Unmanned Aerial Vehicle Remote Sensing for Monitoring Fractional Vegetation Cover in Creeping Plants: A Case Study of Thymus mongolicus Ronniger

Hao Zheng, Wentao Mi, Kaiyan Cao, Weibo Ren (), Yuan Chi, Feng Yuan and Yaling Liu
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Hao Zheng: Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot 010010, China
Wentao Mi: Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot 010010, China
Kaiyan Cao: Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot 010010, China
Weibo Ren: Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot 010010, China
Yuan Chi: Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot 010010, China
Feng Yuan: Key Laboratory of Forage Breeding and Seed Production of Inner Mongolia, National Grass Seed Technology Innovation Center (Preparation), Hohhot 010010, China
Yaling Liu: Key Laboratory of Forage Breeding and Seed Production of Inner Mongolia, National Grass Seed Technology Innovation Center (Preparation), Hohhot 010010, China

Agriculture, 2025, vol. 15, issue 5, 1-19

Abstract: Fractional vegetation cover (FVC) is a key indicator of plant growth. Unmanned aerial vehicle (UAV) imagery has gained prominence for FVC monitoring due to its high resolution. However, most studies have focused on single phenological stages or specific crop types, with limited research on the continuous temporal monitoring of creeping plants. This study addresses this gap by focusing on Thymus mongolicus Ronniger ( T. mongolicus ). UAV-acquired visible light and multispectral images were collected across key phenological stages: green-up, budding, early flowering, peak flowering, and fruiting. FVC estimation models were developed using four algorithms: multiple linear regression (MLR), random forest (RF), support vector regression (SVR), and artificial neural network (ANN). The SVR model achieved optimal performance during the green-up (R 2 = 0.87) and early flowering stages (R 2 = 0.91), while the ANN model excelled during budding (R 2 = 0.93), peak flowering (R 2 = 0.95), and fruiting (R 2 = 0.77). The predictions of the best-performing models were consistent with ground truth FVC values, thereby effectively capturing dynamic changes in FVC. FVC growth rates exhibited distinct variations across phenological stages, indicating high consistency between predicted and actual growth trends. This study highlights the feasibility of UAV-based FVC monitoring for T. mongolicus and indicates its potential for tracking creeping plants.

Keywords: vegetation monitoring; phenological stages; growth dynamics; vegetation index; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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