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Forests
Special Issues
Image Processing for Forest Characterization
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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: 30 May 2024 | Viewed by 2275

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: forest structure; biomass; land use; land cover; LiDAR; SAR; remotely estimation; mapping of forest vertical structure and aboveground biomass
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

Published Papers (2 papers)

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Research

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://0-doi-org.brum.beds.ac.uk/10.3390/f15010197 - 19 Jan 2024
Viewed by 862
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)
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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://0-doi-org.brum.beds.ac.uk/10.3390/f14101984 - 01 Oct 2023
Viewed by 1010
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)
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