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Remote Sensing and Smart Forestry II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 30 July 2024 | Viewed by 3551

Special Issue Editors


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Computer Science and Technology, College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: artificial intelligence; image processing; remote sensing classification
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College of Forestry, Central South University of Forestry & Technology, Changsha 410004, China
Interests: quantitative remote sensing in forestry; application of LiDAR in forestry; digital forest resource monitoring
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School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
Interests: smart forestry; smart landscape; information processing for remote sensing; software engineering
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Research Institute of Forestry Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Interests: artificial intelligence; visualization simulation and virtual reality for forestry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue entitled “Remote Sensing and Smart Forestry II” welcomes papers dealing with smart forestry construction and presents the scientific research achievements of remote sensing applications in the field of forestry in a concentrated way. 

Forests are the basis of human survival and development, and rapid and accurate acquisition of forest change information is of great significance for the sustainable development of ecological environment. Remote sensing plays a big role in studying and providing management decision support mainly including: forest resources survey, forest fire monitoring, forest pests and diseases monitoring, and forest resources dynamic monitoring.

The vertical structure of forest ecosystem and the rough terrain under the forest canopy are very complex, which presents a great challenge to the existing remote sensing monitoring technology. The improvement of spatial resolution and spectral resolution of remote sensing technology, as well as the development of radar remote sensing, aerial remote sensing and UAV remote sensing, which provide rich information sources for forestry remote sensing and broaden the applications of forestry remote sensing.

Special attention will be paid to the application of remote sensing-based smart forests, and this Special Issue aims to do just that. The papers will be reviewed and selected by the academic committee and recommended for publication in Remote Sensing. We kindly invite experts and scholars in related fields to contribute novel and original research to enrich our research community.

Prof. Dr. Weipeng Jing
Prof. Dr. Qiaolin Ye
Prof. Dr. Hua Sun
Dr. Houbing Song
Prof. Dr. Fu Xu
Prof. Dr. Huaiqing 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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • remote sensing
  • smart forestry
  • intelligent forestry
  • forestry technology
  • virtual reality
  • artificial intelligence
  • image processing
  • remote sensing classification

Published Papers (3 papers)

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Research

19 pages, 6411 KiB  
Article
Multi-Label Remote Sensing Image Land Cover Classification Based on a Multi-Dimensional Attention Mechanism
by Haihui You, Juntao Gu and Weipeng Jing
Remote Sens. 2023, 15(20), 4979; https://doi.org/10.3390/rs15204979 - 16 Oct 2023
Cited by 1 | Viewed by 1058
Abstract
For the multi-label classification task of remote sensing images (RSIs), it is difficult to accurately extract feature information from complex land covers, and it is easy to generate redundant features by ordinary convolution extraction features. This paper proposes a multi-label classification model for [...] Read more.
For the multi-label classification task of remote sensing images (RSIs), it is difficult to accurately extract feature information from complex land covers, and it is easy to generate redundant features by ordinary convolution extraction features. This paper proposes a multi-label classification model for multi-source RSIs that combines dense convolution and an attention mechanism. This method adds fusion channel attention and a spatial attention mechanism to each dense block module of the DenseNet, and the sigmoid activation function replaces the softmax activation function in multi-label classification. The improved model retains the main features of RSIs to the greatest extent and enhances the feature extraction of the images. The model can integrate local features, capture global dependencies, and aggregate contextual information to improve the multi-label land cover classification accuracy of RSIs. We conducted comparative experiments on the SEN12-MS and UC-Merced land cover dataset and analyzed the evaluation indicators. The experimental results show that this method effectively improves the multi-label classification accuracy of RSIs. Full article
(This article belongs to the Special Issue Remote Sensing and Smart Forestry II)
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19 pages, 7089 KiB  
Article
Retrieving Sub-Canopy Terrain from ICESat-2 Data Based on the RNR-DCM Filtering and Erroneous Ground Photons Correction Approach
by Yang Wu, Rong Zhao, Qing Hu, Yujia Zhang and Kun Zhang
Remote Sens. 2023, 15(15), 3904; https://doi.org/10.3390/rs15153904 - 07 Aug 2023
Cited by 1 | Viewed by 768
Abstract
Currently, the new space-based laser altimetry mission, Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), is widely used to obtain terrain information. Photon cloud filtering is a crucial step toward retrieving sub-canopy terrain. However, an unsuccessful photon cloud filtering performance weakens the retrieval of [...] Read more.
Currently, the new space-based laser altimetry mission, Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), is widely used to obtain terrain information. Photon cloud filtering is a crucial step toward retrieving sub-canopy terrain. However, an unsuccessful photon cloud filtering performance weakens the retrieval of sub-canopy terrain. In addition, sub-canopy terrain retrieval would not be accurate in densely forested areas due to existing sparse ground photons. This paper proposes a photon cloud filtering method and a ground photon extraction method to accurately retrieve sub-canopy terrain from ICESat-2 data. First, signal photon cloud data were derived from ICESat-2 data using the proposed photon cloud filtering method. Second, ground photons were extracted based on a specific percentile range of elevation. Third, erroneous ground photons were identified and corrected to obtain accurate sub-canopy terrain results, assuming that the terrain in the local area with accurate ground photons was continuous and therefore could be fitted appropriately through a straight line. Then, the signal photon cloud data obtained by the proposed method were compared with the reference signal photon cloud data. The results demonstrate that the overall accuracy of the signal photon identification achieved by the proposed filtering method exceeded 96.1% in the study areas. The sub-canopy terrain retrieved by the proposed sub-canopy terrain retrieval method was compared with the airborne LiDAR terrain measurements. The root-mean-squared error (RMSE) values in the two study areas were 1.28 m and 1.19 m, while the corresponding R2 (coefficient of determination) values were 0.999 and 0.999, respectively. We also identified and corrected erroneous ground photons with an RMSE lower than 2.079 m in densely forested areas. Therefore, the results of this study can be used to improve the accuracy of sub-canopy terrain retrieval, thus pioneering the application of ICESat-2 data, such as the generation of global sub-canopy terrain products. Full article
(This article belongs to the Special Issue Remote Sensing and Smart Forestry II)
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19 pages, 5254 KiB  
Article
Weakly Supervised Forest Fire Segmentation in UAV Imagery Based on Foreground-Aware Pooling and Context-Aware Loss
by Junling Wang, Yupeng Wang, Liping Liu, Hengfu Yin, Ning Ye and Can Xu
Remote Sens. 2023, 15(14), 3606; https://doi.org/10.3390/rs15143606 - 19 Jul 2023
Cited by 2 | Viewed by 922
Abstract
In recent years, tragedies caused by forest fires have been frequently reported. Forest fires not only result in significant economic losses but also cause environmental damage. The utilization of computer vision techniques and unmanned aerial vehicles (UAVs) for forest fire monitoring has become [...] Read more.
In recent years, tragedies caused by forest fires have been frequently reported. Forest fires not only result in significant economic losses but also cause environmental damage. The utilization of computer vision techniques and unmanned aerial vehicles (UAVs) for forest fire monitoring has become a primary approach to accurately locate and extinguish fires during their early stages. However, traditional computer-based methods for UAV forest fire image segmentation require a large amount of pixel-level labeled data to train the networks, which can be time-consuming and costly to acquire. To address this challenge, we propose a novel weakly supervised approach for semantic segmentation of fire images in this study. Our method utilizes self-supervised attention foreground-aware pooling (SAP) and context-aware loss (CAL) to generate high-quality pseudo-labels, serving as substitutes for manual annotation. SAP collaborates with bounding box and class activation mapping (CAM) to generate a background attention map, which aids in the generation of accurate pseudo-labels. CAL further improves the quality of the pseudo-labels by incorporating contextual information related to the target objects, effectively reducing environmental noise. We conducted experiments on two publicly available UAV forest fire datasets: the Corsican dataset and the Flame dataset. Our proposed method achieved impressive results, with IoU values of 81.23% and 76.43% for the Corsican dataset and the Flame dataset, respectively. These results significantly outperform the latest weakly supervised semantic segmentation (WSSS) networks on forest fire datasets. Full article
(This article belongs to the Special Issue Remote Sensing and Smart Forestry II)
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