REMOTE IDENTIFICATION OF MICROSEDIMENTAL RELIEF FORMS AND SOIL SECTIONS OF AGROLANDSCAPES OF THE FORESTS OF UKRAINE WITH SIGNS OF HYDROMORPHISM
-
Published:2024
Issue:1 (104)
Volume:
Page:98-106
-
ISSN:1728-2713
-
Container-title:Visnyk of Taras Shevchenko National University of Kyiv. Geology
-
language:
-
Short-container-title:VKNUGEOL
Author:
, TROFYMENKO PetroORCID, TOMCHENKO ОlhaORCID, , PORALO Rostyslav, , ZATSERKOVNYI VitaliiORCID, , STAKHIV ІrynaORCID,
Abstract
Background. Agricultural lands play a key role in ensuring the food security of the population and the development of the country's economy. However, excessive wetting poses a significant threat to these lands, as a result of which the conditions for the formation of soils with signs of glaciation and low fertility are formed within the lower relief elements, which significantly reduces their potential. In order to highlight the problems of geospatial identification of micro-recessed landforms (MRLF) on agricultural lands, the article uses spectral indices based on the data of RSE. Methods. 6 spectral indices were selected for the research. They were used to obtain data on areas of soil subsidence on arable lands, namely: NDWI, NWI, NDMI, NDVI, OSAVI, WRI. Solving research tasks involved the use of data from the Sentinel-2A satellite system. In order to visualize the spread of MRLF on the research territory, a high-resolution image (0.2 m per 1 pixel) obtained in the "Digitals Professional 5.0" software was used. Processing and geospatial visualization of the RSE data were performed in the Arc Map environment of the Arc GIS 10.8 program using the raster calculator tool. Results. Within the reference fields, the dynamics of the values of water and vegetation indices were constructed and analyzed, and the identification ability for the geospatial separation of soil areas with signs of hydromorphism was evaluated. It is shown that the identification capacity of the indices depends not only on the level of soil moisture, but also on the biomass of vegetation (scales of crop damage), which is indicated by the high information capacity of the traditional vegetation index NDVI. The most informative index ranges were established: for NDVI, the range is from -0.117 to -0.024 with an identification percentage of 98.0 %; for OSAVI – 78.0 % with a range of 0.255–0.313; for NDMI with a range variation of -0.041 to -0.149 and an identification percentage of 56.0. Сonclusions. The results of remote identification of the areas of the MRLF enabled to obtain information about the moisture content of the soils of the arable lands of the research area. The ability of the specified indices during the geospatial identification of microrecessed landforms (MRLF) and soil areas within them with signs of hydromorphism was evaluated. It is demonstrated that the use of orthophotos with a resolution of 0.2 m per 1 pixel serves as important supporting aids of successful completion of the specified tasks. It was found that the identification ability of water indices on test fields without existing vegetation is too low. On the other hand, the shielding of the soil surface by vegetation with areas of damaged crops makes it possible to isolate MRLF. The obtained information can be used during the development of the methodology of soil science surveying and planning of largescale soil survey activities
Publisher
Taras Shevchenko National University of Kyiv
Reference17 articles.
1. Belenok, V., Noszczyk, T., Hebryn-Baidy, L., & Kryachok, S. (2021). Investigating anthropogenically transformed landscapes with remote sensing. Remote Sensing Applications Society and Environment, 24, 100635. https://doi.org/10.1016/j.rsase.2021.100635. 2. Bruce, R. R., Myhre, D. L., & Sanford, J. O. (1968). Water capture in soil surface microdepressions for crop use. 9th Inter. Cong. Soil Sci. Transactions, 325-330. 3. Dovhyi, S., Babiiuchuk, S., Kuchma, T., Tomchenko, O., & Yurkiv, L. (2020). Remote Sensing of the Earth: Analysis of Satellite Images in Geoinformation Systems [in Ukrainian]. 4. FAO, Rome, AGR, & Statistics, UN/FAO. (2023). Applications of remote sensing to agricultural statistics. Report. XF2006285449. France, Rome & May, 2023. 5. Gerardo, R., & Lima, I. (2022). Sentinel-2 Satellite Imagery-Based Assessment of Soil Salinity in Irrigated Rice Fields in Portugal. Agriculture, 12, 1490. https://doi.org/10.3390/agriculture12091490.
|
|