The shift in the use of agriculture land area had been undergone for recent years. In Taiwan, there are many cases where the agriculture area is not being cultivated but it is used for different purpose. In this study, we call this area as the impervious area. Impervious area evolves many kinds of purposes such as housing, factory, warehouse, graveyard, etc. The loss of farmland could further lead to the threat of national food production shortage and also soil pollution. For this reason, a periodical assessment to record the change of total farmland area is important to be carried out. The task could be laborious since Taiwan has an immense agriculture land area. An approach was proposed by utilizing the integration of Pleiades high-resolution satellite imagery and LiDAR data. The pansharpened Pleiades image was processed by using Normalized Digital Vegetation Index (NDVI) algorithm. The height information derived from LiDAR data was processed to produce the Normalized Digital Surface Model (nDSM). The candidate impervious area was selected by setting a NDVI threshold and a nDSM threshold. A pixel-based unsupervised classification would be carried out to this smaller extent of Pleiades. As the result, the time required for processing the images to delineate the impervious area could be reduced.
|Publication status||Published - 2017|
|Event||38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017 - New Delhi, India|
Duration: 2017 Oct 23 → 2017 Oct 27
|Other||38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017|
|Period||17-10-23 → 17-10-27|
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications