spatial and high temporal resolution.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Wageningen University & Research
Award date17 Sept 2008
Place of PublicationWageningen
Publisher
Print ISBNs978-90-8504-988-3
Publication statusPublished - 2008

Keywords

  • ADLIB-BOOK-627

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