High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning DOI Creative Commons

Yicheng Zhou,

Lingbo Yang, Lin Yuan

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(12), P. 2986 - 2986

Published: Dec. 15, 2024

Accurate mapping of tea plantations is crucial for agricultural management and economic planning, yet it poses a significant challenge due to the complex variable nature cultivation landscapes. This study presents high-precision approach in Anji County, Zhejiang Province, China, utilizing multi-source remote sensing data advanced deep learning models. We employed combination Sentinel-2 optical imagery, Sentinel-1 synthetic aperture radar digital elevation models capture rich spatial, spectral, temporal characteristics plantations. Three models, namely U-Net, SE-UNet, Swin-UNet, were constructed trained semantic segmentation Cross-validation point-based accuracy assessment methods used evaluate performance The results demonstrated that Swin-UNet model, transformer-based capturing long-range dependencies global context superior feature extraction, outperformed others, achieving an overall 0.993 F1-score 0.977 when using multi-temporal data. integration with slightly improved classification accuracy, particularly areas affected by cloud cover, highlighting complementary imagery all-weather monitoring. also analyzed influence terrain factors, such as elevation, slope, aspect, on plantation mapping. It was found at higher altitudes or north-facing slopes exhibited improves increasing likely simpler land cover types tea’s preference shade. findings this research not only provide valuable insights into precision but contribute broader application

Language: Английский

Deep neural network and transfer learning for annual wetland vegetation mapping using sentinel-2 time-series data in the heterogeneous lake floodplain environment DOI
Jinquan Ai,

Xinxing Han,

Lijuan Chen

et al.

International Journal of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 24

Published: Jan. 15, 2025

The lake-floodplain wetlands are characterized by high biodiversity, difficult access, and significant environmental changes. Traditional remote sensing mapping methods struggle to generate consistent time-series data on wetland vegetation communities. Current research has endeavoured address this issue through the application of deep learning methodologies. However, a limitation these models is their reliance substantial volume training samples, which contradicts difficulty cost obtaining samples from wetlands. Whether it possible construct transferable model under small sample conditions apply an urgent that needs be addressed. To solve problem, study first constructed neural network (DNN) designed specifically for complex limited size. Subsequently, using 2021 as reference year, novel histogram threshold method was proposed identify unchanged target transfer years 2019, 2020, 2022, 2023. Finally, annual performed in Poyang Lake DNN (STL). results showed high-quality can generated STL, with all overall accuracies exceeding 80%. method, combines SAD NDVI indicators key phenological period, effectively problem determining heterogeneous lake Furthermore, performance STL based significantly superior those support vector machine random forest algorithms communities samples. This demonstrates effective will highly beneficial long-term monitoring wetlands, particularly where availability limited.

Language: Английский

Citations

1
YOLOv8-LDH: A lightweight model for detection of conveyor belt damage based on multispectral imaging DOI
Yue Chen, Mengran Zhou, Feng Hu

et al.

Measurement, Journal Year: 2025, Volume and Issue: 245, P. 116675 - 116675

Published: Jan. 5, 2025

Language: Английский

Citations

0
Coastal environmental changes in Ninh Thuan Province, South-Central Vietnam DOI Creative Commons
Bijeesh Kozhikkodan Veettil, Siham Acharki, Vikram Puri

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0313382 - e0313382

Published: Feb. 5, 2025

Vietnam’s coastal regions are highly vulnerable to natural hazards and human-induced changes, posing significant challenges their ecological socio-economic systems. The country’s mangrove vegetation spans its entire coastline has been depleted for decades in many regions. Notably, proactive stance on climate change mitigation received recognition during the 26th Conference of Parties (COP26) United Nations Framework Convention Climate Change. This study investigated five critical environmental features (shoreline dynamics, drought conditions, soil salinity trends, deforestation, reforestation, as well spatiotemporal variations aquaculture salt farming areas) using satellite data geospatial analysis. Findings revealed a 58% decline areas between 1989 2023, with sharp 2001, followed by gradual recovery. Furthermore, along Ninh Thuan coast indicated continuous increase, except strong La Niña period 2001. Additionally, marshes have expanded significantly, changing land use patterns. These findings highlight urgent need integrated zone management mitigate degradation enhance ecosystem resilience. Future studies should investigate implications these changes evaluate restoration strategies sustainable development.

Language: Английский

Citations

0
Estimation of mangrove heights and aboveground biomass using UAV-LiDAR, Sentinel-1 and ZY-3 stereo images DOI Creative Commons
Bolin Fu, Yingying Wei,

Linhang Jiang

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103160 - 103160

Published: April 1, 2025

Language: Английский

Citations

0
A novel spectral index for estimating leaf water content using infrared atmospheric window edge bands DOI
Zhaoyang Han, Qingjiu Tian, Jia Tian

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 233, P. 110170 - 110170

Published: Feb. 26, 2025

Language: Английский

Citations

0
An Approach for Detecting Mangrove Areas and Mapping Species Using Multispectral Drone Imagery and Deep Learning DOI Creative Commons
Xingyu Chen, Xiuyu Zhang,

Changwei Zhuang

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(8), P. 2540 - 2540

Published: April 17, 2025

Mangrove ecosystems are important in tropical and subtropical coastal zones, contributing to marine biodiversity maintaining ecological balance. It is crucial develop more efficient, intelligent, accurate monitoring methods for mangroves understand better protect mangrove ecosystems. This study promotes a novel model, MangroveNet, integrating multi-scale spectral spatial information detecting area. In addition, we also present an improved AttCloudNet+, identify the distribution of species based on high-resolution multispectral drone images. These models incorporate attention mechanisms have been shown effectively address limitations traditional methods, which prone inaccuracy low efficiency identification. this study, compare results from MangroveNet with SegNet, UNet, DeepUNet, etc. The findings demonstrate that exhibits superior generalization learning capabilities extraction outcomes than other deep models. accuracy, F1_Score, mIoU, precision were 99.13%, 98.84%, 98.11%, 99.14%, respectively. terms identifying species, prediction AttCloudNet+ compared those obtained supervised unsupervised classifications various machine methods. include K-means clustering, ISODATA cluster analysis, Random Forest (RF), Support Vector Machines (SVM), others. comparison demonstrates identification using exhibit most optimal performance Kappa coefficient overall accuracy (OA) index, reaching 0.81 0.87, two confirm effectiveness developed their species. Overall, provide efficient solution dual mechanism acceptable real-time imagery.

Language: Английский

Citations

0
Exploring new mangrove horizons: A scalable remote sensing approach with Planet-NICFI and Sentinel-2 images DOI Creative Commons
Adam Irwansyah Fauzi, Markus Immitzer, Clement Atzberger

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103152 - 103152

Published: April 1, 2025

Language: Английский

Citations

0
Scalable Mangrove Monitoring with Limited Field Data: Integrating MREDT and DACN-M DOI Open Access

Yuchen Zhao,

Shulei Wu, Xianyao Zhang

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(10), P. 1696 - 1696

Published: Sept. 25, 2024

Mangroves play a crucial ecological and economic role but face significant threats, particularly on Hainan Island, which has the highest mangrove species diversity in China. Remote sensing AI techniques offer potential solutions for monitoring these ecosystems, challenges persist due to difficult access field sampling. To address issues, we propose novel model combining Mangrove Rough Extraction Decision Tree (MREDT) Dynamic Attention Convolutional Network (DACN-M). Initially, used drones surveys conduct multiple observations Dongzhaigang Nature Reserve, identifying boundaries of mangroves. Based features, constructed MREDT mitigate failure caused by light instability, simplifying transfer other study areas without requiring annotated samples or extensive surveys. Next, developed DACN-M model, refines rough extraction features from incorporates contextual information more accurate detection. Experimental results demonstrate that our proposed method effectively differentiates mangroves vegetation, achieving F1 Scores above 75% IoU values greater than 60% across six areas. In conclusion, not only accurately identifies monitors distribution also offers advantage being transferable need This provides robust scalable solution protecting preserving critical ecosystems supports effective conservation efforts various regions.

Language: Английский

Citations

1
Long-term changes of mangrove distribution and its response to anthropogenic impacts in the Vietnamese Southern Coastal Region DOI Creative Commons
Thuong V. Tran, Ruth Reef, Xuan Zhu

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 370, P. 122658 - 122658

Published: Sept. 30, 2024

Language: Английский

Citations

1
A multi-source approach to mapping habitat diversity: Comparison and combination of single-date hyperspectral and multi-date multispectral satellite imagery in a Mediterranean Natural Reserve DOI Creative Commons

Chiara Zabeo,

Gaia Vaglio Laurin, Birhane Gebrehiwot Tesfamariam

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102867 - 102867

Published: Oct. 1, 2024

Language: Английский

Citations

1