Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery
Mapping impervious surface area (ISA) dynamics at the regional and global scales is an important task that supports the management of the urban environment and urban ecological systems. In this study, we aimed to develop a new method for ISA percentage (ISA%) mapping using Nighttime Light (NTL) and...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/10/1900 |
| _version_ | 1797534320420716544 |
|---|---|
| author | Yun Tang Zhenfeng Shao Xiao Huang Bowen Cai |
| author_facet | Yun Tang Zhenfeng Shao Xiao Huang Bowen Cai |
| author_sort | Yun Tang |
| collection | DOAJ |
| description | Mapping impervious surface area (ISA) dynamics at the regional and global scales is an important task that supports the management of the urban environment and urban ecological systems. In this study, we aimed to develop a new method for ISA percentage (ISA%) mapping using Nighttime Light (NTL) and MODIS products. The proposed method consists of three major steps. First, we calculated the Enhanced Vegetation Index (EVI)-adjusted NTL index (EANTLI) and performed intra-annual and inter-annual corrections on the DMSP-OLS data. Second, based on the geographically weighted regression (GWR) model, we built a consistent NTL product from 2000 to 2019 by performing an intercalibration between DMSP-OLS and VIIRS images. Third, we adopted a GA-BP neural network model to monitor ISA% dynamics using NTL imagery, MODIS imagery, and population data. Taking the Guangdong–Hong Kong–Macao Greater Bay as the study area, our results indicate that the ISA% in our study area increased from 7.97% in 2000 to 17.11% in 2019, with a mean absolute error (MAE) of 0.0647, root mean square error (RMSE) of 0.1003, Pearson’s coefficient of 0.9613, and R<sup>2</sup> (R-squared) of 0.9239. Specifically, these results demonstrate the effectiveness of the proposed method in mapping ISA and investigating ISA dynamics using temporal features extracted from consistent NTL and MODIS products. The proposed method is feasible when generating ISA% at a large scale at high frequency, given the ease of implementation and the availability of input data sources. |
| first_indexed | 2024-03-10T11:27:56Z |
| format | Article |
| id | doaj.art-9f3ae01e1e3d49949396e440bbe0f42f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:27:56Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9f3ae01e1e3d49949396e440bbe0f42f2023-11-21T19:31:21ZengMDPI AGRemote Sensing2072-42922021-05-011310190010.3390/rs13101900Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS ImageryYun Tang0Zhenfeng Shao1Xiao Huang2Bowen Cai3School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaDepartment of Geosciences, University of Arkansas, Fayetteville, AR 72701, USASchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaMapping impervious surface area (ISA) dynamics at the regional and global scales is an important task that supports the management of the urban environment and urban ecological systems. In this study, we aimed to develop a new method for ISA percentage (ISA%) mapping using Nighttime Light (NTL) and MODIS products. The proposed method consists of three major steps. First, we calculated the Enhanced Vegetation Index (EVI)-adjusted NTL index (EANTLI) and performed intra-annual and inter-annual corrections on the DMSP-OLS data. Second, based on the geographically weighted regression (GWR) model, we built a consistent NTL product from 2000 to 2019 by performing an intercalibration between DMSP-OLS and VIIRS images. Third, we adopted a GA-BP neural network model to monitor ISA% dynamics using NTL imagery, MODIS imagery, and population data. Taking the Guangdong–Hong Kong–Macao Greater Bay as the study area, our results indicate that the ISA% in our study area increased from 7.97% in 2000 to 17.11% in 2019, with a mean absolute error (MAE) of 0.0647, root mean square error (RMSE) of 0.1003, Pearson’s coefficient of 0.9613, and R<sup>2</sup> (R-squared) of 0.9239. Specifically, these results demonstrate the effectiveness of the proposed method in mapping ISA and investigating ISA dynamics using temporal features extracted from consistent NTL and MODIS products. The proposed method is feasible when generating ISA% at a large scale at high frequency, given the ease of implementation and the availability of input data sources.https://www.mdpi.com/2072-4292/13/10/1900impervious surfacenighttime light dataMODISspatiotemporal dynamics |
| spellingShingle | Yun Tang Zhenfeng Shao Xiao Huang Bowen Cai Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery Remote Sensing impervious surface nighttime light data MODIS spatiotemporal dynamics |
| title | Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery |
| title_full | Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery |
| title_fullStr | Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery |
| title_full_unstemmed | Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery |
| title_short | Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery |
| title_sort | mapping impervious surface areas using time series nighttime light and modis imagery |
| topic | impervious surface nighttime light data MODIS spatiotemporal dynamics |
| url | https://www.mdpi.com/2072-4292/13/10/1900 |
| work_keys_str_mv | AT yuntang mappingimpervioussurfaceareasusingtimeseriesnighttimelightandmodisimagery AT zhenfengshao mappingimpervioussurfaceareasusingtimeseriesnighttimelightandmodisimagery AT xiaohuang mappingimpervioussurfaceareasusingtimeseriesnighttimelightandmodisimagery AT bowencai mappingimpervioussurfaceareasusingtimeseriesnighttimelightandmodisimagery |