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Ushbu ilmiy tadqiqot Orolbo‘yi hududining 2000–2024 yillar oralig‘ida kuzatilgan yer qoplami hamda yashil
biomassa dinamikasini masofadan zondlash va geoaxborot texnologiyalari yordamida baholashga qaratildi. Buning uchun
MODIS, Landsat va Sentinel-2 sensorlariga tegishli jami 21 ta bulutsiz sahna (taxminiy hajmi 35 GB) Google Earth Engine
muhitida Sen2Cor atmosferaviy tuzatishlari va Fmask niqoblash jarayonlaridan o‘tkazildi. Mavsumiy median kompozitlar
asosida NDVI va LAI qatlamlari ishlab chiqildi. NDVI va dalada o‘lchangan biomassa ma’lumotlari o‘rtasida Biomassa =
7,63 × e^(2,85 × NDVI) tenglamasi aniqlanib, R² = 0,79 va RMSE = 3,4 t/ga ko‘rsatkichlari qayd etildi. Yakuniy natijada 10
m (UTM 42N) rezolyutsiyadagi NDVI, LAI va biomassa qatlamlari turli yer qoplami sinflari ustiga joylashtirilib, hududdagi
degradatsiya sur’ati, rekultivatsiya qilingan maydonlar va sug‘orma dehqonchilik zonalarida biomassaning o‘sish-kamayish
tendensiyalari aniq ifodalandi. Tadqiqot natijalari suvtejovchi texnologiyalarni rejalashtirish, ekologik monitoring va mintaqaviy
hududiy rejalashtirish strategiyalarini ishlab chiqishda muhim analitik asos bo‘lib xizmat qiladi.

  • Web Address
  • DOI10.63241/2025352akhv
  • Date of creation in the UzSCI system 24-07-2025
  • Read count 12
  • Date of publication 21-07-2025
  • Main LanguageO'zbek
  • Pages150-155
Ўзбек

Ushbu ilmiy tadqiqot Orolbo‘yi hududining 2000–2024 yillar oralig‘ida kuzatilgan yer qoplami hamda yashil
biomassa dinamikasini masofadan zondlash va geoaxborot texnologiyalari yordamida baholashga qaratildi. Buning uchun
MODIS, Landsat va Sentinel-2 sensorlariga tegishli jami 21 ta bulutsiz sahna (taxminiy hajmi 35 GB) Google Earth Engine
muhitida Sen2Cor atmosferaviy tuzatishlari va Fmask niqoblash jarayonlaridan o‘tkazildi. Mavsumiy median kompozitlar
asosida NDVI va LAI qatlamlari ishlab chiqildi. NDVI va dalada o‘lchangan biomassa ma’lumotlari o‘rtasida Biomassa =
7,63 × e^(2,85 × NDVI) tenglamasi aniqlanib, R² = 0,79 va RMSE = 3,4 t/ga ko‘rsatkichlari qayd etildi. Yakuniy natijada 10
m (UTM 42N) rezolyutsiyadagi NDVI, LAI va biomassa qatlamlari turli yer qoplami sinflari ustiga joylashtirilib, hududdagi
degradatsiya sur’ati, rekultivatsiya qilingan maydonlar va sug‘orma dehqonchilik zonalarida biomassaning o‘sish-kamayish
tendensiyalari aniq ifodalandi. Tadqiqot natijalari suvtejovchi texnologiyalarni rejalashtirish, ekologik monitoring va mintaqaviy
hududiy rejalashtirish strategiyalarini ishlab chiqishda muhim analitik asos bo‘lib xizmat qiladi.

Русский

Данное научное исследование было посвящено оценке динамики почвенно-растительного покрова и
зеленой биомассы в регионе Аральского моря в период с 2000 по 2024 год с использованием технологий дистанционного
зондирования и геоинформации. Для этой цели в общей сложности 21 безоблачная сцена (объемом около 35 ГБ) с
датчиков MODIS, Landsat и Sentinel-2 были подвергнуты атмосферным коррекциям Sen2Cor и процессам маскирования
Fmask в среде Google Earth Engine. Слои NDVI и LAI были разработаны на основе сезонных медианных композитов.
Уравнение Биомасса = 7,63 × e^(2,85 × NDVI) было определено между NDVI и данными полевых измерений биомассы,
при этом R² = 0,79 и RMSE = 3,4 т/га. Конечный результат состоял в том, что слои NDVI, LAI и биомассы с
разрешением 10 м (UTM 42N) были наложены на различные классы почвенно-растительного покрова, что наглядно
показало скорость деградации в этом районе, тенденции роста и снижения биомассы на мелиорированных
территориях и в зонах орошаемого земледелия. Результаты исследований послужат важной аналитической основой для
планирования водосберегающих технологий, мониторинга окружающей среды и разработки стратегий регионального
пространственного планирования.

English

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.

№ Name of reference
1 1. O‘zbekiston Respublikasi Prezidentining 2022-yil 28-yanvardagi «O‘zbekiston Respublikasini Taraqqiyot strategiyasi to‘g‘risida»gi PF-60-son Farmoni
2 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 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 4. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.
5 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 6. Huete, A. R., Didan, K., Miura, T. va boshq. (2002). MODIS vegetation indices. Remote Sensing of Environment, 83(1-2), 195-213
7 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 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 9. Zhu, Z., Woodcock, C. E., & Olofsson, P. (2012). Improved cloud detection for Landsat imagery. Remote Sensing of Environment, 118, 83-94.
10 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 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 12. Sen2Cor Team. (2023). Sen2Cor v2.11 User Guide. European Space Agency.
13 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 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
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