Building consistent time series night-time light data from average DMSP/OLS images for indicating human activities in a large-scale oceanic area
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 inten...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
|
| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843222002114 |
| _version_ | 1811243519847694336 |
|---|---|
| author | Rongyong Huang Wenqian Wu Kefu Yu |
| author_facet | Rongyong Huang Wenqian Wu Kefu Yu |
| author_sort | Rongyong Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T14:09:53Z |
| format | Article |
| id | doaj.art-9c21670b3cea4a448b28ba1fcef5fd58 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-12T14:09:53Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-9c21670b3cea4a448b28ba1fcef5fd582022-12-22T03:29:56ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322022-11-01114103023Building consistent time series night-time light data from average DMSP/OLS images for indicating human activities in a large-scale oceanic areaRongyong Huang0Wenqian Wu1Kefu Yu2Guangxi Laboratory on the Study of Coral Reefs in South China Sea, Coral Reef Research Center of China, School of Marine Sciences, Guangxi University, Nanning 530004, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, ChinaGuangxi Laboratory on the Study of Coral Reefs in South China Sea, Coral Reef Research Center of China, School of Marine Sciences, Guangxi University, Nanning 530004, ChinaGuangxi Laboratory on the Study of Coral Reefs in South China Sea, Coral Reef Research Center of China, School of Marine Sciences, Guangxi University, Nanning 530004, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China; Corresponding author at: Guangxi Laboratory on the Study of Coral Reefs in South China Sea, Coral Reef Research Center of China, School of Marine Sciences, Guangxi University, Nanning 530004, China.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.http://www.sciencedirect.com/science/article/pii/S1569843222002114OceanDMSP/OLS average imageRandom ForestTime seriesIntercalibration |
| spellingShingle | Rongyong Huang Wenqian Wu Kefu Yu 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 Observations and Geoinformation Ocean DMSP/OLS average image Random Forest Time series Intercalibration |
| title | Building consistent time series night-time light data from average DMSP/OLS images for indicating human activities in a large-scale oceanic area |
| title_full | Building consistent time series night-time light data from average DMSP/OLS images for indicating human activities in a large-scale oceanic area |
| title_fullStr | Building consistent time series night-time light data from average DMSP/OLS images for indicating human activities in a large-scale oceanic area |
| title_full_unstemmed | Building consistent time series night-time light data from average DMSP/OLS images for indicating human activities in a large-scale oceanic area |
| title_short | Building consistent time series night-time light data from average DMSP/OLS images for indicating human activities in a large-scale oceanic area |
| title_sort | building consistent time series night time light data from average dmsp ols images for indicating human activities in a large scale oceanic area |
| topic | Ocean DMSP/OLS average image Random Forest Time series Intercalibration |
| url | http://www.sciencedirect.com/science/article/pii/S1569843222002114 |
| work_keys_str_mv | AT rongyonghuang buildingconsistenttimeseriesnighttimelightdatafromaveragedmspolsimagesforindicatinghumanactivitiesinalargescaleoceanicarea AT wenqianwu buildingconsistenttimeseriesnighttimelightdatafromaveragedmspolsimagesforindicatinghumanactivitiesinalargescaleoceanicarea AT kefuyu buildingconsistenttimeseriesnighttimelightdatafromaveragedmspolsimagesforindicatinghumanactivitiesinalargescaleoceanicarea |