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Unmanned Aerial Vehicle (UAV) and Satellite Synergy for Assessment and Monitoring Biodiversity

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 7754

Special Issue Editors

Dr. Azade Deljouei

E-Mail Website
Guest Editor
School of Forest, Fisheries and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA
Interests: remote sensing; GIS; biodiversity; land use and land cover change; quantitative soil bioengineering; natural hazards; root reinforcement; shallow landslides; soil erosion; forest hydrology
Dr. Hormoz Sohrabi

E-Mail Website
Guest Editor
Department of Forest Science and Engineering, Faculty of Natural Resources, Tarbiat Modares University, Tehran 14115-111, Iran
Interests: forest remote sensing; forest biometrics; UAV; forest management; forest biomass and carbon
Dr. Parth Sarathi Roy

E-Mail Website
Guest Editor
Sustainable Landscapes and Restoration, World Resources Institute India, New Delhi 110016, India
Interests: earth observation science; GIS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Several decades ago, it became evident that biodiversity in the world is declining at an unprecedented rate. Global biodiversity loss has drawn attention to the relationship between biodiversity and ecosystem services. Biodiversity plays a vital role in maintaining ecosystem stability, seriously impacts human health and societies, and provides diverse production and living necessities. Although traditional approaches to measuring biodiversity provide valuable information, with recent advances in remote sensing (RS), the characterization of biodiversity over a large area can be performed systematically, repeatedly, spatially exhaustively, timely, cost-effectively, and accurately.

Periodic repeats of satellite-based RS are incredibly useful for monitoring change and providing valuable input to conservation management, assessments, and international agreements. This Special Issue, “Unmanned Aerial Vehicle (UAV) and Satellite Synergy for Assessment and Monitoring Biodiversity”, provides a baseline set of information about using RS for monitoring biodiversity in terrestrial, marine, and freshwater realms. A brief overview of how RS can be used for biodiversity and conservation applications, emphasizing to promote knowledge and adopt effective strategies to maintain diversity. Satellite- and airborne-based remote sensing applications with regular repeat cycles provide global coverage, local and regional scales under a variety range of biotic or abiotic drivers in ecosystems with daily view of the entire Earth.

All studies relevant to using satellite and UAV data processing (optical or radar) addressing biodiversity monitoring at different spatial and temporal scales, describing what is required for monitoring biodiversity by remote sensing, recent methodological innovations to achieve biodiversity monitoring, and success stories in applying various techniques for monitoring biodiversity are welcome. All types of original research (including review papers, proof-of-concept manuscripts, validation exercises, and application-oriented contributions) will be considered.

Dr. Azade Deljouei
Dr. Hormoz Sohrabi
Dr. Parth Sarathi Roy
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

  • biodiversity
  • species distribution modeling
  • ecological modeling
  • ecosystem monitoring
  • remote sensing
  • sensors
  • spatial resolution
  • ecoinformatics
  • land use change
  • landscape fragmentation

Published Papers (3 papers)

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Research

19 pages, 20699 KiB  
Article
Applying High-Resolution Satellite and UAS Imagery for Detecting Coldwater Inputs in Temperate Streams of the Iowa Driftless Region
by Niti B. Mishra, Michael J. Siepker and Greg Simmons
Remote Sens. 2023, 15(18), 4445; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15184445 - 09 Sep 2023
Viewed by 1391
Abstract
Coldwater streams are crucial habitats for many biota including Salmonidae and Cottidae species that are unable to tolerate warmer water temperatures. Accurate classification of coldwater streams is essential for their conservation, restoration, and management, especially in light of increasing human disturbance and climate [...] Read more.
Coldwater streams are crucial habitats for many biota including Salmonidae and Cottidae species that are unable to tolerate warmer water temperatures. Accurate classification of coldwater streams is essential for their conservation, restoration, and management, especially in light of increasing human disturbance and climate change. Coldwater streams receive cooler groundwater inputs and, as a result, typically remain ice-free during the winter. Based on this empirical thermal evidence, we examined the potential of very high-resolution (VHR) satellite and uncrewed aerial system (UAS) imagery to (i) detect coldwater streams using semi-automatic classification versus visual interpretation approaches, (ii) examine the physical factors that contribute to inaccuracies in detecting coldwater habitats, and (iii) use the results to identify inaccuracies in existing thermal stream classification datasets and recommend coverage updates. Due to complex site conditions, semi-automated classification was time consuming and produced low mapping accuracy, while visual interpretation produced better results. VHR imagery detected only the highest quality coldwater streams while lower quality streams that still met the thermal and biological criteria to be classified as coldwater remained undetected. Complex stream and site variables (narrow stream width, canopy cover, terrain shadow, stream covered by ice and drifting snow), image quality (spatial resolution, solar elevation angle), and environmental conditions (ambient temperature prior to image acquisition) make coldwater detection challenging; however, UAS imagery is uniquely suited for mapping very narrow streams and can bridge the gap between field data and satellite imagery. Field-collected water temperatures and stream habitat and fish community inventories may be necessary to overcome these challenges and allow validation of remote sensing results. We detected >30 km of coldwater streams that are currently misclassified as warmwater. Overall, visual interpretation of VHR imagery it is a relatively quick and inexpensive approach to detect the location and extent of coldwater stream resources and could be used to develop field monitoring programs to confirm location and extent of coldwater aquatic resources. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle (UAV) and Satellite Synergy for Assessment and Monitoring Biodiversity)
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31 pages, 44817 KiB  
Article
Mangrove Biodiversity Assessment Using UAV Lidar and Hyperspectral Data in China’s Pinglu Canal Estuary
by Yichao Tian, Hu Huang, Guoqing Zhou, Qiang Zhang, Xiaokui Xie, Jinhai Ou, Yali Zhang, Jin Tao and Junliang Lin
Remote Sens. 2023, 15(10), 2622; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15102622 - 18 May 2023
Cited by 4 | Viewed by 2747
Abstract
Mangrove forests are a valuable resource for biological and species diversity, and play a critical role in maintaining biodiversity. However, traditional plant biodiversity survey methods, which rely on labor-intensive field surveys, are not suitable for large-scale continuous spatial observations. To overcome this challenge, [...] Read more.
Mangrove forests are a valuable resource for biological and species diversity, and play a critical role in maintaining biodiversity. However, traditional plant biodiversity survey methods, which rely on labor-intensive field surveys, are not suitable for large-scale continuous spatial observations. To overcome this challenge, we propose an innovative framework for mangrove biodiversity assessment and zoning management based on drone low-altitude remote sensing, integrating data such as vertical structure features and spectral diversity features extracted from on-site measurements, airborne LiDAR, and hyperspectral data. This study focuses on the Maowei Sea mangrove community, located in the estuary of China’s first Pinglu Canal since the founding of the People’s Republic of China. Using the proposed framework, we construct an evaluation index for mangrove biodiversity at the levels of species diversity, ecosystem diversity, and landscape diversity, achieving a quantitative calculation of mangrove biodiversity and an evaluation of spatial distribution patterns. The results show that the biodiversity index of mangroves ranges from 0 to 0.63, with an average value of 0.29, and high-biodiversity areas are primarily concentrated in the southwest of the study area, while low-value areas are mainly located in the north. We also select the elevation and offshore distance of mangrove growth for the spatial zoning of biodiversity. The core area of biodiversity occupies the smallest area, at 2.32%, and is mainly distributed in areas with an elevation of 1.43–1.59 m and an offshore distance of 150.08–204.28 m. Buffer zones and experimental zones account for a significant proportion, with values of 35.99% and 61.69%, respectively. Compared to traditional methods for monitoring mangrove biodiversity, such as community field-sample surveys, the proposed method using unmanned-aerial-vehicle LiDAR and hyperspectral coupling technology to assess mangrove biodiversity and establish a zoning management framework is more conducive to formulating mangrove biodiversity conservation strategies. The study provides a feasible solution for the large-scale biodiversity mapping of mangroves in the Maowei Sea at the estuary of the Pinglu Canal. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle (UAV) and Satellite Synergy for Assessment and Monitoring Biodiversity)
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22 pages, 11982 KiB  
Article
Prediction of Plant Diversity Using Multi-Seasonal Remotely Sensed and Geodiversity Data in a Mountainous Area
by Soroor Rahmanian, Vahid Nasiri, Atiyeh Amindin, Sahar Karami, Sedigheh Maleki, Soheila Pouyan and Stelian Alexandru Borz
Remote Sens. 2023, 15(2), 387; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020387 - 08 Jan 2023
Cited by 1 | Viewed by 2656
Abstract
Plant diversity measurement and monitoring are required for reversing biodiversity loss and ensuring sustainable management. Traditional methods have been using in situ measurements to build multivariate models connecting environmental factors to species diversity. Developments in remotely sensed datasets, processing techniques, and machine learning [...] Read more.
Plant diversity measurement and monitoring are required for reversing biodiversity loss and ensuring sustainable management. Traditional methods have been using in situ measurements to build multivariate models connecting environmental factors to species diversity. Developments in remotely sensed datasets, processing techniques, and machine learning models provide new opportunities for assessing relevant environmental parameters and estimating species diversity. In this study, geodiversity variables containing the topographic and soil variables and multi-seasonal remote-sensing-based features were used to estimate plant diversity in a rangeland from southwest Iran. Shannon’s and Simpson’s indices, species richness, and vegetation cover were used to measure plant diversity and attributes in 96 plots. A random forest model was implemented to predict and map diversity indices, richness, and vegetation cover using 32 remotely sensed and 21 geodiversity variables. Additionally, the linear regression and Spearman’s correlation coefficient were used to assess the relationship between the spectral diversity, expressed as the coefficient of variation in vegetation indices, and species diversity metrics. The results indicated that the synergistic use of geodiversity and multi-seasonal remotely sensed features provide the highest accuracy for Shannon, Simpson, species richness, and vegetation cover indices (R2 up to 0.57), as compared to a single model for each date (February, April, and July). Furthermore, the strongest relationship between species diversity and the coefficient of variation in vegetation indices was based on the remotely-sensed data of April. The approach of multi-model evaluations using the full geodiversity and remotely sensed variables could be a useful method for biodiversity monitoring. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle (UAV) and Satellite Synergy for Assessment and Monitoring Biodiversity)
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