Normalized difference haze index: a new spectral index for monitoring urban air pollution

Zha, Yong; Gao, Jay; Jiang, Jianjun; Lü, Heng; Huang, Jiazhu

doi:10.1080/01431161.2011.595018

Cited by 14 publications

(6 citation statements)

References 21 publications

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“…In order to effectively monitor aeolian desertification distribution, band1 and band2 of MODIS were selected to calculate NDDI using the following equation23.…”

Section: Methodsmentioning

confidence: 99%

The dynamic monitoring of aeolian desertification land distribution and its response to climate change in northern China

Feng

Jia

2016

Sci Rep

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Aeolian desertification is poorly understood despite its importance for indicating environment change. Here we exploit Gaofen-1(GF-1) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to develop a quick and efficient method for large scale aeolian desertification dynamic monitoring in northern China. This method, which is based on Normalized Difference Desertification Index (NDDI) calculated by band1 & band2 of MODIS reflectance data (MODIS09A1). Then we analyze spatial-temporal change of aeolian desertification area and detect its possible influencing factors, such as precipitation, temperature, wind speed and population by Convergent Cross Mapping (CCM) model. It suggests that aeolian desertification area with population indicates feedback (bi-directional causality) between the two variables (P < 0.05), but forcing of aeolian desertification area by population is weak. Meanwhile, we find aeolian desertification area is significantly affected by temperature, as expected. However, there is no obvious forcing for the aeolian desertification area and precipitation. Aeolian desertification area with wind speed indicates feedback (bi-directional causality) between the two variables with significant signal (P < 0.01). We infer that aeolian desertification is greatly affected by natural factors compared with anthropogenic factors. For the desertification in China, we are greatly convinced that desertification prevention is better than control.

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“…In order to effectively monitor aeolian desertification distribution, band1 and band2 of MODIS were selected to calculate NDDI using the following equation23.…”

Section: Methodsmentioning

confidence: 99%

The dynamic monitoring of aeolian desertification land distribution and its response to climate change in northern China

Feng

Jia

2016

Sci Rep

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“…Xu C and Li S et al used Haze Optical Thickness of satellite data to monitor the haze events [5,8] .Zha Y et al adopted MODIS 1 and 4 band with ground resolution of 500m to calculate NDHI (Normalized difference haze index) to monitor haze events in Nanjing and Yangzhou city [1] . Li Y et al proposed remote sensing retrieval aerosol data with high-resolution over urban areas by synergetic usage of HJ-1 CCD and MODIS data [9] .…”

Section: Introductionmentioning

confidence: 99%

“…The methods of haze observation could be divided into three categories [4] : ground survey, remote sensing monitoring [1,5,6] , combination ground and satellite monitoring. Ground monitoring ability is limited greatly by the distributing density of monitoring stations, and only could fulfill monitor in the position.…”

Section: Introductionmentioning

confidence: 99%

“…Haze is composed of dust, fog and other solid suspend [1] .The visibility <10km with the relative humidity range from 80% to 95% is defined by the China Meteorological Administration in 2010 [2] ;. The haze not only has negative impact on people's daily life, but also is an indicator of high concentrations of PM2.5 which is potential to adversely impact public health [3] .…”

Section: Introductionmentioning

confidence: 99%

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Establishing monitoring model of Haze event with multi-satellite data and application-a case: Yangtze River Delta

Jiang

Zhang

et al. 2014

SPIE Proceedings

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Based on Aerosol Optical Thickness (AOT) in the 550nm NPP-EDR data, GOCI\AOT in the 555nm for detecting and tracing sudden haze event on 2013 December in the Yangtze River Delta, while CDAS-NCEP/NCAR Reanalysis wind data is utilized to analyze the migrating routine The results show that: 1)GOCI\AOT(555nm)>1and NPP\AOT(550nm)>1 is an effective indictor of distinguishing the haze event.2) The two-stream approximation algorithm can be used to retrieve GOCI\AOT especially in China with high concentration of aerosol.3)Combined with high-temporal resolution of GOCI\AOT,NPP\AOT is utilized for analyzing the forming mechanism of a sudden outbreak of haze event in Yangtze River Delta .The migration driven mechanism is diagnosed with CDAS-NCEP/NCAR Reanalysis wind data.4)the study suggests that the haze was formed in Hebei and Henan province on December 3, 2013.Under the strong northwest wind with the average rate of 4m/s, the haze rapidly moved to the Yangtze River Delta on December 4 th 2013, resulting in the most serious haze event in 2013 in there. Under the northwest wind control, the haze area expanded rapidly from 70,000km 2 to 200,000km 2 during its migration. The research suggests that it could be a feasible routine monitoring pattern in detecting the occurrence, migration of haze events in Yangtze River Delta. Nontechnical Research Summary:This research is focus on detecting haze by multi-satellite. Using these data, we could detect the haze and trace the movement of haze. The environment in China should be protected.

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“…After deriving difference vegetation index (DVI) from NOAA AVHRR data, Yu et al [21] constructed a relationship between DVI and PM 10 so that the latter can be retrieved from the former via the established relationship quantitatively. Zha et al [22] put forward a normalized difference haze index (NDHI) for studying atmospheric haze weather from winter Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. These methods are common in that in situ measured data were statistically analyzed to establish the relationship between apparent reflectance and the physical and chemical properties of aerosols.…”

mentioning

confidence: 99%

Aerosol Indices Derived from MODIS Data for Indicating Aerosol-Induced Air Pollution

Zha

Zhang

et al. 2014

Remote Sensing

Self Cite

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Aerosol optical depth (AOD) is a critical variable in estimating aerosol concentration in the atmosphere, evaluating severity of atmospheric pollution, and studying their impact on climate. With the assistance of the 6S radiative transfer model, we simulated apparent reflectancein relation to AOD in each Moderate Resolution Imaging Spectroradiometer (MODIS) waveband in this study. The closeness of the relationship was used to identify the most and least sensitive MODIS wavebands. These two bands were then used to construct three aerosol indices (difference, ratio, and normalized difference) for estimating AOD quickly and effectively. The three indices were correlated, respectively, with in situ measured AOD at the Aerosol Robotic NETwork (AERONET) Lake Taihu, Beijing, and Xianghe stations. It is found that apparent reflectance of the blue waveband (band 3) is the most sensitive to AOD while the mid-infrared wavelength (band 7) is the least sensitive. The difference aerosol index is the most accurate in indicating aerosol-induced atmospheric pollution with a correlation coefficient of 0.585, 0.860, 0.685, and 0.333 at the Lake Taihu station, 0.721, 0.839, 0.795, and 0.629 at the Beijing station, and 0.778, 0.782, 0.837, and 0.643 at the Xianghe station in spring, summer, autumn and OPEN ACCESSRemote Sens. 2014, 6 1588 winter, respectively. It is concluded that the newly proposed difference aerosol index can be used effectively to study the level of aerosol-induced air pollution from MODIS satellite imagery with relative ease.

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Normalized difference haze index: a new spectral index for monitoring urban air pollution

Cited by 14 publications

References 21 publications

The dynamic monitoring of aeolian desertification land distribution and its response to climate change in northern China

The dynamic monitoring of aeolian desertification land distribution and its response to climate change in northern China

Establishing monitoring model of Haze event with multi-satellite data and application-a case: Yangtze River Delta

Aerosol Indices Derived from MODIS Data for Indicating Aerosol-Induced Air Pollution

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