Remote monitoring of reforestation on the abandoned agricultural lands in the Republic of Mari El using the method of principal component analysis

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

The 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 was 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 labour costs. Minimum noise fraction transformed images (Landsat-8 OLI-8) were used in a combination of sixth (mid-infrared), fifth (near-infrared) and second (blue) spectral channels for the research. The findings revealed 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 was 763.69 thousand hectares. Reforestation with deciduous species is observed on a territory of 135.5 thousand hectares, 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 26.7 thousand hectares, 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 socio-economic factors.

Full Text

Restricted Access

About the authors

S. A. Lezhnin

Volga State University of Technology

Author for correspondence.
Email: lejninsa@volgatech.net
Russian Federation, Yoshkar-Ola

A. V. Gubaev

Volga State University of Technology

Email: lejninsa@volgatech.net
Russian Federation, Yoshkar-Ola

O. N. Vorobev

Volga State University of Technology

Email: lejninsa@volgatech.net
Russian Federation, Yoshkar-Ola

E. A. Kurbanov

Volga State University of Technology

Email: lejninsa@volgatech.net
Russian Federation, Yoshkar-Ola

D. M. Dergunov

Volga State University of Technology

Email: lejninsa@volgatech.net
Russian Federation, Yoshkar-Ola

References

  1. Belyaev V.V., Kononov O.D., Karaban A.A., Staritsyn V.V. Sostoyanie drevesnoi rastitel'nosti na zemlyakh, vybyvshikh iz khozyaistvennogo oborota v Arkhangel'skoi oblasti [The state of woody vegetation on lands that have retired from economic circulation in the Arkhangelsk region] // Vestnik Severnogo (Arkticheskogo) federal'nogo universiteta. 2013. Vyp. 2. P. 5–11. (In Russian).
  2. Doklad o sostoyanii i ispol'zovanii zemel' v Respublike Marii El v 2018 godu [Report on the state and use of land in the Republic of Maria El in 2018]. Electronic resource: https://rosreestr.gov.ru/upload/to/respublika-mariy-el/%D0%BF%D1% 80%D0%BE%D1%82%D0%BE%D0%BA%D0%BE%D0%BB%D1%8B/%D0%94%D0%BE%D0%BA%D0%BB%D0%B0%D0%B4%20%D0%B7%D0%B0%202018%20%D0%A0%D0%9C%D0%AD.doc (01.03. 2023). (In Russian).
  3. Doklad o sostoyanii i ispol'zovanii zemel' sel'skokhozyaistvennogo naznacheniya Rossiiskii Federatsii v 2019 godu [Report on the state and use of agricultural lands in the Russian Federation in 2019]. Electronic resource: https://mcx.gov.ru/ upload/iblock/fb1/fb12ab74bc70b5091b0533f44a4d8dba.pdf (01.03.2023). (In Russian).
  4. Erusalimskii V.I. Les i pashnya [Forest and arable land] // Lesnoe khozyaistvo. 2011. № 2. P. 14–15. (In Russian).
  5. Kireicheva L.V., Shevchenko V.A., Yurchenko I.F. Otsenka ekonomicheskoi effektivnosti vvoda v agroproizvodstvo zalezhnykh zemel' Nechernozemnoi zony RF [Evaluation of the economic efficiency of introducing fallow lands of the Non-Chernozem zone of the Russian Federation into agricultural production] // Moskovskii ekonomicheskii zhurnal. 2021. № 3. P. 245–255. (In Russian).
  6. Kurbanov E.A., Vorob'ev O.N., Nezamaev S.A., Gubaev A.V., Lezhnin S.A., Polevshchikova Yu.A. Tematicheskoe kartirovanie i stratifikatsiya lesov Mariiskogo Zavolzh'ya po sputnikovym snimkam Landsat-8 OLI [Thematic mapping and stratification of the forests of the Mari Trans-Volga region based on Landsat-8 OLI satellite images] // Ioshkar-Ola: Vestnik PGTU, Ser.: Les. Ekologiya. Prirodopol'zovanie. 2013. № 3. P. 72–82. (In Russian).
  7. Lezhnin S.A. Distantsionnyi metod otsenki formirovaniya molodnyakov na zalezhakh Mariiskogo lesnogo Zavolzh'ya po sputnikovym snimkam [A remote method for assessing the reforestation on the abandoned agricultural lands of the Mari forest Trans-Volga region using satellite images] // Dis. … kand. s.-kh. nauk. Ioshkar-Ola. 2013. 167 p. (In Russian).
  8. Lezhnin S.A., Muzurova R.L. Otsenka vtorichnoi suktsessii na zalezhakh Respubliki Marii El [Assessment of secondary succession on the deposits of the Republic of Mari El] // Lesnye ekosistemy v usloviyakh izmeneniya klimata: biologicheskaya produktivnost' i distantsionnyi monitoring: materialy mezhdunarodnoi konferentsii. Ioshkar-Ola: PGTU, 2019. P. 126–135. (In Russian).
  9. Stytsenko E.A. Vozmozhnosti raspoznavaniya sel'skokhozyaistvennykh ugodii s ispol'zovaniem metodiki sovmestnoi avtomatizirovannoi obrabotki raznosezonnykh mnogozonal'nykh kosmicheskikh izobrazhenii [Possibilities of recognition of agricultural lands using the method of joint automated processing of multi-seasonal multi-zone space images] // Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2017. T. 14. № 5. P. 172–183. (In Russian).
  10. Shikhov A.N., Gerasimov A.P., Ponomarchuk A.I., Perminova E. S. Tematicheskoe deshifrirovanie i interpretatsiya kosmicheskikh snimkov srednego i vysokogo prostranstvennogo razresheniya [Ehlektronnyi resurs]: uchebnoe posobie [Thematic interpretation and interpretation of satellite images of medium and high spatial resolution] // Permskii gosudarstvennyi natsional'nyi issledovatel'skii universitet. 2020. 191 p. Rezhim dostupa: http://www.psu.ru/files/docs/science/books/uchebnie-posobiya/shikhov-gerasimov-ponomarchukperminova-tematicheskoe-deshifrovanie-i-interpretaciyakosmicheskih-snimkov.pdf. (In Russian).
  11. Alcantara C., Kuemmerle T., Prishchepov A.V., Radeloff V.C. Mapping abandoned agriculture with multitemporal MODIS satellite data // Remote Sensing of Environment. 2012. Vol. 124. P. 334–347.
  12. Boardman J.W. Automated spectral analysis: a geological example using AVIRIS data, north Grapevine Mountains, Nevada: in Proceedings, ERIM Tenth Thematic Conference on Geologic Remote Sensing // Environmental Research Institute of Michigan. 1994. P. 407–418.
  13. Dannenberg M. P., Hakkenberg C.R., Song C. Consistent classification of Landsat-8 OLI time series with an improved automatic adaptive signature generalization algorithm // Remote Sensing. 2016. https://doi.org/10.3390/rs8080691
  14. Defourny P., Bontemps S., Bellemans N., Cara C., Dedieu G., Guzzonato E., Hagolle O., Inglada J., Nicola L., Rabaute T., Savinaud M., Udroiu C., Valero S., Bégué A., Dejoux J.-F., El Harti A., Ezzahar J., Kussul N., Labbassi K., Lebourgeois V., Miao Z., Newby T., Nyamugama A., Salh N., Shelestov A., Simonneaux V., Traore P.S., Traore S.S., Koetz B. Near real-time agriculture monitoring at national scale at parcel resolution: performance assessment of the Sen2-Agri automated system in various cropping systems around the world // Remote Sensing of Environment. 2019. № 221. P. 551–568. https://doi.org/10.1016/J.RSE.2018.11.007
  15. Estel S., Kuemmerle T., Alcántara C., Levers C., Prishchepov A., Hostert P. Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series // Remote Sensing of Environment. 2015. № 163. P. 312–325. https://doi.org/10.1016/J.RSE.2015.03.028
  16. Fradette O., Marty C., Faubert P., Dessureault P.-L., Paré M., Bouchard S., Villeneuve C. Additional carbon sequestration potential of abandoned agricultural land afforestation in the boreal zone: A modelling approach // Forest Ecology and Management. 2021. Vol. 499. 119565. https://doi.org/10.1016/j.foreco.2021.119565
  17. Fraser R.H., Oltho, I., Pouliot D. Monitoring land cover change and ecological integrity in Canada’s national parks // Remote Sensing of Environment. 2009. № 113. P. 1397–1409. https://doi.org/10.1016/j.rse.2008.06.019
  18. Friedl M.A., Sulla-Menashe D., Tan B., Schneider A., Ramankutty N., Sibley A., Huang X. M. MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets // Remote Sensing of Environment. 2010. № 114. P. 168–182. https://doi.org/10.1016/j.rse.2009.08.016
  19. Gómez C., White J.C., Wulder M.A. Optical remotely sensed time series data for land cover classification: a review // ISPRS Journal of Photogrammetry and Remote Sensing. 2016. № 116. P. 55–72. https://doi.org/10.1016/J.ISPRSJPRS.2016.03.008
  20. Grădinaru S.R., Kienast F., Psomas A. Using multi-seasonal Landsat-8 OLI imagery for rapid identification of abandoned land in areas affected by urban sprawl // Ecological Indicators. 2019. Vol. 96, Pt. 2. P. 79–86.
  21. Gray J., Song C.H. Consistent classification of image time series with automatic adaptive signature generalization // Remote Sensing of Environment. 2013. № 134. P. 333–341. https://doi.org/10.1016/j.rse.2013.03.022
  22. Janus J., Bożek P., Mitka B., Taszakowski J., Doroż A. Long-term forest cover and height changes on abandoned agricultural land: An assessment based on historical stereometric images and airborne laser scanning data // Ecological Indicators. 2021. Vol. 120. 106904. https://doi.org/10.1016/j.ecolind.2020.106904
  23. Kolecka N., Kozak J., Kaim D., Dobosz M., Ginzler C., Psomas A. Mapping secondary forest succession on abandonet agricultural land in the Polish Carpathians // Remote Sensing and Spatial Information Sciences. 2016. Vol. XLI-B8. P. 931–935.
  24. Levers C., Schneider M., Prishchepov A.V., Estel S. Spatial variation in determinants of agricultural land abandonment in Europe // Science of The Total Environment. 2018. Vol. 644. P. 95–111.
  25. Liu J., Kuang W.H., Zhang Z.X., Xu X.L., Qin Y.W., Ning J., Zhou W.C., Zhang S.W., Li R.D., Yan C.Z., Wu S.X., Shi X.Z., Jiang N., Yu D.S., Pan X.Z., Chi W.F. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s // Journal of Geographical Sciences. 2014. № 24. P. 195–210. https://doi.org/10.1007/s11442-014-1082-6
  26. Liu B., Song W. Mapping abandoned cropland using Within-Year Sentinel-2 time series // CATENA. 2023. Vol. 223. 106924. https://doi.org/10.1016/j.catena.2023.106924
  27. Löw F., Fliemann E., Abdullaev I., Conrad C., Lamers J. P.A. Mapping abandoned agricultural land in Kyzyl-Orda, Kazakhstan using satellite remote sensing // Applied Geography. 2015. Vol. 62. P. 377–390. https://doi.org/10.1016/j.apgeog.2015.05.009
  28. Mainali K., Evans M., Saavedra D., Mills E., Madsen B., Minnemeyer S. Convolutional neural network for high-resolution wetland mapping with open data: Variable selection and the challenges of a generalizable model // Science of The Total Environment. 2023. Vol. 861. 160622. https://doi.org/10.1016/j.scitotenv.2022.160622
  29. Nordén B., Olsen S.L., Haug S., Rusch G. Recent forest on abandoned agricultural land in the boreonemoral zone – Biodiversity of plants and fungi in relation to historical and present tree cover // Forest Ecology and Management. 2021. Vol. 489. 119045. https://doi.org/10.1016/j.foreco.2021.119045
  30. Parés-Ramos I.K., Gould W.A., Aide T.M. Agricultural abandonment, suburban growth, and forest expansion in Puerto Rico between 1991 and 2000 // Ecology and Society. 2008. № 13(8). https://doi.org/10.5751/ES-02479-130201
  31. Pax-Lenney M., Woodcock C. E., Macomber S.A., Gopal S., Song C. Forest mapping with a generalized classifier and Landsat-8 OLI TM data // Remote Sensing of Environment. 2001. № 77. P. 241–250. https://doi.org/10.1016/S0034-4257(01)00208-5
  32. Pedersen N.K., Schmidt I.K., Kepfer-Rojas S. Drivers of tree colonization, species richness, and structural variation during the initial three decades of natural forest colonization in abandoned agricultural soils // Forest Ecology and Management. 2023. Vol. 543. https://doi.org/10.1016/j.foreco.2023.121138
  33. Phalke A.R., Özdoğan M. Large area cropland extent mapping with Landsat-8 OLI data and a generalized classifier // Remote Sensing of Environment. 2018. Vol. 219. P. 180–195. https://doi.org/10.1016/j.rse.2018.09.025
  34. Pointereau P., Coulon F.P., Girard M.L., Stuczynski T., Ortega V.S., Del Rio A. Analysis of farmland abandonment and the extent and location of agricultural areas that are actually abandoned or are in risk to be abandoned // JRC Scientific and Technical Reports. 2008 (EUR23411 EN).
  35. Song W. Mapping Cropland Abandonment in Mountainous Areas Using an Annual Land-Use Trajectory Approach // Sustainability. 2019. Vol. 11. P. 1–24
  36. Thenkabail P.S., Knox J.W., Ozdogan M., Gumma M.K., Congalton R.G., Zhuoting W.U., Milesi C., Finkral A., Marshall M., Mariotto I. Assessing future risks to agricultural productivity, water resources and food security: how can remote sensing help? // Photogrammetric Engineering & Remote Sensing. 2012. № 78(8). P. 773–782.
  37. Wittke S., Xiaowei Y., Karjalainen M., Hyyppä J., Puttonen E. Comparison of two-dimensional multitemporal Sentinel-2 data with three-dimensional remote sensing data sources for forest inventory parameter estimation over a boreal forest // International Journal of Applied Earth Observation and Geoinformation. 2019. Vol. 76. P. 167–178.
  38. Xu Y., Yu L., Zhao F.R., Cai X., Zhao J., Lu H., Gong P. Tracking annual cropland changes from 1984 to 2016 using time-series Landsat-8 OLI images with a change detection and post-classification approach: experiments from three sites in Africa // Remote Sensing of Environment. 2018. № 218. P. 13–31. https://doi.org/10.1016/J.RSE.2018.09.008

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. Distribution of test sites across the study area.

Download (274KB)
Indexing metadata
3. Fig. 2. Fragment of the Landsat-8 OLI-8 image: a – in natural colors; b – after synthesis of the sixth (mid-infrared), fifth (near-infrared) and second (blue) spectral channels; c – after MNF transformation, in which mature mixed forest, which is part of the forest fund, is visually distinguished (shown in dark green on the image, a fragment of the forest is highlighted in blue), deciduous young forests on fallow lands (shown in shades of yellow on the image, a fragment of them is highlighted in red) and coniferous young forests on fallow lands (shown in light green on the image, a fragment of them is highlighted in yellow).

Download (410KB)
Indexing metadata
4. Fig. 3. Map of the Mari El Republic for 2022 with highlighted areas of young coniferous and deciduous trees on fallow lands.

Download (514KB)
Indexing metadata
5. Fig. 4. Comparison of fallow land overgrowth processes in 2011 and 2022 in Landsat images and maps of 2011 and 2022: a – fragment of Landsat 7 ETM+ image for 2011 in natural colors; b – fragment of Landsat 8 OLI image for 2022 in natural colors; c – fragment of map of fallow lands with overgrowth areas for 2011; d – fragment of map of fallow lands with overgrowth areas for 2022.

Download (409KB)
Indexing metadata

Copyright (c) 2024 Russian Academy of Sciences

Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».