scholarly journals Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features

2021 ◽  
Vol 13 (1) ◽  
pp. 153
Author(s):  
Xuegang Dong ◽  
Zhiguo Meng ◽  
Yongzhi Wang ◽  
Yuanzhi Zhang ◽  
Haoteng Sun ◽  
...  
Keyword(s):  
Random Forest ◽  
Urban Areas ◽  
Impervious Surface ◽  
Capital City ◽  
Textural Features ◽  
Impervious Surfaces ◽  
Beijing City ◽  
Ring Road ◽  
Regional Feature ◽  
Exterior Regions

As the capital city of China, Beijing has experienced unprecedented economic and population growth and dramatic impervious surface changes during the last few decades. An application of the classification method combining the spectral and textural features based on Random Forest was conducted to monitor the spatial and temporal changes of Beijing’s impervious surfaces. This classification strategy achieved excellent performance in the impervious surface extraction in complex urban areas, as the Kappa coefficient reached 0.850. Based on this strategy, the impervious surfaces inside Beijing’s sixth ring road in 1997, 2002, 2007, 2013, and 2017 were extracted. As the development of Beijing has a special regional feature, the changes of impervious surfaces within the sixth ring road were assessed. The findings are as follows: (1) the textural features can significantly improve the classification accuracy of land cover in urban areas, especially for the impervious surface with high albedo. (2) Impervious surfaces within the sixth ring road expanded dramatically from 1997 to 2017, had three expanding periods: 1997–2002, 2002–2007, and 2013–2017, and only shrank in 2007–2013. There are different possible major driving factors for each period. (3) The region between the fifth and sixth ring roads in Beijing underwent the most significant changes in the two decades. (4) The inner three regions are relatively highly urbanized areas compared to the outer two regions. Urbanization processes in the interior regions tend to be completed compared to the exterior regions.

scholarly journals A Comparative Study of various Indices for extraction urban impervious surface of Landsat 8 OLI

2019 ◽  
Vol 33 (2) ◽  
pp. 162-172
Author(s):  
Iswari Nur Hidayati ◽  
R Suharyadi
Keyword(s):  
Land Use ◽  
Urban Areas ◽  
Spectral Characteristics ◽  
Impervious Surface ◽  
Urban Heat Islands ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Landsat 8 Oli ◽  
Heat Islands ◽  
Urban Heat

Impervious surface is one of the major land cover types of urban and suburban environment. Conversion of rural landscapes and vegetation area to urban and suburban land use is directly related to the increase of the impervious surface area. The impervious surface expansion is straight-lined with decreasing green spaces in urban areas. Impervious surface is one of indicator for detecting urban heat islands. This study compares various indices for mapping impervious surfaces using Landsat 8 OLI imagery by optimizing the different spectral characteristics of Landsat 8 OLI imagery. The research objectives are (1) to apply various indices for impervious surface mapping and (2) identifies impervious surfaces in urban areas based on multiple indices and provide recommendations and find the best index for mapping impervious surface in urban areas. In addition to utilizing the index, land use supervised classification method, maximum likelihood classification used for extracting built-up, and non-built-up areas. Accuracy assessment of this research used field data collection as primary data for calculating kappa coefficient, producer accuracy, and user accuracy. The study can also be extended to find the land surface temperature and correlate the impervious surface extraction data with urban heat islands.

scholarly journals A Novel Remote Sensing Index for Extracting Impervious Surface Distribution from Landsat 8 OLI Imagery

2019 ◽  
Vol 9 (13) ◽  
pp. 2631 ◽  
Author(s):  
Hong Fang ◽  
Yuchun Wei ◽  
Qiuping Dai
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Impervious Surface ◽  
Landsat 8 ◽  
The Novel ◽  
Impervious Surfaces ◽  
Surface Distribution ◽  
Remote Sensing Imagery ◽  
Normalised Difference Vegetation Index ◽  
Multispectral Remote Sensing

The area of urban impervious surfaces is one of the most important indicators for determining the level of urbanisation and the quality of the environment and is rapidly increasing with the acceleration of urbanisation in developing countries. This paper proposes a novel remote sensing index based on the coastal band and normalised difference vegetation index for extracting impervious surface distribution from Landsat 8 multispectral remote sensing imagery. The index was validated using three images covering urban areas of China and was compared with five other typical index methods for the extraction of impervious surface distribution, namely, the normalised difference built-up index, index-based built-up index, normalised difference impervious surface index, normalised difference impervious index, and combinational built-up index. The results showed that the novel index provided higher accuracy and effectively distinguished impervious surfaces from bare soil, and the average values of the recall, precision, and F1 score for the three images were 95%, 91%, and 93%, respectively. The novel index provides better applicability in the extraction of urban impervious surface distribution from Landsat 8 multispectral remote sensing imagery.

Installation of impervious surface in urban areas affects microbial biomass, activity (potential C mineralisation), and functional diversity of the fine earth

Soil Research ◽  
2013 ◽  
Vol 51 (1) ◽  
pp. 59 ◽  
Author(s):  
Zongqiang Wei ◽  
Shaohua Wu ◽  
Shenglu Zhou ◽  
Chen Lin
Keyword(s):  
Microbial Biomass ◽  
Functional Diversity ◽  
Urban Areas ◽  
Urban Soil ◽  
Impervious Surface ◽  
Soil Microbial Activity ◽  
Impervious Surfaces ◽  
Fine Earth ◽  
Soil Microbial ◽  
Soil Sealing

Artificial soil sealing in urban areas has attracted increasing attention due to its potential hazard to urban ecosystems. It has negative impacts on soil function and the urban environment, since the impervious surface can hamper the exchange of material and energy between the soil and other environmental compartments. However, information about the effects of artificial soil sealing in urban area on soil quality and properties, especially the microbiological components, is still limited. Ten plots which differed in land use were selected from Nanjing City, China, to investigate the effects of impervious surfaces on microbiological characteristics in urban soil. Plot types were paved road, residential paved square, residential paved alley, and grassed area. Soil microbial biomass carbon (Cmic) and nitrogen (Nmic), and activities were analysed, and the microbial functional diversity of fine earth (<2 mm material) was characterised by the Biolog EcoPlate technique. Mean concentrations of soil organic carbon (SOC), Cmic, and Nmic in fine earth from the impervious areas (0–20 cm) were, respectively, 6.5 g kg–1, 55.8 mg kg–1, and 12.2 mg kg–1, which were significantly lower than concentrations from grass areas. Urban sealing also resulted in decreases in soil microbial activity and functional diversity, but the influences on soil microbial diversity varied among land uses, with road pavement having the most negative effect. Substrate use patterns showed that microorganisms in urban sealed soils had higher utilisation of polymers (P < 0.05) but lower use of carbohydrates and amines/amides (P < 0.05). These findings demonstrate that SOC in the sealed soils was more stable than in open soils, and the installation of impervious surfaces such as asphalt and concrete, which are very common in urban areas, can result in decreases in SOC content, soil microbial activity, and diversity in urban soil.

scholarly journals Development of a global 30-m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform

2020 ◽  
Author(s):  
Xiao Zhang ◽  
Liangyun Liu ◽  
Changshan Wu ◽  
Xidong Chen ◽  
Yuan Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Human Activity ◽  
Impervious Surface ◽  
Google Earth ◽  
Impervious Surfaces ◽  
Sar Images ◽  
Training Samples ◽  
Multi Temporal ◽  
Google Earth Engine

Abstract. The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. The use of remote sensing techniques is the only means of accurately carrying out global mapping of impervious surfaces covering large areas. Optical imagery can capture surface reflectance characteristics, while synthetic aperture radar (SAR) images can be used to provide information on the structure and dielectric properties of surface materials. In addition, night-time light (NTL) imagery can detect the intensity of human activity and thus provide important a priori probabilities of the occurrence of impervious surfaces. In this study, we aimed to generate an accurate global impervious surface map at a resolution of 30-m for 2015 by combining Landsat-8 OLI optical images, Sentinel-1 SAR images and VIIRS NTL images based on the Google Earth Engine (GEE) platform. First, the global impervious and non-impervious training samples were automatically derived by combining the GlobeLand30 land-cover product with VIIRS NTL and MODIS enhanced vegetation index (EVI) imagery. Then, based on global training samples and multi-source and multi-temporal imagery, a random forest classifier was trained and used to generate corresponding impervious surface maps for each 5°×5° cell of a geographical grid. Finally, a global impervious surface map, produced by mosaicking numerous 5°×5° regional maps, was validated by interpretation samples and then compared with three existing impervious products (GlobeLand30, FROM_GLC and NUACI). The results indicated that the global impervious surface map produced using the proposed multi-source, multi-temporal random forest classification (MSMT_RF) method was the most accurate of the maps, having an overall accuracy of 96.6 % and kappa coefficient of 0.903 as against 92.5 % and 0.769 for FROM_GLC, 91.1 % and 0.717 for GlobeLand30, and 87.43 % and 0.585 for NUACI. Therefore, it is concluded that a global 30-m impervious surface map can accurately and efficiently be generated by the proposed MSMT_RF method based on the GEE platform. The global impervious surface map generated in this paper are available at https://doi.org/10.5281/zenodo.3505079 (Zhang et al., 2019).

scholarly journals Mapping Urban Impervious Surfaces by Using Spectral Mixture Analysis and Spectral Indices

2019 ◽  
Vol 12 (1) ◽  
pp. 94 ◽  
Author(s):  
Wenliang Li
Keyword(s):  
Urban Areas ◽  
Feature Space ◽  
Impervious Surface ◽  
Spectral Mixture Analysis ◽  
Impervious Surfaces ◽  
Spectral Indices ◽  
Mixture Analysis ◽  
Comparable Performance ◽  
Fayette County ◽  
Spectral Mixture

Impervious surface is the major component of urban areas, and it has been widely considered as the key for assessing the degree of urban sprawl. While the effectiveness of applying spectral mixture analysis (SMA) and spectral indices in mapping urban impervious surface has been proved, most studies have relied either on SMA or spectral indices without considering both. In this study, the SMA and spectral indices were integrated together to map impervious surfaces distributions in both Milwaukee County in the Wisconsin State and Fayette County in the Kentucky State. Specifically, spectral indices were used for identifying major land covers. Two-dimensional feature space plots were generated by calculated spectral indices images for endmember selection and extraction. Linear constrained SMA was finally applied to quantify the fractional impervious surfaces. Research results indicate that the proposed method has achieved a promising accuracy, and better performance was achieved in less developed areas than the developed areas. Moreover, a comparative analysis shows that the proposed method performs better than the conventional method in both the whole study area and the developed areas, and a comparable performance has been achieved in the less developed areas.

scholarly journals Expansion of Impervious Surfaces and Their Driving Forces in Highly Urbanized Cities in Kyrgyzstan

2020 ◽  
Vol 17 (1) ◽  
pp. 362 ◽  
Author(s):  
Gulkaiyr Omurakunova ◽  
Anming Bao ◽  
Wenqiang Xu ◽  
Eldiiar Duulatov ◽  
Liangliang Jiang ◽  
...  
Keyword(s):  
Natural Resources ◽  
Urban Areas ◽  
Driving Forces ◽  
Land Use Changes ◽  
Impervious Surface ◽  
Good Evidence ◽  
Sustainable Urban Development ◽  
Population Increase ◽  
Impervious Surfaces ◽  
Landsat Images

The expansion of urban areas due to population increase and economic expansion creates demand and depletes natural resources, thereby causing land use changes in the main cities. This study focuses on land cover datasets to characterize impervious surface (urban area) expansion in select cities from 1993 to 2017, using supervised classification maximum likelihood techniques and by quantifying impervious surfaces. The results indicate an increasing trend in the impervious surface area by 35% in Bishkek, 75% in Osh, and 15% in Jalal-Abad. The overall accuracy (OA) for the image classification of two different datasets for the three cities was between 82% and 93%, and the kappa coefficients (KCs) were approximately 77% and 91%. The Landsat images with other supplementary data showed positive urban growth in all of the cities. The GDP, industrial growth, and urban population growth were driving factors of impervious surface sprawl in these cities from 1993 to 2017.Landscape Expansion Index (LEI) results also provided good evidence for the change of impervious surfaces during the study period. The results emphasize the idea of applying future planning and sustainable urban development procedures for sustainable use of natural resources and their management, which will increase life quality in urban areas and environments.

scholarly journals URBAN IMPERVIOUS SURFACE DETECTION WITH LANDSAT AND FULL POLARIMETRIC ALOS/PALSAR IMAGES

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 111-115
Author(s):  
S. Attarchi ◽  
M. Rahnama
Keyword(s):  
Classification Accuracy ◽  
Urban Areas ◽  
Texture Features ◽  
Impervious Surface ◽  
Urban Region ◽  
Positive Contribution ◽  
Impervious Surfaces ◽  
Urban Impervious Surface ◽  
Surface Detection ◽  
Sar Data

Abstract. Impervious surface detection in urban area is of great importance because impervious surfaces have been recognized as an urbanization index. In other words, an increase of impervious surface extent accounts for urban sprawl. Remote sensing images in optical wavelength proved to be beneficial for urban impervious surfaces detection and mapping. However, the use of optical images is limited in cloud-prone regions. Also, there are some complexities which make it challenging to impervious surface detection especially in urban areas. In recent years, in the light of increasing availability of SAR data, the use of SAR data for urban impervious surface detection is increased as well. In this study the efficiency of full polarimetric L-band images was compared to Landsat images. Furthermore, the role of SAR’ texture features was assessed. Results show that the classification accuracy of SAR images cannot overcome the Landsat’s classification accuracy. However, SAR texture features provide positive contribution in impervious surface detection in homogeneous urban region.

scholarly journals Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform

2020 ◽  
Vol 12 (3) ◽  
pp. 1625-1648 ◽  
Author(s):  
Xiao Zhang ◽  
Liangyun Liu ◽  
Changshan Wu ◽  
Xidong Chen ◽  
Yuan Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Human Activity ◽  
Impervious Surface ◽  
Google Earth ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Sar Images ◽  
Training Samples ◽  
Google Earth Engine

Abstract. The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. The use of remote sensing techniques is the only means of accurately carrying out global mapping of impervious surfaces covering large areas. Optical imagery can capture surface reflectance characteristics, while synthetic-aperture radar (SAR) images can be used to provide information on the structure and dielectric properties of surface materials. In addition, nighttime light (NTL) imagery can detect the intensity of human activity and thus provide important a priori probabilities of the occurrence of impervious surfaces. In this study, we aimed to generate an accurate global impervious surface map at a resolution of 30 m for 2015 by combining Landsat 8 Operational Land Image (OLI) optical images, Sentinel-1 SAR images and Visible Infrared Imaging Radiometer Suite (VIIRS) NTL images based on the Google Earth Engine (GEE) platform. First, the global impervious and nonimpervious training samples were automatically derived by combining the GlobeLand30 land-cover product with VIIRS NTL and MODIS enhanced vegetation index (EVI) imagery. Then, the local adaptive random forest classifiers, allowing for a regional adjustment of the classification parameters to take into account the regional characteristics, were trained and used to generate regional impervious surface maps for each 5∘×5∘ geographical grid using local training samples and multisource and multitemporal imagery. Finally, a global impervious surface map, produced by mosaicking numerous 5∘×5∘ regional maps, was validated by interpretation samples and then compared with five existing impervious products (GlobeLand30, FROM-GLC, NUACI, HBASE and GHSL). The results indicated that the global impervious surface map produced using the proposed multisource, multitemporal random forest classification (MSMT_RF) method was the most accurate of the maps, having an overall accuracy of 95.1 % and kappa coefficient (one of the most commonly used statistics to test interrater reliability; Olofsson et al., 2014) of 0.898 as against 85.6 % and 0.695 for NUACI, 89.6 % and 0.780 for FROM-GLC, 90.3 % and 0.794 for GHSL, 88.4 % and 0.753 for GlobeLand30, and 88.0 % and 0.745 for HBASE using all 15 regional validation data. Therefore, it is concluded that a global 30 m impervious surface map can accurately and efficiently be generated by the proposed MSMT_RF method based on the GEE platform. The global impervious surface map generated in this paper is available at https://doi.org/10.5281/zenodo.3505079 (Zhang and Liu, 2019).

scholarly journals Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier

2021 ◽  
Vol 13 (15) ◽  
pp. 3040
Author(s):  
Binita Shrestha ◽  
Haroon Stephen ◽  
Sajjad Ahmad
Keyword(s):  
Random Forest ◽  
Growth Rates ◽  
Impervious Surface ◽  
Optical Data ◽  
Accurate Information ◽  
Impervious Surfaces ◽  
Source Information ◽  
Radar Images ◽  
High Resolution Images ◽  
Increase In Accuracy

Urbanization increases the amount of impervious surfaces, making accurate information on spatial and temporal expansion trends essential; the challenge is to develop a cost- and labor-effective technique that is compatible with the assessment of multiple geographical locations in developing countries. Several studies have identified the potential of remote sensing and multiple source information in impervious surface quantification. Therefore, this study aims to fuse datasets from the Sentinel 1 and 2 Satellites to map the impervious surfaces of nine Pakistani cities and estimate their growth rates from 2016 to 2020 utilizing the random forest algorithm. All bands in the optical and radar images were resampled to 10 m resolution, projected to same coordinate system and geometrically aligned to stack into a single product. The models were then trained, and classifications were validated with land cover samples from Google Earth’s high-resolution images. Overall accuracies of classified maps ranged from 85% to 98% with the resultant quantities showing a strong linear relationship (R-squared value of 0.998) with the Copernicus Global Land Services data. There was up to 9% increase in accuracy and up to 12 % increase in kappa coefficient from the fused data with respect to optical alone. A McNemar test confirmed the superiority of fused data. Finally, the cities had growth rates ranging from 0.5% to 2.5%, with an average of 1.8%. The information obtained can alert urban planners and environmentalists to assess impervious surface impacts in the cities.

scholarly journals Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data

2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Fanggang Li ◽  
Erzhu Li ◽  
Ce Zhang ◽  
Alim Samat ◽  
Wei Liu ◽  
...  
Keyword(s):  
Random Forest ◽  
Prediction Model ◽  
Infrared Imaging ◽  
Impervious Surface ◽  
Impervious Surfaces ◽  
Large Area ◽  
Nighttime Light Data ◽  
Multi Temporal ◽  
Nighttime Light ◽  
Accuracy Of Prediction

Impervious surfaces have important effects on the natural environment, including promoting hydrological run-off and impeding evapotranspiration, as well as increasing the urban heat island effect. Obtaining accurate and timely information on the spatial distribution and dynamics of urban surfaces is, thus, of paramount importance for socio-economic analysis, urban planning, and environmental modeling and management. Previous studies have indicated that the fusion of multi-source remotely sensed imagery can increase the accuracy of prediction for impervious surface information across large areas. However, the majority of them are limited to the use of specific data sources to construct a few features with which it can be challenging to characterize adequately the variation in impervious surfaces over large areas. Thus, impervious surface maps are often presented with high uncertainty. In response to this problem, we proposed the use of multi-temporal MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data to construct a more general and robust feature set for large-area artificial impervious surface percentage (AISP) prediction. Three fusion methods were proposed for application to multi-temporal MODIS surface reflectance product (MOD09A1) and Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) data to construct three different types of features: spectral features, index features (band calculations), and fusion features. These features were then used as variables in a random-forest-based AISP prediction model. The model was fitted to China and then applied to predict AISP across Asia. Fifteen typical cities from different regions of Asia were selected to assess the accuracy of the prediction model. The use of multi-temporal MODIS and VIIRS DNB data was found to significantly increase the accuracy of prediction for large-area AISP. The feature set constructed in this research was demonstrated to be suitable for large-area AISP prediction, and the random forest model based on optimization of the selected features achieved the highest accuracy, amongst benchmarks, with testing R2 of 0.690, and testing RMSE of 0.044 in 2018, respectively. In addition, to further test the performance of the proposed method, three existing impervious products (GAIA, HBASE, and NUACI) were used to compare quantitatively. The results showed that the predicted AISP achieved superior performance in comparison with others in some areas (e.g., arid areas and cloudy areas).

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