在後疫情時代，許多跨國企業為分散供應鏈風險而轉移工廠回台灣，在工業用地需求增加下，導致部分工廠選擇建設於非工業用地，若缺乏適當地監督及管理，可能威脅鄰近農田或住宅環境。為避免此一情況，定期地監測土地利用及土地覆蓋變化有助於偵測違規土地開發行為。
本研究區域為臺灣北部的桃園市，近年來因都市發展迅速，若以人力進行土地變異查緝曠日廢時，而使用光學衛星影像進行變遷偵測，可借助其高即時性及大面積觀測優勢，提高監測效率，若再透過整合不同來源的衛星影像，將能夠進一步地提高監測頻率。
過去文獻中曾提出許多不同變遷偵測方法，然而對於季節性變化顯著的區域如水稻田等農業區，植物生長周期之地物覆蓋因季節不同而常被誤判為變異區域，成為判斷是否確為變遷的問題。對此，本研究蒐集2017至2021年間涵蓋研究區的Sentinel-2及SPOT-6/7影像，得到長時間跨度的影像序列，將時序資料進行相對正規化處理，並透過最小二乘法擬合週期性變化分析其藍、綠和近紅外光波段反射率在五年內的震盪，從而建立出時間與反射率強度之關係，並基於該成果為不同季節設定門檻值，再透過門檻值與新拍攝之影像進行比較，偵測出新拍攝影像中的異常區域，可降低誤判具有季節性物候區域為變異點的情況。
經與驗證資料分析比對，研究成果能偵測出總數71%之變異點，若以面積計算其整體精度高達99%、Kappa Coefficient達0.86，顯示本研究方法能成功排除誤判季節性變遷的區域，並有效偵測出真實地表變異。

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.

[1] 古芷諼. (2022). 台商回流的趨勢展望與課題. 臺灣經濟研究月刊, 45(7), 33-41. doi:10.29656/term.202207_45(7).0005
[2] 農業部. 2021年全台盤查成果統計表. Retrieved from https://talis.moa.gov.tw/ALIES/farmlandsurveyreport/110/sheet001.htm
[3] 內政部營建署. 監測通報回報統計成果. Retrieved from https://landchg.tcd.gov.tw/Module/RWD/Web/pub_result.aspx
[4] (UCS), U. o. C. S. (2023/1/1). UCS Satellite Database. Retrieved from https://www.ucsusa.org/resources/satellite-database
[5] Singh, A. (1989). Review Article Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989-1003. doi:10.1080/01431168908903939
[6] Wulder, M. A., Hilker, T., White, J. C., Coops, N. C., Masek, J. G., Pflugmacher, D., & Crevier, Y. (2015). Virtual constellations for global terrestrial monitoring. Remote Sensing of Environment, 170, 62-76. doi:https://doi.org/10.1016/j.rse.2015.09.001
[7] Li, J., & Roy, D. P. (2017). A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring. Remote Sensing, 9(9), 902.
[8] Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., . . . Justice, C. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219, 145-161. doi:https://doi.org/10.1016/j.rse.2018.09.002
[9] Sánchez-Espinosa, A., & Schröder, C. (2019). Land use and land cover mapping in wetlands one step closer to the ground: Sentinel-2 versus landsat 8. Journal of Environmental Management, 247, 484-498. doi:https://doi.org/10.1016/j.jenvman.2019.06.084
[10] Dubovik, O., Schuster, G. L., Xu, F., Hu, Y., Bösch, H., Landgraf, J., & Li, Z. (2021). Grand Challenges in Satellite Remote Sensing. Frontiers in Remote Sensing, 2. doi:10.3389/frsen.2021.619818
[11] Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., . . . Waske, B. (2016). A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring. Remote Sensing, 8(1). Retrieved from doi:10.3390/rs8010070
[12] Qadir, A., & Mondal, P. (2020). Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India. Remote Sensing, 12(3). Retrieved from doi:10.3390/rs12030522
[13] Seo, D. K., Kim, Y. H., Eo, Y. D., Lee, M. H., & Park, W. Y. (2018). Fusion of SAR and Multispectral Images Using Random Forest Regression for Change Detection. ISPRS International Journal of Geo-Information, 7(10). Retrieved from doi:10.3390/ijgi7100401
[14] Zhu, Z. (2017). Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130. doi:10.1016/j.isprsjprs.2017.06.013
[15] Lee, H. (2008). Mapping Deforestation and Age of Evergreen Trees by Applying a Binary Coding Method to Time-Series Landsat November Images. IEEE Transactions on Geoscience and Remote Sensing, 46(11), 3926-3936. doi:10.1109/TGRS.2008.2001158
[16] Afify, H. A. (2011). Evaluation of change detection techniques for monitoring land-cover changes: A case study in new Burg El-Arab area. Alexandria Engineering Journal, 50(2), 187-195. doi:https://doi.org/10.1016/j.aej.2011.06.001
[17] Lawes, R. A., & Wallace, J. F. (2008). Monitoring an invasive perennial at the landscape scale with remote sensing. Ecological Management & Restoration, 9(1), 53-59. doi:10.1111/j.1442-8903.2008.00387.x
[18] Decuyper, M., Chávez, R. O., Lohbeck, M., Lastra, J. A., Tsendbazar, N., Hackländer, J., . . . Vågen, T.-G. (2022). Continuous monitoring of forest change dynamics with satellite time series. Remote Sensing of Environment, 269. doi:10.1016/j.rse.2021.112829
[19] Fu, P., & Weng, Q. (2016). A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment, 175, 205-214. doi:10.1016/j.rse.2015.12.040
[20] 內政部國土測繪中心. (2022). 109-110年國土利用現況調查成果鄉鎮市區統計資料. Retrieved from: https://www.tgos.tw/TGOS/Web/MetaData/TGOS_Query_MetaData.aspx?key=TW-07-301000100G-614015
[21] Son, N.-T., Chen, C.-F., Chen, C.-R., & Guo, H.-Y. (2020). Classification of multitemporal Sentinel-2 data for field-level monitoring of rice cropping practices in Taiwan. Advances in Space Research, 65(8), 1910-1921. doi:https://doi.org/10.1016/j.asr.2020.01.028
[22] Ramadhani, F., Pullanagari, R., Kereszturi, G., & Procter, J. (2020). Automatic Mapping of Rice Growth Stages Using the Integration of SENTINEL-2, MOD13Q1, and SENTINEL-1. Remote Sensing, 12(21). Retrieved from doi:10.3390/rs12213613
[23] 桃園市政府都市發展局. (2023). 桃園市政府都市發展局 工作報告. Retrieved from https://www.tycg.gov.tw/ch/home.jsp?id=10470&parentpath=0,10406
[24] (ESA), E. S. A. (2015). Sentinel-2 User Handbook.
[25] Center for Space and Remote Sensing Research, N. C. U. System and Product. Retrieved from https://www1.csrsr.ncu.edu.tw/rsrs/rsrs_product.php
[26] Astrium. (2013). SPOT 6 & SPOT 7 Imagery User Guide.
[27] 國立中央大學. (2023). 內政部營建署城鄉發展分署 111 年度「國土利用監測整合作業」委託專業服務案 總結報告書 (修正版). Retrieved from https://landchg.tcd.gov.tw/Module/RWD/Web/download.aspx
[28] 內政部營建署城鄉發展分署. (2020). 國土利用監測變異資料標準. In: 內政部營建署城鄉發展分署.
[29] Panuju, D. R., Paull, D. J., & Griffin, A. L. (2020). Change Detection Techniques Based on Multispectral Images for Investigating Land Cover Dynamics. Remote. Sens., 12, 1781.
[30] 鄭偉成. (2019). 使用動態門檻值選取對衛星影像進行非監督式變遷偵測. (碩士), 國立中央大學, Retrieved from http://thesis.lib.nccu.edu.tw/record/#GC107022004%22.
[31] Paolini, L., Grings, F., Sobrino, J., Jimenez, J.-C., & Karszenbaum, H. (2006). Radiometric correction effects in Landsat multi-date/multi-sensor change detection studies. International Journal of Remote Sensing - INT J REMOTE SENS, 27, 685-704. doi:10.1080/01431160500183057
[32] Syariz, M. A., Lin, B.-Y., Denaro, L. G., Jaelani, L. M., Van Nguyen, M., & Lin, C.-H. (2019). Spectral-consistent relative radiometric normalization for multitemporal Landsat 8 imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 147, 56-64. doi:https://doi.org/10.1016/j.isprsjprs.2018.11.007
[33] Schott, J. R., Salvaggio, C., & Volchok, W. J. (1988). Radiometric scene normalization using pseudoinvariant features. Remote Sensing of Environment, 26(1), 1-16. doi:https://doi.org/10.1016/0034-4257(88)90116-2
[34] Xu, H., Zhou, Y., Wei, Y., Liu, C., Li, X., & Chen, W. (2023). A Relative Radiometric Normalization Method for Enhancing Radiometric Consistency of Landsat Time-Series Imageries. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 5797-5812. doi:10.1109/JSTARS.2023.3288973
[35] 戴于恒, & 蔡富安. (2018). 應用雷達差分干涉技術監測基礎設施之長期變形. Paper presented at the 第37屆測量及空間資訊研討會.
[36] Landis, J. R., & Koch, G. G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics, 33(1), 159-174. doi:10.2307/2529310