The rapid increase in urbanization due to population growth leads to the degradation of vegetation in major cities. This study investigated the spatial patterns of the ecoenvironmental conditions of inhabitants of two distinct Asian capital cities, Beijing of China and Islamabad of Pakistan, by utilizing Earth observation data products. The significance of urban vegetation for the cooling effect was studied in local climate zones, i.e., urban, suburban, and rural areas within 1-km2 quantiles. Landsat-8 (OLI) and Gaofen-1 satellite imagery were used to assess vegetation cover and land surface temperature, while population datasets were used to evaluate environmental impact. Comparatively, a higher cooling effect of vegetation presence was observed in rural and suburban zones of Beijing as compared to Islamabad, while the urban zone of Islamabad was found comparatively cooler than Beijing’s urban zone. The urban thermal field variance index calculated from satellite imagery was ranked into the ecological evaluation index. The worst ecoenvironmental conditions were found in urban zones of both cities where the fraction of vegetation is very low. Meanwhile, this condition is more serious in Beijing, as more than 90% of the total population is living under the worst ecoenvironment conditions, while only 7% of the population is enjoying comfortable conditions. Ecoenvironmental conditions of Islamabad are comparatively better than Beijing where ∼61% of the total population live under the worst ecoenvironmental conditions, and ∼24% are living under good conditions. Thus, Islamabad at this early growth stage can learn from Beijing’s ecoenvironmental conditions to improve the quality of living by controlling the associated factors in the future.
We use passive optical high-resolution GeoEye-1 imagery and active synthetic aperture radar (SAR) Advanced Land Observing Satellite (ALOS-1) phased array type L-band synthetic aperture radar (PALSAR) L-band horizontal–horizontal-polarization imagery to estimate forest aboveground biomass (AGB) of the tropical mountainous forest test site in Kayar Khola watershed, Chitwan district, Nepal. Object-based tools were used to delineate tree crowns from the orthorectified pan-sharpened GeoEye-1 optical imagery. AGB modeling with crown projection area extracted from the optical imagery shows a good linear relationship with R2=0.76. The terrain-corrected, radiometrically calibrated, and speckle-filtered ALOS-1 PALSAR backscatter image was utilized for AGB modeling; the nonlinear modeling of AGB with the SAR backscatter (dB) shows R2=0.52. The validation R2 values for AGB estimates from GeoEye-1 and ALOS-1 PALSAR are 0.83 and 0.44, respectively. The direct comparison of AGB estimates from both sensors is made possible by the utilization of the same set of ground survey points for both training and validation of the statistical models for both datasets. The final AGB output maps from both sensors show that the spatial patterns of AGB are in reasonable agreement at lower elevation, while SAR seems to underestimate AGB values as compared with optical-based estimates in the higher elevation zones.
This paper presents the development of index to detect haze from moderate resolution imaging spectroradiometer remote sensing data. Detection of haze over a large area has always been a problem. This study focuses on Beijing, Tianjin, and Shijiazhuang cities in China. These cities have suffered the worst hazy weather in recent years. The spectral influence of haze on surface features was determined through analysis of the spectral variations of surface covers between hazy and haze-free days. A spectral index known as modified normalized difference haze index (m-NDHI) is developed that can be used to monitor haze distribution and intensity. Correlation analysis of the derived m-NDHI and previously developed NDHI with in situPM2.5 (particulate matter with diameter <2.5 μm) data reveals that m-NDHI over water bodies has a coefficient of 0.7096, 0.5864, and 0.4857 and NDHI has coefficient of 0.5625, 0.5321, and 0.4618 with PM2.5 for Beijing, Tianjin, and Shijiazhuang, respectively, in winter. Moreover, the correlation of m-NDHI with PM2.5 is 0.4097, 0.8092, and 0.5546 during the spring, summer, and autumn, respectively, in Beijing. This developed index can be a much easier and more effective method to detect haze in large scales from remotely sensing data and characterize the situation of urban atmospheric pollution.
This paper provides novel analysis of existing interpolation techniques and suggests improvement for more
accurate orthorectification of satellite imagery. Traditional methods for measuring geo-location use Ground
Control Points (GCPs). The accuracy of these methods depends on the accuracy of GCPs. The accuracy of geolocations
can also be improved by using Digital Elevation Model (DEM) which incorporates topographic relief
displacement to measure geographic locations. Since the accuracy of geographic locations is dependent on the
resolution of DEM, in our study, the accuracy of geo-locations was assessed using interpolated DEMs of multiple
resolutions. The comparative analysis showed that the accuracy of geo-locations can be improved by increasing
the resolution of DEM using interpolation.
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