Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.4
2.4
Journals
Forests
Special Issues
Image Processing for Forest Characterization
share announcement

Image Processing for Forest Characterization

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 8270

Special Issue Editors

Dr. Wenjuan Shen

E-Mail Website
Guest Editor
College of Forestry, Nanjing Forestry University, Nanjing 210037, China
Interests: multi-source remote sensing; forest structural parameters modelling; forest change detection; forest biomass
Dr. Wenli Huang

E-Mail Website
Guest Editor
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
Interests: image classification; radiometric normalization of satellite image; data fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Danxia Song

E-Mail Website
Guest Editor
1. Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan 430079, China
2. College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
Interests: multispectral remote sensing; vegetation parameter retrieval and spatio-temporal reconstruction; forest cover change monitoring
Prof. Dr. Chengquan Huang

E-Mail Website
Guest Editor
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Interests: land cover and land use change; vegetation structure and dynamics; disturbance, habitat, and carbon
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Effective strategies for forest characterization and monitoring are important for supporting sustainable forest management. Recent advances in remote sensing, such as optical, radar, and LiDAR sensors, provide valuable information to describe forests at stand, plot, and tree level. In particular, time series remotely sensed data have been considered to be an effective spatial detection tool for obtaining long-term forest characterization at different scales. Optical imagery such as Landsat and Sentinel-2 contains meaningful spectral, textures, spatial features of forests. Radar data such as Sentinel-1 and PALSAR data have been shown to be beneficial for monitoring forests in cloudy and rainy tropical or sub-tropical areas, while LiDAR data offer alternatives for analyzing structural properties of canopies.

This Special Issue welcomes studies about some new insights, novel approaches or findings in forest cover change detection, forest structural parameters modeling, forest biomass, and carbon evaluation. We are also inviting articles that examine one or more of the following general themes:

  • Big data and deep learning-based forestry application;
  • Forest fragmentation;
  • Forest disturbance (e.g., fire, insect disease, logging) mapping;
  • Landscape dynamics;
  • Forest and climate.

Dr. Wenjuan Shen
Dr. Wenli Huang
Dr. Danxia Song
Prof. Dr. Chengquan Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multi-source remote sensing
  • forest structural parameters modeling
  • forest cover change detection
  • forest disturbance
  • forest biomass
  • forest carbon storage

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 9401 KiB  
Article
Fire Severity Outperforms Remote Sensing Indices in Exploring Post-Fire Vegetation Recovery Dynamics in Complex Plateau Mountainous Regions
by Pengfei Liu, Weiyu Zhuang, Weili Kou, Leiguang Wang, Qiuhua Wang and Zhongjian Deng
Forests 2025, 16(2), 263; https://doi.org/10.3390/f16020263 - 1 Feb 2025
Viewed by 710
Abstract
Understanding post-fire vegetation recovery dynamics is crucial for damage assessment and recovery planning, yet spatiotemporal patterns in complex plateau environments remain poorly understood. This study addresses this gap by focusing on Yunnan Province, a mountainous plateau region with high fire incidence. We developed [...] Read more.
Understanding post-fire vegetation recovery dynamics is crucial for damage assessment and recovery planning, yet spatiotemporal patterns in complex plateau environments remain poorly understood. This study addresses this gap by focusing on Yunnan Province, a mountainous plateau region with high fire incidence. We developed an innovative approach combining differenced Normalized Burn Ratio (dNBR) and visual interpretation on Google Earth Engine (GEE) to generate high-quality training samples from Landsat 5 TM/7 ETM+/8 OLI imagery. Four supervised machine learning algorithms were evaluated, with Random Forest (RF) demonstrating superior accuracy (OA = 0.90) for fire severity classification compared to Support Vector Machine (SVM) OA of 0.88, Classification and Regression Tree(CART) OA o f0.85, and Naive Bayes(NB) OA of 0.78. Using RF, we generated annual fire severity maps alongside the Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR) from 2005 to 2020. Key findings include the following: (1) fire severity classification outperformed traditional remote sensing indices in characterizing vegetation recovery; (2) distinct recovery trajectories emerged across severity levels, with moderate areas recovering in 7 years, severe areas transitioning within 2 years, and low severity areas peaking at 2 years post-fire; (3) southern mountainous regions exhibited 1–2 years faster recovery than northern areas. These insights advance understanding of post-fire ecosystem dynamics in complex terrains and support more effective recovery strategies. Full article
(This article belongs to the Special Issue Image Processing for Forest Characterization)
►▼ Show Figures

Figure 1

19 pages, 6073 KiB  
Article
Effective UAV Photogrammetry for Forest Management: New Insights on Side Overlap and Flight Parameters
by Atman Dhruva, Robin J. L. Hartley, Todd A. N. Redpath, Honey Jane C. Estarija, David Cajes and Peter D. Massam
Forests 2024, 15(12), 2135; https://doi.org/10.3390/f15122135 - 2 Dec 2024
Cited by 2 | Viewed by 1806
Abstract
Silvicultural operations such as planting, pruning, and thinning are vital for the forest value chain, requiring efficient monitoring to prevent value loss. While effective, traditional field plots are time-consuming, costly, spatially limited, and rely on assumptions that they adequately represent a wider area. [...] Read more.
Silvicultural operations such as planting, pruning, and thinning are vital for the forest value chain, requiring efficient monitoring to prevent value loss. While effective, traditional field plots are time-consuming, costly, spatially limited, and rely on assumptions that they adequately represent a wider area. Alternatively, unmanned aerial vehicles (UAVs) can cover large areas while keeping operators safe from hazards including steep terrain. Despite their utility, optimal flight parameters to ensure flight efficiency and data quality remain under-researched. This study evaluated the impact of forward and side overlap and flight altitude on the quality of two- and three-dimensional spatial data products from UAV photogrammetry (UAV-SfM) for assessing stand density in a recently thinned Pinus radiata D. Don plantation. A contemporaneously acquired UAV laser scanner (ULS) point cloud provided reference data. The results indicate that the optimal UAV-SfM flight parameters are 90% forward and 85% side overlap at a 120 m altitude. Flights at an 80 m altitude offered marginal resolution improvement (2.2 cm compared to 3.2 cm ground sample distance/GSD) but took longer and were more error-prone. Individual tree detection (ITD) for stand density assessment was then applied to both UAV-SfM and ULS canopy height models (CHMs). Manual cleaning of the detected ULS tree peaks provided ground truth for both methods. UAV-SfM had a lower recall (0.85 vs. 0.94) but a higher precision (0.97 vs. 0.95) compared to ULS. Overall, the F-score indicated no significant difference between a prosumer-grade photogrammetric UAV and an industrial-grade ULS for stand density assessments, demonstrating the efficacy of affordable, off-the-shelf UAV technology for forest managers. Furthermore, in addressing the knowledge gap regarding optimal UAV flight parameters for conducting operational forestry assessments, this study provides valuable insights into the importance of side overlap for orthomosaic quality in forest environments. Full article
(This article belongs to the Special Issue Image Processing for Forest Characterization)
►▼ Show Figures

Graphical abstract

23 pages, 8717 KiB  
Article
Net Forest Carbon Loss Induced by Forest Cover Change and Compound Drought and Heat Events in Two Regions of China
by Chenfeng Gu, Tongyu Wang, Wenjuan Shen, Zhiguo Tai, Xiaokun Su, Jiaying He, Tao He, Weishu Gong and Chengquan Huang
Forests 2024, 15(11), 2048; https://doi.org/10.3390/f15112048 - 20 Nov 2024
Cited by 1 | Viewed by 974
Abstract
Compound drought and heat events (CDHEs) and forest cover change influence regional forest carbon dynamics. Changes in regional vegetation biomass and soil carbon storage induced by forest cover change often exhibit considerable uncertainty, and previous research on the impacts of CDHEs on forest [...] Read more.
Compound drought and heat events (CDHEs) and forest cover change influence regional forest carbon dynamics. Changes in regional vegetation biomass and soil carbon storage induced by forest cover change often exhibit considerable uncertainty, and previous research on the impacts of CDHEs on forest carbon dynamics is limited. To accurately quantify the specific effects of forest cover change and CDHEs on forest carbon dynamics in different regions, we employed a combined algorithm of the Carnegie–Ames–Stanford Approach (CASA) and bookkeeping empirical models to examine the impact of regional forest cover changes on forest carbon dynamics during 2000–2022 in Nanjing and Shaoguan, Southern China. Using the Geographical Detector model, we then analyzed the effects of CDHEs on forest carbon dynamics. Next, we used the photosynthesis equation and the optimal response time of forests to drought (heat) events to calculate the changes in forest carbon sequestration caused by CDHEs in both regions during 2000–2022. The results indicated that afforestation and deforestation led to +0.269 TgC and +1.509 TgC of carbon sequestration and 0.491 TgC and 2.802 TgC of carbon emissions in Nanjing and Shaoguan, respectively. The overall effects of CDHEs on the change in forest carbon sequestration were manifested as net carbon loss. In Nanjing, the net carbon loss caused by CDHEs (0.186 TgC) was lower than the loss due to forest cover change (0.222 TgC). In Shaoguan, the net forest carbon loss caused by CDHEs (3.219 TgC) was much more significant than that caused by forest cover change (1.293 TgC). This study demonstrated that forest carbon dynamics are dominated by different factors in different regions, which provides a scientific basis for local governments to formulate targeted forest management policies. Full article
(This article belongs to the Special Issue Image Processing for Forest Characterization)
►▼ Show Figures

Figure 1

18 pages, 7473 KiB  
Article
Green Space Reverse Pixel Shuffle Network: Urban Green Space Segmentation Using Reverse Pixel Shuffle for Down-Sampling from High-Resolution Remote Sensing Images
by Mingyu Jiang, Hua Shao, Xingyu Zhu and Yang Li
Forests 2024, 15(1), 197; https://doi.org/10.3390/f15010197 - 19 Jan 2024
Cited by 2 | Viewed by 1920
Abstract
Urban green spaces (UGS) play a crucial role in the urban environmental system by aiding in mitigating the urban heat island effect, promoting sustainable urban development, and ensuring the physical and mental well-being of residents. The utilization of remote sensing imagery enables the [...] Read more.
Urban green spaces (UGS) play a crucial role in the urban environmental system by aiding in mitigating the urban heat island effect, promoting sustainable urban development, and ensuring the physical and mental well-being of residents. The utilization of remote sensing imagery enables the real-time surveying and mapping of UGS. By analyzing the spatial distribution and spectral information of a UGS, it can be found that the UGS constitutes a kind of low-rank feature. Thus, the accuracy of the UGS segmentation model is not heavily dependent on the depth of neural networks. On the contrary, emphasizing the preservation of more surface texture features and color information contributes significantly to enhancing the model’s segmentation accuracy. In this paper, we proposed a UGS segmentation model, which was specifically designed according to the unique characteristics of a UGS, named the Green Space Reverse Pixel Shuffle Network (GSRPnet). GSRPnet is a straightforward but effective model, which uses an improved RPS-ResNet as the feature extraction backbone network to enhance its ability to extract UGS features. Experiments conducted on GaoFen-2 remote sensing imagery and the Wuhan Dense Labeling Dataset (WHDLD) demonstrate that, in comparison with other methods, GSRPnet achieves superior results in terms of precision, F1-score, intersection over union, and overall accuracy. It demonstrates smoother edge performance in UGS border regions and excels at identifying discrete small-scale UGS. Meanwhile, the ablation experiments validated the correctness of the hypotheses and methods we proposed in this paper. Additionally, GSRPnet’s parameters are merely 17.999 M, and this effectively demonstrates that the improvement in accuracy of GSRPnet is not only determined by an increase in model parameters. Full article
(This article belongs to the Special Issue Image Processing for Forest Characterization)
►▼ Show Figures

Figure 1

12 pages, 9147 KiB  
Article
An Earlier Spring Phenology Reduces Vegetation Growth Rate during the Green-Up Period in Temperate Forests
by Boheng Wang, Zunchi Liu, Ji Lu, Mao Cai, Chaofan Zhou, Gaohui Duan, Peng Yang and Jinfeng Hu
Forests 2023, 14(10), 1984; https://doi.org/10.3390/f14101984 - 1 Oct 2023
Cited by 2 | Viewed by 1834
Abstract
Climatic warming advances the start of the growing season (SOS) and sequentially enhances the vegetation productivity of temperate forests by extending the carbon uptake period and/or increasing the growth rate. Recent research indicates that the vegetation growth rate is a main driver for [...] Read more.
Climatic warming advances the start of the growing season (SOS) and sequentially enhances the vegetation productivity of temperate forests by extending the carbon uptake period and/or increasing the growth rate. Recent research indicates that the vegetation growth rate is a main driver for the interannual changes in vegetation carbon uptake; however, the specific effects of an earlier SOS on vegetation growth rate and the underlying mechanisms are still unclear. Using 268 year-site PhenoCam observations in temperate forests, we found that an earlier SOS reduced the vegetation growth rate and mean air temperature during the green-up period (i.e., from the SOS to the peak of the growing period), but increased the accumulation of shortwave radiation during the green-up period. Interestingly, an earlier-SOS-induced reduction in the growth rate was weakened in the highly humid areas (aridity index ≥ 1) when compared with that in the humid areas (aridity index < 1), suggesting that an earlier-SOS-induced reduction in the growth rate in temperate forests may intensify with the ongoing global warming and aridity in the future. The structural equation model analyses indicated that an earlier-SOS-induced decrease in the temperature and increase in shortwave radiation drove a low vegetation growth rate. Our findings highlight that the productivity of temperate forests may be overestimated if the negative effect of an earlier SOS on the vegetation growth rate is ignored. Full article
(This article belongs to the Special Issue Image Processing for Forest Characterization)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop