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Remote Sensing Applications to Ecology: Opportunities and Challenges

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

Deadline for manuscript submissions: closed (1 November 2023) | Viewed by 30830

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Prof. Dr. Paul Fergus

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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
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Dr. Carl Chalmers

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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
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Prof. Dr. Serge Wich

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School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: great apes; conservation; drones
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Prof. Dr. Steven Longmore

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Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 5RF, UK
Interests: physics and astronomy; earth and planetary sciences
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Special Issue Information

Dear Colleagues,

We are pleased to announce a new Special Issue entitled “Remote Sensing Applications to Ecology: Opportunities and Challenges”. 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 would encourage submissions with a particular focus on artificial intelligence (AI) algorithms and their use in ecological studies. The 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, monitor forest health and quality, as well as 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

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Related Special Issue

Remote Sensing Applications to Ecology: Opportunities and Challenges II in Remote Sensing (7 articles)

Published Papers (9 papers)

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29 pages, 44990 KiB

Open AccessArticle

SDRSwin: A Residual Swin Transformer Network with Saliency Detection for Infrared and Visible Image Fusion

by Shengshi Li, Guanjun Wang, Hui Zhang and Yonghua Zou

Remote Sens. 2023, 15(18), 4467; https://doi.org/10.3390/rs15184467 - 11 Sep 2023

Cited by 3 | Viewed by 1825

Abstract

Infrared and visible image fusion is a solution that generates an information-rich individual image with different modal information by fusing images obtained from various sensors. Salient detection can better emphasize the targets of concern. We propose a residual Swin Transformer fusion network based on saliency detection, termed SDRSwin, aiming to highlight the salient thermal targets in the infrared image while maintaining the texture details in the visible image. The SDRSwin network is trained with a two-stage training approach. In the first stage, we train an encoder–decoder network based on residual Swin Transformers to achieve powerful feature extraction and reconstruction capabilities. In the second stage, we develop a novel salient loss function to guide the network to fuse the salient targets in the infrared image and the background detail regions in the visible image. The extensive results indicate that our method has abundant texture details with clear bright infrared targets and achieves a better performance than the twenty-one state-of-the-art methods in both subjective and objective evaluation. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges)

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12 pages, 7782 KiB

Open AccessCommunication

Landscape Ecological Risk Assessment for the Tarim River Basin on the Basis of Land-Use Change

by Guangyao Wang, Guangyan Ran, Yaning Chen and Zhengyong Zhang

Remote Sens. 2023, 15(17), 4173; https://doi.org/10.3390/rs15174173 - 25 Aug 2023

Cited by 9 | Viewed by 1764

Abstract

Land-use variation indicates the spatial differentiation of regional ecological risk. Landscape ecological risk assessment (LERA) has been used for the measurement and prediction of environmental quality. In the present study, the land-use dynamics of the Tarim River Basin from 2000 to 2020 were quantitatively analyzed using ENVI 5.6 software based on Landsat TM and ETM+ images (2000, 2010, and 2020). Moreover, the ecological risk level and its spatiotemporal differentiation features were explored using geostatistical methods based on landscape pattern indices. The results show that: (1) From 2000 to 2020, the arable land area increased the most (12,130.272 km²), and the woodland, wetland, water bodies, and building-land areas increased by 2416.541 km², 4103.789 km², 3331.230 km², and 2330.860 km², respectively. However, the bare-land area decreased the most (18,933.943 km²). (2) From 2000 to 2020, a decrease was detected in the landscape ecological risk index (LERI) of the basin, and the very low-, low-, and moderate-risk areas had the largest decrease. In addition, the area of the low- and moderate-risk areas gradually increased, while that of the high-risk areas was reduced. (3) The conversion rate of low-risk areas to very low-risk areas was the largest (5144.0907 km²/a), followed by that of high-risk areas to moderate-risk areas (4994.4765 km²/a). Therefore, the overall landscape ecological risk (LER) of the basin was reduced from 2000 to 2020, but the ecological risk of some areas, especially that of the glaciers and permanent snow-covered areas, still needs close attention. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges)

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21 pages, 7755 KiB

Open AccessArticle

Identification and Optimization of County-Level Ecological Spaces under the Dual-Carbon Target: A Case Study of Shaanxi Province, China

by Jianfeng Li, Siqi Liu, Biao Peng, Huping Ye and Zhuoying Zhang

Remote Sens. 2023, 15(16), 4009; https://doi.org/10.3390/rs15164009 - 13 Aug 2023

Cited by 2 | Viewed by 2002

Abstract

County-level ecological space, as a crucial level in optimizing the land spatial system, plays a pivotal role in “undertaking superior planning and guiding subordinate implementation”. From a spatial optimization perspective, effectively implementing the dual-carbon goal by increasing carbon sinks in specific ecological space units is essential. This study focused on 107 districts and counties in Shaanxi Province, China, aiming to construct a comprehensive multivariate identification system for ecological space under the dual-carbon target based on an analysis of the spatiotemporal distribution characteristics and driving factors of county-level carbon sinks. Furthermore, by analyzing the ecological spatial distribution pattern, carbon sink land structure, and county clustering characteristics, the study explored differential optimization strategies for ecological spaces of different county types to enhance carbon sinks in the ecosystem. The results demonstrated that: (1) From 2000 to 2020, the total carbon sink in Shaanxi Province exhibited an initial increase followed by a decrease, with a decline from 864.39 × 10⁴ t to 863.21 × 10⁴ t. The county-level distribution of total carbon sink displayed significant spatial heterogeneity, with an overall pattern of south > north > central. (2) The interaction among factors enhanced the explanatory power for spatial differentiation of county-level carbon sinks compared to individual factors, exerting an important impact on the spatial distribution pattern of carbon sinks. (3) The distribution of ecological space in Shaanxi Province was highly uneven, with the core ecological space primarily concentrated in the southern and north-central regions. The proportions of low carbon sink (Type I), medium carbon sink (Type II), and high carbon sink (Type III) counties were 35.51%, 18.69%, and 45.80%, respectively. For different types of county-level ecological spaces, this study proposed a differentiated optimization strategy aimed at reducing carbon emissions and enhancing carbon sink. The results will provide theoretical and technical support for regional ecological construction and land spatial optimization, holding significant practical implications for achieving the dual-carbon goal and addressing climate change. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges)

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14 pages, 3003 KiB

Open AccessArticle

Sentinel-2 Time Series and Classifier Fusion to Map an Aquatic Invasive Plant Species along a River—The Case of Water-Hyacinth

by Nuno Mouta, Renato Silva, Eva M. Pinto, Ana Sofia Vaz, Joaquim M. Alonso, João F. Gonçalves, João Honrado and Joana R. Vicente

Remote Sens. 2023, 15(13), 3248; https://doi.org/10.3390/rs15133248 - 23 Jun 2023

Cited by 10 | Viewed by 3434

Abstract

Freshwater ecosystems host high levels of biodiversity but are also highly vulnerable to biological invasions. Aquatic Invasive Alien Plant Species (aIAPS) can cause detrimental effects on freshwater ecosystems and their services to society, raising challenges to decision-makers regarding their correct management. Spatially and temporally explicit information on the occurrence of aIAPS in dynamic freshwater systems is essential to implement efficient regional and local action plans. The use of unmanned aerial vehicle imagery synchronized with free Sentinel-2 multispectral data allied with classifier fusion techniques may support more efficient monitoring actions for non-stationary aIAPS. Here, we explore the advantages of such a novel approach for mapping the invasive water-hyacinth (Eichhornia crassipes) in the Cávado River (northern Portugal). Invaded and non-invaded areas were used to explore the evolution of spectral attributes of Eichhornia crassipes through a time series (processed by a super-resolution algorithm) that covers March 2021 to February 2022 and to build an occurrence dataset (presence or absence). Analysis of the spectral behavior throughout the year allowed the detection of spectral regions with greater capacity to distinguish the target plant from the surrounding environment. Classifier fusion techniques were implemented in the biomod2 predictive modelling package and fed with selected spectral regions to firstly extract a spectral signature from the synchronized day and secondly to identify pixels with similar reflectance values over time. Predictions from statistical and machine-learning algorithms were ensembled to map invaded spaces across the whole study area during all seasons with classifications attaining high accuracy values (True Skill Statistic, TSS: 0.932; Area Under the Receiver Operating Curve, ROC: 0.992; Kappa: 0.826). Our results provide evidence of the potential of our approach to mapping plant invaders in dynamic freshwater systems over time, applicable in the assessment of the success of control actions as well as in the implementation of long-term strategic monitoring. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges)

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27 pages, 8989 KiB

Open AccessArticle

Estimating Three-Dimensional Distribution of Leaf Area Using Airborne LiDAR in Deciduous Broad-Leaved Forest

by Yoshio Awaya and Kazuho Araki

Remote Sens. 2023, 15(12), 3043; https://doi.org/10.3390/rs15123043 - 10 Jun 2023

Cited by 3 | Viewed by 1865

Abstract

We examined the performance of airborne light detection and ranging (LiDAR) data obtained in 2011 for leaf area estimation in deciduous broad-leaved forest using the Beer–Lambert law in Takayama, Gifu, Japan. We estimated leaf area index (LAI, allometry-LAI) and vertical leaf area density (LAD) using field survey data by applying allometric equations to estimate leaf-area of trees and a Weibull distribution equation to estimate vertical leaf distribution. We then estimated extinction coefficients (Ke) of LiDAR data for three height layers from the ground to the canopy top using the vertical LAD and vertical laser pulse distribution. The estimated PAI (LiDAR-PAI) using the Beer–Lambert law and Ke, when treating the canopies as three height layers, showed a significant linear relationship with allometry-LAI (p < 0.001). However, LiDAR-PAI when treating the canopies as single layer saturated at a PAI of six. It was similar to the lesser PAI estimation by hemispherical photography or relative photosynthetic photon flux density which treated the canopy as a single layer, compared to LAI measurements by litter traps. It is therefore important to allocate distinct Ke values to each of the multiple height layers for an accurate estimation of PAI and vertical PAD when applying the Beer–Lambert law to airborne LiDAR data. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges)

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29 pages, 3113 KiB

Open AccessArticle

Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation Using Deep Learning and 3/4G Camera Traps

by Paul Fergus, Carl Chalmers, Steven Longmore, Serge Wich, Carmen Warmenhove, Jonathan Swart, Thuto Ngongwane, André Burger, Jonathan Ledgard and Erik Meijaard

Remote Sens. 2023, 15(11), 2730; https://doi.org/10.3390/rs15112730 - 24 May 2023

Cited by 13 | Viewed by 4028

Abstract

The biodiversity of our planet is under threat, with approximately one million species expected to become extinct within decades. The reason: negative human actions, which include hunting, overfishing, pollution, and the conversion of land for urbanisation and agricultural purposes. Despite significant investment from charities and governments for activities that benefit nature, global wildlife populations continue to decline. Local wildlife guardians have historically played a critical role in global conservation efforts and have shown their ability to achieve sustainability at various levels. In 2021, COP26 recognised their contributions and pledged USD 1.7 billion per year; however this is a fraction of the global biodiversity budget available (between USD 124 billion and USD 143 billion annually) given they protect 80% of the planets biodiversity. This paper proposes a radical new solution based on “Interspecies Money”, where animals own their own money. Creating a digital twin for each species allows animals to dispense funds to their guardians for the services they provide. For example, a rhinoceros may release a payment to its guardian each time it is detected in a camera trap as long as it remains alive and well. To test the efficacy of this approach, 27 camera traps were deployed over a 400 km

^{2}

area in Welgevonden Game Reserve in Limpopo Province in South Africa. The motion-triggered camera traps were operational for ten months and, using deep learning, we managed to capture images of 12 distinct animal species. For each species, a makeshift bank account was set up and credited with GBP 100. Each time an animal was captured in a camera and successfully classified, 1 penny (an arbitrary amount—mechanisms still need to be developed to determine the real value of species) was transferred from the animal account to its associated guardian. The trial demonstrated that it is possible to achieve high animal detection accuracy across the 12 species with a sensitivity of 96.38%, specificity of 99.62%, precision of 87.14%, F1 score of 90.33%, and an accuracy of 99.31%. The successful detections facilitated the transfer of GBP 185.20 between animals and their associated guardians. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges)

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22 pages, 13762 KiB

Open AccessArticle

Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning

by Carl Chalmers, Paul Fergus, Serge Wich, Steven N. Longmore, Naomi Davies Walsh, Philip A. Stephens, Chris Sutherland, Naomi Matthews, Jens Mudde and Amira Nuseibeh

Remote Sens. 2023, 15(10), 2638; https://doi.org/10.3390/rs15102638 - 18 May 2023

Cited by 13 | Viewed by 3297

Abstract

Birds are important indicators for monitoring both biodiversity and habitat health; they also play a crucial role in ecosystem management. Declines in bird populations can result in reduced ecosystem services, including seed dispersal, pollination and pest control. Accurate and long-term monitoring of birds to identify species of concern while measuring the success of conservation interventions is essential for ecologists. However, monitoring is time-consuming, costly and often difficult to manage over long durations and at meaningfully large spatial scales. Technology such as camera traps, acoustic monitors and drones provide methods for non-invasive monitoring. There are two main problems with using camera traps for monitoring: (a) cameras generate many images, making it difficult to process and analyse the data in a timely manner; and (b) the high proportion of false positives hinders the processing and analysis for reporting. In this paper, we outline an approach for overcoming these issues by utilising deep learning for real-time classification of bird species and automated removal of false positives in camera trap data. Images are classified in real-time using a Faster-RCNN architecture. Images are transmitted over 3/4G cameras and processed using Graphical Processing Units (GPUs) to provide conservationists with key detection metrics, thereby removing the requirement for manual observations. Our models achieved an average sensitivity of 88.79%, a specificity of 98.16% and accuracy of 96.71%. This demonstrates the effectiveness of using deep learning for automatic bird monitoring. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges)

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17 pages, 3297 KiB

Open AccessTechnical Note

Validating a Tethered Balloon System and Optical Technologies for Marine Wildlife Detection and Tracking

by Alicia Amerson, Ilan Gonzalez-Hirshfeld and Darielle Dexheimer

Remote Sens. 2023, 15(19), 4709; https://doi.org/10.3390/rs15194709 - 26 Sep 2023

Cited by 1 | Viewed by 8315

Abstract

The interactions between marine wildlife and marine energy devices are not well understood, leading to regulatory delays for device deployments and testing. Technologies that enable marine wildlife observations can help to fill data gaps and reduce uncertainties about animal–device interactions. A validation test conducted in Galveston Bay near La Porte, Texas, in December 2022 used a technology package consisting of a tethered balloon system and three independent sensor systems, including three-band visible, eight-band multispectral, and single-band thermal to detect three marine-mammal-shaped surrogates. The field campaign aimed to provide an initial step to evaluating the use of the TBS and the effectiveness of the sensor suite for marine wildlife observations and detection. From 2 December to 7 December 2022, 6 flights were conducted under varying altitudes and environmental conditions resulting in the collection of 5454 images. A subset of the images was classified and analyzed with two collection criteria including Beaufort wind force scale and TBS altitude to assess a range of observations of a surrogate from near-shore to offshore based on pixel count. The results of this validation test demonstrate the potential for using TBSs and imaging sensors for marine wildlife observations and offer valuable information for further development and application of this technology for marine energy and other blue economy sectors. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges)

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11 pages, 1662 KiB

Open AccessTechnical Note

Using Drones to Determine Chimpanzee Absences at the Edge of Their Distribution in Western Tanzania

by Serge A. Wich, Noémie Bonnin, Anja Hutschenreiter, Alex K. Piel, Adrienne Chitayat, Fiona A. Stewart, Lilian Pintea and Jeffrey T. Kerby

Remote Sens. 2023, 15(8), 2019; https://doi.org/10.3390/rs15082019 - 11 Apr 2023

Cited by 3 | Viewed by 2217

Abstract

Effective species conservation management relies on detailed species distribution data. For many species, such as chimpanzees (Pan troglodytes), distribution data are collected during ground surveys. For chimpanzees, such ground surveys usually focus on detection of the nests they build instead of detection of the chimpanzees themselves due to their low density. However, due to the large areas they still occur in, such surveys are very costly to conduct and repeat frequently to monitor populations over time. Species distribution models are more accurate if they include presence as well as absence data. Earlier studies used drones to determine chimpanzee presence using nests. In this study, therefore, we explored the use of drones to determine the absence of chimpanzee nests in areas we flew over on the edge of the chimpanzee distribution in western Tanzania. We conducted 13 flights with a fixed-wing drone and collected 3560 images for which manual inspection took 180 h. Flights were divided into a total of 746 25 m² plots for which we determined the absence probability of nests. In three flights, we detected nests, in eight, absence was assumed based on a 95% probability criterion, and in two flights, nest absence could not be assumed. Our study indicates that drones can be used to cover relatively large areas to determine the absence of chimpanzees. To fully benefit from the usage of drones to determine the presence and absence of chimpanzees, it is crucial that methods are developed to automate nest detection in images. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges)

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