检索问题
帮我深度研究夜光遥感领域从机制反演到深度学习的演讲过程，并撰写1k字的综述
夜光遥感领域从机制反演到深度学习的演进过程综述
夜光遥感（Nighttime Light, NTL）技术自问世以来，以其独特的视角捕捉人类活动的空间范围和强度，在城市化监测、社会经济评估、生态环境评价等多个领域展现出巨大的应用潜力。从最初基于机制反演的传统方法，到如今融合深度学习和多源大数据的先进技术，夜光遥感的研究范式发生了显著的演变。本文将深入探讨这一演进过程，分析不同阶段的技术特点、挑战与突破，并展望未来发展方向。
1. 夜光遥感的早期探索：机制反演与经验模型
夜光遥感的最早应用可以追溯到上世纪90年代初，主要依赖国防气象卫星计划（DMSP/OLS）的夜光数据。这一阶段的特点是基于物理机制或经验模型进行反演和估算。
- 社会经济参数估算： 夜光数据被广泛用作衡量人口、GDP等社会经济指标的代理数据。研究发现夜光强度与碳排放之间存在正相关关系，这为碳排放的估算提供了重要依据。例如，有研究利用DMSP/OLS数据和NPP/VIIRS数据预测中国各省的GDP，结果显示引入夜光数据作为外生变量显著提高了GDP预测的准确性。在城市化监测方面，NTL数据自1992年以来就被广泛用于理解城市化进程。
- 环境监测与评估： 夜光数据也被用于评估环境状况，如PM2.5浓度估算和生态环境质量监测。
- PM2.5浓度估算： 基于辐射传输理论，研究人员建立了夜间光辐射与地面PM2.5浓度之间的相关模型。例如，一项研究利用珞珈一号01（LJ1-01）夜光图像，结合气象和地形要素，对珠三角城市群的PM2.5浓度进行了估算，模型估算值与实测值之间的R²达到0.82，显示出较高的估算精度，弥补了传统地面监测成本高、空间分辨率低的不足。
- 碳排放反演： 针对缺乏能源统计数据地区（尤其是小尺度区域）碳排放估算困难的问题，研究人员提出了基于夜光遥感数据和改进的STIRPAT模型（ISTIRPAT）进行碳排放反演。该模型通过面板数据回归将省级碳排放清单数据下采样到市级，在湖北省17个城市和地区的应用中，反演精度达到0.9，高于原始模型。这表明夜光数据在获取高精度小尺度区域碳排放数据方面具有重要意义。
- 城市扩展与建成区提取： 传统上，夜光数据被用于识别城市建成区和监测城市扩张。然而，单一夜光数据在客观准确识别城市建成区方面存在局限性。为了更准确地评估城市建成区，研究人员开始尝试融合多源数据。例如，一项研究将2019年3月的POI（兴趣点）数据和2018年10月至2019年3月的珞珈一号01（Luojia1-A）数据通过小波变换融合，发现融合后的图像识别城市建成区分类精度显著提高，达到96.27%，F1分数达到0.8343，远高于单独使用夜光数据的84.00%精度和0.5487的F1分数，这使得提取结果更加客观准确。
- 地表城市热岛（SUHI）估算： DMSP/OLS夜光数据也被用于多中心城市群地表城市热岛强度（SUHII）的估算，研究表明夜光数据可以解释粤港澳大湾区SUHII空间变异的90%以上。
这一阶段的方法通常依赖于统计回归模型、经验阈值法或简单的物理模型。其优势在于模型相对简单，易于理解和实现。然而，挑战也显而易见：DMSP/OLS数据存在空间分辨率低、饱和效应、缺乏星上辐射定标等问题，限制了其在精细化研究中的应用。同时，不同传感器的夜光数据存在显著差异，导致长期序列分析的困难。
2. 数据融合与校准：应对多源数据的挑战
随着新型夜光遥感卫星（如NPP-VIIRS、SDGSAT-1、LJ1-01）的出现，夜光遥感数据源日益丰富，空间分辨率和辐射精度也显著提高。然而，不同传感器之间的数据差异成为了进行长期、一致性研究的主要障碍。因此，数据融合和交叉校准成为了这一阶段的核心任务。
- DMSP/OLS与NPP-VIIRS的交叉校准： DMSP/OLS和NPP-VIIRS是两种广泛使用的NTL数据集，但它们在空间分辨率和传感器设计上存在差异，使得直接用于长期城市化分析变得困难。
- 生成扩展时间序列数据： 研究人员通过交叉校准DMSP-OLS数据（2000-2012年）和月度NPP-VIIRS数据（2013-2018年）的组合，构建了NPP-VIIRS-like的扩展时间序列夜光数据（2000-2018年）。这种校准方法通过植被指数和自动编码器模型进行图像增强，在像素和城市层面与2012年的年度合成NPP-VIIRS数据显示出良好的一致性（R²分别为0.87和0.95），并与DMSP-OLS辐射校准NTL数据具有良好的精度。
- U-Net超分辨率网络： 为了解决现有NTL数据集的巨大差异和有限时间覆盖问题，研究人员提出了夜光U-Net超分辨率网络，用于DMSP-OLS和NPP-VIIRS之间的交叉传感器校准，并生成了1992年至2023年连续一致的500米全球年度模拟VIIRS夜光数据集（SVNL）。该数据集在捕获更长的NTL动态、保持更高时间一致性和呈现更大空间细节方面优于其他数据集，可用于长期人类活动监测和城市化研究。
- 线性拟合不变目标区域法： 在中国-巴基斯坦经济走廊区域，研究人员提出了一种基于线性拟合提取不变目标区域的方法，实现了DMSP/OLS图像之间以及DMSP/OLS和NPP/VIIRS两种数据之间的相互校准，校准后DMSP/OLS图像的总灰度与GDP和人口数据的相关性显著提高。
- SDGSAT-1与多光谱数据： 新一代卫星如SDGSAT-1（可持续发展科学卫星1号）的“Glimmer Imager (GI)”传感器提供了新的多光谱、高分辨率夜光图像数据，空间分辨率达到40米。
- 多光谱反演城市夜间光谱： 传统夜光遥感主要使用单波段观测，对城市夜间光谱特征的研究相对较弱。SDGSAT-1等多光谱遥感技术的发展使得分波段光谱反演成为可能。一项研究基于SDGSAT-1夜间多光谱图像和地面测量数据，检索了城市夜间RGB波段光谱分布，并构建了光环境反演图和蓝光比图。结果显示，参考人类视觉光谱范围配置的波段更适合遥感光谱反演，其中B波段与地面观测相关性最高（0.879）。
- 去条带算法： SDGSAT-1的L1A GI图像中存在大量带状坏像素或损坏像素，直接影响数据应用的准确性和可用性。为此，研究人员提出了一种基于异常检测和光谱相似性恢复（ADSSR）的去条带算法，该算法在视觉和定量指标方面均优于其他代表性算法，并能在不同尺寸GI图像中保持出色的性能和鲁棒性。
- 道路提取： SDGSAT-1/GIU数据的高空间分辨率使其能够作为道路提取的数据源。一项名为Band Operation and Marker-based Watershed Segmentation Algorithm (BO-MWSA) 的新道路提取方法，其F1分数达到84.65%，分别比支持向量机（SVM）和最优阈值（OT）算法高出11.02%和9.43%。
数据融合和校准显著提升了夜光遥感数据的时间连续性和空间一致性，为长期、大规模的研究奠定了基础。然而，校准过程本身仍然面临挑战，如如何准确处理不同传感器间的差异，如何最大限度地保留数据的原始信息等。
3. 深度学习与大数据赋能：从反演到智能分析
近年来，随着深度学习和空间大数据分析技术的快速发展，夜光遥感研究进入了一个全新的阶段。深度学习模型在处理复杂模式、特征提取和非线性关系方面表现出强大优势，极大地拓展了夜光遥感的应用范围和精度。
- 多源大数据集成： 现代城市研究正受益于遥感数据和空间大数据（如电信、移动技术、在线搜索引擎和社交媒体平台的地理标记数据）的结合。这种结合带来了“爆炸性”的发现，使得城市学者能够利用实时数据建模、模拟和预测城市景观变化，为可持续城市规划和发展提供宝贵信息。夜光遥感数据与POI数据、陆面温度（LST）产品等多种地理空间数据的结合，成为趋势。
- 复杂关系建模： 深度学习模型能够更好地捕捉夜光数据与各种社会经济、环境现象之间的复杂非线性关系。
- 城市夜间光谱反演： 在城市夜间光谱反演中，随机森林、反向传播（BP）神经网络和支持向量回归等机器学习模型在整体上优于线性模型，并且这三种机器学习方法取得了可比的精度，交叉验证的R²值约为0.65-0.70。其中，随机森林模型被选作主要的夜间光谱反演模型，揭示了交通走廊、商业区和景观照明等高亮度功能区的空间模式。
- 生态环境与城市化耦合协调度： 综合夜光指数（CNLI）和遥感生态指数（RSEI）被用于客观评估城市化和生态环境。结合耦合协调度（CCD）模型和面板向量自回归模型（PVAR），可以进一步探究两者之间的相互作用和影响机制，揭示城市化发展对生态环境的影响以及生态环境对城市化发展的限制。在中国的沿海地区，通过整合昼间和夜间遥感数据，发现城市化进程不断推进，而环境改善主要来自非城市化区域，城市化区域的生态环境压力依然不容乐观。
- 动态监测与预测： 深度学习结合长短期记忆网络（LSTM）等技术，可以更好地处理时间序列夜光数据，实现对动态变化的精确监测和未来趋势的预测。
- 难民人口变化估算： 夜光数据被用于量化俄乌战争前后乌克兰夜光变化和难民人口变化。结合Theil-Sen估计器和M-K检验探索夜光趋势，并利用夜光数据和联合国难民署部分难民数据构建线性模型。结果显示，战后一周内，乌克兰夜光区域和平均夜光DN值急剧下降约50%。通过模型和夜光数据计算的难民人口变化与联合国难民署数据基本一致，表明夜光数据可能直接用于动态估算战争期间难民流动的变化，这对于国际人道主义援助和战后重建具有重要意义。
- 长期人类活动监测： 诸如SVNL这样的长序列数据集，结合深度学习方法，可以用于长期的全球或区域城市化监测，并深入研究人口和社会经济活动的动态。
- 新兴应用： 多角度观测夜光数据为遥感带来了额外信息，可以改进现有基于夜光的遥感反演，甚至实现全新的遥感分析，尽管这将是具有挑战性的工作。此外，高分辨率夜光图像在夜间海洋船舶检测方面也显示出巨大潜力，LJ1-01数据在船舶检测方法中的检测精度显著高于NPP/VIIRS数据，可以记录更多潜在的船舶灯光，并区分船舶灯光和背景噪声，为高精度监测夜间海洋船舶提供参考。
4. 挑战与展望
尽管夜光遥感领域取得了显著进展，但仍面临一些挑战：
- 尺度效应与变异源： 对夜光数据的尺度效应和变异源需要更深入的理解。
- 多源数据融合与协同： 如何更好地整合多源夜光数据，并与其他类型地理空间数据协同使用以提高NTL的利用率，仍然是一个重要的研究方向。
- 全球南方地区的研究： 目前研究主要集中在发达地区，对全球南方地区的研究相对较少，需要更多关注。
- 新型数据产品的应用： 随着新夜光数据产品的不断涌现，如何开发新的城市应用是未来的研究重点。
- 夜光与碳浓度的复杂关系： 在中国工业改革背景下，夜光强度与碳排放之间的正相关关系可能不完全正确。夜光与碳浓度之间可能存在不同的相关性，这取决于城市工业结构和发展规划，因此需要通过探索夜光与碳浓度的关系来修改现有碳排放估算模型，提高精度。
展望未来，夜光遥感将继续朝着高精度、智能化、多维度和广覆盖的方向发展。以下几个方面将是未来的研究热点：
- 更精细化的人类活动刻画： 结合更高分辨率的夜光数据和更复杂的深度学习模型，将能够更精细地刻画人类活动的空间分布、强度和类型，例如区分不同类型的照明和功能区。
- 时空动态的深度挖掘： 深度学习特别是循环神经网络（RNN）和Transformer模型，将在处理长期时间序列夜光数据，预测城市发展趋势、人口迁移、经济波动等方面发挥更大作用。
- 多传感器数据融合的标准化与自动化： 开发更 robust 和通用的多传感器数据融合与校准算法，实现不同来源夜光数据的无缝集成，为全球范围内的长期监测提供一致性数据。
- 夜光遥感与其他遥感技术的协同： 结合光学遥感、SAR（合成孔径雷达）遥感、激光雷达（LiDAR）等多种遥感数据，形成互补优势，提升城市环境监测和管理的能力。
- 结合AI的可解释性研究： 随着深度学习模型复杂度的增加，提高模型的可解释性将变得至关重要，以更好地理解模型决策过程，增强用户对结果的信任。
- 基于夜光遥感的智能决策支持系统： 将夜光遥感分析结果集成到智能城市管理平台中，为城市规划、能源管理、环境保护、灾害响应等提供实时、智能的决策支持。
综上所述，夜光遥感领域经历了从基于机制反演的经验方法到融合多源数据和深度学习的智能分析的深刻演变。这一过程不仅提升了夜光遥感数据的应用潜力，也为理解和解决全球城市化、环境变化等复杂问题提供了强大的工具和新的视角。未来，随着技术的不断创新和多学科的交叉融合，夜光遥感必将在可持续发展领域发挥越来越重要的作用。
References
1Carbon Emission Inversion Model from Provincial to Municipal Scale Based on Nighttime Light Remote Sensing and Improved STIRPATOpenAlex
Wang Qi, Jiejun Huang, Han Zhou, et al.
Carbon emissions and consequent climate change directly affect the sustainable development of ecological environment systems and human society, which is a pertinent issue of concern for all countries globally. The construction of a carbon emission inversion model has significant theoretical importance and practical significance for carbon emission accounting and control. Established carbon emission models usually adopt socio-economic parameters or energy statistics to calculate carbon emissions. However, high-precision estimates of carbon emissions in administrative regions lacking energy statistics are difficult. This problem is especially prominent in small-scale regions. Methods to accurately estimate carbon emissions in small-scale regions are needed. Based on nighttime light remote-sensing data and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, combined with the environmental Kuznets curve, this paper proposes an ISTIRPAT (Improved Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Through the improved STIRPAT model (ISTIRPAT) and panel data regression, provincial carbon emission inventory data were downscaled to the municipal level, and municipal scale carbon emission inventories were obtained. This study took the 17 cities and prefectures of Hubei Province, China, as an example to verify the accuracy of the model. Carbon emissions for 17 cities and prefectures from 2012 to 2018 calculated from the original STIRPAT model and the ISTIRPAT model were compared with real values. The results show that using the ISTIRPAT model to downscale the provincial carbon emission inventory to the municipal level, the inversion accuracy reached 0.9, which was higher than that of the original model. Overall, carbon emissions in Hubei Province showed an upward trend. Regarding the spatial distribution, the main carbon emission area was formed in the central part of Hubei Province as a ring-shaped mountain peak. The lowest carbon emissions in the central area expanded outward, increased, and gradually decreased to the edge of the province. The overall composition of carbon emissions in eastern Hubei was higher than those in western Hubei.
2Estimation and Analysis of the Nighttime PM2.5 Concentration Based on LJ1-01 Images: A Case Study in the Pearl River Delta Urban Agglomeration of ChinaOpenAlex
Yanjun Wang, Mengjie Wang, Bo Huang, et al.
At present, fine particulate matter (PM2.5) has become an important pollutant in regard to air pollution and has seriously harmed the ecological environment and human health. In the face of increasingly serious PM2.5 air pollution problems, feasible large-scale continuous spatial PM2.5 concentration monitoring provides great practical value and potential. Based on radiative transfer theory, a correlation model of the nighttime light radiance and ground PM2.5 concentration is established. A multiple linear regression model is proposed with the light radiance, meteorological elements (temperature, relative humidity, and wind speed) and terrain elements (elevation, slope, and terrain relief) as variables to estimate the ground PM2.5 concentration at 56 air quality monitoring stations in the Pearl River Delta (PRD) urban agglomeration from 2018 to 2019, and the accuracy of model estimation is tested. The results indicate that the R2 value between the model-estimated and measured values is 0.82 in the PRD region, and the model attains a high estimation accuracy. Moreover, the estimation accuracy of the model exhibits notable temporal and spatial heterogeneity. This study, to a certain extent, mitigates the shortcomings of traditional ground PM2.5 concentration monitoring methods with a high cost and low spatial resolution and complements satellite remote sensing technology. This study extends the use of LJ1-01 nighttime light remote sensing images to estimate nighttime PM2.5 concentrations. This yields a certain practical value and potential in nighttime ground PM2.5 concentration inversion.
3Construction and visualization analysis of urban night multispectral inversion model based on SDGSAT-1 glimmer imageryOpenAlex
Ming Liu, Ruicong Li, Lie Feng, et al.
• Multispectral data advances urban night spectral research. • Random forest model used to invert urban night spectral data. • Construct a blue light ratio inversion map for the entire city. The spectrum is a key physical quantity characterizing the urban nighttime light environment. However, due to the prevalent use of single-band observations in conventional nighttime light remote sensing, studies on urban nighttime spectral characteristics remain relatively weak. With the advancement of multispectral remote sensing, band-wise spectral retrieval has become feasible. In this study, based on SDGSAT-1 nighttime multispectral imagery and ground-based measurements, we compare the spectral ranges of human visual perception and satellite sensors, retrieve the urban nighttime RGB-band spectral distribution, and construct a light-environment inversion map and a blue-light ratio map. The results show that: (1) configuring bands with reference to the human visual spectral range is more suitable for remote sensing spectral retrieval, among which the B band exhibits the highest correlation with ground observations (correlation coefficient 0.879), followed by the R and G bands (0.700 and 0.688, respectively). (2) A comparison of six linear regression models with three machine learning models—random forest, back-propagation (BP) neural network, and support vector regression—indicates that machine learning models overall outperform linear models, while the three machine learning approaches achieve comparable accuracies, with cross-validated R 2 values of approximately 0.65–0.70. (3) Considering residual characteristics and uncertainty analysis, the random forest model is selected as the primary inversion model to retrieve the nighttime spectra of the main urban area of Dalian. The band-wise and blue-light ratio maps reveal the spatial patterns of high-luminance functional areas such as traffic corridors, commercial districts, and landscape lighting, demonstrating that multispectral nighttime light remote sensing can provide important support for urban lighting planning and sustainable urban development.
4An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibrationOpenAlex
Zuoqi Chen, Bailang Yu, Chengshu Yang, et al.
Abstract. The nighttime light (NTL) satellite data have been widely used to investigate the urbanization process. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light data and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely used NTL datasets. However, the difference in their spatial resolutions and sensor design requires a cross-sensor calibration of these two datasets for analyzing a long-term urbanization process. Different from the traditional cross-sensor calibration of NTL data by converting NPP-VIIRS to DMSP-OLS-like NTL data, this study built an extended time series (2000–2018) of NPP-VIIRS-like NTL data through a new cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL data (2013–2018). The proposed cross-sensor calibration is unique due to the image enhancement by using a vegetation index and an auto-encoder model. Compared with the annual composited NPP-VIIRS NTL data in 2012, our product of extended NPP-VIIRS-like NTL data shows a good consistency at the pixel and city levels with R2 of 0.87 and 0.95, respectively. We also found that our product has great accuracy by comparing it with DMSP-OLS radiance-calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our extended NPP-VIIRS-like NTL data (2000–2018) have an excellent spatial pattern and temporal consistency which are similar to the composited NPP-VIIRS NTL data. In addition, the resulting product could be easily updated and provide a useful proxy to monitor the dynamics of demographic and socioeconomic activities for a longer time period compared to existing products. The extended time series (2000–2018) of nighttime light data is freely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et al., 2020).
5Nighttime light remote sensing for urban applications: Progress, challenges, and prospectsOpenAlex
Qiming Zheng, Karen C. Seto, Yuyu Zhou, et al.
Nighttime light (NTL) remote sensing data offer unique capabilities to characterize both the extent and intensity of human activities and have been extensively used to understand urbanization since 1992. The recent proliferation of NTL sensors, algorithms, and products creates new opportunities to understand contemporary urbanization and the associated socioeconomic and environmental changes. We conducted a comprehensive literature review of 688 peer-reviewed papers published between 1992 and 2022 to understand the trends in how NTL data have been used to study urbanization (e.g., with which data products, during which time span, and in which geographies) and to synthesize the progress and challenges of key urban application topics. Based on our review, we identified four research directions for future NTL-based urban applications: (1) a better understanding of scale effects and sources of variations in NTL data; (2) integrating multi-source NTL data and synergizing NTL data with other types of geospatial data for improved NTL utilization; (3) more research on the Global South; and (4) developing new urban applications with new NTL data products. Addressing research gaps in these areas will generate new insights into the urbanization process under different geographical and socioeconomic settings.
6Exploration of eco-environment and urbanization changes in coastal zones: A case study in China over the past 20 yearsOpenAlex
Zihao Zheng, Zhifeng Wu, Yingbiao Chen, et al.
With the rapid development of urbanization and population migration, since the 20th century, the natural and eco-environment of coastal areas have been under tremendous pressure due to the strong interference of human response. To objectively evaluate the coastal eco-environment condition and explore the impact from the urbanization process, this paper, by integrating daytime remote sensing and nighttime remote sensing, carried out a quantitative assessment of the coastal zone of China in 2000–2019 based on Remote Sensing Ecological Index (RSEI) and Comprehensive Nighttime Light Index (CNLI) respectively. The results showed that: 1) the overall eco-environmental conditions in China's coastal zone have shown a trend of improvement, but regional differences still exist; 2) during the study period, the urbanization process of cities continued to advance, especially in seaside cities and prefecture-level cities in Jiangsu and Shandong, which were much higher than the average growth rate; 3) the Coupling Coordination Degree (CCD) between the urbanization and eco-environment in coastal cities is constantly increasing, but the main contribution of environmental improvement comes from non-urbanized areas, and the eco-environment pressure in urbanized areas is still not optimistic. As a large-scale, long-term series of eco-environment and urbanization process change analysis, this study can provide theoretical support for mesoscale development planning, eco-environment condition monitoring and environmental protection policies from decision-makers.
7A global annual simulated VIIRS nighttime light dataset from 1992 to 2023OpenAlex
Xiuxiu Chen, Zeyu Wang, Feng Zhang, et al.
Nighttime light (NTL) data is recognized as a reliable proxy for measuring the scope and intensity of human activity, finding wide application in studies such as urbanization monitoring, socioeconomic estimation, and ecological environment assessment. However, the substantial discrepancies and limited temporal coverage of existing NTL datasets have constrained their potential for long-term research applications. To address this, a Nighttime Light U-Net super-resolution network is proposed for the cross-sensor calibration between the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) NTL data and the Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data. This network is applied to generate a continuous and consistent 500-meter global annual simulated VIIRS NTL dataset (SVNL) from 1992 to 2023. Validation results indicate a high confidence in the quality of the SVNL data, demonstrating its superiority in capturing longer NTL dynamics, maintaining higher temporal consistency, and presenting greater spatial detail compared with other NTL datasets. The SVNL could be utilized for prolonged human activities monitoring, and further research on regional or global urbanization.
8Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective of Urban Studies in Recent DecadesOpenAlex
Danlin Yu, Chuanglin Fang
During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite images, unmanned drone images, and hyperspectral images, among many others, have provided fresh opportunities to examine the dynamics of urban landscapes. In the meantime, the rapid development of telecommunications and mobile technology, alongside the emergence of online search engines and social media platforms with geotagging technology, has fundamentally changed how human activities and the urban landscape are recorded and depicted. The combination of these two types of data sources results in explosive and mind-blowing discoveries in contemporary urban studies, especially for the purposes of sustainable urban planning and development. Urban scholars are now equipped with abundant data to examine many theoretical arguments that often result from limited and indirect observations and less-than-ideal controlled experiments. For the first time, urban scholars can model, simulate, and predict changes in the urban landscape using real-time data to produce the most realistic results, providing invaluable information for urban planners and governments to aim for a sustainable and healthy urban future. This current study reviews the development, current status, and future trajectory of urban studies facilitated by the advancement of remote sensing and spatial big data analytical technologies. The review attempts to serve as a bridge between the growing “big data” and modern urban study communities.
9An extended time-series (2000–2018) of global NPP-VIIRS-likenighttime light data from a cross-sensor calibrationOpenAlex
Zuoqi Chen, Bailang Yu, Chengshu Yang, et al.
Abstract. The nighttime light (NTL) satellite data have been widely used to investigate urbanization process. The Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) stable nighttime light data and Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely used NTL datasets. However, the difference of their spatial resolutions and sensor design makes it difficult to directly use these two datasets together for a long-term analysis of urbanization. To solve this issue, an extended time-series (2000–2018) of NPP-VIIRS-like NTL data were proposed in this study through a cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL data (2013–2018). Compared with the annual composited NPP-VIIRS NTL data in 2012, our product of extended NPP-VIIRS-like NTL data shows a good consistency at the pixel and city levels with R2 of 0.87 and 0.95, respectively. We also found that our product has a good accuracy by comparing with DMSP-OLS radiance calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our extended NPP-VIIRS-like NTL data (2000–2018) have a good spatial pattern and temporal consistency, which are similar to the composited NPP-VIIRS NTL data. In addition, the resulting product could be easily updated and provide a useful proxy to monitor the dynamics of demographic and socio-economic activities for a longer time period compared to existing products. The extended time-series (2000–2018) of nighttime light data are freely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et al., 2020).
10Consistent intercalibration of nighttime light data between DMSP/OLS and NPP/VIIRS in the China⁃Pakistan Economic CorridorOpenAlex
Li Liang, BIAN Jinhu, Ainong Li, et al.
ç±ç¾å½å½é²æ°è±¡å«ææè½½çå¯è§å æå线æ§æ«æä¸å¡ç³»ç»ï¼DMSP/OLSï¼åå½å®¶æ轨å«ææè½½çå¯è§å è¿çº¢å¤æåè¾å°ä»ªï¼NPP/VIIRSï¼è·åçå¤é´ç¯å å½±åæ¯çæµäººç±»ç¤¾ä¼ç»æµæ´»å¨åèªç¶ç°è±¡ï¼å¦æç«ãæ²¹æ°çç§çï¼ç主è¦æ°æ®æºãç¶èï¼ç°æçå¤é´ç¯å æ°æ®åå¨ç¼ºä¹æä¸çè¾å°å®æ ãåå 饱åãæ¶é´å°ºåº¦ä¸è¿ç»ãå¤æºå¤é´ç¯å å½±åè¾å°ä¸ä¸è´çé®é¢ãåºäºæ¤ï¼æ¬æ以ä¸å·´ç»æµèµ°å»åºå为ç 究åºï¼æåºäºä¸ç§åºäºçº¿æ§æåæåä¸åç®æ åºåçæ¹æ³ï¼å®ç°äºDMSP/OLSå½±åé´ãDMSP/OLSä¸NPP/VIIRS两ç§æ°æ®é´çç¸äºæ ¡æ£ãç¶å对ä¸å·´ç»æµèµ°å»çæ ¡æ£ç»æå¨ä¸å空é´å°ºåº¦ä¸éç¨åºåç°åº¦æ»éãæ ååå·®å¼ææ°ä»¥åæ ååå·®å¼ææ°åä½ä¸ºè¯ä»·ææ è¿è¡æ£éªãç»æ表æ:两ç§æ ¡æ£æ¨¡åçæåä¼åº¦åå¨0.78以ä¸ï¼æ ¡æ£åçDMSP/OLSå½±åç°åº¦æ»éä¸GDPå人å£æ°æ®çç¸å ³æ§æ¾èæé«(GDP:R2=0.7689;人å£:R2=0.9033)ï¼ä¸æ ååå·®å¼ææ°ææ¾éä½ï¼NPP/VIIRSå½±åç»è¿ä¸DMSP/OLSäºæ ¡æ£åå¨è¾å°äº®åº¦ãæ¶ç©ºåå¸ä¸ä¸DMSP/OLSæ´å ä¸è´ï¼ç©ºé´ç»èä¿¡æ¯æ´å çªåºï¼ä»èå¢å¼ºäºå¤æºå¤é´ç¯å å½±åçä¸è´æ§ï¼æ´å éåç¨äºé¿æ¶é´åºå社ä¼ç»æµè¦ç´ åå±è¶å¿çåæã
11A Destriping Algorithm for SDGSAT-1 Nighttime Light Images Based on Anomaly Detection and Spectral Similarity RestorationOpenAlex
Degang Zhang, Bo Cheng, Lu Shi, et al.
Remote sensing nighttime lights (NTLs) offers a unique perspective on human activity, and NTL images are widely used in urbanization monitoring, light pollution, and other human-related research. As one of the payloads of sustainable development science Satellite-1 (SDGSAT-1), the Glimmer Imager (GI) provides a new multi-spectral, high-resolution, global coverage of NTL images. However, during the on-orbit testing of SDGSAT-1, a large number of stripes with bad or corrupted pixels were observed in the L1A GI image, which directly affected the accuracy and availability of data applications. Therefore, we propose a novel destriping algorithm based on anomaly detection and spectral similarity restoration (ADSSR) for the GI image. The ADSSR algorithm mainly consists of three parts: pretreatment, stripe detection, and stripe restoration. In the pretreatment, salt-pepper noise is suppressed by setting a minimum area threshold of the connected components. Then, during stripe detections, the valid pixel number sequence and the total pixel value sequence are analyzed to determine the location of stripes, and the abnormal pixels of each stripe are estimated by a clustering algorithm. Finally, a spectral-similarity-based method is adopted to restore all abnormal pixels of each stripe in the stripe restoration. In this paper, the ADSSR algorithm is compared with three representative destriping algorithms, and the robustness of the ADSSR algorithm is tested on different sizes of GI images. The results show that the ADSSR algorithm performs better than three representative destriping algorithms in terms of visual and quantitative indexes and still maintains outstanding performance and robustness in differently sized GI images.
12Using Wavelet Transforms to Fuse Nighttime Light Data and POI Big Data to Extract Urban Built-Up AreasOpenAlex
Xiong He, Chunshan Zhou, Jun Zhang, et al.
Urban built-up areas are not only the embodiment of urban expansion but also the main space carrier of urban activities. Accurate extraction of urban built-up areas is of great practical significance for measuring the urbanization process and judging the urban environment. It is difficult to identify urban built-up areas objectively and accurately with single data. Therefore, to evaluate urban built-up areas more accurately, this study uses the new method of fusing wavelet transforms and images on the basis of utilization of the POI data of March 2019 and the Luojia1-A data from October 2018 to March 2019. to identify urban built-up areas. The identified urban built-up areas are mainly concentrated in the areas with higher urbanization level and night light value, such as the northeast of Dianchi Lake and the eastern bank around the Dianchi Lake. It is shown in the accuracy verification result that the classification accuracy identified by night-light data of urban build-up area accounts for 84.00% of the total area with the F1 score 0.5487 and the Classification accuracy identified by the fusion of night-light data and POI data of urban build-up area accounts for 96.27% of the total area with the F1 score 0.8343. It is indicated that the built-up areas identified after image fusion are significantly improved with more realistic extraction results. In addition, point of interest (POI) data can better account for the deficiency in nighttime light (NTL) data extraction of urban built-up areas in the urban spatial structure, making the extraction results more objective and accurate. The method proposed in this study can extract urban built-up areas more conveniently and accurately, which is of great practical significance for urbanization monitoring and sustainable urban planning and construction.
13A Novel SUHI Referenced Estimation Method for Multicenters Urban Agglomeration using DMSP/OLS Nighttime Light DataOpenAlex
Jiufeng Li, Fangfang Wang, Yingchun Fu, et al.
The surface urban heat island (SUHI) of urban agglomeration has always been an important topic in the studies of urban heat island, especially with the development of satellite-based land surface temperature (LST) products. However, most studies are limited to the perspective of a single city, ignoring the impact of urban agglomeration and the changes of LST at day and night on the reference LST (RLST) (e.g., rural areas). Consequently, this article proposed a novel method about SUHI intensity estimation for the multicenters (mcSUHII) of urban agglomeration in Guangdong-Hong Kong-Macao Greater Bay Area (GHMBay) using nighttime light (NTL) data (i.e., DMSP/OLS) obtained in October, 2010. The mcSUHII method considered the RLST of SUHII estimation based on multicenter structure, and was more flexible to adapt the impact of human activity intensity. The study showed that compared with other RLSTs, such as suburban and forest, mcSUHII mitigates the underestimation bias caused by ignoring the multicenter structure. Importantly, the change in SUHII for urban agglomerations is greater than for a single city. Moreover, it was illustrated that the variation of SUHII presented an obvious inverted U-shape along the gradient from the inland to the coastal cities. The highest SUHIIs in the delta cities at day and night are ~7.27 ± 1.71 °C and ~4.46 ± 1.42 °C, respectively. Additionally, NTL served as the dominator together with other factors that were capable of explaining more than 90% of the spatial variation in SUHII in GHMBay. Therefore, considering multicenters more in estimation of SUHII of urban agglomeration for the sustainable development.
14Mapping of nighttime light trends and refugee population changes in Ukraine during the Russian–Ukrainian WarOpenAlex
Chaoqing Huang, Song Hong, Xiaoxiao Niu, et al.
The nighttime lights accurately and coherently depict how humans live. This study uses nighttime light measurements to quantify changes in nighttime lighting and refugee population in Ukraine before and after the war. We combined the Theil–Sen estimator with the M-K test to explore the trends of nighttime light. In addition, we constructed a linear model using nighttime light data and a portion of the UNHCR refugee data. Our results reveal that 1 week after the start of the Russo-Ukrainian War, the nighttime light area and the average nighttime light DN value in Ukraine exhibited a steep decline of about 50 percent. Our findings showed taht refugee population changes calculated through models and nighttime light data were mostly consistent with UNHCR data. We thought that the nighttime light data might be used directly to dynamically estimate changes in the refugee movement throughout the war. Nighttime light changes has significant implications for international humanitarian assistance and post-war reconstruction.
15Spatiotemporal Change of Eco-Environmental Quality in the Oasis City and Its Correlation with Urbanization Based on RSEI: A Case Study of Urumqi, ChinaOpenAlex
Jingjing Zhang, Qian Zhou, Min Cao, et al.
As an important node city of “The Belt and Road” strategy, Urumqi has a non-negligible impact on the ecological environment in the process of rapid development. It is of great significance to understand the coupling and coordination between urbanization and the ecological environment for regional sustainable development. However, previous studies on the coupling coordination degree (CCD) model of urbanization and ecological environment are limited, and they ignore the endogenous relationship between the two. Therefore, this study aims to introduce an econometric model, the panel vector autoregression model (PVAR), to further explore the relationship between them and the influencing mechanism. Firstly, urbanization and ecological environment were evaluated objectively by the comprehensive nighttime light index (CNLI) and remote sensing ecological index (RSEI), respectively. Then, the coupling coordination degree of urbanization and the ecological environment were evaluated comprehensively by a typical coupling coordination degree model. Finally, the PVAR model is used to analyze the interaction between the two systems and the mechanism of action. The results showed that: (1) in the recent 25 years, the mean value of RSEI in Urumqi decreased gradually, and the overall ecological environment deteriorated, but the differences among districts and counties were still significant; (2) the urbanization level of Urumqi is on the rise, while UC, DBC(B), and MD have the highest increase in CNLI although they are at a low level; and (3) in the interactive relationship between urbanization and the ecological environment, the development of Urumqi’s ecological environment is mainly affected by its development inertia, and the development of urbanization is limited by the ecological environment. This study fills the gap in the study of the interaction mechanism between urbanization and the ecological environment and provides a new perspective for the study of sustainable urban development worldwide.
16Research on Road Extraction Method Based on Sustainable Development Goals Satellite-1 Nighttime Light DataOpenAlex
Dingkun Chang, Qinjun Wang, Jingyi Yang, et al.
Road information plays a fundamental role in many applications. However, at present, it is difficult to extract road information from the traditional nighttime light images in view of their low spatial and spectral resolutions. To fill the gap in high-resolution nighttime light (NTL) data, the Sustainable Development Goals Satellite-1(SDGSAT-1) developed by the Chinese Academy of Sciences (CAS) was successfully launched on 5 November 2021. With 40 m spatial resolution, NTL data acquired by the Glimmer Imager Usual (GIU) sensor on the SDGSAT-1 provide a new data source for road extraction. To evaluate the ability of SDGSAT-1 NTL data to extract road information, we proposed a new road extraction method named Band Operation and Marker-based Watershed Segmentation Algorithm (BO-MWSA). Comparing with support vector machine (SVM) and optimum threshold (OT) algorithms, the results showed that: (1) the F1 scores of the roads in the test area extracted by SVM, OT, and BO-MWSA were all over 70%, indicating that SDGSAT-1/GIU data could be used as a data source for road extraction. (2) The F1 score of road extraction by BO-MWSA is 84.65%, which is 11.02% and 9.43% higher than those of SVM and OT, respectively. In addition, the F1 scores of BO-MWSA road extraction in Beijing and Wuhan are both more than 84%, indicating that BO-MWSA is an effective method for road extraction using NTL imagery. (3) In road extraction experiments for Lhasa, Beijing, and Wuhan, the results showed that the greater the traffic flow was, the lower the accuracy of the extracted roads became. Therefore, BO-MWSA is an effective method for road extraction using SDGSAT-1 NTL data.
17GDP Forecasting Model for China’s Provinces Using Nighttime Light Remote Sensing DataOpenAlex
Yan Gu, Zhenfeng Shao, Xiao Huang, et al.
In order to promote the economic development of China’s provinces and provide references for the provinces to make effective economic decisions, it is urgent to investigate the trend of province-level economic development. In this study, DMSP/OLS data and NPP/VIIRS data were used to predict economic development. Based on the GDP data of China’s provinces from 1992 to 2016 and the nighttime light remote sensing (NTL) data of corresponding years, we forecast GDP via the linear model (LR model), ARIMA model, ARIMAX model, and SARIMA model. Models were verified against the GDP records from 2017 to 2019. The experimental results showed that the involvement of NTL as exogenous variables led to improved GDP prediction.
18Explore the application of high-resolution nighttime light remote sensing images in nighttime marine ship detection: A case study of LJ1-01 dataOpenAlex
Liang Zhong, Xiaosheng Liu, Peng Yang, et al.
Abstract Nighttime light remote sensing images show significant application potential in marine ship monitoring, but in areas where ships are densely distributed, the detection accuracy of the current methods is still limited. This article considered the LJ1-01 data as an example, compared with the National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) data, and explored the application of high-resolution nighttime light images in marine ship detection. The radiation values of the aforementioned two images were corrected to achieve consistency, and the interference light sources of the ship light were filtered. Then, when the threshold segmentation and two-parameter constant false alarm rate methods are combined, the ships’ location information was with obtained, and the reliability of the results was analyzed. The results show that the LJ1-01 data can not only record more potential ship light but also distinguish the ship light and background noise in the data. The detection accuracy of the LJ1-01 data in both ship detection methods is significantly higher than that of the NPP/VIIRS data. This study analyzes the characteristics, performance, and application potential of the high-resolution nighttime light data in the detection of marine vessels. The relevant results can provide a reference for the high-precision monitoring of nighttime marine ships.
19Multiple Angle Observations Would Benefit Visible Band Remote Sensing Using Night LightsOpenAlex
Christopher C. M. Kyba, Martin Aubé, Salvador Bará, et al.
Abstract The spatial and angular emission patterns of artificial and natural light emitted, scattered, and reflected from the Earth at night are far more complex than those for scattered and reflected solar radiation during daytime. In this commentary, we use examples to show that there is additional information contained in the angular distribution of emitted light. We argue that this information could be used to improve existing remote sensing retrievals based on night lights, and in some cases could make entirely new remote sensing analyses possible. This work will be challenging, so we hope this article will encourage researchers and funding agencies to pursue further study of how multi‐angle views can be analyzed or acquired.
20Correlation Analysis of CO2 Concentration Based on DMSP-OLS and NPP-VIIRS Integrated DataOpenAlex
Zuozhi Chen, Wei Gong, Zhiyu Gao, et al.
In view of global warming, caused by the increase in the concentration of greenhouse gases, China has proposed a series of carbon emission reduction policies. It is necessary to obtain the spatiotemporal distribution of carbon emissions accurately. Nighttime light data is recognized as an important basis for carbon emission estimation. A large number of research results show that there is a positive correlation between nighttime light intensity and carbon emission. However, in the current context of China’s industrial reforms, this positive relationship may not be entirely correct. First, we correct the nighttime light data from different satellites and established a long-term series data set. Then, we verify the positive correlation between nighttime light and carbon emission. However, the time scale of emission data often lags, and the carbon concentration data are released earlier and are more accurate than emission data. Therefore, we propose to investigate the relationship between nighttime light and carbon concentration. It is found that there may be different correlations between nighttime light and the carbon concentration, due to different urban industrial structure and development planning. Therefore, by exploring the relationship between nighttime light and the carbon concentration, the existing carbon emission estimation model can be modified to improve the accuracy of the emission model.