![]() Guo Q, Fu B, Shi P, Cudahy T, Zhang J, Xu H (2017) Satellite monitoring the spatial-temporal dynamics of desertification in response to climate change and human activities across the Ordos Plateau. Įasdale MH, Fariña C, Hara S, León NP, Umaña F, Tittonell P, Bruzzone O (2019) Trend-cycles of vegetation dynamics as a tool for land degradation assessment and monitoring. ĭuan H, Wang T, Xue X, Yan C (2019) Dynamic monitoring of aeolian desertification based on multiple indicators in Horqin Sandy Land, China. ĭuan H, Wang T, Xue X, Liu S, Guo J (2014) Dynamics of aeolian desertification and its driving forces in the Horqin Sandy Land Northern China. ĭ’Odorico P, Bhattachan A, Davis KF, Ravi S, Runyan CW (2013) Global desertification: drivers and feedbacks. ĭing C, Huang W, Li Y, Zhao S, Huang F (2020) Nonlinear changes in dryland vegetation greenness over east Inner Mongolia, China, in recent years from satellite time series. ĭeng H, Cui H, Zhao Q, Pan R, Zhou J, Lan A (2019) Constrained François’ Langur (Trachypithecus francoisi) in Yezhong Nature Reserve, Guizhou. Ĭuo L, Zhang Y, Wu Y, Hou M (2020) Desertification affecting the Tibetan Plateau between 1971–2015: viewed from a climate perspective. ( ) (in Chinese)Ĭui BL, Li XY (2015) Runoff processes in the Qinghai Lake Basin, Northeast Qinghai-Tibet Plateau, China: insights from stable isotope and hydrochemistry. Ĭhen J, Zhang WY, Jiang ZX, Zhou BJ, Liu C, Xu WM (2022) Characteristics of the wind field in the region around Qinghai Lake and its influence on the sedimentary system. Ĭhen A, Yang X, Xu B et al (2021) Monitoring the spatiotemporal dynamics of aeolian desertification using Google Earth Engine. Ĭanora F, D’Angella A, Aiello A (2015) Quantitative assessment of the sensitivity to desertification in the Bradano River basin (Basilicata, southern Italy). īezerra FGS, Aguiar APD, Alvalá RCDS et al (2020) Analysis of areas undergoing desertification, using EVI2 multi-temporal data based on MODIS imagery as indicator. īecerril-Piña R, Díaz-Delgado C, Mastachi-Loza CA, González-Sosa E (2016) Integration of remote sensing techniques for monitoring desertification in Mexico. The findings indicate that desertification in the region around Qinghai Lake has been effectively controlled, and the overall desertification trend is improving.Īmani M, Ghorbanian A, Ahmadi SA et al (2020) Google earth engine cloud computing platform for remote sensing big data applications: a comprehensive review. Natural factors, such as climate change from warm-dry to warm-wet and decreased wind speed, and human factors improved the desertification situation. The desertification land area fluctuated downward in the study area from 2000 to 2020, and the overall desertification status improved. GEE offers significant advantages, such as massive data processing and long-term dynamic monitoring. Our results showed that the desertification difference index based on the Albedo-NDVI feature space could reflect the degree of desertification in the region around Qinghai Lake. Using cloud computing via Google Earth Engine (GEE), we collected Landsat 5 TM, Landsat 8 OLI/TIRS, and MODIS Albedo images from 2000 to 2020 in the region around Qinghai Lake, acquired land surface albedo (Albedo), and normalized vegetation index (NDVI) to build a remote sensing monitoring model of desertification. The region around Qinghai Lake, in the northeastern part of the Qinghai–Tibet Plateau in China, is a special ecological function area and a climate change sensitive area, making its environmental conditions a great concern. Monitoring the spatiotemporal dynamics of desertification is crucial for its control. Desertification is one of the most serious ecological environmental problems in the world. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |