A Study On Floodplain Inundation Analysis And Modeling Method Based On Remote Sensing And GIS

Flood is one of the most common natural phenomena across lots of regions in the world. It not only affects the living and production of human being, but also affects the survival and prosperity of flora and fauna communities along the rivers and around the lakes. As a most important characteristic of flood, inundation has always been a focus in flood studies. Researches on flood inundation involve sensing, mapping and modeling of flood inundation represented by inundation probability estimation and inundation extent prediction. With the development of space technologies, Geographic Information System (GIS) and Remote Sensing (RS) are playing a more and more important role in these studies, with increasing number of related researches emerging. However, there are still a lot of issues that need to be investigated. The first one is the accuracy and stability issue of inundation detection using remote sensing technology, as well as the impact from mutual restrain of spatial and temporal resolutions of remotely sensed imagery on flood inundation detection and mapping. In addition to that, for a specific region, analysing the historical inundation as well as future inundation probability is equally important. However, there are only some flood frequency analysis methods utilizing time-series observed flow data to reveal the flood probability. Few of these methods used GIS and remote sensing technologies, which hampered the derivation of flood inundation probability that has spatial characteristics. One more thing, for floodplain area, inundation has a close relationship with the water quantity upstream, as well as the terrain. Several studies have established empirical relationship models between in-channel observed flow and floodplain inundation, but these models did not take the terrain into consideration. Therefore, the analysis and modeling of floodplain inundation based on these relationship models did not have strong theoretical basis.After elaborated the progress of flood inundation studies, this thesis proposed a floodplain inundation analysis and modeling method based on RS and GIS. The method involved the sensing, mapping and modeling of flood inundation. Main contents and contributions of this thesis were summarized as follows: (1) MODIS (Moderate-resolution Imaging Spectroradiometer) data have several advantages such as broad coverage, short revisit time and high accessibility, which makes them an ideal tool for flood inundation detection over broad areas. This thesis made a detailed introduction on the MODIS data, and then described its application in flood inundation detection. The methods for inundation delineation from MODIS image were summarized, represented by the water index methods. It was found that most of these methods were not suitable for automatic delineation because they generally required human intervention. Therefore, an Open Water Likelihood (OWL) index was introduced here. An advantage of the OWL index is that it appears to be stable and consistent on a time-series of images. Using Landsat images which cover the same period but have a much higher resolution, a comprehensive evaluation was then conducted to ensure its accuracy and reliability in flood inundation detection from MODIS OWL imagery. Evaluation results demonstrated that inundation extents detected from a time-series of MODIS imagery using OWL index have high accuracy and strong stability, which means a universal threshold is applicable to automatically delineate inundation extent.(2) Observed flow data have a long record history, while remotely sensed data are able to reflect the spatial distribution of flood inundation quickly and efficiently. This thesis thus proposed a method for analyzing the spatio-temporal dynamics of floodplain inundation using a combination of these two types of data. This method made use of the advantages of both data to derive the spatial and temporal characteristics of flood inundation at large basin scale. The Murray-Darling Basin (MDB) in Australia was selected as a case study area. A zoning process was conducted using stream network data and gauge location data, in order to ensure the close relationship between the observed flow and remotely sensed inundation within each zone. Based on the zoning results, a series of flood inundation characteristic maps, including inundation duration map, annual inundation map, inundation frequency map and inundation probability map, were derived for this big river basin through the method of combining time-series observed flow data and MODIS imagery. (3) Existing flood inundation modeling methods that are based on the empirical relationship models between in-channel observed flow and floodplain inundation did not have strong theoretical basis. Therefore, through introducing terrain data, this thesis proposed an analysis and modeling method for flood inundation connectivity and depth at river reach scale using a combination of observed flow data, Landsat imagery and DEM (Digital Elevation Model) data. This method was then applied to a typical river reach in MDB. The relationship between downstream floodplain inundation and upstream observed gauge flow was established with stronger theoretical basis. Based on this relationship, inundation conditions including connectivity and depths over the floodplain area can be predicted under different flow conditions.(4) When remotely sensed data are utilized for flood inundation mapping, the existence of mixed pixel is an important but unavoidable factor that limits the mapping accuracy, especially for those data with coarse spatial resolutions such as MODIS. This thesis investigated the feasibility of using DEM to improve the resolution and accuracy of flood inundation maps through the method of pixel unmixing and reconstruction. It then proposed a DEM-based modified sub-pixel mapping algorithm for enhancing floodplain inundation mapping. Test data were employed to evaluate the performance of this algorithm. Evaluation results demonstrated that the modified algorithm is able to derive a better inundation map than the traditional algorithm, either in the form of shape or accuracy.