基于复合指数模型的DMSP/OLS夜间灯光影像饱和校正方法

由于OLS传感器航行过程中增益记录和交叉定标的缺失,使得DMSP夜光影像在城市中心出现过度饱和等问题,因此探讨灯光数据去饱和方法在人类活动强度评估和城市时空演化分析等方面具有重要意义。为了解决这一难题,有学者提出使用NDVI修正饱和灯光数据的VANUI指数,为研究灯光数据去饱和提供了简单便捷的思路,然而该指数在部分城市中较难有明显的校正效果。本文在VANUI指数思想的基础上,顾及到人口密度随着郊区到城市中心距离的增加呈现指数型增长,提出了基于复合指数模型校正夜间灯光指数CEANI,为人类活动强度评价等研究提供更准确的结果。研究表明:① 与VANUI相比,CEANI在刻画城市内部饱和区域特征时具有更好的细节,较好地凸显城市内部空间异质性;② 在25组随机样本的相关对比中,CEANI（R²mean=0.79）与辐射定标产品比VANUI（R²mean=0.68）具有更高的相关性;③ 三大城市群中CEANI与常住人口的R²分别为0.767、0.676和0.841,比VANUI（R²分别为0.640、0.553和0.775）分别提高了0.127、0.123、0.066,相较于VANUI,CEANI与常住人口具有更强的相关性,对于人口指标的估算能力更强。

Saturation Correction Method of DMSP/OLS Nighttime Lights Image based on Compound Exponential Model

The lack of gain recording and cross-calibration during the OLS sensor navigation makes DMSP nighttime lights image oversaturated in the city center, which affects the accuracy of using night light data to evaluate human activity intensity. In order to suppress the occurrence of saturation, the radiometric calibration night light data developed by Elvedge have been widely used. The radiometric calibration data products have a high accuracy and strong reliability. However, the calibration process is complex, and the required data is usually difficult to obtain. At present, only a few results have applied the calibration data to the continuity analysis. In recent years, many scholars found that NDVI can desaturate DMSP/OLS night light images and enhance the heterogeneity of urban center. Based on this, non-radiation calibration method has been applied to correct the saturation effect and shown a good correction result. On the basis of summarizing the idea by VANUI that the difference between night light intensity and vegetation coverage shows a decreasing trend from the city center to the suburb, this paper considers that the population density increases exponentially with the increase of rural-urban distance. We proposed a correction of nighttime light index based on compound exponential model (CEANI). Results show that (1) compared with VANUI, CEANI showed better details and spatial heterogeneity when characterizing the saturated regions of the city. In addition, CEANI not only identified areas where human activity was concentrated, such as stations, airports, and business areas with high traffic and people flow, but also clearly identified the areas with high vegetation coverage and low DN values such as forests and parks with sparse road network; (2) in the correlation analysis using 25 random samples, CEANI showed a higher correlation (R²mean = 0.79) with radiometric calibration products than VANUI (R²mean = 0.68); (3) CEANI had a stronger correlation with the number of permanent residents and significantly estimated population indicators better than VANUI, which suggests the better calculation index for describing the intensity of human activity. In summary, the CEANI can be used to correct the saturation problem in DMSP/OLS luminous data products. It better shows the internal details of the city and its spatial heterogeneity, and thus can derive more accurate results for the evaluation of human activity intensity.