城市化與人類社會發展息息相關。如何根據遙感數據探索城市化議題是相關重要的。夜間燈光影像為城市化和社會經濟變量的研究提供了一個新的領域，這與傳統衛星遙感有很大的不同。美國國家海洋暨大氣總署的國家氣象衛星計劃/操作線掃描系統（DMSP/OLS）提供夜間燈光數據。許多研究利用DMSP/OLS傳感器監測近紅外光輻射(NIR)，並量化人類活動與社會經濟變量和夜間亮度之間的關係。因此，本研究的宗旨在產製高解析度人口密度圖以了解人口連續性的分佈趨勢。對於夜間燈光影像，由於大氣條件的逐年變化和傳感器的周期性變化，不同年份獲得的夜間光照數據無法直接比較，因此，本研究使用多時期夜間燈光找出彼此的偽不辨特徵(PIFs)，並依相對輻射校準參數校正影像使DN值更加穩定。在本研究中，考慮與人口密度相關的三個因素，如:夜間燈光、開放街圖(OSM)的道路密度和興趣點密度，此三個因素在迴歸模型裡設置為自變數，而依變數為實際人口密度，迴歸模型採用傳統的全域型迴歸(OLS)，探討全域式自變數與人口之間的關係，局部型迴歸使用地理加權迴歸(GWR)，該模型確定每個城市人口密度與自變數的空間變化關係，然而，該模型是一種局部迴歸方法，容易由變數之間的負相關得到無意義的負人口密度，因此，本文提出了一種利用調整最佳帶寬與非負最小二乘的約束條件改良GWR模型，並定義為非負地理加權迴歸(NGWR)，該模型是探索人口密度分佈的有效方法，利用該方法可以解決人口密度負值和優化變數之間的擬合情況。與OLS和GWR相比，NGWR提供最佳的預測人口密度的方法，此外，本研究也考慮到時間變化的關係，考慮時間維度而建立非負時空地理加權迴歸(NGTWR)，由2004~2013年的資料為基準，在此模型確立彼此之間的關係，進而獲得迴歸參數，確定好不同模型的迴歸參數後，藉由空間內插依已知迴歸參數估計未知迴歸參數，再使用網格運算的方式獲取人口密度圖。

本研究對不同的人口密度圖做精度驗證，不考慮時間因素下，最佳人口密度估計模型為NGWR，其樣點交叉驗證結果為相對誤差率17.95%，而NGTWR可降至7.87%，此外，校正後的夜間燈光影像可改善人口密度估計精度，其區域驗證下結果為均方根誤差230降至223，而多變數人口密度估計的誤差比單一變數低，其均方根誤差由230降至221，另一方面，本研究結合OSM水體、森林資料調整人口密度分佈，也針對都市化的議題，進行小區域的人口密度估計，研究區域選取北京市，此地方人口密度估計採用最新夜間燈光影像VIIRS/DNB數據，具有更高空間解析度以及更細部的空間分佈特徵，北京市所估計的人口密度圖為本研究對人口估計方面的延伸，此外，本文從不同研究所產製的人口密度圖進行比較，進而得知可改善的空間以及不同演算法在人口密度估計上的優勢。

Nighttime light imagery offers a unique perspective that greatly differs from that of conventional satellite remote sensing in studying urbanization and socio-economic variables. The National Oceanic and Atmospheric Administration provides night light data, including DMSP/OLS and VIIRS/DNB nighttime light images. However, given the periodic changes in satellite sensors and the yearly variations in atmospheric conditions, the nighttime light data obtained across different years cannot be directly compared. Therefore, the application of multi-temporal nighttime light calibration is proposed to enhance the stability of these images. This study extracts three factors related to population density, namely, nighttime light, road density, and POI density, from OpenStreetMap (OSM) and imports these factors into a regression model. After obtaining the coefficient of this model, population density maps of China and Beijing were generated and adjusted based on the water body and forest land distribution data from OSM. Result shows that the proposed model is more accurate than the existing models as reflected in its RMSE and MRE indexes. Therefore, the regional population of China can be reliably and effectively estimated based on OSM features and nighttime light images.

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