Impact of Anthropogenic Activities on Microbial Community Structure in Riverbed Sediments of East Kazakhstan

In 2023 there was a severe forest fire on the territory of the Semipalatinsk Forest. More than 60 thousand ha were burned. This resulted in high environmental costs, destroying trees, real estate, recreational and even human lives. One of the most pressing issues was the determination of the origin and spread of forest fires. In such cases, remote sensing data is a spatial and temporal measure to obtain fast and accurate data to prevent the further spread of the fire and to neutralize this natural disaster. Using such geospatial information, it is possible to prioritize preventive measures to reduce the risk of forest fires and identify mitigating factors to increase the likelihood of immediate fire suppression in threatened areas. This work proposes to assess the fire potential and to determine the hazard potential. This is done by analyzing and mapping the area of the fire that has occurred. The mapping of the fire potential was approached from a remote sensing point of view by estimating and mapping the total vegetation cover using Landsat-8–9 OLI/TIRS DATA and the geographic information systems QGIS, SAGAGIS. A morphometric, spatial analysis of conditions was also conducted, taking into account many factors affecting fire potential—land exposure, aspect, wind direction, fire statistics, population density, climatic characteristics, etc. As a result, an attempt was made to create indices RBR, RdNBR, dNDVI, dGNDI, GEMI and BAI, which could be the basis for the determination of fire potential. These indices are based on the type of tree, the vegetation and the topographic features. These characteristics make it possible, after the classification of the Landsat images, to evaluate the reliability of the information obtained by determining the area of the fire through the Object Based Image Analysis segmentation method and by assessing the accuracy of the detected data. The index values identified were consistent with reliable information for identifying fire locations and monitoring estimated fire risk. They could be used to map fire potential from regional to local scales.

show abstract

Innovative technology for assessing the degradation of the Earth by sand desertification of soils with specialized processing of space materials

Apshikur,

Alimkulov,

Kapasov

et al. 2024

ICIGIS

View full text Add to dashboard Cite

This study provides an assessment of the process of sand landslide degradation of the Abay Region of the Republic of Kazakhstan and space materials (RS-Remote Sensing, GIS-Geographic Information Systems and machine learning data of the soilgrid platform, QGIS Trends). Using the technology of the Earth module and field research, the amount of landslide sand g/kg at a depth of 5 cm was determined and the SDG land degradation target, the land productivity indicator (productivity), the indicator of the impact assessment of the change in the type of surface cover (land cover), the indicator of the change in the carbon stock of soil organic matter (carbon velocity) were calculated in the machine learning module, which uses integrated research to monitor, map and quantify the threat of land degradation. The study was analyzed and obtained between 2015 and 2022 by interpreting data from machine learning images with visual, interactive interpretation. The data of dynamic reverse changes from Sandy and worn areas, in these two stages, were obtained by overlapping the database. The results of the study showed that in the proportion of years obtained, 3009.29 km2 of newly degraded land was formed, and most of the newly degraded land is the area where sand landslides occur, as is known from the quantitative data of the study, it was found that the share of sand landslide degradation of the surveyed 272 410.4 km2 area within 7 years is 4.56 % of the total area. By analyzing the results of the study, some constructive measures were proposed aimed at ecological protection of forest clusters in the North-West of Abay Region and planting new forest seedlings.

show abstract

Impact of Anthropogenic Activities on Microbial Community Structure in Riverbed Sediments of East Kazakhstan

Cited by 4 publications

References 56 publications

Anthropogenic Perturbations of Arctic Marine Microbial Community

Anthropogenic Perturbations of Arctic Marine Microbial Community

Spatial analysis and mapping of potential wildfires from Landsat satellite data

Innovative technology for assessing the degradation of the Earth by sand desertification of soils with specialized processing of space materials

Contact Info

Product

Resources

About