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Remote Sensing
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Remote Sensing Applications to Ecology: Opportunities and Challenges II
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Remote Sensing Applications to Ecology: Opportunities and Challenges II

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2982

Special Issue Editors

Prof. Dr. Paul Fergus

E-Mail Website
Guest Editor
School of Computer Science and Mathematics, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
Interests: artificial intelligence; machine learning; deep learning; object detection; conservation; e-health
Special Issues, Collections and Topics in MDPI journals
Dr. Carl Chalmers

E-Mail Website
Guest Editor
School of Computer Science and Mathematics, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
Interests: artificial intelligence; machine learning; deep learning; computer vision; technology in conservation and e-health
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Serge Wich

E-Mail Website
Guest Editor
School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: great apes; conservation; drones
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Steven Longmore

E-Mail Website
Guest Editor
Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 5RF, UK
Interests: physics and astronomy; earth and planetary sciences
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the overwhelming support and interest in the previous Special Issue (SI), we are introducing a 2nd edition on “Remote Sensing Applications to Ecology: Opportunities and Challenges”. We would like to thank all the authors and co-authors who made contributions to the success of the 1st edition of this SI.

We are pleased to announce a new Special Issue entitled “Remote Sensing Applications to Ecology: Opportunities and Challenges II”. We are soliciting submissions for both review and original research articles related to the novel use of data obtained from sensors (camera traps, cameras, microphones, unoccupied vehicles (aerial, terrestrial, and aquatic)) and any other sensor platforms you think would support ecology to manage and protect environments globally. We encourage submissions with a particular focus on artificial intelligence (AI) algorithms and their use in ecological studies. This Special Issue is open to contributions ranging from systems that monitor different physical environments, combat poaching and protect wildlife, support wildlife management and conservation, enable animal counting and tracking, support biodiversity assessments, and monitor forest health and quality, to novel approaches to sensor fusion for remote sensing. Original contributions that look at integrated sensor-based technologies and wide-area communications across remote sensing platforms (land, sea, air- and spaceborne) are also encouraged.

Prof. Dr. Paul Fergus
Dr. Carl Chalmers
Prof. Dr. Serge Wich
Prof. Dr. Steven Longmore
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

  • land-, sea-, air-, space-based monitoring
  • multi-spectral remote sensing
  • hyperspectral remote sensing
  • LiDAR
  • sensor fusion
  • time series analysis
  • data fusion and data assimilation
  • wireless (2/3/4/5G/WiFi/satellite) mesh networking in remote areas
  • machine learning (image processing and pattern recognition)
  • high-performance inferencing
  • edge/IoT deployment and inferencing
  • robotics (rovers, drones)
  • remote sensing applications
  • poaching
  • wildlife conservation
  • animal counting
  • environment monitoring
  • change detection

Related Special Issue

Published Papers (4 papers)

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Research

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19 pages, 5541 KiB  
Article
Application of Normalized Radar Backscatter and Hyperspectral Data to Augment Rangeland Vegetation Fractional Classification
by Matthew Rigge, Brett Bunde, Kory Postma, Simon Oliver and Norman Mueller
Remote Sens. 2024, 16(13), 2315; https://doi.org/10.3390/rs16132315 (registering DOI) - 25 Jun 2024
Viewed by 835
Abstract
Rangeland ecosystems in the western United States are vulnerable to climate change, fire, and anthropogenic disturbances, yet classification of rangeland areas remains difficult due to frequently sparse vegetation canopies that increase the influence of soils and senesced vegetation, the overall abundance of senesced [...] Read more.
Rangeland ecosystems in the western United States are vulnerable to climate change, fire, and anthropogenic disturbances, yet classification of rangeland areas remains difficult due to frequently sparse vegetation canopies that increase the influence of soils and senesced vegetation, the overall abundance of senesced vegetation, heterogeneity of life forms, and limited ground-based data. The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project provides fractional vegetation cover maps across western North America using Landsat imagery and artificial intelligence from 1985 to 2023 at yearly time-steps. The objectives of this case study are to apply hyperspectral data from several new data streams, including Sentinel Synthetic Aperture Radar (SAR) and Earth Surface Mineral Dust Source Investigation (EMIT), to the RCMAP model. We run a series of five tests (Landsat-base model, base + SAR, base + EMIT, base + SAR + EMIT, and base + Landsat NEXT [LNEXT] synthesized from EMIT) over a difficult-to-classify region centered in southwest Montana, USA. Our testing results indicate a clear accuracy benefit of adding SAR and EMIT data to the RCMAP model, with a 7.5% and 29% relative increase in independent accuracy (R2), respectively. The ability of SAR data to observe vegetation height allows for more accurate classification of vegetation types, whereas EMIT’s continuous characterization of the spectral response boosts discriminatory power relative to multispectral data. Our spectral profile analysis reveals the enhanced classification power with EMIT is related to both the improved spectral resolution and representation of the entire domain as compared to legacy Landsat. One key finding is that legacy Landsat bands largely miss portions of the electromagnetic spectrum where separation among important rangeland targets exists, namely in the 900–1250 nm and 1500–1780 nm range. Synthesized LNEXT data include these gaps, but the reduced spectral resolution compared to EMIT results in an intermediate 18% increase in accuracy relative to the base run. Here, we show the promise of enhanced classification accuracy using EMIT data, and to a smaller extent, SAR. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges II)
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17 pages, 5035 KiB  
Article
CLIP-Driven Few-Shot Species-Recognition Method for Integrating Geographic Information
by Lei Liu, Linzhe Yang, Feng Yang, Feixiang Chen and Fu Xu
Remote Sens. 2024, 16(12), 2238; https://doi.org/10.3390/rs16122238 - 20 Jun 2024
Viewed by 356
Abstract
Automatic recognition of species is important for the conservation and management of biodiversity. However, since closely related species are visually similar, it is difficult to distinguish them by images alone. In addition, traditional species-recognition models are limited by the size of the dataset [...] Read more.
Automatic recognition of species is important for the conservation and management of biodiversity. However, since closely related species are visually similar, it is difficult to distinguish them by images alone. In addition, traditional species-recognition models are limited by the size of the dataset and face the problem of poor generalization ability. Visual-language models such as Contrastive Language-Image Pretraining (CLIP), obtained by training on large-scale datasets, have excellent visual representation learning ability and demonstrated promising few-shot transfer ability in a variety of few-shot species recognition tasks. However, limited by the dataset on which CLIP is trained, the performance of CLIP is poor when used directly for few-shot species recognition. To improve the performance of CLIP for few-shot species recognition, we proposed a few-shot species-recognition method incorporating geolocation information. First, we utilized the powerful feature extraction capability of CLIP to extract image features and text features. Second, a geographic feature extraction module was constructed to provide additional contextual information by converting structured geographic location information into geographic feature representations. Then, a multimodal feature fusion module was constructed to deeply interact geographic features with image features to obtain enhanced image features through residual connection. Finally, the similarity between the enhanced image features and text features was calculated and the species recognition results were obtained. Extensive experiments on the iNaturalist 2021 dataset show that our proposed method can significantly improve the performance of CLIP’s few-shot species recognition. Under ViT-L/14 and 16-shot training species samples, compared to Linear probe CLIP, our method achieved a performance improvement of 6.22% (mammals), 13.77% (reptiles), and 16.82% (amphibians). Our work provides powerful evidence for integrating geolocation information into species-recognition models based on visual-language models. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges II)
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31 pages, 14044 KiB  
Article
An Ecological Quality Evaluation of Large-Scale Farms Based on an Improved Remote Sensing Ecological Index
by Jun Wang, Lili Jiang, Qingwen Qi and Yongji Wang
Remote Sens. 2024, 16(4), 684; https://doi.org/10.3390/rs16040684 - 15 Feb 2024
Viewed by 808
Abstract
The ecological quality of large-scale farms is a critical determinant of crop growth. In this paper, an ecological assessment procedure suitable for agricultural regions should be developed based on an improved remote sensing ecological index (IRSEI), which introduces an integrated salinity index (ISI) [...] Read more.
The ecological quality of large-scale farms is a critical determinant of crop growth. In this paper, an ecological assessment procedure suitable for agricultural regions should be developed based on an improved remote sensing ecological index (IRSEI), which introduces an integrated salinity index (ISI) tailored to the salinized soil characteristics in farming areas and incorporates ecological indices such as the greenness index (NDVI), the humidity index (WET), the dryness index (NDBSI), and the heat index (LST). The results indicate that between 2013 and 2022, the mean IRSEI increasing from 0.500 in 2013 to 0.826 in 2020 before decreasing to 0.646 in 2022. From 2013 to 2022, the area of the farm that experienced slight to significant improvements in ecological quality reached 1419.91 km2, accounting for 71.94% of the total farm area. An analysis of different land cover types revealed that the IRSEI performed more reliably than did the original RSEI method. Correlation analysis based on crop yields showed that the IRSEI method was more strongly correlated with yield than was the RSEI method. Therefore, the proposed IRSEI method offers a rapid and effective new means of monitoring ecological quality for agricultural planting areas characterized by soil salinization, and it is more effective than the traditional RSEI method. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges II)
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Review

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25 pages, 1490 KiB  
Review
Remote Sensing Application in Ecological Restoration Monitoring: A Systematic Review
by Ruozeng Wang, Yonghua Sun, Jinkun Zong, Yihan Wang, Xuyue Cao, Yanzhao Wang, Xinglu Cheng and Wangkuan Zhang
Remote Sens. 2024, 16(12), 2204; https://doi.org/10.3390/rs16122204 - 17 Jun 2024
Viewed by 530
Abstract
In the context of the continuous degradation of the global environment, ecological restoration has become a primary task in global environmental governance. In this process, remote sensing technology, as an advanced monitoring and analysis tool, plays a key role in monitoring ecological restoration. [...] Read more.
In the context of the continuous degradation of the global environment, ecological restoration has become a primary task in global environmental governance. In this process, remote sensing technology, as an advanced monitoring and analysis tool, plays a key role in monitoring ecological restoration. This article reviews the application of remote sensing technology in ecological restoration monitoring. Based on a comprehensive analysis of the literature in the field of ecological remote sensing, it systematically summarizes the major in-orbit spaceborne and airborne sensors and their related products. This article further proposes a series of evaluation indicators for ecological restoration from four aspects: forests, soil, water, and the atmosphere, and elaborates on the calculation methods for these indicators. In addition, this paper also summarizes the methods for evaluating the effectiveness of ecological restoration, including subjective evaluation, objective evaluation, and comprehensive evaluation methods. Finally, we analyze the challenges faced by remote sensing technology in evaluating ecological restoration effectiveness, such as issues with the precision of indicators extraction, the limitations of spatial resolution, and the diversity of evaluation methods. This review also looks forward to future ecological restoration technologies, such as the potential applications of integrated aerospace and terrestrial remote sensing, multi-data fusion, and machine learning technologies. This study reveals the effectiveness of remote sensing technology in ecological restoration monitoring, aiming to provide efficient tools and innovative strategies for future remote sensing monitoring and assessment of ecological restoration. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges II)
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