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Forests
Special Issues
Imaging Sensors for Monitoring Forest Dynamics
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Imaging Sensors for Monitoring Forest Dynamics

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 May 2024) | Viewed by 3548

Special Issue Editors

Prof. Dr. Huichun Zhang

E-Mail Website
Guest Editor
Department of Forestry and Mechanical Engineering, School of Mechanical and Electrical Engineering, Nanjing Forestry University, Nanjing 210037, China
Interests: forestry information technology and equipment; precise pesticide application technology; phenotypic collection and analysis technology and platform; intelligent forestry machinery
Dr. Yufeng Ge

E-Mail Website
Guest Editor
Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
Interests: sensor-based plant phenotyping; optoelectronic sensor development in agriculture; VIS/NIR/MIR spectroscopy; agricultural remote sensing and image analysis; precision agriculture and spatial statistics
Special Issues, Collections and Topics in MDPI journals
Dr. Sheng Wen

E-Mail Website
Guest Editor
Engineering Foundation Teaching and Training Center, South China Agricultural University, Guangzhou 510642, China
Interests: sensors; precision agricultural and forestry; artificial intelligence, UAV; agricultural artificial intelligence
Dr. Houxi Zhang

E-Mail Website
Guest Editor
Forestry College, Fujian Agriculture and Forestry University, Fuzhou, China
Interests: UAV-based remote sensing; deep learning; multispectral image; tree crown extraction; soil science

Special Issue Information

Dear Colleagues,

Imaging sensors and sensing technologies are used to collect data for the quantitative assessment of complex traits related to forest growth, phenology, performance and adaptation to biotic or abiotic stress (disease, insects, drought, heat and salinity). Enabling technologies for the monitoring of forests are rapidly advancing, including visible, multispectral, hyperspectral, thermal, and fluorescence imaging sensors, and 3D light detection and ranging (LIDAR). The Special Issue includes research papers, reviews, and perspectives that highlight the potential and limitations of image sensor technology for forest monitoring, as well as discuss current and future applications in forest management and conservation. This Special Issue welcomes research on imaging sensors, image processing algorithms, imaging techniques and their applications in forests.

Prof. Dr. Huichun Zhang
Dr. Yufeng Ge
Dr. Sheng Wen
Dr. Houxi Zhang
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

  • imaging technique
  • imaging sensor
  • image process system
  • forest dynamics
  • monitoring
  • remote sensing
  • precision forestry
  • machine learning

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Published Papers (2 papers)

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Research

19 pages, 6420 KiB  
Article
Three-Dimensional Quantification and Visualization of Leaf Chlorophyll Content in Poplar Saplings under Drought Using SFM-MVS
by Qifei Tian, Huichun Zhang, Liming Bian, Lei Zhou and Yufeng Ge
Forests 2024, 15(1), 20; https://doi.org/10.3390/f15010020 - 20 Dec 2023
Cited by 4 | Viewed by 1491
Abstract
As global temperatures warm, drought reduces plant yields and is one of the most serious abiotic stresses causing plant losses. The early identification of plant drought is of great significance for making improvement decisions in advance. Chlorophyll is closely related to plant photosynthesis [...] Read more.
As global temperatures warm, drought reduces plant yields and is one of the most serious abiotic stresses causing plant losses. The early identification of plant drought is of great significance for making improvement decisions in advance. Chlorophyll is closely related to plant photosynthesis and nutritional status. By tracking the changes in chlorophyll between plant strains, we can identify the impact of drought on a plant’s physiological status, efficiently adjust the plant’s ecosystem adaptability, and achieve optimization of planting management strategies and resource utilization efficiency. Plant three-dimensional reconstruction and three-dimensional character description are current research hot spots in the development of phenomics, which can three-dimensionally reveal the impact of drought on plant structure and physiological phenotypes. This article obtains visible light multi-view images of four poplar varieties before and after drought. Machine learning algorithms were used to establish the regression models between color vegetation indices and chlorophyll content. The model, based on the partial least squares regression (PLSR), reached the best performance, with an R2 of 0.711. The SFM-MVS algorithm was used to reconstruct the plant’s three-dimensional point cloud and perform color correction, point cloud noise reduction, and morphological calibration. The trained PLSR chlorophyll prediction model was combined with the point cloud color information, and the point cloud color was re-rendered to achieve three-dimensional digitization of plant chlorophyll content. Experimental research found that under natural growth conditions, the chlorophyll content of poplar trees showed a gradient distribution state with gradually increasing values from top to bottom; after being given a short period of mild drought stress, the chlorophyll content accumulated. Compared with the value before stress, it has improved, but no longer presents a gradient distribution state. At the same time, after severe drought stress, the chlorophyll value decreased as a whole, and the lower leaves began to turn yellow, wilt and fall off; when the stress intensity was consistent with the duration, the effect of drought on the chlorophyll value was 895 < SY-1 < 110 < 3804. This research provides an effective tool for in-depth understanding of the mechanisms and physiological responses of plants to environmental stress. It is of great significance for improving agricultural and forestry production and protecting the ecological environment. It also provides decision-making for solving plant drought problems caused by global climate change. Full article
(This article belongs to the Special Issue Imaging Sensors for Monitoring Forest Dynamics)
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26 pages, 11096 KiB  
Article
Eucalyptus Plantation Area Extraction Based on SLPSO-RFE Feature Selection and Multi-Temporal Sentinel-1/2 Data
by Xiaoqi Lin, Chao Ren, Yi Li, Weiting Yue, Jieyu Liang and Anchao Yin
Forests 2023, 14(9), 1864; https://doi.org/10.3390/f14091864 - 13 Sep 2023
Cited by 4 | Viewed by 1468
Abstract
An accurate and efficient estimation of eucalyptus plantation areas is of paramount significance for forestry resource management and ecological environment monitoring. Currently, combining multidimensional optical and SAR images with machine learning has become an important method for eucalyptus plantation classification, but there are [...] Read more.
An accurate and efficient estimation of eucalyptus plantation areas is of paramount significance for forestry resource management and ecological environment monitoring. Currently, combining multidimensional optical and SAR images with machine learning has become an important method for eucalyptus plantation classification, but there are still some challenges in feature selection. This study proposes a feature selection method that combines multi-temporal Sentinel-1 and Sentinel-2 data with SLPSO (social learning particle swarm optimization) and RFE (Recursive Feature Elimination), which reduces the impact of information redundancy and improves classification accuracy. Specifically, this paper first fuses multi-temporal Sentinel-1 and Sentinel-2 data, and then carries out feature selection by combining SLPSO and RFE to mitigate the effects of information redundancy. Next, based on features such as the spectrum, red-edge indices, texture characteristics, vegetation indices, and backscatter coefficients, the study employs the Simple Non-Iterative Clustering (SNIC) object-oriented method and three different types of machine-learning models: Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM) for the extraction of eucalyptus plantation areas. Each model uses a supervised-learning method, with labeled training data guiding the classification of eucalyptus plantation regions. Lastly, to validate the efficacy of selecting multi-temporal data and the performance of the SLPSO–RFE model in classification, a comparative analysis is undertaken against the classification results derived from single-temporal data and the ReliefF–RFE feature selection scheme. The findings reveal that employing SLPSO–RFE for feature selection significantly elevates the classification precision of eucalyptus plantations across all three classifiers. The overall accuracy rates were noted at 95.48% for SVM, 96% for CART, and 97.97% for RF. When contrasted with classification outcomes from multi-temporal data and ReliefF–RFE, the overall accuracy for the trio of models saw an increase of 10%, 8%, and 8.54%, respectively. The accuracy enhancement was even more pronounced when juxtaposed with results from single-temporal data and ReliefF-RFE, at increments of 15.25%, 13.58%, and 14.54% respectively. The insights from this research carry profound theoretical implications and practical applications, particularly in identifying and extracting eucalyptus plantations leveraging multi-temporal data and feature selection. Full article
(This article belongs to the Special Issue Imaging Sensors for Monitoring Forest Dynamics)
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