Remote Monitoring of Reforestation on Abandoned Agricultural Lands in the Republic of Mari El Using Principal Component Analysis

Abstract—This paper presents the results of monitoring natural forest regrowth on abandoned agricultural land in the Middle Volga Region using remote sensing methods. The Mari El Republic is chosen as the test site for this research. The use of modern remote sensing methods makes it possible to identify and evaluate areas of natural forest regrowth on abandoned agricultural lands with higher accuracy and at lower financial and labor costs. Minimum noise fraction (MNF) transformed images (Landsat-8 OLI-8) are used in a combination of sixth (midinfrared), fifth (near-infrared), and second (blue) spectral channels for the research. The findings reveal that there is a steady process of mass forest regrowth on abandoned agricultural land in Mari El. The total area of agricultural land used in the research is 763 690 ha. Reforestation with deciduous species is observed on a territory of 135 500 ha, which makes up 17.7% of the total area of agricultural land and 49.9% of the territory of fallow land in the Republic of Mari El. Reforestation with coniferous species is observed on 26 700 ha, which amounts to 3.5 and 9.85%, respectively. Future studies can address anthropogenic and natural impacts on the structure and dynamics of new forest stands. A comprehensive analysis of the density of forest regrowth on abandoned agricultural lands should be carried out using existing maps of agricultural land, population density, and other socioeconomic factors.

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Publication Details

Journal
Izvestiya Atmospheric and Oceanic Physics
Published
2024-12-01
DOI
https://doi.org/10.1134/s0001433824701081
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

Remote Monitoring of Reforestation on Abandoned Agricultural Lands in the Republic of Mari El Using Principal Component Analysis

Д.М. Дергунов, С.А. Лежнин, Oleg Vorobev, Eldar Kurbanov et al.
Izvestiya Atmospheric and Oceanic Physics
Remote Sensing in Agriculture
article

Remote Monitoring of Reforestation on Abandoned Agricultural Lands in the Republic of Mari El Using Principal Component Analysis

Д.М. Дергунов, С.А. Лежнин, Oleg Vorobev, Eldar Kurbanov, Aleksandr Gubaev
article en

Abstract

Abstract—This paper presents the results of monitoring natural forest regrowth on abandoned agricultural land in the Middle Volga Region using remote sensing methods. The Mari El Republic is chosen as the test site for this research. The use of modern remote sensing methods makes it possible to identify and evaluate areas of natural forest regrowth on abandoned agricultural lands with higher accuracy and at lower financial and labor costs. Minimum noise fraction (MNF) transformed images (Landsat-8 OLI-8) are used in a combination of sixth (midinfrared), fifth (near-infrared), and second (blue) spectral channels for the research. The findings reveal that there is a steady process of mass forest regrowth on abandoned agricultural land in Mari El. The total area of agricultural land used in the research is 763 690 ha. Reforestation with deciduous species is observed on a territory of 135 500 ha, which makes up 17.7% of the total area of agricultural land and 49.9% of the territory of fallow land in the Republic of Mari El. Reforestation with coniferous species is observed on 26 700 ha, which amounts to 3.5 and 9.85%, respectively. Future studies can address anthropogenic and natural impacts on the structure and dynamics of new forest stands. A comprehensive analysis of the density of forest regrowth on abandoned agricultural lands should be carried out using existing maps of agricultural land, population density, and other socioeconomic factors.

Izvestiya Atmospheric and Oceanic PhysicsVol. 60(9)
Volga State University of Technology (RU)
Zero hunger
Openalex Percentile: Top 74%
Remote Sensing in Agriculture
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Remote Monitoring of Reforestation on Abandoned Agricultural Lands in the Republic of Mari El Using Principal Component Analysis — Д.М. Дергунов, С.А. Лежнин, et al. · Izvestiya Atmospheric and Oceanic Physics (2024) | TGRS Research Map | TGRS