Vegetation Characterization and Classification With Multi-Source Remote Sensing Data

Topic Information

Dear Colleagues,

Geospatial technologies are developing more rapidly than ever before, causing Earth observation (EO) data to be widely available in unprecedented volume and detail. Extracting the useful information buried in all these data is a challenge that calls for innovative solutions. In parallel with this increase in data, there is now an increased demand for the accurate determination of a growing number of attributes of vegetation canopies using remote sensing for inventory and sustainable management; analysis of wild-life habitats and biodiversity; renewable energy production; precision agriculture and forestry; carbon stock estimation, etc. The accurate characterization of vegetation canopies, such as the determination of cover types and species and the retrieval of their biophysical parameters, remains challenging using traditional data and stand-alone approaches. Significant advancements in machine learning, deep learning, and artificial intelligence have wide implications for this field. Data/information fusion has lately been attracting much interest in the remote sensing community to generate maximal values from the available data sets. This topic collection, thus, aims to disseminate innovative research in vegetation characterization using multi-source remotely sensed data and accumulated domain knowledge. The papers may be related to, but not limited to, the following topics:

• Quantification and representation of uncertainty associated with information fusion.
• Integration of physical principles and domain knowledge to data-driven machine learning and deep learning methods.
• Development of novel information fusion algorithms for applications, such as: a. segmentation and classification of vegetation types; b. retrieval of physical/biophysical/biochemical parameters of vegetation canopies; c. change detection.
• Development of frameworks, methods, and tools for large-scale operational implementation in vegetation characterization using multi-source data.

Prof. Dr. Baoxin Hu
Prof. Dr. Linhai Jing
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Forests forests	2.4	4.4	2010	16.2 Days	CHF 2600	Submit
Geomatics geomatics	-	-	2021	22.1 Days	CHF 1000	Submit
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit

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Published Papers (4 papers)

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All Forests Geomatics Remote Sensing Sensors

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23 pages, 26510 KiB  

Open AccessArticle

Improving the Individual Tree Parameters Estimation of a Complex Mixed Conifer—Broadleaf Forest Using a Combination of Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle RGB and Multispectral Imagery

by Jeyavanan Karthigesu, Toshiaki Owari, Satoshi Tsuyuki and Takuya Hiroshima

Geomatics 2025, 5(1), 12; https://doi.org/10.3390/geomatics5010012 - 10 Mar 2025

Viewed by 691

Abstract

Individual tree parameters are essential for forestry decision-making, supporting economic valuation, harvesting, and silvicultural operations. While extensive research exists on uniform and simply structured forests, studies addressing complex, dense, and mixed forests with highly overlapping, clustered, and multiple tree crowns remain limited. This [...] Read more.

Individual tree parameters are essential for forestry decision-making, supporting economic valuation, harvesting, and silvicultural operations. While extensive research exists on uniform and simply structured forests, studies addressing complex, dense, and mixed forests with highly overlapping, clustered, and multiple tree crowns remain limited. This study bridges this gap by combining structural, textural, and spectral metrics derived from unmanned aerial vehicle (UAV) Red–Green–Blue (RGB) and multispectral (MS) imagery to estimate individual tree parameters using a random forest regression model in a complex mixed conifer–broadleaf forest. Data from 255 individual trees (115 conifers, 67 Japanese oak, and 73 other broadleaf species (OBL)) were analyzed. High-resolution UAV orthomosaic enabled effective tree crown delineation and canopy height models. Combining structural, textural, and spectral metrics improved the accuracy of tree height, diameter at breast height, stem volume, basal area, and carbon stock estimates. Conifers showed high accuracy (R² = 0.70–0.89) for all individual parameters, with a high estimate of tree height (R² = 0.89, RMSE = 0.85 m). The accuracy of oak (R² = 0.11–0.49) and OBL (R² = 0.38–0.57) was improved, with OBL species achieving relatively high accuracy for basal area (R² = 0.57, RMSE = 0.08 m² tree⁻¹) and volume (R² = 0.51, RMSE = 0.27 m³ tree⁻¹). These findings highlight the potential of UAV metrics in accurately estimating individual tree parameters in a complex mixed conifer–broadleaf forest. Full article

(This article belongs to the Topic Vegetation Characterization and Classification With Multi-Source Remote Sensing Data)

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28 pages, 5622 KiB  

Open AccessArticle

Advancing Forest Degradation and Regeneration Assessment Through Light Detection and Ranging and Hyperspectral Imaging Integration

by Catherine Torres de Almeida, Lênio Soares Galvão, Jean Pierre H. B. Ometto, Aline Daniele Jacon, Francisca Rocha de Souza Pereira, Luciane Yumie Sato, Celso Henrique Leite Silva-Junior, Pedro H. S. Brancalion and Luiz Eduardo Oliveira e Cruz de Aragão

Remote Sens. 2024, 16(21), 3935; https://doi.org/10.3390/rs16213935 - 22 Oct 2024

Cited by 1 | Viewed by 1438

Abstract

Integrating Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) enhances the assessment of tropical forest degradation and regeneration, which is crucial for conservation and climate mitigation strategies. This study optimized procedures using combined airborne LiDAR, HSI data, and machine learning algorithms across [...] Read more.

Integrating Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) enhances the assessment of tropical forest degradation and regeneration, which is crucial for conservation and climate mitigation strategies. This study optimized procedures using combined airborne LiDAR, HSI data, and machine learning algorithms across 12 sites in the Brazilian Amazon, covering various environmental and anthropogenic conditions. Four forest classes (undisturbed, degraded, and two stages of second-growth) were identified using Landsat time series (1984–2017) and auxiliary data. Metrics from 600 samples were analyzed with three classifiers: Random Forest, Stochastic Gradient Boosting, and Support Vector Machine. The combination of LiDAR and HSI data improved classification accuracy by up to 12% compared with single data sources. The most decisive metrics were LiDAR-based upper canopy cover and HSI-based absorption bands in the near-infrared and shortwave infrared. LiDAR produced significantly fewer errors for discriminating second-growth from old-growth forests, while HSI had better performance to discriminate degraded from undisturbed forests. HSI-only models performed similarly to LiDAR-only models (mean F1 of about 75% for both data sources). The results highlight the potential of integrating LiDAR and HSI data to improve our understanding of forest dynamics in the context of nature-based solutions to mitigate climate change impacts. Full article

(This article belongs to the Topic Vegetation Characterization and Classification With Multi-Source Remote Sensing Data)

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31 pages, 35626 KiB  

Open AccessArticle

Mountain Forest Type Classification Based on One-Dimensional Convolutional Neural Network

by Maoyang Bai, Peihao Peng, Shiqi Zhang, Xueman Wang, Xiao Wang, Juan Wang and Petri Pellikka

Forests 2023, 14(9), 1823; https://doi.org/10.3390/f14091823 - 6 Sep 2023

Cited by 3 | Viewed by 1988

Abstract

Convolutional neural networks (CNNs) have demonstrated their efficacy in remote sensing applications for mountain forest classification. However, two-dimensional convolutional neural networks (2D CNNs) require a significant manual involvement in the visual interpretation to obtain continuous polygon label data. To reduce the errors associated [...] Read more.

Convolutional neural networks (CNNs) have demonstrated their efficacy in remote sensing applications for mountain forest classification. However, two-dimensional convolutional neural networks (2D CNNs) require a significant manual involvement in the visual interpretation to obtain continuous polygon label data. To reduce the errors associated with manual visual interpretation and enhance classification efficiency, it is imperative to explore alternative approaches. In this research, we introduce a novel one-dimensional convolutional neural network (1D CNN) methodology that directly leverages field investigation data as labels for classifying mountain forest types based on multiple remote sensing data sources. The hyperparameters were optimised using an orthogonal table, and the model’s performance was evaluated on Mount Emei of Sichuan Province. Comparative assessments with traditional classification methods, namely, a random forest (RF) and a support vector machine (SVM), revealed superior results obtained by the proposed 1D CNN. Forest type classification using the 1D CNN achieved an impressive overall accuracy (OA) of 97.41% and a kappa coefficient (Kappa) of 0.9673, outperforming the U-Net (OA: 94.45%, Kappa: 0.9239), RF (OA: 88.99%, Kappa: 0.8488), and SVM (OA: 88.79%, Kappa: 0.8476). Moreover, the 1D CNN model was retrained using limited field investigation data from Mount Wawu in Sichuan Province and successfully classified forest types in that region, thereby demonstrating its spatial-scale transferability with an OA of 90.86% and a Kappa of 0.8879. These findings underscore the effectiveness of the proposed 1D CNN in utilising multiple remote sensing data sources for accurate mountain forest type classification. In summary, the introduced 1D CNN presents a novel, efficient, and reliable method for mountain forest type classification, offering substantial contributions to the field. Full article

(This article belongs to the Topic Vegetation Characterization and Classification With Multi-Source Remote Sensing Data)

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23 pages, 10932 KiB  

Open AccessArticle

Fusion Approaches to Individual Tree Species Classification Using Multisource Remote Sensing Data

by Qian Li, Baoxin Hu, Jiali Shang and Hui Li

Forests 2023, 14(7), 1392; https://doi.org/10.3390/f14071392 - 7 Jul 2023

Cited by 6 | Viewed by 1697

Abstract

With the wide availability of remotely sensed data from various sensors, fusion-based tree species classification approaches have emerged as a prominent and ongoing research topic. However, most recent studies primarily focused on combining multisource data at the feature level, while few systematically examined [...] Read more.

With the wide availability of remotely sensed data from various sensors, fusion-based tree species classification approaches have emerged as a prominent and ongoing research topic. However, most recent studies primarily focused on combining multisource data at the feature level, while few systematically examined their positive or negative contributions to tree species classification. This study aimed to investigate fusion approaches at the feature and decision levels deployed with support vector machine and random forest algorithms to classify five dominant tree species: Norway maple, honey locust, Austrian pine, white spruce, and blue spruce in individual crowns. Spectral, textural, and structural features derived from multispectral imagery (MSI), a very high-resolution panchromatic image (PAN), and LiDAR data were systematically exploited to assess their contributions to accurate classifications. Among the various classification schemes that were explored, both feature- and decision-level fusion approaches demonstrated significant improvements in tree species classification compared with the utilization of MSI (0.7), PAN (0.74), or LiDAR (0.8) in isolation. Notably, the decision-level fusion approach achieved the highest overall accuracies (0.86 for SVM and 0.84 for RF) and kappa coefficients (0.82 for SVM and 0.79 for RF). The misclassification analysis of fusion approaches highlighted the potential and flexibility of decision-level fusion in tree species classification. Full article

(This article belongs to the Topic Vegetation Characterization and Classification With Multi-Source Remote Sensing Data)

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