| dc.description.abstract | In the post-pandemic era, many corporations have relocated factories to Taiwan to diversify their supply chain risks. Due to increased demand for industrial land, some factories may build in non-designated zones without appropriate management, threatening land usage in the neighboring residential and agricultural areas. In this situation, periodical monitoring of land use and land cover changes can assist in identifying unauthorized land development. Therefore, our study focuses on monitoring land change in Taoyuan City in northern Taiwan, where urban growth has been accelerated in recent years. Investigating land use change by on-site survey may take a long time and could be more efficient; hence, using optical satellite imagery for change detection has the advantages of high timeliness and extensive observational coverage, improving monitoring efficiency. By integrating satellite images from different sources, the frequency of monitoring can be further increased.
Various methods for change detection have been proposed. Still, for areas with significant seasonal change, such as rice fields and other agricultural zones, changes in the land surface due to plant growth cycles can be misinterpreted as anomalies, posing challenges in assessing surface changes. In the past, various methods have been proposed for change detection. However, regions with the abovementioned ambiguities may be misinterpreted as changes, posing challenges in change detection. In this study, we collected a series of Sentinel-2 and SPOT-6/7 satellite images covering our study area from 2017 to 2021. We process these images through relative radiometric normalization and use a periodical model to fit changes in reflectance (blue, green, and near-infrared bands) in a least squares sense over the five-year time series. This allows us to establish a relationship between time and reflectance, setting a dynamic threshold value for any given time in a year. This model can use these thresholds with a newly captured image to detect areas that had changed in the last epoch, reducing misclassification areas with seasonal phenology as change points. Comparing our results with reference data, our method successfully detects 71% of total change points, achieving an overall accuracy of 99% and a Kappa coefficient of 0.86 in area validation. It is concluded that our method can avoid misclassification from seasonal change areas and effectively detect actual change points. | en_US |