建立海岸线预测系统是为了实现海岸线预测的量化和自动化。系统分由三个部分组成:遥感影像海岸线边缘提取与矢量化、海岸线相关因子计算(变化速率与分维度)和海岸线变化趋势预测。以海岸线遥感影像为数据,通过八邻域法海岸线边缘提取及...建立海岸线预测系统是为了实现海岸线预测的量化和自动化。系统分由三个部分组成:遥感影像海岸线边缘提取与矢量化、海岸线相关因子计算(变化速率与分维度)和海岸线变化趋势预测。以海岸线遥感影像为数据,通过八邻域法海岸线边缘提取及矢量化、海岸线相关因子计算两个功能模块的处理,基于GM(1,1)模型的海岸线变化趋势预测模块对输入的四期矢量海岸线数据建立GM(1,1)模型,根据用户输入的预测原点和预测精度,得到若干预测点,继而得到预测的海岸线轮廓。系统采用C#语言基于ESR I ArcOb jects的开发来实现。该系统在辽宁省国土资源规划地理信息系统中得到应用和推广。展开更多
The prediction of shoreline erosion is vital for coastal management. This study aims to utilize geo-informatics technology to increase accuracy of a shoreline prediction model along two study sites in Samutprakarn pro...The prediction of shoreline erosion is vital for coastal management. This study aims to utilize geo-informatics technology to increase accuracy of a shoreline prediction model along two study sites in Samutprakarn province and in Prachuabkirikhan province. Predicting coastline change using remote sensing together with GIS （geographic information system） is a spat^o-temporal technology, which can continuously provide perspectives of coastal areas. Due to a long term of operational period of LANDSAT satellite, it is useful to enhance accuracy of prediction model. LANDSAT-5 TM images acquired during 1999-2009 were used to produce historical shoreline vectors. Physical data were modified to be input data of digital shoreline analysis system. The model was validated. Linear regressions were applied in order to derive equations of erosion magnitude. The result presents that averaged erosion and accretion rate along Samutprakarn province was 22.30 meters/year and 2.94 meters/year, respectively. On the other hand, the average rate of coastal erosion along Prachuabkirikhan province was much lower, being 2.48 meters/year while the accretion rate was approximately 4.11 meters/year. The predicted shoreline change at Samutprakarn province in 2019 is about -132.69 ~ 0.758 meters while at Prachuabkirikhan is 40.58 ~ 0.0012 meters. In conclusion, this prediction model focused the changing of shoreline in long term and accuracy of the model could be improved by increasing number of shorelines vectors, transect intervals and resolution of satellite images. Clearly, the model is flexible and can be applied in other particular areas for coastal zone management in Thailand.展开更多
文摘建立海岸线预测系统是为了实现海岸线预测的量化和自动化。系统分由三个部分组成:遥感影像海岸线边缘提取与矢量化、海岸线相关因子计算(变化速率与分维度)和海岸线变化趋势预测。以海岸线遥感影像为数据,通过八邻域法海岸线边缘提取及矢量化、海岸线相关因子计算两个功能模块的处理,基于GM(1,1)模型的海岸线变化趋势预测模块对输入的四期矢量海岸线数据建立GM(1,1)模型,根据用户输入的预测原点和预测精度,得到若干预测点,继而得到预测的海岸线轮廓。系统采用C#语言基于ESR I ArcOb jects的开发来实现。该系统在辽宁省国土资源规划地理信息系统中得到应用和推广。
文摘The prediction of shoreline erosion is vital for coastal management. This study aims to utilize geo-informatics technology to increase accuracy of a shoreline prediction model along two study sites in Samutprakarn province and in Prachuabkirikhan province. Predicting coastline change using remote sensing together with GIS （geographic information system） is a spat^o-temporal technology, which can continuously provide perspectives of coastal areas. Due to a long term of operational period of LANDSAT satellite, it is useful to enhance accuracy of prediction model. LANDSAT-5 TM images acquired during 1999-2009 were used to produce historical shoreline vectors. Physical data were modified to be input data of digital shoreline analysis system. The model was validated. Linear regressions were applied in order to derive equations of erosion magnitude. The result presents that averaged erosion and accretion rate along Samutprakarn province was 22.30 meters/year and 2.94 meters/year, respectively. On the other hand, the average rate of coastal erosion along Prachuabkirikhan province was much lower, being 2.48 meters/year while the accretion rate was approximately 4.11 meters/year. The predicted shoreline change at Samutprakarn province in 2019 is about -132.69 ~ 0.758 meters while at Prachuabkirikhan is 40.58 ~ 0.0012 meters. In conclusion, this prediction model focused the changing of shoreline in long term and accuracy of the model could be improved by increasing number of shorelines vectors, transect intervals and resolution of satellite images. Clearly, the model is flexible and can be applied in other particular areas for coastal zone management in Thailand.
