IMPLEMENTING SIFT AND BI-TRIANGULAR PLANE TRANSFORMATION FOR INTEGRATING DIGITAL TERRAIN MODELS

Since their inception in the middle of the twentieth century, Digital Terrain Models (DTMs) have played an important role in many fields and applications that are used by geospatial professionals, ranging from commercial companies to government agencies. Thus, both the scientific community and the industry have introduced many methods and technologies for DTM generation and data handling. These resulted in a high volume and variety of DTM databases, each having different coverage and data-characteristics, such as accuracy, resolution, level-of-detail – amongst others. These various factors can cause a dilemma for scientists, mappers, and engineers that now have to choose a DTM to work with, let alone if several of these representations exist for a specified area. Traditionally, researchers tackled this problem by using only one DTM (e.g., the most accurate or detailed one), and only rarely tried to implement data fusion approaches, combining several DTMs into one cohesive unit. Although to some extent this was successful in reducing errors and improving the overall integrated DTM accuracy, two prominent problems are still scarcely addressed. The first is that the horizontal datum distortions and discrepancies between the DTMs are mostly ignored, with only the height dimension taken into account, even though in most cases these are evident. The second is that most approaches operate on a global scale, and thus do not address the more localized variations and discrepancies that are presented in the different DTMs. Both problems affect the resulting integrated DTM quality, which retains these unresolved distortions and discrepancies, resulting in a representation that is to some extent inferior and ambiguous. In order to tackle this, we propose an image based fusion approach: using the SIFT algorithm for matching and registration of the different representations, alongside localized morphing. Implementing the proposed approach and algorithms on various DTMs, the results are promising, with the capacity correctly geospatially align the DTMs, thus reducing the mean height difference variance between the databases to close to zero, as well as reducing the standard deviation between them by more than 30 %

ASSESSMENTS OF MULTISCALE PRECIPITATION DATA FUSION AND SOIL MOISTURE DATA ASSIMILATION AND THEIR ROLES IN HYDROLOGICAL FORECASTS

Precipitation is the most important input for hydrological simulations and soil moisture contents (SMCs) are the most important state variables of hydrological system. We can improve hydrological simulations by improving the quality of precipitation data and assimilating satellite-measured SMC data into land surface simulation. Multiscale data fusion is an effective approach to derive precipitation data due to the multiscale characteristics of precipitation measurements. Multiscale data assimilation is the exact approach to assimilate satellite-measured SMC data into land surface simulations when measurements and model simulations are not at the same spatial resolution. To date, no systematic assessments of these approaches have been conducted in hydrological simulations. For the purpose of improving hydrological forecast, this study assesses influences of precipitation data fusion and soil moisture data assimilation on the simulations of streamflow, SMCs and evapotranspiration over 14 watersheds selected from the Ohio River Basin. As the technical basis of this study, a large-scale flow routing scheme and a parameter calibration scheme with multiple precipitation inputs are developed for Noah LSM. A multiscale data fusion algorithm, namely Multiscale Kalman Smoother (MKS) based framework, which plays an important role in multiscale precipitation data fusion and multiscale soil moisture data assimilation, is assessed in a large experimental site with 2246 precipitation events in 2003. Three precipitation data products are derived by fusing NLDAS-2 precipitation data product and NEXRAD MPE precipitation data product with the MKS-based framework. For the assessment over the 14 watersheds in three individual years, essential improvements of hydrological simulation have been found for a half number of cases. Findings of this assessment show that precipitation data fusion is a statistically effective approach to improve hydrological simulations. To assess the influences of soil moisture data assimilation on hydrological simulation, AMSR-E SMC data are assimilated into land surface simulation by Noah LSM. Results show that soil moisture data assimilation has not improved hydrological simulations for most of cases because AMSR-E data underestimate SMC compared with model simulations. However, for those cases in which precipitation data overestimate real precipitation, the soil moisture data assimilation has been proved as an effective approach to improve hydrological simulations