scholarly journals ASSESMENT OF ATMOSPHERIC DUST CONTAMINATION WITH GROUND-BASED AND REMOTE SENSING METHODS (ON THE EXAMPLE OF THE TOWN OF TOBOLSK)

2021 ◽  
pp. 121-134
Author(s):  
Alexander A. Tigeev ◽  
◽  
Nikolay V. Aksenov ◽  
Dmitriy V. Moskovchenko ◽  
Roman Yu. Pozhitkov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Remote Sensing Data ◽  
Information Value ◽  
Petrochemical Plant ◽  
Insoluble Particles ◽  
Functional Zones ◽  
Long Time ◽  
The Town ◽  
Dust Load

Snow cover is an effective accumulator of dust fallout and provides objective information on the level of pollution, but its sampling in large areas takes a long time. The use of remote sensing data (RSD) makes it possible to significantly simplify the assessment of the dust load in the atmosphere. Based on RSD from the town of Tobolsk, we evaluated the information value of various indices used to assess the distribution and properties of snow cover (NDSI, normalized S3 index, and SCI). Data on dust load and physicochemical properties of the snow obtained during sampling and subsequent analysis were compared with the spectral properties of the snow cover. It was determined that the dust load in the town averaged 32,1 mg/m2 per day, which is approximately 8 times higher than the background values. The degree of alkalinization is moderate, an increase in pH and salinity of snowmelt waters is observed. In comparison with other functional zones of the town, no increase in dust fallout was detected in the industrial zone (Tobolsk Petrochemical Plant). The level of dustiness is maximum in the zone of multistory buildings and on the streets with the highest traffic intensity. It was established that spectral indices indicate the amount of solid impurities in snow and the level of alkalinization. A statistically significant correlation was found between the amount of insoluble particles in snow and the S3 index as well as between pH and the SCI index. The paper concludes that these indices can be used to assess the environmental situation in urbanized areas.

Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

2021 ◽  
Vol 973 (7) ◽  
pp. 21-31
Author(s):  
Е.А. Rasputina ◽  
A.S. Korepova
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Baikal Region ◽  
Remote Sensing Data ◽  
Meteorological Station ◽  
Sensing Data ◽  
Modis Data ◽  
Weather Stations ◽  
The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

scholarly journals A snow cover climatology for the Pyrenees from MODIS snow products

2014 ◽  
Vol 11 (11) ◽  
pp. 12531-12571 ◽  
Author(s):  
S. Gascoin ◽  
O. Hagolle ◽  
M. Huc ◽  
L. Jarlan ◽  
J.-F. Dejoux ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Landsat Images ◽  
Forested Areas ◽  
Land Cover Map ◽  
Hydropower Production ◽  
Snow Cover Duration

Abstract. The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (we) and 105 mm respectively, for both MOD10A1 and MYD10A1. Kappa coefficients are within 0.74 and 0.92 depending on the product and the variable. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both datasets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decreases over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gapfilling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band. We finally analyze the snow patterns for the atypical winter 2011–2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

scholarly journals Experience of the application of remote sensing data in the problems of geo-ecological monitoring of the coal mining area

2019 ◽  
Vol 75 ◽  
pp. 02001
Author(s):  
Olga Giniyatullina ◽  
Evgeniy Schastlivtsev ◽  
Vladimir Kovalev
Keyword(s):  
Remote Sensing ◽  
Coal Mining ◽  
Remote Sensing Data ◽  
Ecological Monitoring ◽  
Mining Area ◽  
The State ◽  
Sensing Data ◽  
Coal Mining Area ◽  
Mining Region ◽  
Dust Load

The experience of solving problems of geoecological monitoring of coal mining region with the use of remote sensing data is presented. The results of control over the boundaries of coal-mining enterprises, assessment of the degree of self-growth of dumps, monitoring of the state of vegetation near objects of coal mining and dust load of the area are shown.

scholarly journals Snow depth derived from passive microwave remote-sensing data in China

2008 ◽  
Vol 49 ◽  
pp. 145-154 ◽  
Author(s):  
Tao Che ◽  
Xin Li ◽  
Rui Jin ◽  
Richard Armstrong ◽  
Tingjun Zhang
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Temporal Distribution ◽  
Remote Sensing Data ◽  
Passive Microwave ◽  
Spatial And Temporal Distribution ◽  
Sensing Data ◽  
Seasonal Snow ◽  
Snow Mass

AbstractIn this study, we report on the spatial and temporal distribution of seasonal snow depth derived from passive microwave satellite remote-sensing data (e.g. SMMR from 1978 to 1987 and SMM/ I from 1987 to 2006) in China. We first modified the Chang algorithm and then validated it using meteorological observation data, considering the influences from vegetation, wet snow, precipitation, cold desert and frozen ground. Furthermore, the modified algorithm is dynamically adjusted based on the seasonal variation of grain size and snow density. Snow-depth distribution is indirectly validated by MODIS snow-cover products by comparing the snow-extent area from this work. The final snow-depth datasets from 1978 to 2006 show that the interannual snow-depth variation is very significant. The spatial and temporal distribution of snow depth is illustrated and discussed, including the steady snow-cover regions in China and snow-mass trend in these regions. Though the areal extent of seasonal snow cover in the Northern Hemisphere indicates a weak decrease over a long period, there is no clear trend in change of snow-cover area extent in China. However, snow mass over the Qinghai–Tibetan Plateau and northwestern China has increased, while it has weakly decreased in northeastern China. Overall, snow depth in China during the past three decades shows significant interannual variation, with a weak increasing trend.

Comparison Operator Edge Detection Based on Remote Sensing of Marine Oil Spill

2012 ◽  
Vol 610-613 ◽  
pp. 3747-3751 ◽  
Author(s):  
Mei Ping Sun ◽  
Chun Yan Shi ◽  
Hai Ying Li
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Remote Sensing Data ◽  
Laplacian Operator ◽  
The Sustainable Development ◽  
Marine Oil ◽  
Long Time ◽  
Multi Temporal ◽  
Comparison Operator ◽  
Important Means

In recent years, marine oil spill is frequent and seriously threats to the sustainable development of coastal areas and marine environment. Large Marine oil spill is difficult to clean up and pollution range is large, lasting for a long time, for the biological and ecological environment destruction, particularly serious. It is very important to take corresponding measures that how to quickly master the location of the oil spill when this event occurs. Satellite remote sensing has advantages of large, multi-temporal, all-weather, real-time, quick and economic and has become an important means of monitoring marine oil spill, playing an important role in the monitoring of marine oil spill treatment. This paper illustrates by Penglai 19-3 oil spill accident, using MODIS remote sensing data, the use of the Robert operator, Sobel operator, Laplacian operator and LOG operator to extract the oil spill edge, and extraction accuracy of the comparison and analysis.

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