[HTML][HTML] Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet Plateau

P Lou, T Wu, S Yang, X Wu, J Chen, X Zhu, J Chen… - Ecological …, 2023 - Elsevier
Abstract Vegetation dynamics in Qinghai-Tibet Plateau (QTP) have been debated in recent
decades. Most studies suggest that wetter and warmer climatic conditions would release low …

[HTML][HTML] Abrupt thaw and its effects on permafrost carbon emissions in the Tibetan Plateau: A remote sensing and modeling perspective

Y Yi, T Wu, M Wu, H Jiang, Y Yang, BM Rogers - Earth-Science Reviews, 2024 - Elsevier
Abstract The Tibetan Plateau (TP) has the largest permafrost area in the low-and mid-
latitudes. With warmer ground temperatures and ice-rich terrain, the TP permafrost is …

Enhancing precision in coastal dunes vegetation map**: ultra-high resolution hierarchical classification at the individual plant level

E Belcore, M Latella, M Piras… - International Journal of …, 2024 - Taylor & Francis
The classification of ultra-high-resolution (UHR) imagery, characterized by spatial
resolutions exceeding 10 cm, presents opportunities and challenges distinct from lower …

User-Relevant Land Cover Products for Informed Decision-Making in the Complex Terrain of the Peruvian Andes

V Mantas, C Caro - Remote Sensing, 2023 - mdpi.com
Land cover in mountainous regions is shaped by a complex web of stressors arising from
natural and anthropogenic processes. The co-design process implemented with regional …

Effects of thaw slump on soil bacterial communities on the Qinghai-Tibet Plateau

G Liu, P Ma, Z Cheng, Y Wang, Y Li, X Wu - Catena, 2023 - Elsevier
The rapid permafrost degradation caused by climate warming can lead to thermokarst
development, which in turn greatly alter soil parameters and impact the soil bacterial …

Predicting seasonal deformation using InSAR and machine learning in the permafrost regions of the Yangtze River source region

J Chen, X Lin, T Wu, J Hao, X Wu, D Zou… - Water Resources …, 2024 - Wiley Online Library
Quantifying seasonal deformation is essential for accurately determining the thickness of the
active layer and the distribution of water content within it, providing insights into the freeze …

[HTML][HTML] Enhanced detection of freeze‒thaw induced landslides in Zhidoi county (Tibetan Plateau, China) with Google Earth Engine and image fusion

JH Yang, YC Gao, L Jia, WJ Wang, QB Wu… - Advances in Climate …, 2024 - Elsevier
Freeze‒thaw induced landslides (FTILs) in grasslands on the Tibetan Plateau are a
geological disaster leading to soil erosion. These landslides reduce biodiversity and …

A new framework for GEOBIA: accurate individual plant extraction and detection using high-resolution RGB data from UAVs

K Yang, Z Ye, H Liu, X Su, C Yu… - International Journal of …, 2023 - Taylor & Francis
Citrus (Citrus reticulata), which is an important economic crop worldwide, is often managed
in a labor-intensive and inefficient manner in develo** countries, thereby necessitating …

[HTML][HTML] Time-series simulation of alpine grassland cover using transferable stacking deep learning and multisource remote sensing data in the Google Earth Engine

X Lin, J Chen, T Wu, S Yi, J Chen, X Han - International Journal of Applied …, 2024 - Elsevier
The growth of vegetation on the Qinghai Tibet Plateau (QTP) is experiencing significant
changes due to climate change. There is still a lack of high-precision simulation methods for …

[HTML][HTML] A novel framework for multiple thermokarst hazards risk assessment and controlling environmental factors analysis on the Qinghai-Tibet Plateau

P Lou, T Wu, G Yin, J Chen, X Zhu, X Wu, R Li, S Yang - Catena, 2024 - Elsevier
Due to the influence of climate warming, the degradation of permafrost on the Qinghai-Tibet
Plateau (QTP) has become evident. The formation of thermokarst hazards induced by the …