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Local-Scale Remote Sensing for Biodiversity, Ecology and Conservation
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Local-Scale Remote Sensing for Biodiversity, Ecology and Conservation

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 9799

Special Issue Editor

Dr. Jesper Erenskjold Moeslund

E-Mail Website
Guest Editor
Department of Ecoscience, Aarhus Universitet, Aarhus, Denmark
Interests: biodiversity; remote sensing; lidar; ecology; vegetation; local-scale natural dynamics; conservation; ecoinformatics; biogeography; threatened species

Special Issue Information

Dear Colleagues,

Remote sensing techniques have been used widely to study nature for several decades. In recent times, remotely sensed data have increased massively in quantity and spectral and spatial resolution, rendering them increasingly suitable for insights into local-scale (i.e., a few meters) natural processes and ditto biodiversity patterns—to large extents (i.e., over several thousand km2). This now opens possibilities to study local-scale ecological phenomena across broad geographic and abiotic gradients. Such studies are needed to understand the determinants of biodiversity patterns and to ensure effective and management-relevant next-generation mapping and monitoring systems which are core elements in stopping the ongoing global biodiversity crisis.

This Special Issue aims to present the latest advances on current work to enhance our understanding of local-scale ecological phenomena and the mapping and monitoring thereof by use of remote sensing methods and data. It has a particular focus on biodiversity- and conservation-related studies. This SI will cover only terrestrial biodiversity, meaning biodiversity that is not confined strictly to aquatic or marine environments. In this SI, the term ‘biodiversity’ will be considered broadly and therefore covers not only species diversity but also functional diversity and species and functional composition. “Remote sensing” is considered to mean sensing systems that gather data over a certain spatial extent by means of moving or mobile (e.g., tripod or backpack-mounted) platforms. We welcome contributions in all fields where remote sensing is being developed and applied for local-scale studies of ecology, biodiversity, and conservation, including but not limited to:

  • Application of satellite RS data;
  • Application of piloted and remotely piloted aircrafts;
  • Application of multisensor methods;
  • Integration of plot and coverage ecological or biodiversity related data;
  • Remote sensing for studying both observed and dark diversity;
  • Remote sensing for aiding in conservation;
  • Integration of remote sensing biodiversity data with eDNA biodiversity data;
  • Mappings made with remote sensing methods, applied as baselines for biodiversity monitoring, conservation, or restoration;
  • Studies of the relationships between remote sensing measures and biodiversity measures.

Dr. Jesper Erenskjold Moeslund
Guest Editor

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
  • ecology
  • local scale
  • remote sensing
  • conservation
  • restoration
  • laser scanning
  • satellite sensors
  • drones

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Published Papers (5 papers)

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Research

13 pages, 5370 KiB  
Communication
Predicting Abiotic Soil Characteristics Using Sentinel-2 at Nature-Management-Relevant Spatial Scales and Extents
by Jesper Erenskjold Moeslund and Christian Frølund Damgaard
Remote Sens. 2024, 16(16), 3094; https://doi.org/10.3390/rs16163094 - 22 Aug 2024
Viewed by 747
Abstract
Knowledge of local plant community characteristics is imperative for practical nature planning and management, and for understanding plant diversity and distribution drivers. Today, retrieving such data is only possible by fieldwork and is hence costly both in time and money. Here, we used [...] Read more.
Knowledge of local plant community characteristics is imperative for practical nature planning and management, and for understanding plant diversity and distribution drivers. Today, retrieving such data is only possible by fieldwork and is hence costly both in time and money. Here, we used nine bands from multispectral high-to-medium resolution (10–60 m) satellite data (Sentinel-2) and machine learning to predict local vegetation plot characteristics over a broad area (approx. 30,000 km2) in terms of plants’ preferences for soil moisture, soil fertility, and pH, mirroring the levels of the corresponding actual soil factors. These factors are believed to be among the most important for local plant community composition. Our results showed that there are clear links between the Sentinel-2 data and plants’ abiotic soil preferences, and using solely satellite data we achieved predictive powers between 26 and 59%, improving to around 70% when habitat information was included as a predictor. This shows that plants’ abiotic soil preferences can be detected quite well from space, but also that retrieving soil characteristics using satellites is complicated and that perfect detection of soil conditions using remote sensing—if at all possible—needs further methodological and data development. Full article
(This article belongs to the Special Issue Local-Scale Remote Sensing for Biodiversity, Ecology and Conservation)
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22 pages, 6348 KiB  
Article
Analysis of Vegetation Canopy Spectral Features and Species Discrimination in Reclamation Mining Area Using In Situ Hyperspectral Data
by Xu Wang, Hang Xu, Jianwei Zhou, Xiaonan Fang, Shuang Shuai and Xianhua Yang
Remote Sens. 2024, 16(13), 2372; https://doi.org/10.3390/rs16132372 - 28 Jun 2024
Cited by 1 | Viewed by 993
Abstract
The effective identification of reclaimed vegetation species is important for the subsequent management of ecological restoration projects in mining areas. Hyperspectral remote sensing has been used for identifying vegetation species. However, few studies have focused on mine-reclaimed vegetation. Even if there are studies [...] Read more.
The effective identification of reclaimed vegetation species is important for the subsequent management of ecological restoration projects in mining areas. Hyperspectral remote sensing has been used for identifying vegetation species. However, few studies have focused on mine-reclaimed vegetation. Even if there are studies in this field, the methods used by the researches are mainly traditional discriminant analyses. The environmental conditions of reclaimed mining areas lead to significant intraclass spectral differences in reclaimed vegetation, and there is uncertainty in the identification of reclaimed vegetation species using traditional classification models. In this study, in situ hyperspectral data were used to analyze the spectral variation in the reclaimed vegetation canopy in mine restoration areas and evaluate their potential in the identification of reclaimed vegetation species. We measured the canopy spectral reflectance of five vegetation species in the study area using the ASD FieldSpec 4. The spectral characteristics of vegetation canopy were analyzed by mathematically transforming the original spectra, including Savitzky–Golay smoothing, first derivative, reciprocal logarithm, and continuum removal. In addition, we calculated indicators for identifying vegetation species using mathematically transformed hyperspectral data. The metrics were submitted to a feature selection procedure (recursive feature elimination) to optimize model performance and reduce its complexity. Different classification algorithms (regularized logistic regression, back propagation neural network, support vector machines with radial basis function kernel, and random forest) were constructed to explore optimal procedures for identifying reclaimed vegetation species based on the best feature metrics. The results showed that the separability between the spectra of reclaimed vegetation can be improved by applying different mathematical transformations to the spectra. The most important spectral metrics extracted by the recursive feature elimination (RFE) algorithm were related to the visible and near-infrared spectral regions, mainly in the vegetation pigments and water absorption bands. Among the four identification models, the random forest had the best recognition ability for reclaimed vegetation species, with an overall accuracy of 0.871. Our results provide a quantitative reference for the future exploration of reclaimed vegetation mapping using hyperspectral data. Full article
(This article belongs to the Special Issue Local-Scale Remote Sensing for Biodiversity, Ecology and Conservation)
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Graphical abstract

20 pages, 14478 KiB  
Article
Terrestrial and Airborne Lidar to Quantify Shrub Cover for Canada Lynx (Lynx canadensis) Habitat Using Machine Learning
by Jonathan L. Batchelor, Andrew T. Hudak, Peter Gould and L. Monika Moskal
Remote Sens. 2023, 15(18), 4434; https://doi.org/10.3390/rs15184434 - 9 Sep 2023
Viewed by 1918
Abstract
The Canada lynx is listed as a threatened species, and as such, the identification and conservation of lynx habitats is of significant concern. Lynxes require areas with high amounts of horizontal cover made up of ground vegetation. Lidar offers a robust method of [...] Read more.
The Canada lynx is listed as a threatened species, and as such, the identification and conservation of lynx habitats is of significant concern. Lynxes require areas with high amounts of horizontal cover made up of ground vegetation. Lidar offers a robust method of quantifying vegetation structure, and airborne lidar has been acquired across large areas of potential lynx habitat. Unfortunately, airborne lidar is often not able to directly measure understory horizontal cover due to occlusion from the upper branches. Terrestrial lidar does directly measure understory horizontal cover and can be used as training data for larger area models using airborne lidar. In this study, we acquired 168 individual terrestrial lidar scans (TLS) across 42 sites in north-central Washington state. We generated metrics from the single-scan TLS plots using depth maps, a digital cover board, and voxels. Using our TLS metrics as the training data for the airborne lidar acquired for the entire Loomis State Forest, we were able to produce a model using xgboost with 85% accuracy. We believe our study shows that single-scan TLS plots can be used effectively to quantify fine-scale forest structure elements relevant to species habitat, to then inform larger area models using airborne lidar. Full article
(This article belongs to the Special Issue Local-Scale Remote Sensing for Biodiversity, Ecology and Conservation)
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14 pages, 4365 KiB  
Communication
Towards Prediction and Mapping of Grassland Aboveground Biomass Using Handheld LiDAR
by Jeroen S. de Nobel, Kenneth F. Rijsdijk, Perry Cornelissen and Arie C. Seijmonsbergen
Remote Sens. 2023, 15(7), 1754; https://doi.org/10.3390/rs15071754 - 24 Mar 2023
Cited by 2 | Viewed by 2649
Abstract
The Oostvaardersplassen nature reserve in the Netherlands is grazed by large herbivores. Due to their increasing numbers, the area became dominated by short grazed grasslands and biodiversity decreased. From 2018, the numbers are controlled to create a diverse landscape. Fine-scale mapping and monitoring [...] Read more.
The Oostvaardersplassen nature reserve in the Netherlands is grazed by large herbivores. Due to their increasing numbers, the area became dominated by short grazed grasslands and biodiversity decreased. From 2018, the numbers are controlled to create a diverse landscape. Fine-scale mapping and monitoring of the aboveground biomass is a tool to evaluate management efforts to restore a heterogeneous and biodiverse area. We developed a random forest model that describes the correlation between field-based samples of aboveground biomass and fifteen height-related vegetation metrics that were calculated from high-density point clouds collected with a handheld LiDAR. We found that two height-related metrics (maximum and 75th percentile of all height points) produced the best correlation with an R2 of 0.79 and a root-mean-square error of 0.073 kg/m2. Grassland segments were mapped by applying a segmentation routine on the normalized grassland’s digital surface model. For each grassland segment, the aboveground biomass was mapped using the point cloud and the random forest AGB model. Visual inspection of video recordings of the scanned trajectories and field observations of grassland patterns suggest that drift and stretch effects of the point cloud influence the map. We recommend optimizing data collection using looped trajectories during scanning to avoid point cloud drift and stretch, test horizontal vegetation metrics in the model development and include seasonal influence of the vegetation status. We conclude that handheld LiDAR is a promising technique to retrieve detailed height-related metrics in grasslands that can be used as input for semi-automated spatio-temporal modelling of grassland aboveground biomass for supporting management decisions in nature reserves. Full article
(This article belongs to the Special Issue Local-Scale Remote Sensing for Biodiversity, Ecology and Conservation)
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15 pages, 4258 KiB  
Article
Assessment of the Capability of Landsat and BiodivMapR to Track the Change of Alpha Diversity in Dryland Disturbed by Mining
by Yan Zhang, Jiajia Tang, Qinyu Wu, Shuai Huang, Xijun Yao and Jing Dong
Remote Sens. 2023, 15(6), 1554; https://doi.org/10.3390/rs15061554 - 12 Mar 2023
Cited by 3 | Viewed by 2113
Abstract
Remotely sensed spectral diversity is a promising method for investigating biodiversity. However, studies designed to assess the effectiveness of tracking changes in diversity using historical satellite imagery are lacking. This study employs open-access multispectral Landsat imagery and the BiodivMapR package to estimate the [...] Read more.
Remotely sensed spectral diversity is a promising method for investigating biodiversity. However, studies designed to assess the effectiveness of tracking changes in diversity using historical satellite imagery are lacking. This study employs open-access multispectral Landsat imagery and the BiodivMapR package to estimate the multi-temporal alpha diversity in drylands affected by mining. Multi-temporal parameters of alpha diversity were identified, such as vegetation indices, buffer zone size, and the number of clusters. Variations in alpha diversity were compared for various plant communities over time. The results showed that this method could effectively assess the alpha diversity of vegetation (R2, 0.68). The optimal parameters used to maximize the accuracy of alpha diversity were NDVI threshold, 0.01; size of buffer zones, 120 m × 120 m; number of clusters, 100. The root mean square error of the alpha diversity of herbs was lowest (0.26), while those of shrub and tree communities were higher (0.34–0.41). During the period 1990–2020, the study area showed an overall trend of increasing diversity, with surface mining causing a significant decrease in diversity when compared with underground mining. This illustrates that the quick development of remote sensing and image processing techniques offers new opportunities for monitoring diversity in both single and multiple time phases. Researchers should consider the plant community types involved and select locally suitable parameters. In the future, the generation of long-time series and finer resolution maps of diversity should be studied further in the aspects of spatial, functional, taxonomic, and phylogenetic diversity. Full article
(This article belongs to the Special Issue Local-Scale Remote Sensing for Biodiversity, Ecology and Conservation)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Predicting and Mapping Grassland aboveground biomass using handheld LiDAR
Authors: Arie C. Seijmonsbergen
Affiliation: Institute for Biodiversity and Ecosystem DynamicsComputational Geo-EcologyUniversity of AmsterdamPO Box 942481090 GEThe Netherlands

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