浅析隧道贯通测量中提高精度的方法

通过对磁浮隧道工程、轨道交通工程等大量工程实践的分析,探讨了减少隧道贯通横向误差的有效方法。     

Vegetation discrimination using irs-p3 wifs temporal dataset - a case study from rampa forests, eastern ghats, a.p.

Multitemporal data sets from coarse resolution sensors of Indian Remote Sensing Satellites provides an opportunity to classify various forest types using their phenological attributes reflected in temporal NDVI profiles. The present study attempts to classify various vegetation classes using time integrated NDVI (T-NDVI) values derived from IRS-P3 WiFS data. The algorithm explores the differential characteristics in T-NDVI values of different features and the results suggest the possible use of the methodology for forest type classification.     

Prediction of Land Cover Change Using Markov and Cellular Automata Models: Case of Al-Ain,UAE, 1992-2030

The UAE has witnessed rapid urban development and economic growth in recent years. With its ambitious vision to become one of the advanced nations by 2021, planners and policy-makers need to know the most likely direction of future urban development. In this study, remotely sensed imagery coupled with cellular automata models were used to predict land cover in Al Ain, the second largest city in the Emirate of Abu Dhabi. Markov and cellular automata models were used for 1992 and 2006 to predict land cover in 2012. Land Use and Land Cover maps for the study area were derived from 1992, 2006, and 2012 Landsat satellite images (TM, ETM⁺). The models achieved an overall accuracy of approximately 80 %. A Markov model was applied for 2006 and 2012 to predict land cover in 2030. The results conformed to the general trend of the Al Ain Master Plan 2030. This study demonstrates that remote sensing, with the availability of free Landsat data, is a viable technology that could be used to help in the prediction process especially in developing countries, where data availability is a problem.