Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data

Abstract

Building density, as a component of impervious surface fraction, is a significant indicator of population distribution as essentially all humans live and conduct activities in buildings. Because population spatialization usually occurs over large areas, large-scale building density estimation through a proper, time-efficient, and relatively precise way is urgently required. Therefore, this study constructed a decision tree by the Classification and Regression Tree (CART) algorithm combining synthetic aperture radar (SAR) with optical images. The input features included four spectral bands (B 1-4 ) of GF-1 PMS imagery; Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Built-up Index (RBI) derived from them; and backscatter intensity (BI) of Radarsat-2 SAR data. In addition, a new index called amended backscatter intensity (ABI), which takes the influence created by different spatial patterns into account, was introduced and calculated through fractal dimension and lacunarity. Result showed that before the integration use of multisource data, a model using B 1-4 , NDVI, NDWI, and RBI had the highest accuracy, with RMSE of 10.28 and R 2 of 0.63 for Jizhou and RMSE of 20.34 and R 2 of 0.36 for Beijing. In Comparison, the best model after combining two data sources (i.e., the model employing B 1-4 , NDVI, NDWI, RBI and ABI) reduced the RMSE to 8.93 and 16.21 raised the R 2 to 0.80 and 0.64, respectively. The result indicated that the synergistic use of optical and SAR data has the potential to improve the building density estimation performance and the addition of ABI has a better capacity for improving the model than other input features.

Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery

“…In this way, the average LST in the area gradually increases with the spatial distribution of the UHI area to become more continuous and concentrated, forming a high-temperature area centered on artificial buildings in the city [1,9,10]. However, our comprehensive literature research found that past studies focused on the analysis of seasonal, daily, and annual variations of the UHI, which revealed the temporal and spatial variation of the UHI, the distribution characteristics of the horizontal and vertical directions, etc., while there has been less quantitative analysis of the single factors of UHIs, especially discussion of the ubiquitous relationship between impervious surfaces and UHI in the city.As an integral part of the urban impervious surface percentage, the urban building density (UBD) (percentage of built area) can directly reflect the degree of intensification and land-use efficiency in cities to some extent [11]. It is also an essential comprehensive social indicator that can function as an urban planning layout measure [12,13], resource utilization efficiency measure [14,15], and assessment of the urban ecological environment and livability [16][17][18][19].…”

“…Using remote sensing images to extract UBD information is also a fast and effective method to make up for the shortcomings of traditional manual mapping methods. Existing studies on UBD tend to use high-resolution remote sensing satellites for research because they are more accurate [11,28,29], but their shortcomings are also obvious, such as complex building analysis, the cumbersome automated extraction process, and a large number of computations.NTL remote sensing is widely used in macroeconomic and social parameter estimation [30][31][32], urban monitoring [33][34][35][36], great event change [37,38], energy consumption [39,40], ecological environment assessment [41,42], and other fields. Due to the low spatial resolution of NTL images, previous studies focused on macroscopic dimensions, such as national or urban scales.…”

“…As an integral part of the urban impervious surface percentage, the urban building density (UBD) (percentage of built area) can directly reflect the degree of intensification and land-use efficiency in cities to some extent [11]. It is also an essential comprehensive social indicator that can function as an urban planning layout measure [12,13], resource utilization efficiency measure [14,15], and assessment of the urban ecological environment and livability [16][17][18][19].…”

“…Using remote sensing images to extract UBD information is also a fast and effective method to make up for the shortcomings of traditional manual mapping methods. Existing studies on UBD tend to use high-resolution remote sensing satellites for research because they are more accurate [11,28,29], but their shortcomings are also obvious, such as complex building analysis, the cumbersome automated extraction process, and a large number of computations.…”

Rural Settlement Subdivision by Using Landscape Metrics as Spatial Contextual Information

“…Besides those frequently-used Very High Resolution (VHR) Imagery (e.g., IKONOS, QuickBird and WorldView), Chinese Gaofen Satellites (GF-1 and GF-2) are regarded as suitable inputs in LULC application [43][44][45]. In this research, PMS sensor imagery of GF-2 satellite was selected.…”