TY - JOUR A1 - Kuenzer, Claudia A1 - Klein, Igor A1 - Ullmann, Tobias A1 - Georgiou, Efi Foufoula A1 - Baumhauer, Roland A1 - Dech, Stefan T1 - Remote Sensing of River Delta Inundation: Exploiting the Potential of Coarse Spatial Resolution, Temporally-Dense MODIS Time Series JF - Remote Sensing N2 - River deltas belong to the most densely settled places on earth. Although they only account for 5% of the global land surface, over 550 million people live in deltas. These preferred livelihood locations, which feature flat terrain, fertile alluvial soils, access to fluvial and marine resources, a rich wetland biodiversity and other advantages are, however, threatened by numerous internal and external processes. Socio-economic development, urbanization, climate change induced sea level rise, as well as flood pulse changes due to upstream water diversion all lead to changes in these highly dynamic systems. A thorough understanding of a river delta's general setting and intra-annual as well as long-term dynamic is therefore crucial for an informed management of natural resources. Here, remote sensing can play a key role in analyzing and monitoring these vast areas at a global scale. The goal of this study is to demonstrate the potential of intra-annual time series analyses at dense temporal, but coarse spatial resolution for inundation characterization in five river deltas located in four different countries. Based on 250 m MODIS reflectance data we analyze inundation dynamics in four densely populated Asian river deltas-namely the Yellow River Delta (China), the Mekong Delta (Vietnam), the Irrawaddy Delta (Myanmar), and the Ganges-Brahmaputra (Bangladesh, India)-as well as one very contrasting delta: the nearly uninhabited polar Mackenzie Delta Region in northwestern Canada for the complete time span of one year (2013). A complex processing chain of water surface derivation on a daily basis allows the generation of intra-annual time series, which indicate inundation duration in each of the deltas. Our analyses depict distinct inundation patterns within each of the deltas, which can be attributed to processes such as overland flooding, irrigation agriculture, aquaculture, or snowmelt and thermokarst processes. Clear differences between mid-latitude, subtropical, and polar deltas are illustrated, and the advantages and limitations of the approach for inundation derivation are discussed. KW - difference water index KW - ENVISAT ASAR WSM KW - TerraSAR-X KW - central asia KW - SAR imagery KW - synthetic aperture radar KW - mekong delta KW - mangrove ecosystems KW - flood detection KW - dynamics Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-151552 VL - 7 SP - 8516 EP - 8542 ER - TY - JOUR A1 - Nguyen, Duy Ba A1 - Kersten, Clauss A1 - Senmao, Cao A1 - Vahid, Naeimi A1 - Kuenzer, Claudia A1 - Wagner, Wolfgang T1 - Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data JF - Remote Sensing N2 - Rice is the most important food crop in Asia, and the timely mapping and monitoring of paddy rice fields subsequently emerged as an important task in the context of food security and modelling of greenhouse gas emissions. Rice growth has a distinct influence on Synthetic Aperture Radar (SAR) backscatter images, and time-series analysis of C-band images has been successfully employed to map rice fields. The poor data availability on regional scales is a major drawback of this method. We devised an approach to classify paddy rice with the use of all available Envisat ASAR WSM (Advanced Synthetic Aperture Radar Wide Swath Mode) data for our study area, the Mekong Delta in Vietnam. We used regression-based incidence angle normalization and temporal averaging to combine acquisitions from multiple tracks and years. A crop phenology-based classifier has been applied to this time series to detect single-, double- and triple-cropped rice areas (one to three harvests per year), as well as dates and lengths of growing seasons. Our classification has an overall accuracy of 85.3% and a kappa coefficient of 0.74 compared to a reference dataset and correlates highly with official rice area statistics at the provincial level (R-2 of 0.98). SAR-based time-series analysis allows accurate mapping and monitoring of rice areas even under adverse atmospheric conditions. KW - band SAR data KW - SAR KW - rice KW - WSM KW - ASAR KW - Envisat KW - MODIS image KW - Southeast China KW - polarimetric SAR KW - cropping systems KW - time-series KW - paddy rice KW - radar KW - paddy KW - rice mapping KW - Vietnam KW - Mekong-Delta KW - synthetic aperture radar KW - multitemporal ALOS/PALSAR imagery KW - soil moisture retrieval Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-137554 VL - 7 IS - 12 ER -