Determination of the state of artificial plantings of the green belt of Astana using remote sensing data of the Earth
https://doi.org/10.21266/2079-4304.2024.247.137-153
Abstract
To determine the further strategy of conservation and maintenance of artificial plantings of the green belt of Astana, it was necessary to assess the vital condition of woody and shrubby plants, which became the primary task of our research. For this purpose, ground-based taxational observations were carried out, multispectral data of remote sensing of the Earth (remote sensing) and GIS tools were used. The purpose of the research was to determine the areas of weakened and dying forest crops in the green belt of Astana using remote sensing data. The research methodology consisted in laying reference plots in forest crops of various species composition and age, in which trees were taxed and their vital condition was determined. Based on the decoding of multispectral images of reference sites, the identification of the rock composition and the living condition of artificial plantings of the entire green zone of Astana was carried out. As a result of ground-based research and remote sensing data processing, it was revealed that in the green belt of Astana, the main area is occupied by forest crops related to the «weakened» state of life – 40.3%. «Healthy» forest crops occupy 31.5% of the area, «dying» – 28.3%.
About the Authors
A. N. KabanovKazakhstan
KABANOV Andrey N. – senior researcher; postgraduate student
021704. Kirov str. 58. Shchuchinsk. Republic of Kazakhstan
A. M. Bekbaeva
Kazakhstan
BEKBAEVA Aigul M. – Deputy Director of the Center for Technological Competence in the Field of Digitalization of Agriculture, Master of Science
010000. Zhenis av. 62B. Nursultan. Kazakhstan
S. A. Kabanova
Kazakhstan
KABANOVA Svetlana A. – PhD (Biological), Associate Professor, Head of the Department of Reforestation and Afforestation
021704. Kirov str. 58. Shchuchinsk. Republic of Kazakhstan
I. S. Kochegarov
Russian Federation
KOCHEGAROV Igor S. – junior researcher, master of science
021704. Kirov str. 58. Shchuchinsk. Republic of Kazakhstan
M. A. Danchenko
Russian Federation
DANCHENKO Matvey A. – PhD (Geographical), Associate Professor
634050. Lenin str. 36. Tomsk. Russia
S. A. Skott
United States
SCOTT Sabina A. – adjunct of microbiological sciences
43015. Sptring str. 550 E. Columbus city. Ohio State. USA
References
1. Alekseev V.A. Diagnostika zhiznennogo sostoyaniya derevev i drevostoev. Lesovedenie. 1989, no. 4, pp. 51–57. (In Russ.)
2. Kabanova S.A., Danchenko M.A., Skott S.A., Kabanov A.N., Cvetkova N.V., Kirillov V.Yu. Sravnitelnyj analiz nakopleniya tyazhelyh metallov v hvoe introducentov v zelenoj zone g. Nur-Sultan. Lesotekhnicheskij zhurnal. 2021, no 4 (44), pp. 57–67. (In Russ.)
3. Kabanova S.A., Kabanov A.N., Hasenov A.A., Danchenko M.A. Nauchnoe soprovozhdenie proizvodstvennyh opytov v lesnyh kulturah zelenogo poyasa g. Nur- Sultan. Vestnik Rossijskogo universiteta druzhby narodov. Seriya: Agronomiya i zhivotnovodstvo, 2019, no 4, pp. 437–452. (In Russ.)
4. Barta V., Hanus J., Dobrovolný L., Homolova L. Comparison of field survey and remote sensing techniques for detection of bark beetle-infested trees. Forest Ecology and Management, 2022, 506, p. 119984. DOI: 10.1016/j.foreco.2021.119984.
5. Breiman L. Random Forests. Machine Learning, 2001, 45(1), pp. 5–32. DOI: 10.1023/A:1010933404324.
6. Garza BN., Ancona V., Enciso J., Perotto-Baldivieso HL., Kunta M., Simpson C. Quantifying Citrus Tree Health Using True Color UAV Images. Remote Sensing, 2020, 12(1), p.170. DOI: 10.3390/rs12010170
7. Hastie T., Tibshirani R., Friedman J. Random Forests. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer-Verlag, 2009, p. 764.
8. Kabanova S.A., Zenkova Z.N., Danchenko M.A. Regional risks of artificial forestation in the steppe zone of Kazakhstan (case study of the green belt of Astana). IOP Conf. Series: Earth and Environmental Science, 2018, р. 211. DOI: 10.1088/1755-1315/211/1/012055
9. Laze K. Preliminary findings on remote sensing of forest cover change, forest and tree health in Southeastern Europe. ISPRS – International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2022, XLIII-B4-2022, pp. 133–139. DOI: 10.5194/isprs-archives-XLIII-B4-2022-133-2022.
10. Li X., Zheng Z., Xu C., Zhao P., Chen J., Wu J., Zhao X., Mu X., Zhao D., Zeng Y. Individual tree-based forest species diversity estimation by classification and clustering methods using UAV data. Frontiers in Ecology and Evolution, 2023, 11. DOI: 10.3389/fevo.2023.1139458.
11. Li Z., Yang R., Cai W., Xue Y., Hu Y., Li L. LLAM-MDC Net for Detecting Remote Sensing Images of Dead Tree Clusters. Remote Sensing, 2022, 14, p. 3684. DOI: 10.3390/rs14153684.
12. Meng J., Li S., Wang W., Liu Q., Xie S., Ma W. Mapping forest health using spectral and textural information extracted from spot-5 satellite images. Remote Sensing, 2016, 8(9), p. 719.
13. Mielczarek D., Sikorski P., Archiciński P., Ciężkowski W., Zaniewska E., Chormanski J. The Use of an Airborne Laser Scanner for Rapid Identification of Invasive Tree Species Acer negundo in Riparian Forests. Remote Sensing, 2022, 15, p. 212. DOI: 10.3390/rs15010212.
14. Moreno-Fernandez D., Camarero J., Garcia M., Lines E., Sanchez-Davila J., Tijerin-Trivino J., Valeriano C., Viana-Soto A., Zavala M., Ruiz-Benito P. The Interplay of the Tree and Stand-Level Processes Mediate Drought-Induced Forest Dieback: Evidence from Complementary Remote Sensing and Tree-Ring Approaches. Ecosystems, 2022, 25, pp. 1–16. DOI: 10.1007/s10021-022-00793-2.
15. Qiao Y., Zheng G., Du Z., Ma X., Li J., & Moskal L. Tree-Species Classification and Individual-Tree-Biomass Model Construction Based on Hyperspectral and LiDAR Data. Remote Sensing, 2023, 15, p. 1341. DOI: 10.3390/rs15051341.
16. Poblete-Echeverria C., Duncan S.J., McLeod A. Detection of the spectral signature of Phytophthora root rot (PRR) symptoms using hyperspectral imaging. Acta Hortic, 2023, 1360, pp. 77–84. DOI: 10.17660/ActaHortic.2023.1360.10
17. Wan H., Tan, Y., Jing L., Li H., Qiu F., Wu W. Tree Species Classification of Forest Stands Using Multisource Remote Sensing Data. Remote Sensing, 2021, 13, p. 144. DOI: 10.3390/rs13010144.
18. Zhang S., Qin J., Tang X., Wang Y., Huang J., Song Q., Min J. Spectral Characteristics and Evaluation Model of Pinus massoniana Suffering from Bursaphelenchus Xylophilus Disease. Spectroscopy and Spectral Analysis, 2019, 39, pp. 865–872. DOI: 10.3964/j.issn.1000-0593(2019)03-0865-08.
19. Zhang Z., Liu X., Zhu L., Li J., Zhang Y. Remote Sensing Extraction Method of Illicium verum Based on Functional Characteristics of Vegetation Canopy. Remote Sensing, 2022, 14, p. 6248. DOI: 10.3390/rs14246248.
20. Zhang Z., Jiang D., Chang Q., Zheng Z., Fu X., Li K., Mo H. Estimation of Anthocyanins in Leaves of Trees with Apple Mosaic Disease Based on Hyperspectral Data. Remote Sensing, 2023, 15(7), p. 1732. DOI: 10.3390/rs15071732.
21. Zhu J., Xu Q., Yao J., Zhang X., Xu C. The Changes of Leaf Reflectance Spectrum and Leaf Functional Traits of Osmanthus fragrans Are Related to the Parasitism of Cuscuta japonica. Applied Sciences, 2021, 11(4), p. 1937. DOI: 10.3390/app11041937
Review
For citations:
Kabanov A.N., Bekbaeva A.M., Kabanova S.A., Kochegarov I.S., Danchenko M.A., Skott S.A. Determination of the state of artificial plantings of the green belt of Astana using remote sensing data of the Earth. Izvestia Sankt-Peterburgskoj lesotehniceskoj akademii. 2024;1(247):137-153. (In Russ.) https://doi.org/10.21266/2079-4304.2024.247.137-153