柬埔寨為東南亞常發生洪水災害的國家之一，其地理位置位於湄公河下游地區，地勢上受到高原與高山環繞，中央地區則為低窪的洪氾區。季風季節期間，降於洞里薩湖與湄公河河岸之劇烈強降雨往往導致災難性洪災。由於當地居民以農業生產維生，水稻生態系統中洪災損失評估在此一地區扮演著相當重要的角色。本研究針對2013年10月侵襲柬埔寨中央地區之劇烈颱風個案為研究案例，利用 Landsat 8 OLI 與 MODIS 資料，提出以物件導向之方法用於洪水區域劃定與影響水稻田範圍評估。在洪水區域劃定部分，Landsat 8 OLI Level 1產品被用於偵測洪水區域，然而使用之衛星影像西北部分被雲層遮蔽，因此選用增強時空適應反射率融合模式（ESTARFM）產製具Landsat空間解析度之無雲合成影像以進行洪水範圍偵測。於水稻田辨識部分，依據植被與水體指標於插秧時期之季節間變化，使用Terra（MOD13Q1）和Aqua（MYD13Q1）MODIS植被指數產品以辨識水稻田範圍。在此方法中，以物件之局部方差變化用於估計最佳尺度參數以進行影像分割處理。結果顯示，洪水範圍偵測部分以高空間解析度之影像作為驗證，其整體精度高於95%；而水稻田辨識部分，辨識成果與統計資料呈現良好相關性(R2 = 0.675)。洪水範圍與水稻田位置圖則進行圖層套疊進一步分析受洪水災害影響之水稻田範圍。洪水區域劃定與影響水稻田範圍成果將有助於提供地方政府寶貴之資訊，以利洪水減災及災害補償與重建。本研究提出之方法將可應用於其他研究區域，或針對無現地洪水觀測或雷達遙測資料之區域，以進行大尺度之即時觀測與洪水災害評估。;Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains. It usually experiences devastating floods induced by an overwhelming concentration of rainfall water over the Tonle Sap Lake’s and Mekong River’s banks during monsoon seasons. Flood damage assessment in the rice ecosystem plays an important role in this region as local residents rely heavily on agricultural production. This study introduced an object-based approach to flood mapping and affected rice field estimation in central Cambodia after the flood event induced by a severse typhoon occurred in October 2013 using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data. For flood mapping, the Landsat 8 OLI Level 1 products were used to detect inundation regions. However, the images over northwestern region was covered by cloud; therefore, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was appied to generate cloud-free Landsat-scale synthetic data for flood detection in this area. For rice identification, Terra (MOD13Q1) and Aqua (MYD13Q1) MODIS vegetation index products were utilized to identify the paddy rice field based on seasonal inter-variation between vegetation and water index during the transplanting stage. In this approach, image segmentation process was conducted with optimal scale parameter estimation based on the variation of objects’ local variances. The inundated area was identified with an overall accuracy of higher than 95% compared to those derived from finer spatial resolution images. The rice classification result was well correlated with the statistical data at a commune level (R2 = 0.675). The inundation and paddy rice maps were overlaid and further analyzed to estimate rice area impacted by the disaster. The flood mapping and affected rice estimation result is really useful as it provides local governments with valuable information for flooding mitigation and post-flooding compensation and restoration. The success and findings of this procedure could be promisingly applied in other areas to timely observe and assess the impacts of flood disasters at a large scale and in the areas where in situ flooding observation is inoperable or radar remotely sensed data is unavailable.