The Changes in Nighttime Lights Caused by the Turkey–Syria Earthquake Using NOAA-20 VIIRS Day/Night Band Data

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(13), P. 3438 - 3438

Published: July 7, 2023

The Turkey–Syria earthquake on 6 February 2023 resulted in losses such as casualties, road damage, and building collapses. We mapped quantified the areas impacted by at different distances directions using NOAA-20 VIIRS nighttime light (NTL) data. then explored relationship between average changes NTL intensity, population density, density bivariate local indicators of spatial association (LISA) method. In Turkey, Hatay, Gaziantep, Sanliurfa experienced largest losses. Ar Raqqah was most affected city Syria, with highest loss rate. A correlation analysis showed that number injured populations provinces Turkey pixels a decreased intensity exhibited linear correlation, an R-squared value 0.7395. Based changing NTL, large were located 50 km from epicentre east-by-south north-by-west 130 southwest direction. increase distributed north-by-east 180 northeast direction, indicating high resilience effective rescue. had densities, particularly approximately south-by-west direction within 40 which can be seen low–high (L-H) pattern LISA results. Our findings provide insights for evaluating natural disasters help decision makers to plan post-disaster reconstruction determine risk levels national or regional scale.

Language: Английский

---

Utilizing Building Offset and Shadow to Retrieve Urban Building Heights with ICESat-2 Photons

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(15), P. 3786 - 3786

Published: July 30, 2023

Building height serves as an essential feature of urban morphology that provides valuable insights into human socio-cultural behaviors and their impact on the environment in milieu. However, openly accessible building information at individual level is still lacking remains sorely limited. Previous studies have shown ICESat-2′s ATL03/08 products are good accuracy for heights retrieval, however, these limited to areas with available data coverage. To this end, we propose a method extracting by using ICESat-2 ATL03 photons high-resolution remote sensing images. We first extracted roof footprint offsets shadows from high resolution imagery multitasking CNN frameworks. Using samples calculated photons, developed estimation combines offset shadow length information. assessed efficacy proposed Wujiaochang area Shanghai city, China. The results indicated able extract MAE 4.7 m, outperforms traditional shadow-based offset-based method. believe candidate accurately retrieving city-wide scale.

Language: Английский

---

Integrating NTL Intensity and Building Volume to Improve the Built-Up Areas’ Extraction from SDGSAT-1 GLI Data

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(13), P. 2278 - 2278

Published: June 21, 2024

Urban built-up areas are the main space carrier of population and urban activities. It is great significance to accurately identify area for monitoring urbanization dynamics their impact on Sustainable Development Goals. Using only nighttime light (NTL) remote sensing data will lead omission phenomena in extraction, especially SDGSAT-1 glimmer imager (GLI) with high spatial resolution. Therefore, this study proposed a novel Lights integrate Building Volume (LitBV) index by integrating NTL intensity information from GLI building volume Digital Surface Model (DSM) extract more accurately. The results indicated that LitBV achieved remarkable extraction areas, overall accuracy 81.25%. based better than volume. Moreover, experiments at different resolutions (10 m, 100 500 m) types (SDGSAT-1 data, Luojia-1 NASA’s Black Marble data) showed can significantly improve areas. has good application ability prospect extracting high-resolution data.

Language: Английский

---

Coupling and Coordination Association between Night Light Intensity and Women Safety – A Comparative Assessment of Indian Metropolitan Cities

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 481, P. 144135 - 144135

Published: Nov. 1, 2024

Language: Английский

---

Towards building floor-level nighttime light exposure assessment using SDGSAT-1 GLI data

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2025, Volume and Issue: 223, P. 375 - 397

Published: March 27, 2025

Language: Английский

---

Spatiotemporal Evolution and Correlation Analysis of Carbon Emissions in the Nine Provinces along the Yellow River since the 21st Century Using Nighttime Light Data

Land, Journal Year: 2023, Volume and Issue: 12(7), P. 1469 - 1469

Published: July 23, 2023

Monitoring carbon emissions is crucial for assessing and addressing economic development climate change, particularly in regions like the nine provinces along Yellow River China, which experiences significant urbanization development. However, to best of our knowledge, existing studies mainly focus on national provincial scales, with fewer municipal county scales. To address this issue, we established a emission assessment model based “NPP-VIIRS-like” nighttime light data, aiming analyze spatiotemporal variation three different levels since 21st century. Further, spatial correlation at level was explored using Moran’s I analysis method. Results show that, from 2000 2021, region continued rise, but growth rate declined, showing an overall convergence trend. Per capita intensity showed upward trend, while per unit GDP downward Its distribution generally high eastern low western region. The each city trend “several”; that is, urban area around has higher emissions. Meanwhile, there capitals. decreasing first then increasing gradually tended stable state later stage, pattern agglomeration relatively fixed. “High–High” “Low–Low” were main types local autocorrelation, number counties increased significantly, decreased. findings study provide valuable insights into trends area, as well references help achieve peaking neutrality goals proposed by China.

Language: Английский

---

Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data

Remote Sensing, Journal Year: 2022, Volume and Issue: 15(1), P. 22 - 22

Published: Dec. 21, 2022

The COVID-19 pandemic has presented unprecedented disruptions to human society worldwide since late 2019, and lockdown policies in response the have directly drastically decreased socioeconomic activities. To quantify assess extent of pandemic’s impact on economy Hebei Province, China, nighttime light (NTL) data, vegetation information, provincial quarterly gross domestic product (GDP) data were jointly utilized estimate GDP for prefecture-level cities county-level cities. Next, an autoregressive integrated moving average model (ARIMA) was applied predict 2020 2021. Finally, economic recovery intensity (ERI) used Province during pandemic. results show that, at level, had not yet recovered; prefectural county levels, three prefectures forty counties still struggling restore their economies by end 2021, even though these economies, as a whole, gradually recovering. In addition, number new infected cases correlated positively with urban NTL period, but post-pandemic period. study are informative local government’s strategies allocating financial resources short- long-term.

Language: Английский

---

Spatial Population Distribution Data Disaggregation Based on SDGSAT-1 Nighttime Light and Land Use Data Using Guilin, China, as an Example

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(11), P. 2926 - 2926

Published: June 3, 2023

A high-resolution population distribution map is crucial for numerous applications such as urban planning, disaster management, public health, and resource allocation, it plays a pivotal role in evaluating making decisions to achieve the UN Sustainable Development Goals (SDGs). Although there are many products derived from remote sensing nighttime light (NTL) other auxiliary data, they limited by coarse spatial resolution of NTL data. As result, outcomes’ restricted, cannot meet requirements some applications. To address this limitation, study employs data provided SDGSAT-1 satellite, which has 10 m, land use disaggregate WorldPop (100 m resolution) high m. The case conducted Guilin, China, using multi-class weighted dasymetric mapping method shows that total error during disaggregation 0.63%, accuracy 146 towns area represented an R2 0.99. In comparison result’s information entropy frequency increases 345% 1142%, respectively, demonstrates effectiveness approach studying distributions with resolution.

Language: Английский

---

A Vegetation Nighttime Condition Index Derived From the Triangular Feature Space Between Nighttime Light Intensity and Vegetation Index

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2023, Volume and Issue: 61, P. 1 - 15

Published: Jan. 1, 2023

Nighttime light (NTL) data have been commonly used as a proxy for characterizing socioeconomic activities. Vegetation coverage has found to be closely and inversely correlated with NTL intensity (NTLI). Although the combination of NTLI vegetation indices studied in various applications, complex relationship between is not yet clear. By analyzing NDVI mainland China from 2013 2021, we that scatterplot exhibits triangular shape physical meaning, which called NTLI-NDVI feature space. Using space, proposed Condition Index (VNCI), defined ratio differences among pixels specific value. VNCI associated local urban characteristics ability increase variation conditions within areas. To demonstrate application potential space VNCI, two applications (urban area extraction parameters estimation) were conducted. In terms extracting areas, shows better detection (with an average overall accuracy 85.01%) than original NPP-VIIRS other existing Moreover, further simple novel correcting approach correct using effectively eliminates impact enhances estimating parameters. Our findings demonstrated VNCI-corrected show superior performance R ² 0.9) both gross domestic product electric power consumption at provincial level. We believe hold great NTL-based studies.

Language: Английский

---

Multi-Type Features Embedded Deep Learning Framework for Residential Building Prediction

ISPRS International Journal of Geo-Information, Journal Year: 2023, Volume and Issue: 12(9), P. 356 - 356

Published: Aug. 31, 2023

Building type prediction is a critical task for urban planning and population estimation. The growing availability of multi-source data presents rich semantic information building prediction. However, existing residential methods have problems with feature extraction fusion from multi-type multi-level interactions between features. To overcome these limitations, we propose deep learning approach that takes both the internal external characteristics buildings into consideration features are shape buildings, include location proximity to nearest road areas interest (AOI), mainly threefold: spatial co-location patterns points (POI), nighttime light, land use buildings. A model, DeepFM, embedded, was deployed train predict types. Comparative ablation experiments using OpenStreetMap light dataset were carried out. results showed our model had significantly higher classification performance compared other models, F1 score 0.9444. It testified enhanced predicted performance. Moreover, good in transfer different regions. This research not only enhances accuracy identification but also offers valuable insights ideas related studies.

Language: Английский

---