Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives

Abstract

Nighttime light observations from remote sensing provide us with a timely and spatially explicit measure of human activities, and therefore enable a host of applications such as tracking urbanization and socioeconomic dynamics, evaluating armed conflicts and disasters, investigating fisheries, assessing greenhouse gas emissions and energy use, and analyzing light pollution and health effects. The new and improved sensors, algorithms, and products for nighttime lights, in association with other Earth observations and ancillary data (e.g., geo-located big data), together offer great potential for a deep understanding of human activities and related environmental consequences in a changing world. This paper reviews the advances of nighttime light sensors and products and examines the contributions of nighttime light remote sensing to perceiving the changing world from two aspects (i.e., human activities and environmental changes). Based on the historical review of the advances in nighttime light remote sensing, we summarize the challenges in current nighttime light remote sensing research and propose four strategic directions, including: Improving nighttime light data; developing a long time series of consistent nighttime light data; integrating nighttime light observations with other data and knowledge; and promoting multidisciplinary and interdisciplinary analyses of nighttime light observations.

Adaptive CNN Ensemble for Complex Multispectral Image Analysis

“…e experiments are carried out on the Google Cloud Platform (GCP). In the GCP server (us-west1-b region), we installed the Compute Engine 2//For feature extraction task (2) Perform convolution operation with F � 64 and nonlinearity, via equations 3and 4(3) Add Gaussian noise, via equation 6(4) Apply max-pooling, via equation 7(5) Activate and deactivate neurons using dropout with p, via equation 8 Forward accuracy to the performance feedback module (5) Determine the ensemble accuracies using voting, via Algorithm 2 //Spectral band stream are classifying with their respective instances in the DEC module (6) if % accuracy for B ≥ //if sample does not misclassify (7) Repeat steps 3, 4, and 5 (8) if % accuracy for B ≤ //if sample misclassify (9) Save the B //Save misclassified sample in training repository //as potential new spectral band (10) Counter++ (11) Repeat steps 3, 4, and 5 (12) if counter � 50 //number of misclassified instances reached to 50 (13) Cluster M c using K-mean where K � 1, [35]. //Cluster all the misclassified data samples using the K-means approach with //K � 1, K � 1 the case is assigned to the class of its nearest neighbor (14) Determine optimized centroid, [35]//to optimize the similar sample instances (15) Compare cosine distance cluster sample with cluster centroid, via equation (12) //to segregate most relevant samples in cluster (16) Assign the all nearest samples a hypothetical class X � B n+1 //A new class with additional spectral band information (17) Create new instance classifier i n+1 //New Single Instance, 20 layered architecture (18) Train new instance classifier I � i n+1 with hypothetical class X � B n+1 , via Table 3 //Online training with selected hyperparameters as depicted in Table 3 ALGORITHM 3: Continued.…”

“…e distinct types of multispectral images have different processing needs and thus also comes with new challenges to algorithms that analyze the data [7], such as optimization of model computational complexity, accuracy, robustness, and adaptability. A recent survey [8] discusses the potential applications and challenges associated with the nighttime light observations. is study highlights the issues of timeseries data analysis for multispectral images.…”

Constructing a New Inter-Calibration Method for DMSP-OLS and NPP-VIIRS Nighttime Light

“…However, the longer-term analysis and application of NTL can be deeply facilitated by connecting the two sensors. Accordingly, the inter-calibration between these two sensors is now one of the most eagerly awaited researches [18][19][20].…”

“…Wu et al [26] adopted a similar method to correct the annual NTL data of Beijing region, while their result has similar problems with that of Li et al [25]. The city-scale NPP-VIIRS data were converted into DMSP-like data from 1996 to 2017 by a residuals-corrected geographically weighted regression model [27], while the time range of NTL was not completely expanded [20]. All of these works have promoted the applications of NTL, but these functions still have some limitations in different aspects.…”

Assessment of Night-Time Lighting for Global Terrestrial Protected and Wilderness Areas

“…It is necessary to study the lighting conditions of reserves [29][30][31]. DMSP/OLS NTL is an excellent data source for analyzing the impact of human development on various ecosystems [32], vegetation [33], and biodiversity [34] over long-term sequences and over large geographical areas and in remote locations [35].…”