Classification of plants based on time-series SAR coherence and intensity data in Yancheng coastal wetland DOI

Shuaichen Bian,

Chou Xie, Bangsen Tian

et al.

International Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: 46(2), P. 859 - 881

Published: Nov. 26, 2024

Investigating coastal wetland plant communities is of great significance for monitoring due to the important functions wetlands, such as maintaining biodiversity and mitigating global climate change. Current studies on plants mostly rely optical data, with few utilizing synthetic aperture radar (SAR) data. Moreover, these often analysed single temporal SAR which limited exploration valuable information present in time-series Therefore, this paper, we proposed a technique mapping types based coherence intensity data fully utilize from We utilized Sentinel-1 Single Look Complex (SLC) images covering Yancheng entire year 2021 investigate effectiveness using dual-polarization interferometric intensity-derived features classification. Plant classification was conducted support vector machine (SVM) random forest (RF) methods. Our results demonstrated that integrating resulted best accuracy, an overall accuracy (OA) 89.79% Kappa coefficient 0.858. This highlights combining cover wetlands.

Language: Английский

Habitat quality evaluation and pattern simulation of coastal salt marsh wetlands DOI
Yuting Huang,

Guanghui Zheng,

Xianglan Li

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 945, P. 174003 - 174003

Published: June 14, 2024

Language: Английский

Citations

11
Cross-scale observation of riparian vegetation: Testing the potential of satellite-UAV-Field integrated observations for large-scale herbaceous species DOI Creative Commons

Weiwei Jiang,

Chenyu Li, Henglin Xiao

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103016 - 103016

Published: Jan. 1, 2025

Language: Английский

Citations

0
Seasonal Land Use and Land Cover Mapping in South American Agricultural Watersheds Using Multisource Remote Sensing: The Case of Cuenca Laguna Merín, Uruguay DOI Creative Commons
Giancarlo Alciaturi, Shimon Wdowinski, María del Pilar García Rodríguez

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(1), P. 228 - 228

Published: Jan. 3, 2025

Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers extract insights from Multisource Remote Sensing. This study aims use these technologies for mapping summer winter Land Use/Land Cover features Cuenca de la Laguna Merín, Uruguay, while comparing performance Random Forests, Support Vector Machines, Gradient-Boosting Tree classifiers. The materials include Sentinel-2, Sentinel-1 Shuttle Radar Topography Mission imagery, Google Engine, training validation datasets quoted methods involve creating a multisource database, conducting feature importance analysis, developing models, supervised classification performing accuracy assessments. Results indicate low significance microwave inputs relative optical features. Short-wave infrared bands transformations such as Normalised Vegetation Index, Surface Water Index Enhanced demonstrate highest importance. Accuracy assessments that various classes is optimal, particularly rice paddies, which play vital role country’s economy highlight significant environmental concerns. However, challenges persist reducing confusion between classes, regarding natural vegetation versus seasonally flooded vegetation, well post-agricultural fields/bare land herbaceous areas. Forests Trees exhibited superior compared Machines. Future research should explore approaches Deep Learning pixel-based object-based integration address identified challenges. These initiatives consider data combinations, including additional indices texture metrics derived Grey-Level Co-Occurrence Matrix.

Language: Английский

Citations

0
Urban green space vegetation height modeling and intelligent classification based on UAV multi-spectral and oblique high-resolution images DOI Creative Commons
Ronghua Li,

Zhican Bai,

Chao Ye

et al.

Urban forestry & urban greening, Journal Year: 2025, Volume and Issue: unknown, P. 128785 - 128785

Published: March 1, 2025

Language: Английский

Citations

0
Mature and immature oil palm classification from image Sentinel-2 using Google earth engine (GEE) DOI
Siti Aminah Anshah,

Nurul Ain Nabilah Sharuddin,

Siti Aekbal Salleh

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 465 - 487

Published: Jan. 1, 2025

Language: Английский

Citations

0
Application of Image Recognition Methods to Determine Land Use Classes DOI Creative Commons

Julius Jancevičius,

Diana Kalibatienė

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(9), P. 4765 - 4765

Published: April 25, 2025

The increasing availability of satellite data and advances in machine learning (ML) have significantly enhanced land use image classification for environmental monitoring. However, the primary challenge using imagery lies presence cloud cover, variations resolution, seasonal changes, which impact accuracy reliability. This paper aims to improve assessment cover changes by proposing a hybrid ML, interpolation, vegetation indices-based approach. proposed approach was implemented random forest (RF) classifier, combined with interpolation indices, classify Sentinel-2 Baltic States. experimental results demonstrate that achieves an rate above 90%, effectively demonstrating its capacity distinguish between various types. We believe this study will inspire researchers practitioners further work towards applying ML algorithms offer valuable insights future tasks involving noise digitalization research.

Language: Английский

Citations

0
Mapping herbaceous wetlands using combined phenological and hydrological features from time-series Sentinel-1/2 imagery DOI Creative Commons

Zhaolong Yang,

Xiaodong Na

International Journal of Digital Earth, Journal Year: 2025, Volume and Issue: 18(1)

Published: April 27, 2025

Language: Английский

Citations

0
Soil moisture content estimation of drip-irrigated citrus orchard based on UAV images and machine learning algorithm in Southwest China DOI Creative Commons

Quanshan Liu,

Zongjun Wu,

Ningbo Cui

et al.

Agricultural Water Management, Journal Year: 2024, Volume and Issue: 303, P. 109069 - 109069

Published: Sept. 21, 2024

Language: Английский

Citations

3
Mapping and Classification of the Liaohe Estuary Wetland Based on the Combination of Object-Oriented and Temporal Features DOI Creative Commons

Sien Guo,

Ziyi Feng, Peng Wang

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 60496 - 60512

Published: Jan. 1, 2024

For the protection, restoration, and sustainable management of wetland ecosystems, precision in extracting high-quality land cover information is crucial. This study focused on National Nature Reserve Liaohe Estuary Panjin City, Liaoning Province, China. To overcome challenge spectral similarity among covers occurrence "salt-and-pepper" effect where certain parcels get misclassified into multiple categories by conventional methods, an approach combining object-oriented techniques temporal features was employed for accurate classification. The analysis utilized multi-temporal Sentinel-2 multispectral images. Initially, images underwent segmentation using SNIC method to generate uniform polygons, effectively mitigating misclassification issues. Subsequently, texture, geometry, band reflectance, deviation were extracted each segmented object. A total 57 features, including vegetation moisture components, integrated construct characteristics. By applying Random Forest (RF) algorithm combination with Recursive Feature Elimination (ERT), 18 significant influencing extraction identified. These selected then train a model classifying area. findings revealed that feature classification achieved impressive overall accuracy 95.52% Kappa coefficient 0.95 region. various types reached 0.87 both user mapping accuracy. Compared alternative machine learning algorithms such as SegUnet++, SVM, RF, proposed demonstrated performance increase 16.35%, 14.06%, 6.14%, respectively. incorporation notably reduced misclassifications, resulting 6.14% 0.06 improvement compared lacking features. Particularly like canals, aquaculture, rivers, reservoirs, producer improved over 7.5% more than 9%, except rivers. effectiveness evident addressing effect, showcasing rise 2.81% 0.03% not utilizing techniques. In summary, method, integrating methods offers superior fine mapping.

Language: Английский

Citations

2
A study on the classification of coastal wetland vegetation based on the Suaeda salsa index and its phenological characteristics DOI Creative Commons

Weicheng Huang,

Xianyun Fei, Weiwei Yang

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 170, P. 113021 - 113021

Published: Dec. 30, 2024

Language: Английский

Citations

2