DMSP/OLS nighttime light （NTL） image is a widely used data source for urbaniza- tion studies. Although OLS NTL data are able to map nighttime luminosity, the identification accuracy of distribution of urban areas （...DMSP/OLS nighttime light （NTL） image is a widely used data source for urbaniza- tion studies. Although OLS NTL data are able to map nighttime luminosity, the identification accuracy of distribution of urban areas （UAD） is limited by the overestimation of the lit areas resulting from the coarse spatial resolution. In view of geographical condition, we integrate NTL with Biophysical Composition Index （BCl） and propose a new spectral index, the BCl Assisted NTL Index （BANI） to capture UAD. Comparisons between BANI approach and NDVl-assisted SVM classification are carried out using UAD extracted from Landsat TM/ETM＋ data as reference. Results show that BANI is capable of improving the accuracy of UAD extraction using NTL data. The average overall accuracy （OA） and Kappa coefficient of sample cities increased from 88.53% to 95.10% and from 0.56 to 0.84, respectively. Moreover with regard to cities with more mixed land covers, the accuracy of extraction results is high and the improvement is obvious. For other cities, the accuracy also increased to varying de- grees. Hence, BANI approach could achieve better UAD extraction results compared with NDVl-assisted SVM method, suggesting that the proposed method is a reliable alternative method for a large-scale urbanization study in China＇s mainland.展开更多
文摘DMSP/OLS nighttime light （NTL） image is a widely used data source for urbaniza- tion studies. Although OLS NTL data are able to map nighttime luminosity, the identification accuracy of distribution of urban areas （UAD） is limited by the overestimation of the lit areas resulting from the coarse spatial resolution. In view of geographical condition, we integrate NTL with Biophysical Composition Index （BCl） and propose a new spectral index, the BCl Assisted NTL Index （BANI） to capture UAD. Comparisons between BANI approach and NDVl-assisted SVM classification are carried out using UAD extracted from Landsat TM/ETM＋ data as reference. Results show that BANI is capable of improving the accuracy of UAD extraction using NTL data. The average overall accuracy （OA） and Kappa coefficient of sample cities increased from 88.53% to 95.10% and from 0.56 to 0.84, respectively. Moreover with regard to cities with more mixed land covers, the accuracy of extraction results is high and the improvement is obvious. For other cities, the accuracy also increased to varying de- grees. Hence, BANI approach could achieve better UAD extraction results compared with NDVl-assisted SVM method, suggesting that the proposed method is a reliable alternative method for a large-scale urbanization study in China＇s mainland.
