Refined Classification of Mountainous Vegetation Based on Multi-Source and Multi-Temporal High-Resolution Images DOI Open Access
Dan Chen, Xianyun Fei, Jing Li

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 707 - 707

Published: April 21, 2025

Distinguishing vegetation types from satellite images has long been a goal of remote sensing, and the combination multi-source multi-temporal sensing for classification is currently hot topic in field. In species-rich mountainous environments, this study selected four different seasons (two aerial images, one WorldView-2 image, UAV image) proposed method integrating hierarchical extraction object-oriented approaches 11 types. This innovatively combines Random Forest algorithm with decision tree model, constructing strategy based on feature combinations to progressively address challenge distinguishing similar spectral characteristics. Compared traditional single-temporal methods, our approach significantly enhances accuracy through fusion comparative experimental validation, offering novel technical framework fine-grained under complex land cover conditions. To validate effectiveness features, we additionally performed classifications individual images. The results indicate that (1) classification, best performance was achieved autumn reaching an overall 72.36%, while spring had worst performance, only 58.79%; (2) features reached 89.10%, which improvement 16.74% compared (autumn). Notably, producer species such as Quercus acutissima Carr., Tea plantations, Camellia sinensis (L.) Kuntze, Pinus taeda L., Phyllostachys spectabilis C.D.Chu et C.S.Chao, thunbergii Parl., Castanea mollissima Blume all exceeded 90%, indicating relatively ideal outcome.

Language: Английский

Digital mapping of soil organic carbon in a plain area based on time-series features DOI Creative Commons
Kun Yan, Decai Wang,

Yongkang Feng

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 171, P. 113215 - 113215

Published: Feb. 1, 2025

Language: Английский

Citations

0
Forest Height and Volume Mapping in Northern Spain with Multi-Source Earth Observation Data: Method and Data Comparison DOI Open Access
Iyán Teijido-Murias, Oleg Antropov, Carlos A. López‐Sánchez

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 563 - 563

Published: March 24, 2025

Accurate forest monitoring is critical for achieving the objectives of European Green Deal. While national inventories provide consistent information on state forests, their temporal frequency inadequate fast-growing species with 15-year rotations when are conducted every 10 years. However, Earth observation (EO) satellite systems can be used to address this challenge. Remote sensing satellites enable continuous acquisition land cover data high (annually or shorter), at a spatial resolution 10-30 m per pixel. This study focused northern Spain, highly productive region. aimed improve models predicting variables in plantations Spain by integrating optical (Sentinel-2) and imaging radar (Sentinel-1, ALOS-2 PALSAR-2 TanDEM-X) datasets supported climatic terrain variables. Five popular machine learning algorithms were compared, namely kNN, LightGBM, Random Forest, MLR, XGBoost. The findings show an improvement R2 from 0.24 only Sentinel-2 MultiLinear Regression 0.49 XGboost multi-source EO data. It concluded that combination datasets, regardless model used, significantly enhances performance, TanDEM-X standing out remarkable ability valuable height volume, particularly complex such as Spain.

Language: Английский

Citations

0
Refined Classification of Mountainous Vegetation Based on Multi-Source and Multi-Temporal High-Resolution Images DOI Open Access
Dan Chen, Xianyun Fei, Jing Li

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 707 - 707

Published: April 21, 2025

Distinguishing vegetation types from satellite images has long been a goal of remote sensing, and the combination multi-source multi-temporal sensing for classification is currently hot topic in field. In species-rich mountainous environments, this study selected four different seasons (two aerial images, one WorldView-2 image, UAV image) proposed method integrating hierarchical extraction object-oriented approaches 11 types. This innovatively combines Random Forest algorithm with decision tree model, constructing strategy based on feature combinations to progressively address challenge distinguishing similar spectral characteristics. Compared traditional single-temporal methods, our approach significantly enhances accuracy through fusion comparative experimental validation, offering novel technical framework fine-grained under complex land cover conditions. To validate effectiveness features, we additionally performed classifications individual images. The results indicate that (1) classification, best performance was achieved autumn reaching an overall 72.36%, while spring had worst performance, only 58.79%; (2) features reached 89.10%, which improvement 16.74% compared (autumn). Notably, producer species such as Quercus acutissima Carr., Tea plantations, Camellia sinensis (L.) Kuntze, Pinus taeda L., Phyllostachys spectabilis C.D.Chu et C.S.Chao, thunbergii Parl., Castanea mollissima Blume all exceeded 90%, indicating relatively ideal outcome.

Language: Английский

Citations

0