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Remote Sensing
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Remote Sensing of Biodiversity in Tropical Forests
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Remote Sensing of Biodiversity in Tropical Forests

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

Deadline for manuscript submissions: closed (10 July 2021) | Viewed by 6607

Special Issue Editors

Dr. Gabriela Zuquim

E-Mail Website1 Website2
Guest Editor
Department of Biology, Aarhus University, Nordre Ringgade 1, 8000 Aarhus C, Denmark
Interests: Amazonia; biogeography; ecology; species diversity patterns; understory herbs; ferns
Dr. Juliana Schietti

E-Mail Website
Guest Editor
Department of Biology, Federal University of Amazonas, 69080-900 Manaus, Amazonas, Brazil
Interests: forest structure; species diversity; environmental gradients; functional plant ecology.
Dr. Stéphane Takoudjou Momo

E-Mail Website
Guest Editor
1. IRD/UMR AMAP, Botanique et Modélisation de l'Architecture des Plantes et des végétations, F-34000 Montpellier, France
2. Plant Systematic and Ecology Laboratory, Department of Biology, Higher Teachers' Training College, Yaounde, Cameroon
Interests: ecology; remote sensing (LiDAR); biomass; Congo basin; Allometric equations

Special Issue Information

Dear Colleagues,

Tropical forests contain the richest species communities of the world and are crucial in regulating the global climate and providing ecosystem services. Despite its importance, these forests are also heavily threatened by fire, deforestation, mining, and other human activities.

Tropical forest biodiversity remains poorly understood and mapped. Its vastness and high diversity pose challenges for sampling over representative areas.

Remote sensing data can provide spatially and temporally continuous information useful for biodiversity assessments and significantly improve our understanding of forest dynamics, function, biodiversity distribution, reduce knowledge gaps, and help to identify threats and priority areas for land use planning and conservation of tropical forests.

Increased availability of data from terrestrial, airborne, and satellite sensors and improved analytical methods are allowing a broader range of approaches, from descriptions of spatial patterns and processes to predictions in field-unsampled areas in tropical forest ecosystems. These have applications in mapping biogeographical units, carbon stocks, habitats, species occurrence, functional and chemical variation, tracking animal movement, and monitoring human-driven changes due to land use and effects of climate change.

We thus call on the global tropical forest biodiversity community for contributions of innovative work on remote sensing of tropical forest. We are seeking a broad range of approaches that provide insights in linking field and remote sensing measurements, improving our ability to measure, map, and monitor species, traits, and structural diversity of tropical forests, regardless of the taxonomic focus.

The papers presented in this Special Issue will help to improve scientific-based decisions on tropical forests and expand the general readership of remote sensing and forest research. We thus also encourage authors from tropical countries to contribute.

Dr. Gabriela Zuquim
Dr. Juliana Schietti
Dr. Stéphane Takoudjou Momo
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

  • species distribution
  • functional diversity
  • upscaling
  • ecological patterns and processes
  • conservation
  • monitoring
  • mapping
  • sensors

Published Papers (2 papers)

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Research

22 pages, 4605 KiB  
Article
A New CBAM-P-Net Model for Few-Shot Forest Species Classification Using Airborne Hyperspectral Images
by Long Chen, Xiaomin Tian, Guoqi Chai, Xiaoli Zhang and Erxue Chen
Remote Sens. 2021, 13(7), 1269; https://doi.org/10.3390/rs13071269 - 26 Mar 2021
Cited by 24 | Viewed by 3163
Abstract
High-precision automatic identification and mapping of forest tree species composition is an important content of forest resource survey and monitoring. The airborne hyperspectral image contains rich spectral and spatial information, which provides the possibility of high-precision classification and mapping of forest tree species. [...] Read more.
High-precision automatic identification and mapping of forest tree species composition is an important content of forest resource survey and monitoring. The airborne hyperspectral image contains rich spectral and spatial information, which provides the possibility of high-precision classification and mapping of forest tree species. Few-shot learning, as an application of deep learning, has become an effective method of image classification. Prototypical networks (P-Net) is a simple and practical deep learning network, which has significant advantages in solving few-shot classification problems. Considering the high band correlation and large data volume associated with airborne hyperspectral images, how to fully extract effective features, filter or reduce redundant features is the key to improving the classification accuracy of P-Net, in order to extract effective features in hyperspectral images and obtain a high-precision forest tree species classification model with limited samples. In this research, we embedded the convolutional block attention module (CBAM) between the convolution blocks of P-Net, the CBAM-P-Net was constructed, and a method to improve the feature extraction efficiency of the P-Net was proposed, although this method makes the network more complex and increases the computational cost to a certain extent. The results show that the combination strategy using Channel First for CBAM greatly improves the feature extraction efficiency of the model. In different sample windows, CBAM-P-Net has an average increase of 1.17% and 0.0129 in testing overall accuracy (OA) and kappa coefficient (Kappa). The optimal classification window is 17 × 17, the OA reaches 97.28%, and Kappa reaches 0.97, which is an increase of 1.95% and 0.0214 along with just 49 s of training time expended, respectively, compared with P-Net. Therefore, using a suitable sample window and applying the proposed CBAM-P-Net to classify airborne hyperspectral images can achieve high-precision classification and mapping of forest tree species. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity in Tropical Forests)
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23 pages, 3808 KiB  
Article
Improved Prototypical Network Model for Forest Species Classification in Complex Stand
by Xiaomin Tian, Long Chen, Xiaoli Zhang and Erxue Chen
Remote Sens. 2020, 12(22), 3839; https://doi.org/10.3390/rs12223839 - 23 Nov 2020
Cited by 9 | Viewed by 2665
Abstract
Deep learning has become an effective method for hyperspectral image classification. However, the high band correlation and data volume associated with airborne hyperspectral images, and the insufficiency of training samples, present challenges to the application of deep learning in airborne image classification. Prototypical [...] Read more.
Deep learning has become an effective method for hyperspectral image classification. However, the high band correlation and data volume associated with airborne hyperspectral images, and the insufficiency of training samples, present challenges to the application of deep learning in airborne image classification. Prototypical networks are practical deep learning networks that have demonstrated effectiveness in handling small-sample classification. In this study, an improved prototypical network is proposed (by adding L2 regularization to the convolutional layer and dropout to the maximum pooling layer) to address the problem of overfitting in small-sample classification. The proposed network has an optimal sample window for classification, and the window size is related to the area and distribution of the study area. After performing dimensionality reduction using principal component analysis, the time required for training using hyperspectral images shortened significantly, and the test accuracy increased drastically. Furthermore, when the size of the sample window was 27 × 27 after dimensionality reduction, the overall accuracy of forest species classification was 98.53%, and the Kappa coefficient was 0.9838. Therefore, by using an improved prototypical network with a sample window of an appropriate size, the network yielded desirable classification results, thereby demonstrating its suitability for the fine classification and mapping of tree species. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity in Tropical Forests)
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