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Building consistent time series night-time light data from average DMSP/OLS images for indicating human activities in a large-scale oceanic area
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-22 , DOI: 10.1016/j.jag.2022.103023
Rongyong Huang, Wenqian Wu, Kefu Yu

Human activities in the ocean have never been chronically and continuously investigated on a large scale. Night-time light (NTL) images collected by the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) have been used as a proxy for monitoring the distribution and intensity of some human activities in the ocean from 1992 to 2013. However, systematic radiometric biases exist among the average visible-light DMSP/OLS NTL images (DMSPavg) derived from different satellites. Moreover, the high randomness of fishing vessel locations and the large amount of noise impede the intercalibration of DMSPavg. To address these issues, this study has developed a method for generating a series of consistent NTL images from 1992 to 2013 for a large-scale oceanic area. A composite image was first constructed by combining the original DMSPavg, median, and standard deviation filter images derived from the DMSPavg, and a bathymetry image. Thereafter, Random Forest (RF) algorithm was employed to classify the composite image into effective and noisy pixels. Finally, a stepwise intercalibration method was adopted to reduce the systematic radiometric biases in the denoised images. The experimental results showed that RF had an overall accuracy of 96% and a Kappa coefficient of 0.775. Furthermore, the intercalibration was shown to significantly reduce the systematic radiometric biases owing to the noises being effectively discarded by the RF. Specifically, the Sum Normalized Different Index (SNDI) of the images intercalibrated by the proposed method can reach 0.61, which is 68.2% less than that of the original DMSPavg. In addition, the correlation coefficients between the intercalibrated DMSPavg and fishery catches in the exclusive economic zones (EEZs) of Japan and Malaysia can reach 0.949 and 0.901, respectively, which are higher than other values, such as the one intercalibrated using the Pseudo-Invariant Features (PIFs) method. In summary, the proposed method has been proven to be effective and feasible for generating consistent time-series NTL data for a large-scale oceanic area, and the derived Total Light Index (TLI) is an effective indicator of ocean fishery activities for ocean ecosystem research and related applications.



中文翻译:

从平均 DMSP/OLS 图像构建一致的时间序列夜间灯光数据,以指示大规模海洋区域的人类活动

人类在海洋中的活动从未进行过长期且持续的大规模调查。国防气象卫星计划/业务线扫描系统 (DMSP/OLS) 收集的夜间光 (NTL) 图像已被用作监测 1992 年至 2013 年海洋中某些人类活动的分布和强度的代理。然而,来自不同卫星的平均可见光 DMSP/OLS NTL 图像 (DMSP avg ) 之间存在系统辐射偏差。此外,渔船位置的高度随机性和大量的噪声阻碍了 DMSP avg的相互校准。. 为了解决这些问题,本研究开发了一种方法,用于生成 1992 年至 2013 年期间大尺度海洋区域的一系列一致 NTL 图像。首先通过组合从 DMSP avg导出的原始 DMSP avg、中值和标准偏差滤波器图像来构建合成图像,以及测深图像。此后,采用随机森林(RF)算法将合成图像分类为有效像素和噪声像素。最后,采用逐步相互校准的方法来减少去噪图像中的系统辐射偏差。实验结果表明,RF 的总体准确率为 96%,Kappa 系数为 0.775。此外,由于RF有效地丢弃了噪声,因此表明相互校准显着减少了系统辐射偏差。具体来说,通过该方法相互校准的图像的和归一化不同指数(SNDI)可以达到0.61,比原始DMSP avg降低68.2% 。此外,相互校准的 DMSP avg之间的相关系数日本和马来西亚的专属经济区 (EEZ) 的渔获量分别可以达到 0.949 和 0.901,高于其他值,例如使用伪不变特征 (PIF) 方法相互校准的值。综上所述,该方法已被证明在为大范围海洋区域生成一致的时间序列 NTL 数据是有效和可行的,并且导出的总光指数(TLI)是海洋生态系统海洋渔业活动的有效指标研究和相关应用。

更新日期:2022-09-22
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