scholarly journals Monitoring of Compound Air Pollution by Remote Sensing in Lanzhou City in the Past 10 Years

2021 ◽  
Author(s):  
Tianzhen Ju ◽  
Zhuohong Liang ◽  
Wenjun Liu ◽  
Bingnan Li ◽  
Ruirui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Remote Sensing Data ◽  
Gansu Province ◽  
Transmission Model ◽  
Ozone Monitoring Instrument ◽  
Atmospheric Transmission ◽  
Long Distance ◽  
Temporal And Spatial Distribution ◽  
Compound Pollution

Abstract Based on satellite remote sensing data acquired by the Ozone Monitoring Instrument (OMI), this study used pixel space analysis, a coefficient of variation, stability analysis, and an atmospheric transmission model to determine the concentration of tropospheric ozone (O3), NO2, HCHO, and SO2 columns in Lanzhou from 2010 to 2019. A series of analyses were carried out on the temporal and spatial distribution of concentration, influencing factors and atmospheric transmission path. The results show that the air pollutants in this area present multi-dimensional characteristics and have a complex spatial distribution. In terms of inter-annual changes, in addition to the increase in the concentration of the HCHO column, the ozone, NO2, and SO2 column concentrations have all decreased over time. In terms of monthly average changes, these four pollutants reached their maximum values in April, December, June, and January, respectively. These four types of pollution had a strong spatial correlation, among which HCHO and SO2 had a significant positive correlation, with a correlation coefficient of 0.76. Many factors affect the Atmospheric Compound Pollution in Lanzhou. Among them, pollutants are closely related to urbanization and to the activities of coal-burning industries. Moreover, temperature, precipitation, and sunshine also have certain effects on air quality. The proliferation of pollutants in Gansu Province was one of the sources of pollutants in Lanzhou, while long-distance transportation in the atmosphere from outside the province (Qinghai, Sichuan, and Shaanxi) also exacerbated the pollution in Lanzhou.

scholarly journals RS & GIS-Based Spatialtemporal Analysis of Ecological Footprint and Biocapacity Pattern of Jinghe River Watershed in China: Does Supply Meet Demand?

2011 ◽  
Vol 356-360 ◽  
pp. 2820-2832
Author(s):  
Dong Xia Yue ◽  
Jin Hui Ma ◽  
Jian Jun Guo ◽  
Jia Jing Zhang ◽  
Jun Du ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Ecological Footprint ◽  
Spatial Scales ◽  
Supply And Demand ◽  
Remote Sensing Data ◽  
Spatiotemporal Dynamics ◽  
River Watershed ◽  
Per Capita ◽  
Jinghe River

The Ecological Footprint methodology is a framework that tracks Ecological Footprint (humanity’s demands on the biosphere) by comparing human demand against the regenerative capacity (Biocapacity) of the planet (WWF, 2010) to advance the science of sustainability. As such, the spatiotemporal dynamics of the Ecological Footprint (EF) and Biocapacity (BC) in a given watershed are important topics in the field of sustainability research based on remote sensing (RS) data and geographic information system (GIS) techniques．This paper reports on a case study of the Jinghe River Watershed using improved EF methodology with the help of GIS and high resolution remote sensing data, to quantitatively estimate the relationship between EF demand and BC supply and analyze their spatial distribution patterns at multiple spatial scales for four periods (1986, 1995, 2000 and 2008). We predict the future BC both overall, and of six categories of biological productivity area for the next four decades using the Markov Chain Method．The results showed that the spatial distribution of EF demand and BC supply were significantly uneven in the region, in which the per-capita EF of all counties located in the watershed increased continually from 1986 to 2008, and the EF per person of counties in the middle and lower reaches area was markedly greater than that in the upper reaches over time. On the supply side, the per-capita BC of all counties decreased gradually from 1986 to 2008, and the per-capita BC of counties in the upper reaches area was greater than that in the middle and lower reaches during the period, causing the uneven spatial distribution of Ecological budget-the gap between supply and demand, showed that the Jinghe River Watershed on the whole has begun to be unsustainable since 2008, with each county exhibiting differential temporal patterns. The prediction results showed that the total BC will increase continually from 2020 to 2050, and the BC of six categories will reduce, indicating that unsustainability in the region will escalate. As a whole, The EF demand has exceeded the BC supply, and the gap was widening in the Jinghe Watershed. This paper provided an in-depth portrait of the spatiotemporal dynamics of EF and BC, as well as their interactions with humanity and ecosystems.

Quantitatively monitoring undergoing degradation of plateau grassland by remote sensing data: a case study in Maqu County, Gansu Province, China

2012 ◽  
Vol 32 (6) ◽  
pp. 1663-1676
Author(s):  
周坚华 ZHOU Jianhua ◽  
魏怀东 WEI Huaidong ◽  
陈芳 CHEN Fang ◽  
郭晓华 GUO Xiaohua
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Gansu Province ◽  
Sensing Data

scholarly journals Using remote sensing data in population density estimation

2021 ◽  
Author(s):  
E.G. Shvetsov ◽  
N.M. Tchebakova ◽  
E.I. Parfenova
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Population Density ◽  
Vegetation Index ◽  
Census Data ◽  
Remote Sensing Data ◽  
Satellite System ◽  
Population Census ◽  
Krasnoyarsk Krai ◽  
Using Data

In recent decades, remote sensing methods have often been used to estimate population density, especially using data on nighttime illumination. Information about the spatial distribution of the population is important for understanding the dynamics of cities and analyzing various socio-economic, environmental and political factors. In this work, we have formed layers of the nighttime light index, surface temperature and vegetation index according to the SNPP/VIIRS satellite system for the territory of the central and southern regions of the Krasnoyarsk krai. Using these data, we have calculated VTLPI (vegetation temperature light population index) for the year 2013. The obtained values of the VTLPI calculated for a number of settlements of the Krasnoyarsk krai were compared with the results of the population census conducted in 2010. In total, we used census data for 40 settlements. Analysis of the data showed that the relationship between the value of the VTLPI index and the population density in the Krasnoyarsk krai can be adequately fitted (R 2 = 0.65) using a linear function. In this case, the value of the root-meansquare error was 345, and the relative error was 0.09. Using the obtained model equation and the spatial distribution of the VTLPI index using GIS tools, the distribution of the population over the study area was estimated with a spatial resolution of 500 meters. According to the obtained model and the VTLPI index, the average urban population density in the study area exceeded 500 people/km2 . Comparison of the obtained data on the total population in the study area showed that the estimate based on the VTLPI index is about 21% higher than the actual census data.

scholarly journals ANISOTROPIC SCATTERING SHADOW COMPENSATION METHOD FOR REMOTE SENSING IMAGE WITH CONSIDERATION OF TERRAIN

2016 ◽  
Vol XLI-B1 ◽  
pp. 401-405 ◽  
Author(s):  
Qiongjie Wang ◽  
Li Yan
Keyword(s):  
Remote Sensing ◽  
Detection Method ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Anisotropic Scattering ◽  
Compensation Method ◽  
Transmission Model ◽  
View Factor ◽  
Shadow Area ◽  
Soft Shadow

With the rapid development of sensor networks and earth observation technology, a large quantity of high resolution remote sensing data is available. However, the influence of shadow has become increasingly greater due to the higher resolution shows more complex and detailed land cover, especially under the shadow. Shadow areas usually have lower intensity and fuzzy boundary, which make the images hard to interpret automatically. In this paper, a simple and effective shadow (including soft shadow) detection and compensation method is proposed based on normal data, Digital Elevation Model (DEM) and sun position. First, we use high accuracy DEM and sun position to rebuild the geometric relationship between surface and sun at the time the image shoot and get the hard shadow boundary and sky view factor (SVF) of each pixel. Anisotropic scattering assumption is accepted to determine the soft shadow factor mainly affected by diffuse radiation. Finally, an easy radiation transmission model is used to compensate the shadow area. Compared with the spectral detection method, our detection method has strict theoretical basis, reliable compensation result and minor affected by the image quality. The compensation strategy can effectively improve the radiation intensity of shadow area, reduce the information loss brought by shadow and improve the robustness and efficiency of the classification algorithms.

scholarly journals Quantifying Grazing Intensity Using Remote Sensing in Alpine Meadows on Qinghai-Tibetan Plateau

2019 ◽  
Vol 11 (2) ◽  
pp. 417 ◽  
Author(s):  
Qingqing Ma ◽  
Linrong Chai ◽  
Fujiang Hou ◽  
Shenghua Chang ◽  
Yushou Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Field Experiments ◽  
Grazing Intensity ◽  
Remote Sensing Data ◽  
Gansu Province ◽  
Livestock Grazing ◽  
Scientific Management ◽  
Sensing Data

Remote sensing data have been widely used in the study of large-scale vegetation activities, which have important significance in estimating grassland yields, determining grassland carrying capacity, and strengthening the scientific management of grasslands. Remote sensing data are also used for estimating grazing intensity. Unfortunately, the spatial distribution of grazing-induced degradation remains undocumented by field observation, and most previous studies on grazing intensity have been qualitative. In our study, we tried to quantify grazing intensity using remote sensing techniques. To achieve this goal, we conducted field experiments at Gansu Province, China, which included a meadow steppe and a semi-arid region. The correlation between a vegetation index and grazing intensity was simulated, and the results demonstrated that there was a significant negative correlation between NDVI and relative grazing intensity (p < 0.05). The relative grazing intensity increased with a decrease in NDVI, and when the relative grazing intensity reached a certain level, the response of NDVI to relative grazing intensity was no longer sensitive. This study shows that the NDVI model can illustrate the feasibility of using a vegetation index to monitor the grazing intensity of livestock in free-grazing mode. Notably, it is feasible to use the remote sensing vegetation index to obtain the thresholds of livestock grazing intensity.

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