پیشرفت‌ها، چالش‌ها و دیدگاه‌ها درزَمینۀ تصحیح تصاویر ماهواره‌ای نور شب رایگان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی فتوگرامتری، دانشگاه صنعتی نوشیروانی بابل، بابل، ایران

2 استادیار گروه مهندسی نقشه‌برداری، دانشکدة عمران، دانشگاه صنعتی نوشیروانی بابل، بابل، ایران

چکیده

سابقه و هدف: سنجش ‌از دور منبع داده‌ای قدرتمند برای نقشه‌برداری از مناطق شهری و نظارت بر پویایی شهرنشینی است. از بین داده‌های سنجش‌ازدوری، تصاویری که در شب اخذ می‌شوند راهی مؤثر برای نظارت بر فعالیت‌های انسانی، در مقیاس جهانی، فراهم کرده است؛ زیرا این تصاویر با توجه به ویژگی‌ها و قابلیت‌هایشان می‌توانند مناطق شهری و سایر فعالیت‌های انسانی را که ویژگی اصلی‌شان استفاده از نور در شب است، با اندازه‌گیری صحیح مکانی، از پس‌زمینۀ بدون نور جدا کنند. این تصاویر با نظارت مستمر و مداوم از منظرة شبانۀ جهانی، منبع و نتایج ارزشمندی از فعالیت‌های انسانی را، از گذشته تا امروز، فراهم می‌کند و تجزیه‌وتحلیل سری زمانی این داده‌ها برای کشف، تخمین و نظارت بر پویایی اجتماعی و اقتصادی در کشورها، به‌ویژه مناطق فرعی که آمار رسمی مورد ‌اعتمادی دربارة آنها وجود ندارد، بسیار ارزشمند است. با توجه به پیشرفت سنجنده‌های ماهواره‌ای نور شب در سال‌های اخیر و تحقیقات جدید انجام‌شده درزَمینة داده‌های نور شب، هدف از این تحقیق بررسی پیشرفت‌های سنجندة شبانه، معرفی انواع داده‌ها و محصولات دردسترس، بررسی و بیان مزایا و معایب هریک و همچنین مروری بر روش‌ها و راه‌حل‌های مطرح‌شده در تحقیقات پیشین است تا مشکلات و محدودیت‌های این تصاویر حل شود.
مواد و روش‌ها: هدف اصلی از این تحقیق معرفی و بررسی کلی داده‌های نور شب، مزایا و چالش­های هریک و روش‌های بیان‌شده به‌منظور تصحیح مشکلات و چالش­هاست. مطالعات درزَمینة تصاویر نور شب DMSP اغلب بر دو بعد مکانی و زمانی تمرکز دارد. در بعد مکانی، نواقص ذاتی این مجموعه داده، یعنی مقادیر اشباع‌شدة مقادیر رقومی در مناطق مرکزی شهری و تأثیرات شکوفایی در مناطق حومة شهری و روستایی درخور توجه است. در بعد زمانی، به‌دلیل فقدان کالیبراسیون در پردازنده، به فرایندهای اضافی روی محصولات سالیانة داده‌های پایدار نور شب DMSP برای بررسی پویایی‌های شهری نیاز است؛ روش‌های کنونی تصحیحات مشکلات مکانی در دو دستة طیفی و غیرطیفی قرار می‌گیرد. روش‌های مطرح‌شده برای تصحیح مشکلات زمانی این سنجنده نیز، در دو دستة کالیبراسیون سالیانة داده‌های نور شب و تنظیم الگوی زمانی، بررسی شده است. تصاویر ماهیانة NPP-VIIRS محصولی است که علاوه‌بر مقادیر نورهای ثابت، مانند چراغ‌های شهرها و مسیر‌های حمل‌ونقل، مقادیری نویزی مانند شعله‌های گاز و سوختن زیست‌توده و نویز پس‌زمینه را نیز شامل می‌شود؛ به همین دلیل، پیش‌از استفاده لازم است پردازش شود. همچنین ازآنجا‌که دقت موقعیت‌یابی داده‌های لوجیا کمتر از وضوح مکانی آن است، جابه‌جایی تصویر در برخی مکان‌ها ممکن است به 650 متر برسد؛ ازاین‌رو تصحیح هندسی در این تصویر انجام می‌شود. انواع این روش‌ها در این مقاله بررسی شده است.
بحث و بررسی: طی مقایسه‌ای کلی، می‌توان نتیجه‌ گرفت که در بررسی عملکرد داده‌های نور شب گوناگون، داده‌های نور پایدار شبانة DMSP، به‌رغم مشکلات و محدودیت‌های موجود، دارای سری زمانی طولانی‌تری درقیاس با داده‌های نور شب دیگر است زیرا دورة زمانی 1992 تا 2013 را دربرمی‌گیرد و همچنان، در بسیاری تحقیقات درزَمینة بررسی پویایی شهری و برآورد روند کلی رشد شهر، کاربرد دارد. درمقایسه، NPP-VIIRS از مزایایی برخوردار است و به نور کمتر نیز حساسیت نشان می‌دهد اما زمان عبور این ماهواره ساعت 1:30 بامداد است؛ در این ساعت شب، بسیاری از چراغ‌ها خاموش می‌شوند و به همین علت ممکن است، درمواردی که فقط از دادة نور شب برای بررسی مناطق شهری استفاده می‌شود، مناسب نباشد. همچنین طی بررسی‌های انجام‌شده، این تصویر در تحقیقات درزَمینة فعالیت‌های اقتصادی کاربرد بیشتری داشته است و حساسیت‌نداشتن آن به نور آبی از­LED ها در توانایی سنجنده، در تعیین کمّیت نورهای مصنوعی ساطع‌شده از زمین، تأثیر می‌گذارد.
نتیجه‌گیری: این بررسی با هدف معرفی انواع داده‌های نور شب سنجش‌ازدوری و بررسی آنها انجام شده است و به‌طور خلاصه می‌توان گفت، درحال حاضر، تحقیقات درزَمینة‌ تصحیح مشکلات مکانی اشباع و شکوفایی به دو دستة طیفی و غیرطیفی تقسیم می­شوند. دسته‌های غیرطیفی اغلب فقط با استفاده از دادة نور شب و در برخی موارد، با استفاده از داده‌های غیرسنجش‌ازدوری ترکیب می‌شوند. بررسی روش‌های طیفی نشان می­دهد که اغلب این روش‌ها از شاخص‌های طیفی مربوط به پوشش گیاهی و دمای سطح زمین استفاده می‌کنند. درحال حاضر، تصحیح تصاویر DMSP از بعد زمانی با کالیبراسیون بین داده‌ها، به‌طور خاص، با استفاده از روش مناطق مرجع ثابت یا پیکسل‌های مرجع انجام‌شدنی است. از معتبرترین روش­های مطرح‌شده در این زمینه، روش منطقة مرجع است. پس‌از پایان مأموریت سنجندة DMSP-OLS، سنجندة VIIRS معرفی‌ شده است. برخلاف دادة سالیانة این ماهواره، دادة ماهیانة آن به‌علت وجود نویزهای پس‌زمینه، نورهای سرگردان و مواردی ازاین‌دست، نیاز به تصحیح دارد. طبق بررسی‌های انجام‌شده براساس مطالعات موجود در روند تحقیقات، می‌توان گفت بیشتر مطالعات و روش‌ها سعی در حذف نویزها با استفاده از چارچوبی مشخص، اما با فرض‌های متفاوت، دارند. درنَهایت، با توجه به چالش‌ها و محدودیت‌های فعلی ماهواره‌های نور شب، چند پیشنهاد اصلی برای پیشرفت و توسعه در این زمینه مطرح می‌شود؛ ادغام داده‌های DMSP-OLS با داده‌های NPP-VIIRS یا با وضوح بالاتر داده‌های لوجیا می‌تواند بیشتر مورد مطالعه قرار گیرد تا یک سری زمانی طولانی‌تر برای تحقیقات آینده، به‌منظور بررسی پویایی شهری و موارد مشابه، ایجاد شود.

کلیدواژه‌ها

عنوان مقاله [English]

The Advances, Challenges and Perspectives in the Correction Field of Free Night Light Satellite Image

نویسندگان [English]

  • Fatemeh Ahmadi 1
  • Abbas Kiani 2
  • yasser Ebrahimian Ghajari 2
1 Master Student of Photogrammetry Engineering, Babol Noshirvani University of Technology, Babol, Iran
2 Assistant Prof. of Civil Engineering Dep., Babol Noshirvani University of Technology, Babol, Iran
چکیده [English]

Introduction: Remote sensing provides a powerful data source for the mapping of urban areas and the monitoring of urban dynamics on a range of scales. Among the variues types of  remote sensing data, images captured at night offer an effective means ofmonitoring human activities on a global scale. The distinctive features and capabilities of these images permit the separation of urban areas and other human activities, the main feature of which is the use of light at night by accurately measuring the location, from the background without light. Via providing uninterrupted and continuous monitoring from the night world perspective, these images provide a valuable source of information about human activities over time from the past to the present. The time series analysis of this data is highly valuable for discovering, estimating and monitoring social and economic dynamics in countries, especially sub-regions where there are no official statistics. With recent developments in night-time data satellite sensors and new research conducted in this field, this study aims to review the advances in night-time sensors, introduce the existing data and products, review and express the advantages and disadvantages of each one, and review the methods and solutions presented in previous research for solving the existing problems and limitations in order to improve these images.
Materials and methods: The main objective of this research is to introduce and review the general charactristics of night-time light data, discussing their advantages, challenges, and methods for addressing these challenges. The majority of studies on DMSP night light images focus on two spatial and temporal dimensions. In the spatial dimension, inherent deficiencies of this dataset are observed, such as saturated numerical values in central urban areas and flourishing effects in suburban and rural areas. In the temporal dimension, the lack of calibration in the processor, necessitates the implementation of additional processes on annual products of stable DMSP night light data in order to examine urban dynamics. The existing methods for correcting spatial problems are divided into tow categories: spectral and non-spectral. Similarly, methods for addressing temporal issues are divided into two categories: annual calibration of night light data and adjustment of temporal patterns. NPP-VIIRS monthly images encompass various features including fixed light values such as city lights and transportation routes, as well as noise values such as gas flames, biomass burning, and background noise. Therefore, preprocessing is necessary before utilizing this data. Furthermore, the positioning accuracy of Loujia_01 data is lower than its spatial resolution, resulting in image displacement of up to 650 meters in some areas. Geometric correction is applied to rectify this issue, and various correction methods have been investigated.
Discussion: A general comparison of the data sets reveals that, despite the existing problems and limitations, the DMSP stable night light data outperforms other night light datasets due to its longer time series, which spans from 1992 to 2013. This extended temporal coverage makes it a valuable resource for research on urban dynamics and estimating the overall growth trend of cities. On the other hand, NPP-VIIRS offers advantages and is sensitive to faint light sources. However, its passage time at 1:30 in the morning, when many lights are turned off, limits its utility for urban studies. Consequently, it may not be the optimal choice for exclusively investigating urban areas exclusively. Nevertheless, the NPP-VIIRS data is more useful in research related to economic activities. Furthermore, the sensor's lack of sensitivity to blue light emitted by LEDs impacts its ability to accurately quantify artificial light emissions from the ground.
Conclusion: The objective of this study was to introduce types of remote sensing night light data and their analysis. In short, current research in the field of correcting spatial saturation and blooming problems is divided into two categories: spectral and non-spectral. Non-spectral methods typically rely solely on night light data, although they may also incorporate non-remote sensing data. Spectral methods often employ spectral indices that are related to vegetation and ground surface temperature. Currently, correcting DMSP images from the temporal dimension can be achieved through inter-data calibration, specifically via the fixed reference regions or reference pixels method. One of the most reliable methods in this field is the reference area method. Following the conclusion of the DMSP-OLS mission, the VIIRS was introduced. In contrast to the annual data of this satellite, the monthly data requires correction due to the presence of background noise, and stray lights. A reviews of existing studies indicates that the majority of methods aim to remove noise using specific frameworks although with differing assumptions. Finally, considering the current challenges and limitations of night light satellites, several recommendations for future progress and development in this field are put forth. Further investigation could be conducted into the integration of DMSP-OLS data with NPP-VIIRS data or higher resolution Loujia-01 data, with the objective of developing a longer time series for future research on urban dynamics.

کلیدواژه‌ها [English]

  • Night light satellite images
  • Advantages and disadvantages of night light images
  • Correction and pre-processing methods

مراجع

Abrahams, A., Oram, C. & Lozano-Gracia, N., 2018, Deblurring DMSP Nighttime Lights: A New Method Using Gaussian Filters and Frequencies of Illumination, Remote Sensing of Environment, 210, PP. 242-254, https://doi.org/10.1016/j.rse.2018.03.018.
Alahmadi, M., Mansour, S., Martin, D. & Atkinson, P., 2021, An Improved Index for Urban Population Distribution Mapping Based on Nighttime Lights (DMSP-OLS) Data: An Experiment in Riyadh Province, Saudi Arabia, Remote Sensing, 13, P. 1171, https://doi.org/10.3390/rs13061171.
Bennett, M.M. & Smith, L.C., 2017, Advances in Using Multitemporal Night-Time Lights Satellite Imagery to Detect, Estimate, and Monitor Socioeconomic Dynamics, Remote Sensing of Environment, 192, PP. 176-197, https://doi.org/10.1016/j.rse.2017.01.005.
Bennie, J., Davies, T.W., Duffy, J.P., Inger, R. & Gaston, K.J., 2014, Contrasting Trends in Light Pollution Across Europe Based on Satellite Observed Night Time Lights, Scientific Reports, 4, PP. 1-6, https://doi.org/10.1038/srep03789.
Cao, C. & Bai, Y., 2014, Quantitative Analysis of VIIRS DNB Nightlight Point Source for Light Power Estimation and Stability Monitoring, Remote Sensing, 6, PP. 11915-11935, https://doi.org/10.3390/rs61211915.
Cao, X., Chen, J., Imura, H. & Higashi, O., 2009, A SVM-Based Method to Extract Urban Areas from DMSP-OLS and SPOT VGT Data, Remote Sensing of Environment, 113, PP. 2205-2209, https://doi.org/10.1016/j.rse.2009.06.001.
Cao, X., Wang, J., Chen, J. & Shi, F., 2014, Spatialization of Electricity Consumption of China Using Saturation-Corrected DMSP-OLS Data, International Journal of Applied Earth Observation and Geoinformation, 28, PP. 193-200, https://doi.org/10.1016/j.jag.2013.12.004.
Cao, X., Hu, Y., Zhu, X., Shi, F., Zhuo, L. & Chen, J., 2019, A Simple Self-Adjusting Model for Correcting the Blooming Effects in DMSP-OLS Nighttime Light Images, Remote Sensing of Environment, 224, PP. 401-411, https://doi.org/10.1016/j.rse.2019.02.019.
de Miguel, A.S., Castaño, J.G., Zamorano, J., Pascual, S., Ángeles, M., Cayuela, L., Martinez, G.M., Challupner, P. & Kyba, C.C., 2014, Atlas of Astronaut Photos of Earth at Night, Astronomy & Geophysics, 55(4), P. 4.36, https://doi.org/10.1093/astrogeo/atu165
de Pinho, C.M.D., Fonseca, L.M.G., Korting, T.S., De Almeida, C.M. & Kux, H.J.H., 2012, Land-cover Classification of an Intra-Urban Environment Using High-Resolution Images and Object-Based Image Analysis, International Journal of Remote Sensing, 33, PP. 5973-5995, https://doi.org/10.1080/01431161.2012.675451.
Elvidge, C.D., Baugh, K.E., Dietz, J.B., Bland, T., Sutton, P.C. & Kroehl, H.W., 1999, Radiance Calibration of DMSP-OLS Low-Light Imaging Data of Human Settlements, Remote Sensing of Environment, 68, PP. 77-88, https://doi.org/10.1016/S0034-4257(98)00098-4.
Elvidge, C.D., Imhoff, M.L., Baugh, K.E., Hobson, V.R., Nelson, I., Safran, J., Dietz, J.B. & Tuttle, B.T., 2001, Night-Time Lights of the World: 1994–1995, ISPRS Journal of Photogrammetry and Remote Sensing, 56, PP. 81-99, https://doi.org/10.1016/S0924-2716(01)00040-5.
Elvidge, C.D., Ziskin, D., Baugh, K.E., Tuttle, B.T., Ghosh, T., Pack, D.W., Erwin, E.H. & Zhizhin, M., 2009, A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data, Energies, 2, PP. 595-622, https://doi.org/10.3390/en20300595.
Elvidge, C.D., Baugh, K.E., Zhizhin, M. & Hsu, F.-C., 2013, Why VIIRS Data Are Superior to DMSP for Mapping Nighttime Lights, Proceedings of the Asia-Pacific Advanced Network, 35, P. 62, http://dx.doi.org/10.7125/APAN.35.7.
Elvidge, C.D., Baugh, K., Zhizhin, M., Hsu, F.C. & Ghosh, T., 2017, VIIRS Night-Time Lights, International Journal of Remote Sensing, 38, PP. 5860-5879, https://doi.org/10.1080/01431161.2017.1342050.
Elvidge, C.D., Zhizhin, M., Ghosh, T., Hsu, F.-C. & Taneja, J., 2021, Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019, Remote Sensing, 13, P. 922, https://doi.org/10.3390/rs13050922.
Gao, B., Huang, Q., He, C. & Ma, Q., 2015, Dynamics of Urbanization Levels in China from1992  to 2012: Perspective from DMSP/OLS Nighttime Light Data, Remote Sensing, 7, PP. 1721-1735,  https://doi.org/10.3390/rs70201721.
Hao, R., Yu, D., Sun, Y., Cao, Q., Liu, Y. & Liu, Y., 2015, Integrating Multiple Source Data to Enhance Variation and Weaken the Blooming Effect of DMSP-OLS Light, Remote Sensing, 7, PP. 1440-1442, https://doi.org/10.3390/rs70201422.
Hara, M., Okada, S., Yagi, H., Moriyama, T., Shigehara, K. & Sugimori, Y., 2010, Progress for Stable Artificial Lights Distribution Extrvction Accuracy and Estimation of Electric Power Consumption by Means of Dmsp/Ols Nighttime Imagery, International Journal of Remote Sensing and Earth Sciences (IJReSES), 1.
He, C., Ma, Q., Li, T., Yang, Y. & Liu, Z., 2012, Spatiotemporal Dynamics of Electric Power Consumption in Chinese Mainland from 1995 to 2008 Modeled Using DMSP/OLS Stable Nighttime Lights Data, Journal of Geographical Sciences, 22, PP. 125-136, https://doi.org/10.1007/s11442-012-0916-3.
He, C., Ma, Q., Liu, Z. & Zhang, Q., 2014, Modeling the Spatiotemporal Dynamics of Electric Power Consumption in Mainland China Using Saturation-Corrected DMSP/OLS Nighttime Stable Light Data, International Journal of Digital Earth, 7, PP. 993-1014, https://doi.org/10.1080/17538947.2013.822026.
Hu, Y.n., Peng, J., Liu, Y., Du, Y., Li, H. & Wu, J., 2017, Mapping Development Pattern in Beijing-Tianjin-Hebei Urban Agglomeration Using DMSP/OLS Nighttime Light Data, Remote Sensing, 9, P. 760, https://doi.org/10.3390/rs9070760.
Hu, Y., Chen, J., Cao, X., Chen, X., Cui, X. & Gan, L., 2021, Correcting the Saturation Effect in DMSP/OLS Stable Nighttime Light Products Based on Radiance-Calibrated Data, IEEE Transactions on Geoscience and Remote Sensing, https://doi.org/10.1109/TGRS.2021.3060170.
Huang, X., Schneider, A. & Friedl, M.A., 2016, Mapping Sub-Pixel Urban Expansion in China Using MODIS and DMSP/OLS Nighttime Lights, Remote Sensing of Environment, 175, PP. 92-108, https://doi.org/10.1016/j.rse.2015.12.042.
Huang, X., Shi, K., Cui, Y. & Li, Y., 2021, A Saturated Light Correction Method for DMSP-OLS Nighttime Stable Light Data by Remote and Social Sensing Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, PP. 1885-1894.
Ji, X., Li, X., He, Y. & Liu, X., 2019, A Simple Method to Improve Estimates of County-Level Economics in China Using Nighttime Light Data and GDP Growth Rate, ISPRS International Journal of Geo-Information, 8, P. 419, https://doi.org/10.3390/ijgi8090419.
Jiang, W., He, G., Long, T., Guo, H., Yin, R., Leng, W., Liu, H. & Wang, G., 2018, Potentiality of Using Luojia1-01  Nighttime Light Imagery to Investigate Artificial Light Pollution, Sensors, 18, P. 2900, https://doi.org/10.3390/s18092900.
Jing, X., Shao, X., Cao, C., Fu, X. & Yan, L., 2016, Comparison between the Suomi-NPP Day-Night Band and DMSP-OLS for Correlating Socio-Economic Variables at the Provincial Level in China, Remote Sensing, 8, P. 17, https://doi.org/10.3390/rs8010017.
Kyba, C., Garz, S., Kuechly, H., De Miguel, A.S., Zamorano, J., Fischer, J. & Hölker, F., 2015, High-Resolution Imagery of Earth at Night: New Sources, Opportunities and Challenges, Remote Sensing, 7, PP. 1-23, https://doi.org/10.3390/rs70100001.
Letu, H., Hara, M., Yagi, H., Naoki, K., Tana, G., Nishio, F. & Shuhei, O., 2010, Estimating Energy Consumption from Night-Time DMPS/OLS Imagery after Correcting for Saturation Effects, International Journal of Remote Sensing, 31, PP. 4443-4458, https://doi.org/10.1080/01431160903277464.
Letu, H., Hara, M., Tana, G. & Nishio, F., 2011, A Saturated Light Correction Method for DMSP/OLS Nighttime Satellite Imagery, IEEE Transactions on Geoscience and Remote Sensing, 50, PP. 389-396, https://doi.org/10.1109/TGRS.2011.2178031.
Levin, N. & Phinn, S., 2016, Illuminating the Capabilities of Landsat 8 for Mapping Night Lights, Remote Sensing of Environment, 182, PP. 27-38, https://doi.org/10.1016/j.rse.2016.04.021.
Li, X. & Zhou, Y., 2017a. A Stepwise Calibration of Global DMSP/OLS Stable Nighttime Light Data (1992-2013), Remote Sensing, 9, P. 637, https://doi.org/10.3390/rs9060637.
Li, X. & Zhou, Y., 2017b, Urban Mapping Using DMSP/OLS Stable Night-Time Light: A Review, International Journal of Remote Sensing, 38, PP. 6030-6046, https://doi.org/10.1080/01431161.2016.1274451.
Li, X., Chen, X., Zhao, Y., Xu, J., Chen, F. & Li, H., 2013a, Automatic Intercalibration of Night-Time Light Imagery Using Robust Regression, Remote Sensing Letters, 4, PP. 45-54, https://doi.org/10.1080/2150704X.2012.687471.
Li, X., Xu, H., Chen, X. & Li, C., 2013b, Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China, Remote Sensing, 5, PP. 3057-3081, https://doi.org/10.3390/rs5063057.
Li, Q., Lu, L., Weng, Q., Xie, Y. & Guo, H., 2016, Monitoring Urban Dynamics in the Southeast USA Using Time-Series DMSP/OLS Nightlight Imagery, Remote Sensing, 8, P. 578, https://doi.org/10.3390/rs8070578.
Li, X., Zhan, C., Tao, J. & Li, L., 2018a, Long-Term Monitoring of the Impacts of Disaster on Human Activity Using Dmsp/Ols Nighttime Light Data: A Case Study of the2008  Wenchuan, China Earthquake, Remote Sensing, 10, P. 588, https://doi.org/10.3390/rs10040588.
Li, X., Zhao, L., Li, D. & Xu, H., 2018b, Mapping Urban Extent Using Luojia 1-01 Nighttime Light Imagery, Sensors, 18, P. 3665, https://doi.org/10.3390/s18113665.
Li, X., Li, X., Li, D., He, X. & Jendryke, M., 2019a, A Preliminary Investigation of Luojia-1  Night-Time Light Imagery, Remote Sensing Letters, 10, PP. 526-535, https://doi.org/10.1080/2150704X.2019.1577573.
Li, X., Liu, Z., Chen, X. & Sun, J., 2019b, Assessing the Ability of Luojia 1-01 Imagery to Detect Feeble Nighttime Lights, Sensors, 19, P. 3708, https://doi.org/10.3390/s19173708.
Li, C., Yang, W., Tang, Q., Tang, X., Lei, J., Wu, M. & Qiu, S., 2020a, Detection of Multidimensional Poverty Using Luojia1-01  Nighttime Light Imagery, Journal of the Indian Society of Remote Sensing, 48, PP. 963-977, https://doi.org/10.1007/s12524-020-01126-3.
Li, F., Yan, Q., Bian, Z., Liu, B. & Wu, Z., 2020b, A POI and LST Adjusted NTL Urban Index for Urban Built-Up Area Extraction, Sensors, 20, P, 2918, https://doi.org/10.3390/s20102918.
Li, C., Wang, X., Wu, Z., Dai, Z., Yin, J. & Zhang, C., 2021a, An Improved Method for Urban Built-Up Area Extraction Supported by Multi-Source Data, Sustainability, 13, P. 5042, https://doi.org/10.3390/su13095042.
Li, F., Li, E., Zhang, C., Samat, A., Liu, W., Li, C. & Atkinson, P.M., 2021b, Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data, Remote Sensing, 13, P. 212, https://doi.org/10.3390/rs13020212.
Li, F., Liu, X., Liao, S. & Jia, P., 2021c, The Modified Normalized Urban Area Composite Index: A Satelliate-Derived High-Resolution Index for Extracting Urban Areas, Remote Sensing, 13, P. 2350, https://doi.org/10.3390/rs13122350.
Liu, L. & Leung, Y., 2015, A Study of Urban Expansion of Prefectural-Level Cities in South China Using Night-Time Light Images, International Journal of Remote Sensing, 36, PP. 5557-5575, https://doi.org/10.1080/01431161.2015.1101650.
Liu, Z., He, C. & Yang, Y., 2011, Mapping Urban Areas by Performing Systematic Correction for DMSP/OLS Nighttime Lights Time Series in China from1992  to 2008, 2011 IEEE International Geoscience and Remote Sensing Symposium, IEEE, PP. 1858-1861, https://doi.org/10.1109/IGARSS.2011.6049485.
Liu, Z., He, C., Zhang, Q., Huang, Q. & Yang, Y., 2012, Extracting the Dynamics of Urban Expansion in China Using DMSP-OLS Nighttime Light Data from1992  to 2008, Landscape and Urban Planning, 106, PP. 62-72, https://doi.org/10.1016/j.landurbplan.2012.02.013.
Liu, X., Hu, G., Ai, B., Li, X. & Shi, Q., 2015a, A Normalized Urban Areas Composite Index (NUACI) Based on Combination of DMSP-OLS and MODIS for Mapping Impervious Surface Area, Remote Sensing, 7, PP. 17168-17189, https://doi.org/10.3390/rs71215863.
Liu, Y., Wang, Y., Peng, J., Du, Y., Liu, X., Li, S. & Zhang, D., 2015b., Correlations between Urbanization and Vegetation Degradation across the World’s Metropolises Using DMSP/OLS Nighttime Light Data, Remote Sensing, 7, PP. 2067-2088, https://doi.org/10.3390/rs70202067.
Liu, Y., Yang, Y., Jing, W., Yao, L., Yue, X. & Zhao, X., 2017, A New Urban Index for Expressing Inner-City Patterns Based on MODIS LST and EVI Regulated DMSP/OLS NTL, Remote Sensing, 9, P. 777, https://doi.org/10.3390/rs9080777.
Liu, C., Yang, K., Bennett, M.M., Guo, Z., Cheng, L. & Li, M., 2019, Automated Extraction of Built-Up Areas by Fusing VIIRS Nighttime Lights and Landsat-8 Data, Remote Sensing, 11, P. 1571, https://doi.org/10.3390/rs11131571.
Liu, H., Luo, N. & Hu, C., 2020, Detection of County Economic Development Using LJ1-01  Nighttime Light Imagery: A Comparison with NPP-VIIRS Data, Sensors, 20, P. 6633, https://doi.org/10.3390/s20226633.
Lu, D., Tian, H., Zhou, G. & Ge, H., 2008, Regional Mapping of Human Settlements in Southeastern China with Multisensor Remotely Sensed Data, Remote Sensing of Environment, 112, PP. 3668-3679, https://doi.org/10.1016/j.rse.2008.05.009.
Ma, W. & Li, P., 2018, An Object Similarity-Based Thresholding Method for Urban Area Mapping from Visible Infrared Imaging Radiometer Suite Day/Night Band (Viirs dnb) Data, Remote Sensing, 10, P. 263, https://doi.org/10.3390/rs10020263.
Ma, Q., He, C., Wu, J., Liu, Z., Zhang, Q. & Sun, Z., 2014a, Quantifying Spatiotemporal Patterns of Urban Impervious Surfaces in China: An Improved Assessment Using Nighttime Light Data, Landscape and Urban Planning, 130, PP. 36-49, https://doi.org/10.1016/j.landurbplan.2014.06.009.
Ma, T., Zhou, C., Pei, T., Haynie, S. & Fan, J., 2014b, Responses of Suomi-NPP VIIRS-Derived Nighttime Lights to Socioeconomic Activity in China’s Cities, Remote Sensing Letters, 5, PP. 165-174, https://doi.org/10.1080/2150704X.2014.890758.
Ma, X., Li, C., Tong, X. & Liu, S., 2019, A New Fusion Approach for Extracting Urban Built-Up Areas from Multisource Remotely Sensed Data, Remote Sensing, 11, P. 2516, https://doi.org/10.3390/rs11212516.
Mallick, J., Rahman, A. & Singh, C.K., 2013, Modeling Urban Heat Islands in Heterogeneous Land Surface and Its Correlation with Impervious Surface Area by Using Night-Time ASTER Satellite Data in Highly Urbanizing City, Delhi-India, Advances in Space Research, 52, PP. 639-655, https://doi.org/10.1016/j.asr.2013.04.025.
Mills, S., Weiss, S. & Liang, C., 2013, VIIRS Day/Night Band (DNB) Stray Light Characterization and Correction, Earth Observing Systems XVIII, 8866, https://doi.org/10.1117/12.2023107.
Mukherjee, S., Srivastav, S., Gupta, P.K., Hamm, N. & Tolpekin, V., 2017, An Algorithm for Inter-calibration of Time-Series DMSP/OLS Night-Time Light Images, Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 87, PP. 721-731, https://doi.org/10.1007/s40010-017-0444-8.
Niu, W., Xia, H., Wang, R., Pan, L., Meng, Q., Qin, Y., Li, R., Zhao, X., Bian, X. & Zhao, W., 2020, Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data, ISPRS International Journal of Geo-Information, 10, P. 5, https://doi.org/10.3390/ijgi10010005.
Pan, W., Fu, H. & Zheng, P., 2020, Regional Poverty and Inequality in the Xiamen-Zhangzhou-Quanzhou City Cluster in China Based on NPP/VIIRS Night-Time Light Imagery, Sustainability, 12, P. 2547, https://doi.org/10.3390/su12062547.
Pandey, B., Joshi, P. & Seto, K.C., 2013, Monitoring Urbanization Dynamics in India Using DMSP/OLS Night Time Lights and SPOT-VGT Data, International Journal of Applied Earth Observation and Geoinformation, 23, PP. 49-61, https://doi.org/10.1016/j.jag.2012.11.005.
Ramachandra, T., Bharath, H., Vinay, S., Joshi, N., Kumar, U. & Rao, K.V.,2013 , Modelling Urban Revolution in Greater Bangalore, India, 30th Annual In-House Symposium on Space Science and Technology, ISRO-IISc Space Technology Cell, Indian Institute of Science, Bangalore, PP. 7-8.
Shen, Z., Zhu, X., Cao, X. & Chen, J., 2019, Measurement of Blooming Effect of DMSP-OLS Nighttime Light Data Based on NPP-VIIRS Data, Annals of GIS, 25, PP. 153-165, https://doi.org/10.1080/19475683.2019.1570336.
Shi, K., Yu, B., Huang, Y., Hu, Y., Yin, B., Chen, Z., Chen, L. & Wu, J., 2014, Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data, Remote Sensing, 6, PP. 1705-1724, https://doi.org/10.3390/rs6021705.
Shi, K., Yu, B., Hu, Y., Huang, C., Chen, Y., Huang, Y., Chen, Z. & Wu, J., 2015, Modeling and Mapping Total Freight Traffic in China Using NPP-VIIRS Nighttime Light Composite Data, GIScience & Remote Sensing, 52, PP. 274-289, https://doi.org/10.1080/15481603.2015.1022420.
Shi, K., Chen, Y., Yu, B., Xu, T., Yang, C., Li, L., Huang, C., Chen, Z., Liu, R. & Wu, J., 2016, Detecting Spatiotemporal Dynamics of Global Electric Power Consumption Using DMSP-OLS Nighttime Stable Light Data, Applied Energy, 184, PP. 450-463, https://doi.org/10.1016/j.apenergy.2016.10.032.
Small, C., Pozzi, F. & Elvidge, C.D., 2005, Spatial Analysis of Global Urban Extent from DMSP-OLS Night Lights, Remote Sensing of Environment, 96, PP. 277-291, https://doi.org/10.1016/j.rse.2005.02.002.
Tan, M., 2015, Urban Growth and Rural Transition in China Based on DMSP/OLS Nighttime Light Data, Sustainability, 7, PP. 8768-8781, https://doi.org/10.3390/su7078768.
Tan, M., 2016, Use of an Inside Buffer Method to Extract the Extent of Urban Areas from DMSP/OLS Nighttime Light Data in North China, Giscience & Remote Sensing, 53, PP. 444-458, https://doi.org/10.1080/15481603.2016.1148832.
Townsend, A.C. & Bruce, D.A., 2010, The Use of Night-Time Lights Satellite Imagery as a Measure of Australia's Regional Electricity Consumption and Population Distribution, International Journal of Remote Sensing, 31, PP. 4459-4480, https://doi.org/10.1080/01431160903261005.
Wang, Y. & Shen, Z., 2021, Comparing Luojia 1-01 and VIIRS Nighttime Light Data in Detecting Urban Spatial Structure Using a Threshold-Based Kernel Density Estimation, Remote Sensing, 13, P. 1574, https://doi.org/10.3390/rs13081574.
Wang, Z., Yao, F. & Li, W., 2017, Saturation Correction for Nighttime Lights Data Based on the Relative NDVI, Remote Sensing, 9, P. 759, https://doi.org/10.3390/rs9070759.
Wang, C., Chen, Z., Yang, C., Li, Q., Wu, Q., Wu, J., Zhang, G. & Yu, B., 2020, Analyzing Parcel-Level Relationships between Luojia1-01  Nighttime Light Intensity and Artificial Surface Features across Shanghai, China: A Comparison with NPP-VIIRS Data, International Journal of Applied Earth Observation and Geoinformation, 85, P. 101989, https://doi.org/10.1016/j.jag.2019.101989.
Wang, L., Zhang, H., Xu, H., Zhu, A., Fan, H. & Wang, Y., 2021, Extraction of City Roads Using Luojia1-01  Nighttime Light Data, Applied Sciences, 11, P. 10113, https://doi.org/10.3390/app112110113.
Wu, J., He, S., Peng, J., Li, W. & Zhong, X., 2013, Intercalibration of DMSP-OLS Night-Time Light Data by the Invariant Region Method, International Journal of Remote Sensing, 34, PP. 7356-7368 https://doi.org/10.1080/01431161.2013.820365.
Wu, R., Yang, D., Dong, J., Zhang, L. & Xia, F., 2018a, Regional Inequality in China Based on NPP-VIIRS Night-Time Light Imagery, Remote Sensing , 10, P. 240, https://doi.org/10.3390/rs10020240.
Wu, W., Zhao, H. & Jiang, S., 2018b, A Zipf’s Law-Based Method for Mapping Urban Areas Using NPP-VIIRS Nighttime Light Data, Remote Sensing, 10, P. 130, https://doi.org/10.3390/rs10010130.
Xie, Y., Weng, Q. & Fu, P., 2019, Temporal Variations of Artificial Nighttime Lights and Their Implications for Urbanization in the Conterminous United States, 2013-2017, Remote Sensing of Environment, 225, PP. 174-160, https://doi.org/10.1016/j.rse.2019.03.008.
Xin, X., Liu, B., Di, K., Zhu, Z., Zhao, Z., Liu, J., Yue, Z. & Zhang, G., 2017, Monitoring Urban Expansion Using Time Series of Night-Time Light Data: A Case Study in Wuhan, China, International Journal of Remote Sensing, 38, PP. 6110-6128, https://doi.org/10.1080/01431161.2017.1312623.
Xue, X., Yu, Z., Zhu, S., Zheng, Q., Weston, M., Wang, K., Gan, M. & Xu, H., 2018, Delineating Urban Boundaries Using Landsat8  Multispectral Data and VIIRS Nighttime Light Data, Remote Sensing, 10, P. 799, https://doi.org/10.3390/rs10050799.
Yang, Z., Chen, Y., Zheng, Z. & Wu, Z., 2022, Identifying China’s Polycentric Cities and Evaluating the Urban Centre Development Level Using Luojia-1 A Night-Time Light Data, Annals of GIS, 28(2), PP. 185-195. https://doi.org/10.1080/19475683.2022.2026472.
Yin, Z., Li, X., Tong, F., Li, Z. & Jendryke, M., 2020, Mapping Urban Expansion Using Night-Time Light Images from Luojia1-01  and International Space Station. International Journal of Remote Sensing, 41, PP. 2603-2623, https://doi.org/10.1080/01431161.2019.1693661.
Yuan, X., Jia, L., Menenti, M., Zhou, J. & Chen, Q., 2019, Filtering the NPP-VIIRS Nighttime Light Data for Improved Detection of Settlements in Africa, Remote Sensing, 11, P. 3002, https://doi.org/10.3390/rs11243002.
Zhang, X. & Li, P., 2018, A Temperature and Vegetation Adjusted NTL Urban Index for Urban Area mappiNg and analysis, ISPRS Journal of Photogrammetry and Remote Sensing, 135, PP. 93-111, https://doi.org/10.1016/j.isprsjprs. 2017.11.016.
Zhang, Q., Schaaf, C. & Seto, K.C., 2013, The Vegetation Adjusted NTL Urban Index: A New Approach to Reduce Saturation and Increase Variation in Nighttime Luminosity, Remote Sensing of Environment, 129, PP. 32-41, https://doi.org/10.1016/j.rse.2012.10.022.
Zhang, Q., Pandey, B. & Seto, K.C., 2016, A Robust Method to Generate a Consistent Time Series from DMSP/OLS Nighttime Light Data, IEEE Transactions on Geoscience and Remote Sensing, 54, PP. 5821-5831, https://doi.org/10.1109/TGRS.2016.2572724.
Zhang, G., Guo, X., Li, D. & Jiang, B., 2019, Evaluating the Potential of LJ1-01  Nighttime Light Data for Modeling Socio-Economic Parameters, Sensors, 19, P. 1465, https://doi.org/10.3390/s19061465.
Zhang, C., Pei, Y., Li, J., Qin, Q. & Yue, J., 2020, Application of Luojia1-01  Nighttime Images for Detecting the Light Changes for the2019  Spring Festival in Western Cities, China, Remote Sensing, 12, P. 1416, https://doi.org/10.3390/rs12091416.
Zhao, N., Zhou, Y. & Samson, E.L., 2014, Correcting Incompatible DN Values and Geometric Errors in Nighttime Lights Time-Series Images, IEEE Transactions on Geoscience and Remote Sensing, 53, PP. 2039-2049, https://doi.org/10.1109/TGRS.2014.2352598.
Zhao, M., Cheng, W., Zhou, C., Li, M., Wang, N. & Liu, Q. , 2017, GDP Spatialization and Economic Differences in South China Based on NPP-VIIRS Nighttime Light Imagery, Remote Sensing, 9, P. 673, https://doi.org/10.3390/rs9070673.
Zhao, M., Zhou, Y., Li, X., Cao, W., He, C., Yu, B., Li, X., Elvidge, C.D., Cheng, W. & Zhou, C., 2019, Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives, Remote Sensing, 11, P. 1971, https://doi.org/10.3390/rs11171971.
Zheng, Z., Chen, Y., Wu, Z., Ye, X., Guo, G. & Qian, Q., 2019a, The Desaturation Method of DMSP/OLS Nighttime Light Data Based on Vector Data: Taking the Rapidly Urbanized China as an Example, International Journal of Geographical Information Science, 33, PP. 431-453, https://doi.org/10.1080/13658816.2018.1538516.
Zheng, Z., Yang, Z., Chen, Y., Wu, Z. & Marinello, F., 2019b, The Interannual Calibration and Global Nighttime Light Fluctuation Assessment Based on Pixel-Level Linear Regression Analysis, Remote Sensing, 11, P. 2185, https://doi.org/10.3390/rs11182185.
Zheng, Q., Weng, Q. & Wang, K., 2020, Correcting the Pixel Blooming Effect (PiBE) of DMSP-OLS Nighttime Light Imagery, Remote Sensing of Environment, 240, P. 111707, https://doi.org/10.1016/j.rse.2020.111707.
Zheng, Y., Tang, L. & Wang, H., 2021a, An Improved Approach for Monitoring Urban Built-Up Areas by Combining NPP-VIIRS Nighttime Light, NDVI, NDWI, and NDBI, Journal of Cleaner Production, 328, P. 129488, https://doi.org/10.1016/j.jclepro.2021.129488.
Zheng, Y., Zhou, Q., He, Y., Wang, C., Wang, X. & Wang, H., 2021b, An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI, Remote Sensing, 13, P. 766, https://doi.org/10.3390/rs13040766.
Zhuo, L., Zheng, J., Zhang, X., Li, J. & Liu, L., 2015, An Improved Method of Night-Time Light Saturation Reduction Based on EVI, International Journal of Remote Sensing, 36, PP. 4114-4130, https://doi.org/10.1080/01431161.2015.1073861.
Zhuo, L., Zhang, C., Zhu, X., Huang, T., Hu, Y. & Tao, H., 2021, iSEAM: Improving the Blooming Effect Adjustment for DMSP-OLS Nighttime Light Images by Considering Spatial Heterogeneity of Blooming Distance, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, https://doi.org/10.1109/JSTARS.2021.3065399.
Ziskin, D., Baugh, K., Hsu, F.C., Ghosh, T. & Elvidge, C., 2010, Methods Used for the2006  Radiance Lights, Proceedings of the Asia-Pacific Advanced Network, 30, PP. 131-142, http://dx.doi.org/10.7125/APAN.30.18.

فایل ها

هم رسانی

ارجاع به این مقاله

آمار