scholarly journals REMOTE MONITORING OF ECOLOGICAL STATE OF OIL-PRODUCING AREAS OF TOMSK REGION

2020 ◽  
Vol 4 (2) ◽  
pp. 41-46
Author(s):  
Tatiana O. Peremitina ◽  
Irina G. Yashchenko
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Western Siberia ◽  
Free Access ◽  
Environmental Status ◽  
Tomsk Region ◽  
Access To Data ◽  
Hydrocarbon Deposits

The article discusses the possibility of using satellite data to solve problems of monitoring the environmental status of oil producing territories in Western Siberia. The analysis includes MODIS satellite data of medium spatial resolution, which combine the advantages of free access to data and spatial resolution that is acceptable for detecting changes in the state of vegetation cover. The time series of the values of the vegetation index EVI (Enhanced Vegetation Index) of hydrocarbon deposits vegetation cover in the Tomsk Region: Archinsky, Shinginsky, Kazan, South Tabagansky and West Ostaninsky for the growing periods from 2007 to 2019 were calculated. The analysis of the dynamics of changes in the average values of the advanced EVI index allowed determining the minimum and maximum values of the index for the studied territories, as well as to identify trends in the increase of its values over a 10-year period.

Remote Sensing of Arctic Vegetation: Relations between the NDVI, Spatial Resolution and Vegetation Cover on Boothia Peninsula, Nunavut

ARCTIC ◽  
2009 ◽  
Vol 61 (1) ◽  
pp. 1 ◽  
Author(s):  
Gita J. Laidler ◽  
Paul M. Treitz ◽  
David M. Atkinson
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Moisture Regime ◽  
Percent Cover ◽  
Multispectral Data ◽  
Arctic Vegetation

Arctic tundra environments are thought to be particularly sensitive to changes in climate, whereby alterations in ecosystem functioning are likely to be expressed through shifts in vegetation phenology, species composition, and net ecosystem productivity (NEP). Remote sensing has shown potential as a tool to quantify and monitor biophysical variables over space and through time. This study explores the relationship between the normalized difference vegetation index (NDVI) and percent-vegetation cover in a tundra environment, where variations in soil moisture, exposed soil, and gravel till have significant influence on spectral response, and hence, on the characterization of vegetation communities. IKONOS multispectral data (4 m spatial resolution) and Landsat 7 ETM+ data (30 m spatial resolution) were collected for a study area in the Lord Lindsay River watershed on Boothia Peninsula, Nunavut. In conjunction with image acquisition, percent cover data were collected for twelve 100 m × 100 m study plots to determine vegetation community composition. Strong correlations were found for NDVI values calculated with surface and satellite sensors, across the sample plots. In addition, results suggest that percent cover is highly correlated with the NDVI, thereby indicating strong potential for modeling percent cover variations over the region. These percent cover variations are closely related to moisture regime, particularly in areas of high moisture (e.g., water-tracks). These results are important given that improved mapping of Arctic vegetation and associated biophysical variables is needed to monitor environmental change.

Analysis of change in vegetation cover linked to public policies, case study: Tenosique, Tabasco, Mexico.

2021 ◽  
Author(s):  
Jacob Nieto ◽  
Gabriela Vidal García ◽  
Mariana Patricia Jácome Paz ◽  
Tania Ximena Ruiz Santos ◽  
Juan Manuel Nuñez ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Satellite Images ◽  
Vegetation Index ◽  
Agricultural Sector ◽  
Public Policies ◽  
Anthropogenic Activity ◽  
Time Range ◽  
Free Access ◽  
Ecological Processes ◽  
Landsat 7

<p>Currently, natural areas are being devastated by anthropogenic activity. Activities such as agriculture, illegal logging, non-organic farms, and livestock exploitation, disrupt an ecosystem that has been in balance for many years. Therefore, regulations implemented by governments are required for their preservation. However, these regulations are not always the most used in terms of conservation. Such is the case of the town "Tenosique", in this area is one of the most important rivers in Mesoamerica, the Usumacinta River, which is a great regulator of ecological processes and is connected to Mexico with Guatemala. This site has been under the influence of regulations applied to the economic impulse of the area, whether for agricultural and livestock activities, which has affected the apparent vegetation cover, unlike Guatemala that has opted for regulations with a forest conservation approach. These policies sought to boost the agricultural sector, but many deforested areas to carry out this activity turned out not to be suitable due to the type of soil. With the change of regime, financing ends and with it economic activity decreases, leaving the area quite affected and the communities with financial problems. Recently, conservation and protection actions were implemented in the area together with support for these communities. The proximity between Mexico and Guatemala visually shows the results of the application of different public policies. The objective of this study is to quantify the loss and gain of vegetation over time from satellite images of the area, in order to compare this statistic with the different government programs of each era. For this, at least 10 multispectral satellite images of free access will be used, from the Landsat 7 satellite, which has 30 meters of resolution but visually adjustable to 15 meters with the union of its panchromatic channel, and that cover a time range from 1999 to 2020. On these, two processes will be carried out: 1) a normalized vegetation index calculation and 2) a supervised classification. With which it is intended to measure the area and the greenness of a mask of the vegetation cover. The results will serve to update the projects carried out on the site and detect areas of priority interest resolution for larger projects, as well as the future estimation of the critical state of the site regarding the loss of vegetation cover and quantify the conservation efforts that have been carried out. carried out from 2008 to the present.</p>

Monitoring of forest health using spectral unmixing of multispectral satellite data

2020 ◽  
Author(s):  
Volha Siliuk ◽  
Leonid Katkovsky ◽  
Boris Beliaev
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Vegetation Index ◽  
Forest Health ◽  
Spectral Unmixing ◽  
Burned Area ◽  
High Temporal Resolution ◽  
Strong Impact ◽  
Airborne Measurements ◽  
Spacecraft Data

<p>Forests play an important role in global carbon, hydrological and atmospheric cycles. Current environmental issues have a strong impact on forest health. Satellite remote sensing is widely used for forest state monitoring due to increasing availability of satellite data and high temporal resolution. However, a spatial resolution of satellite data is often insufficient to detect small areas of forest drying. For a clearer detection of affected forest areas, spectral unmixing is required.</p><p>The results of spectral unmixing of Belarusian spacecraft data (4 bands: blue, green, red, NIR; spatial resolution 10 meters) are performed. To detect affected forest areas that need to be specified, the vegetation index NDVI is calculated. Then, spectral mixture analysis is running for these areas. The library of endmembers (pure spectral signatures) was created by ground measurements using spectral instruments that were developed in the department of aerospace researches of Belarusian state university. Comparison of spectral unmixing results and airborne measurements shows high agreement. Airborne measurements of study forest area was carried out using Leica airborne digital sensor. Spatial resolution of airborne data is around 40 centimeters. The developed spectral unmixing approach can be used for other tasks, such as burned area mapping, crop monitoring, etc.</p>

scholarly journals Application of Drone and WorldView-4 Satellite Data in Mapping and Monitoring Grazing Land Cover and Pasture Quality: Pre- and Post-Flooding

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 321
Author(s):  
Clement E. Akumu ◽  
Eze O. Amadi ◽  
Samuel Dennis
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Land Cover Change ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Vegetation Index ◽  
High Spatial Resolution ◽  
Learning Technologies ◽  
Grazing Land ◽  
Pasture Quality

Frequent flooding worldwide, especially in grazing environments, requires mapping and monitoring grazing land cover and pasture quality to support land management. Although drones, satellite, and machine learning technologies can be used to map land cover and pasture quality, there have been limited applications in grazing land environments, especially monitoring land cover change and pasture quality pre- and post-flood events. The use of high spatial resolution drone and satellite data such as WorldView-4 can provide effective mapping and monitoring in grazing land environments. The aim of this study was to utilize high spatial resolution drone and WorldView-4 satellite data to map and monitor grazing land cover change and pasture quality pre-and post-flooding. The grazing land cover was mapped pre-flooding using WorldView-4 satellite data and post-flooding using real-time drone data. The machine learning Random Forest classification algorithm was used to delineate land cover types and the normalized difference vegetation index (NDVI) was used to monitor pasture quality. This study found a seven percent (7%) increase in pasture cover and a one hundred percent (100%) increase in pasture quality post-flooding. The drone and WorldView-4 satellite data were useful to detect grazing land cover change at a finer scale.

scholarly journals Development of the integrated approach for stratigraphic dissolution and correlation of Middle–Upper Jurassic disturbances of south-east of western Siberia

2019 ◽  
Vol 27 (3) ◽  
pp. 24-39
Author(s):  
I. V. Rychkova ◽  
M. I. Shaminova ◽  
V. V. Anosov ◽  
V. P. Ivanov
Keyword(s):  
Western Siberia ◽  
Integrated Approach ◽  
Upper Jurassic ◽  
Organic Mass ◽  
Ir Spectrometry ◽  
Tomsk Region ◽  
The Difference ◽  
Effective Development ◽  
Hydrocarbon Deposits ◽  
Reliable Correlation

Based on complex paleobotanical, lithogeochemical, IR spectrometry and thermochemical studies, stratigraphic dismemberment and correlation of productive Middle–Upper Jurassic sediments, represented by Tyumen and Naunak formations in the Dvojnaya and Snezhnaya areas, in the southeast of Western Siberia (the central part of the Tomsk Region) were carried out. A reliable basis has been created for an optimal correction of the calculation of reserves and effective development of hydrocarbon deposits. It is established that for the Tyumen formation the leading paleobotanical remains are the ferns of Coniopteris vialovae, Raphaelia diamensis and Czekanowski czekanowskia irkutensis, Cz. rigida, Phoenicopsis mogutchevae, and for the Naunak formation – Czekanowski czekanowskia tomskiensis. This is due to the paleoclimatic situation, which predetermined the composition of the plant community and the types of plant-coal-forming plants. For reliable correlation, a lithogeochemical study of sediments was carried out, taking into account the analysis of the origin of the coal. The difference in the composition of plant complexes in the suites was confirmed by the difference in the genetic properties of the marking coal-bearing deposits: the degree of biochemical stability of the organic mass of the peat, the level of gelification and the floral regeneration of the organic mass of the coals, and also the yields of light and heavy hydrocarbons.

scholarly journals AUTOMATED PREDICTION SYSTEM FOR VEGETATION COVER BASED ON MODIS-NDVI SATELLITE DATA AND NEURAL NETWORKS

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 9-15
Author(s):  
S. K. M. Abujayyab ◽  
İ. R. Karaş
Keyword(s):  
Neural Networks ◽  
Satellite Data ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Prediction System ◽  
Modis Ndvi ◽  
Automate Processing ◽  
Automated Prediction ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Around the world, vegetation cover functioning as shelter to wildlife, clean water, food security as well as treat large part of air pollution problem. Accurate predictive data early warn and provide knowledge for decision makers to reduce the effects of changes in vegetation cover. In this paper, an automated prediction system was developed to forecast vegetation cover. Prediction system based on moderate satellite data spatial resolution and global coverage data. The tools of system automate processing Moderate Resolution Imaging Spectroradiometer (MODIS) images and training neural networks (NN) model based on 60,000 observations to forecast future density of Normalized Difference Vegetation Index (NDVI). Zonguldak data, located in north of Turkey as dense vegetation cover area utilized as case study for system application. This system significantly facilitates predictive process for users than previous long and complex models.

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