Transformation of Arable Lands in Russia over Last Half Century—Analysis Based on Detailed Mapping and Retrospective Monitoring of Soil–Land Cover and Decipherment of Big Remote Sensing Data
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Materials
- (1)
- For task of developing decoding features:
- (2)
- Decipherable materials:
- (3)
- Reference materials:
2.3. Methods
2.3.1. Cartographic Analysis
2.3.2. Geographic Information System (GIS) Technologies
- (1)
- (2)
- Vectorization of georeferenced archival materials to support cartographic analysis.
- (3)
- Georeferencing of multi-temporal archives of RSD with a spatial resolution of 10 and 30 m to subpixel accuracy. This is carried out when the geographic location of RSD scenes differs by more than one resolution element (pixel) relative to cartographic materials at a scale of 1:10,000. ArcGIS was used [59].
- (4)
- (5)
2.3.3. Neural Networks
2.3.4. Principles of Interpretation
- (1)
- The map must be topological.
- (2)
- The map must be georeferenced.
- (3)
- The map must not contradict RSD.
- (4)
- The map must not contradict the DEM (digital elevation model).
- (5)
- The map must not contradict the topographic bases, if they in turn do not contradict the RSD.
- (6)
- A map may contradict previously created maps if and only if there is a justification for the changes being made.
- (7)
- Different thematic maps may have contours that do not coincide with each other if and only if this is caused by the difference in the ground location of thematic loads.
2.3.5. Ground Verification
2.3.6. Block Diagram of This Work’s Technology
- (1)
- Objects may appear on the arable land that are not indicated on the cartographic materials. During the field survey, the state of the object is photographically recorded with georeferencing. Forest taxation is carried out on the objects. The photographs are added to the GIS project as a georeferenced layer.
- (2)
- Verification of the interpretation results. Taxation is carried out selectively and compared with the interpretation results.
3. Results
3.1. Primary Results
3.2. Results of Retrospective Monitoring of Arable Lands in Palekhsky District
3.3. Description of the Dynamics of Arable Land in Palekhsky District
3.4. Implementation of the Retrospective Monitoring of Soil–Land Cover
4. Discussion
4.1. Catastrophe or Stabilization
4.2. Detailed Mapping of Arable Land
4.3. Research Area
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ATCOR | Atmospheric and topographic correction |
BSS | Bare soil surface |
CV | Cross-validation |
DEM | Digital elevation model |
ERDAS | Earth Resource Development Assessment System |
FAO | Food and Agriculture Organization of the United Nations |
GIS | Geographic information system |
QGIS | Quantum geographic information system |
MODIS | Moderate Resolution Imaging Spectroradiometer |
RSD | Remote sensing data |
RSFSR | Russian Soviet Federative Socialist Republic |
SRTM | Shuttle Radar Topography Mission |
USSR | Union of Soviet Socialist Republics |
WSV | Woody and shrub vegetation |
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ID | Land Classes by Year | ||||||||
---|---|---|---|---|---|---|---|---|---|
1985 | 1990 | 1995 | 2000 | 2005 | 2010 | 2015 | 2020 | 2025 | |
4127 | 1 | 1 | 1 | 4 | 4 | 4 | 4 | 4 | 4 |
3007 | 14 | 14 | 14 | 1 | 1 | 1 | 1 | 1 | 1 |
1351 | 1 | 1 | 1 | 1 | 3 | 1 | 3 | 9 | 9 |
2120 | 1 | 3 | 1 | 3 | 3 | 3 | 9 | 1 | 1 |
1399 | 14 | 1 | 1 | 1 | 3 | 3 | 10 | 10 | 11 |
1276 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
5461 | 1 | 1 | 1 | 1 | 1 | 9 | 10 | 10 | 11 |
6492 | 1 | 1 | 1 | 1 | 3 | 3 | 9 | 9 | 10 |
2864 | 1 | 1 | 3 | 3 | 3 | 1 | 1 | 1 | 1 |
2249 | 1 | 1 | 1 | 1 | 1 | 6 | 6 | 6 | 1 |
5112 | 14 | 1 | 1 | 1 | 3 | 9 | 10 | 10 | 11 |
409 | 1 | 1 | 3 | 3 | 3 | 3 | 9 | 9 | 9 |
240 | 1 | 1 | 1 | 3 | 3 | 3 | 1 | 1 | 3 |
2302 | 14 | 14 | 14 | 14 | 1 | 3 | 1 | 1 | 1 |
5931 | 1 | 3 | 3 | 3 | 3 | 3 | 9 | 9 | 9 |
3686 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
2288 | 14 | 1 | 3 | 1 | 3 | 9 | 10 | 10 | 10 |
5863 | 1 | 1 | 14 | 14 | 14 | 14 | 14 | 14 | 14 |
3017 | 14 | 14 | 14 | 14 | 14 | 1 | 1 | 1 | 3 |
594 | 1 | 1 | 3 | 1 | 3 | 9 | 10 | 10 | 10 |
1974 | 1 | 1 | 3 | 1 | 1 | 3 | 14 | 14 | 14 |
Class Number | Land Class Name |
---|---|
1 | Arable land |
2 | Land reclamation |
3 | Fallow land |
4 | Waterlogged depression |
5 | Erosion gully–ravine network |
6 | Waterlogged gully–ravine network |
7 | Wetland |
8 | Saline territory (solonchak) |
9 | WSV (slight overgrowth of the field) |
10 | WSV (moderate overgrowth of the field) |
11 | WSV (severe overgrowth of the field) |
12 | WSV (very intensive overgrowth of the field) |
13 | Cultivated woody vegetation |
14 | Anthropogenically modified territories |
Class Number | 1985 | 1990 | 1995 | 2000 | 2005 | 2010 | 2015 | 2020 | 2025 |
---|---|---|---|---|---|---|---|---|---|
1 | 25,876.3 | 24,815.8 | 22,546.5 | 19,298.4 | 11,842.1 | 7678.1 | 5923.9 | 6736.1 | 6662.3 |
2 | 4.4 | 3.0 | 5.3 | ||||||
3 | 239.4 | 1247.5 | 3412.6 | 6491.1 | 13,322.3 | 11,700.3 | 6529.2 | 4877.8 | 4307.1 |
4 | 11.3 | 9.2 | 12.3 | 22.47 | 19.4 | 16.3 | 15.1 | 13.4 | 16.9 |
6 | 7.1 | 14.1 | 12.07 | 12.3 | 13.1 | 9.2 | 6.1 | 4.9 | |
9 | 8.7 | 47.9 | 162.31 | 641.1 | 4838.7 | 6388.4 | 6002.6 | 5397.7 | |
10 | 8.7 | 11.9 | 58.79 | 202.9 | 1655.5 | 5821.9 | 6407.9 | 7136.8 | |
11 | 0.9 | 0.9 | 0.90 | 15.1 | 138.6 | 1227.7 | 1739.3 | 2228.3 | |
12 | 113.4 | 214.7 | 234.1 | ||||||
13 | 0.4 | 0.6 | 0.64 | 0.6 | 1.1 | 1.3 | 1.3 | 1.3 | |
14 | 56.8 | 98.6 | 145.7 | 145.80 | 136.8 | 150.8 | 162.4 | 190.5 | 197.9 |
Class Number | 1985 | 1990 | 1995 | 2000 | 2005 | 2010 | 2015 | 2020 | 2025 |
---|---|---|---|---|---|---|---|---|---|
9 | 0 | 114 | 622 | 2110 | 8334 | 62,903 | 83,050 | 78,034 | 70,170 |
10 | 441 | 0 | 603 | 2969 | 10,244 | 83,602 | 294,008 | 323,597 | 360,409 |
11 | 0 | 100 | 100 | 100 | 1667 | 15,318 | 135,659 | 192,192 | 246,230 |
12 | 0 | 0 | 0 | 0 | 0 | 0 | 20,471 | 38,754 | 42,252 |
Total | 441 | 213 | 1325 | 5179 | 20,245 | 161,823 | 533,187 | 632,577 | 719,060 |
Subject of the Russian Federation | Sowing 1990 | Sowing 2025 | % 2025 from 1990 | Minimum Sowing | Year of Minimum | % of Minimum from 1990 | Arable Land Area 2025 |
---|---|---|---|---|---|---|---|
Ivanovo Oblast | 609.1 | 199.5 | 67 | 194.5 | 2021 | 68 | 562.2 |
Kaluga Oblast | 918.9 | 346.9 | 62 | 299.0 | 2010 | 68 | 955.4 |
Tula Oblast | 1448.1 | 940.3 | 35 | 644.0 | 2007 | 56 | 1556.2 |
Republic of Tatarstan | 3402.4 | 2857.7 | 13 | 2862.9 | 2003 | 16 | 3405.6 |
Tambov Oblast | 2068.3 | 1894.4 | 8 | 1282.0 | 2005 | 38 | 2127.5 |
Rostov Oblast | 5223.9 | 4862.8 | 7 | 3760.3 | 1998 | 28 | 5983.1 |
Stavropol Krai | 3433.9 | 3049.0 | 11 | 2736.7 | 2005 | 20 | 3999.8 |
In the Russian Federation as a whole | 117,705.2 | 80,184.5 | 32 | 74,861.4 | 2010 | 34 | 122,688.4 |
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Rukhovich, D.I.; Koroleva, P.V.; Shapovalov, D.A.; Komissarov, M.A.; Pham, T.G. Transformation of Arable Lands in Russia over Last Half Century—Analysis Based on Detailed Mapping and Retrospective Monitoring of Soil–Land Cover and Decipherment of Big Remote Sensing Data. Sustainability 2025, 17, 6203. https://doi.org/10.3390/su17136203
Rukhovich DI, Koroleva PV, Shapovalov DA, Komissarov MA, Pham TG. Transformation of Arable Lands in Russia over Last Half Century—Analysis Based on Detailed Mapping and Retrospective Monitoring of Soil–Land Cover and Decipherment of Big Remote Sensing Data. Sustainability. 2025; 17(13):6203. https://doi.org/10.3390/su17136203
Chicago/Turabian StyleRukhovich, Dmitry I., Polina V. Koroleva, Dmitry A. Shapovalov, Mikhail A. Komissarov, and Tung Gia Pham. 2025. "Transformation of Arable Lands in Russia over Last Half Century—Analysis Based on Detailed Mapping and Retrospective Monitoring of Soil–Land Cover and Decipherment of Big Remote Sensing Data" Sustainability 17, no. 13: 6203. https://doi.org/10.3390/su17136203
APA StyleRukhovich, D. I., Koroleva, P. V., Shapovalov, D. A., Komissarov, M. A., & Pham, T. G. (2025). Transformation of Arable Lands in Russia over Last Half Century—Analysis Based on Detailed Mapping and Retrospective Monitoring of Soil–Land Cover and Decipherment of Big Remote Sensing Data. Sustainability, 17(13), 6203. https://doi.org/10.3390/su17136203