logo
calendar24 Iyul 2025
view33
Main language:Uzbek

OROLBO‘YI HUDUDIDAGI YER QOPLAMI VA YASHIL BIOMASSA DINAMIKASINING O‘ZGARISHINI TAHLIL QILISH

Field of Science:
pdf

6882657deff21.pdf

PDF

ARTICLE ANNOTATION

quote
This scientific study aimed to assess the dynamics of land cover and green biomass in the Aral Sea region observed between 2000 and 2024 using remote sensing and geoinformation technologies. For this purpose, a total of 21 cloud-free scenes (approximately 35 GB in size) from MODIS, Landsat, and Sentinel-2 sensors were processed using Sen2Cor atmospheric corrections and Fmask masking in Google Earth Engine. NDVI and LAI layers were developed based on seasonal median composites. The equation Biomass = 7.63 × e^(2.85 × NDVI) was determined between NDVI and field-measured biomass data, with R² = 0.79 and RMSE = 3.4 t/ha. The final result was a 10 m (UTM 42N) resolution overlay of NDVI, LAI and biomass layers over different land cover classes, clearly expressing the rate of degradation in the area, the growth and decline trends of biomass in reclaimed areas and irrigated farming zones. The research results serve as an important analytical basis for planning water- saving technologies, environmental monitoring and developing regional spatial planning strategies.

AUTHORS

B.Bektashev

Atrof-muhit va tabiatni muhofaza qilish texnologiyalari ilmiy-tadqiqot instituti

N.Samatov

Atrof-muhit va tabiatni muhofaza qilish texnologiyalari ilmiy-tadqiqot instituti

J.To'layev

Atrof-muhit va tabiatni muhofaza qilish texnologiyalari ilmiy-tadqiqot instituti

G.Keldiyorova

Samarqand davlat universiteti

Tags

# Аральское море# Aral Sea# salinity# reclamation# рекультивация# геоинформационные системы# landsat# ландсат# соленость# дистанционное зондирование# remote sensing# sho‘rlanish# geographic information systems# Sentinel-2# geoaxborot tizimlari# NDVI# MODIS# random forest# случайный лес# Orolbo‘yi# masofadan zondlash# rekultivatsiya# yer qoplami# yashil biomassa# LAI# Google Earth Engine# biomassa–NDVI bog‘lanishi# ekologik degradatsiya# почвопокровные# зеленая биомасса# ЛАИ# Google Планета Земля# МОДИС# Сентинел-2# взаимосвязь биомассы и NDVI# ухудшение состояния окружающей с# land cover# green biomass# biomass–NDVI linkage# ecological degradation

OTHER ARTICLES IN THIS JOURNAL

Rate Article

0
0 ratings
5
4
3
2
1

Article Identifiers

References

1. O‘zbekiston Respublikasi Prezidentining 2022-yil 28-yanvardagi «O‘zbekiston Respublikasini Taraqqiyot strategiyasi to‘g‘risida»gi PF-60-son Farmoni

2. O‘zbekiston Respublikasi Prezidentining 2020-yil 12-fevraldagi «Orolbo‘yi XIM faoliyati samaradorligini oshirishga doir qo‘shimcha chora-tadbirlar to‘g‘risida»gi PQ-4597-son qarori.

3. O‘zbekiston Respublikasi Prezidentining 2023-yil 23-noyabrdagi «“Yashil makon” loyihasini izchil amalga oshirish orqali ekologik barqarorlikni ta’minlash chora-tadbirlari to‘g‘risida»gi PF-199-son Farmoni

4. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.

5. Gorelick, N., Hancher, M., Dixon, M. va boshq. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27

6. Huete, A. R., Didan, K., Miura, T. va boshq. (2002). MODIS vegetation indices. Remote Sensing of Environment, 83(1-2), 195-213

7. Rouse, J. W., Haas, R. H., Schell, J. A. & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA SP-351, 309-317

8. Chander, G., Markham, B. & Helder, D. (2009). Summary of current radiometric calibration coefficients for Landsat 5 TM and Landsat 7 ETM+ sensors. Remote Sensing of Environment, 113(5), 893-903.

9. Zhu, Z., Woodcock, C. E., & Olofsson, P. (2012). Improved cloud detection for Landsat imagery. Remote Sensing of Environment, 118, 83-94.

10. Foody, G. M. & Mathur, A. (2004). Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment, 93(1-2), 107-117.

11. Myneni, R. B., Ross, J. & Asrar, G. (2015). Leaf Area Index (LAI) and NDVI relationship. Advances in Photosynthesis and Respiration, 30, 75-100

12. Sen2Cor Team. (2023). Sen2Cor v2.11 User Guide. European Space Agency.

13. Zhu, Z. & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152-171

14. Foga, S., Scaramuzza, P., Guo, S. va boshq. (2017). Cloud, cloud shadow, and snow detection in Landsat imagery. Remote Sensing of Environment, 202, 218-226