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Hyperspectral Remote Sensing for Biodiversity Mapping

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 34666

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Special Issue Editors

Dr. J. Pablo Arroyo-Mora

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Guest Editor

Flight Research Laboratory, National Research council of Canada, 1920 Research Private, U-61, Ottawa, ON K1V 2B1, Canada
Interests: UAV; airborne; hyperspectral; biodiversity; carbon; tropical; peatlands
Special Issues, Collections and Topics in MDPI journals

Dr. Margaret Kalacska

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Guest Editor

Department of Geography, Université McGill, Montreal, QC, Canada
Interests: hyperspectral; satellite imagery; land cover change; signal processing; biodiversity; thermal imaging; UAV
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Significant advances have been made in the applications of hyperspectral remote sensing over the last 30-years. With the maturation of field spectroscopy techniques (e.g. leaf level data), the continuous improvement of airborne hyperspectral sensors with VISNIR, as well as full range capabilities (including SWIR and LWIR), and the more recent development of UAV based hyperspectral systems, these data represent more than ever, a significant opportunity for biodiversity mapping. Current threats to biodiversity such as climate change, deforestation, and invasive species among others have only intensified, and the need to continuously study biodiversity responses to these threats, is paramount to document and assess the changes across different ecosystems (e.g. forests, corals, fresh water biota, peatlands, etc.). Furthermore, the need for sound baseline methods for mapping biodiversity with hyperspectral data (e.g. proper data acquisition techniques and analysis) at different spectral, spatial and temporal scales is a major requirement.

This Special Issue will include studies focused on the use of hyperspectral data at different spatial scales (e.g. leaf level, canopy, stand, landscape, regional) for biodiversity mapping, with special attention to the use of scientifically sound data collection techniques (e.g. well calibrated data) and given the wealth that hyperspectral data provides, novel approaches for data analysis. We invite authors to submit recent research that encompass the following topics using hyperspectral data:

Biodiversity mapping
Species spectral differences
Species composition
Novel applications to terrestrial and aquatic systems
Multi-scale analyses, including but not limited to field sampling, UAV, airborne and satellite
Scaling approaches between platforms
Calibration/validation and good practices for hyperspectral data collection and analysis for biodiversity assessment
Data fusion between hyperspectral and other sources (e.g. LiDAR)

Dr. J. Pablo Arroyo-Mora
Dr. Margaret Kalacska
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
Hyperspectral
Spectroscopy
UAV
Airborne
Satellite
Species enumeration
Conservation

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Published Papers (6 papers)

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Research

28 pages, 7462 KiB

Open AccessArticle

Spectral Complexity of Hyperspectral Images: A New Approach for Mangrove Classification

by Patrick Osei Darko, Margaret Kalacska, J. Pablo Arroyo-Mora and Matthew E. Fagan

Remote Sens. 2021, 13(13), 2604; https://doi.org/10.3390/rs13132604 - 2 Jul 2021

Cited by 20 | Viewed by 5427

Abstract

Hyperspectral remote sensing across multiple spatio-temporal scales allows for mapping and monitoring mangrove habitats to support urgent conservation efforts. The use of hyperspectral imagery for assessing mangroves is less common than for terrestrial forest ecosystems. In this study, two well-known measures in statistical physics, Mean Information Gain (MIG) and Marginal Entropy (ME), have been adapted to high spatial resolution (2.5 m) full range (Visible-Shortwave-Infrared) airborne hyperspectral imagery. These two spectral complexity metrics describe the spatial heterogeneity and the aspatial heterogeneity of the reflectance. In this study, we compare MIG and ME with surface reflectance for mapping mangrove extent and species composition in the Sierpe mangroves in Costa Rica. The highest accuracy for separating mangroves from forest was achieved with visible-near infrared (VNIR) reflectance (98.8% overall accuracy), following by shortwave infrared (SWIR) MIG and ME (98%). Our results also show that MIG and ME can discriminate dominant mangrove species with higher accuracy than surface reflectance alone (e.g., MIG–VNIR = 93.6% vs. VNIR Reflectance = 89.7%). Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing for Biodiversity Mapping)

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19 pages, 3890 KiB

Open AccessArticle

Foliar Spectra and Traits of Bog Plants across Nitrogen Deposition Gradients

by Alizée Girard, Anna K. Schweiger, Alexis Carteron, Margaret Kalacska and Etienne Laliberté

Remote Sens. 2020, 12(15), 2448; https://doi.org/10.3390/rs12152448 - 30 Jul 2020

Cited by 17 | Viewed by 4899

Abstract

Bogs, as nutrient-poor ecosystems, are particularly sensitive to atmospheric nitrogen (N) deposition. Nitrogen deposition alters bog plant community composition and can limit their ability to sequester carbon (C). Spectroscopy is a promising approach for studying how N deposition affects bogs because of its ability to remotely determine changes in plant species composition in the long term as well as shorter-term changes in foliar chemistry. However, there is limited knowledge on the extent to which bog plants differ in their foliar spectral properties, how N deposition might affect those properties, and whether subtle inter- or intraspecific changes in foliar traits can be spectrally detected. The objective of the study was to assess the effect of N deposition on foliar traits and spectra. Using an integrating sphere fitted to a field spectrometer, we measured spectral properties of leaves from the four most common vascular plant species (Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum and Eriophorum vaginatum) in three bogs in southern Québec and Ontario, Canada, exposed to different atmospheric N deposition levels, including one subjected to a 18-year N fertilization experiment. We also measured chemical and morphological properties of those leaves. We found detectable intraspecific changes in leaf structural traits and chemistry (namely chlorophyll b and N concentrations) with increasing N deposition and identified spectral regions that helped distinguish the site-specific populations within each species. Most of the variation in leaf spectral, chemical, and morphological properties was among species. As such, species had distinct spectral foliar signatures, allowing us to identify them with high accuracy with partial least squares discriminant analyses (PLSDA). Predictions of foliar traits from spectra using partial least squares regression (PLSR) were generally accurate, particularly for the concentrations of N and C, soluble C, leaf water, and dry matter content (<10% RMSEP). However, these multi-species PLSR models were not accurate within species, where the range of values was narrow. To improve the detection of short-term intraspecific changes in functional traits, models should be trained with more species-specific data. Our field study showing clear differences in foliar spectra and traits among species, and some within-species differences due to N deposition, suggest that spectroscopy is a promising approach for assessing long-term vegetation changes in bogs subject to atmospheric pollution. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing for Biodiversity Mapping)

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25 pages, 5524 KiB

Open AccessArticle

Classification of Atlantic Coastal Sand Dune Vegetation Using In Situ, UAV, and Airborne Hyperspectral Data

by Quentin Laporte-Fauret, Bertrand Lubac, Bruno Castelle, Richard Michalet, Vincent Marieu, Lionel Bombrun, Patrick Launeau, Manuel Giraud, Cassandra Normandin and David Rosebery

Remote Sens. 2020, 12(14), 2222; https://doi.org/10.3390/rs12142222 - 11 Jul 2020

Cited by 29 | Viewed by 6624

Abstract

Mapping coastal dune vegetation is critical to understand dune mobility and resilience in the context of climate change, sea level rise, and increased anthropogenic pressure. However, the identification of plant species from remotely sensed data is tedious and limited to broad vegetation communities, while such environments are dominated by fragmented and small-scale landscape patterns. In June 2019, a comprehensive multi-scale survey including unmanned aerial vehicle (UAV), hyperspectral ground, and airborne data was conducted along approximately 20 km of a coastal dune system in southwest France. The objective was to generate an accurate mapping of the main sediment and plant species ground cover types in order to characterize the spatial distribution of coastal dune stability patterns. Field and UAV data were used to assess the quality of airborne data and generate a robust end-member spectral library. Next, a two-step classification approach, based on the normalized difference vegetation index and Random Forest classifier, was developed. Results show high performances with an overall accuracy of 100% and 92.5% for sand and vegetation ground cover types, respectively. Finally, a coastal dune stability index was computed across the entire study site. Different stability patterns were clearly identified along the coast, highlighting for the first time the high potential of this methodology to support coastal dune management. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing for Biodiversity Mapping)

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33 pages, 7146 KiB

Open AccessArticle

Recursive Feature Elimination and Random Forest Classification of Natura 2000 Grasslands in Lowland River Valleys of Poland Based on Airborne Hyperspectral and LiDAR Data Fusion

by Luca Demarchi, Adam Kania, Wojciech Ciężkowski, Hubert Piórkowski, Zuzanna Oświecimska-Piasko and Jarosław Chormański

Remote Sens. 2020, 12(11), 1842; https://doi.org/10.3390/rs12111842 - 6 Jun 2020

Cited by 62 | Viewed by 6249

Abstract

The use of hyperspectral (HS) and LiDAR acquisitions has a great potential to enhance mapping and monitoring practices of endangered grasslands habitats, beyond conventional botanical field surveys. In this study we assess the potentiality of recursive feature elimination (RFE) in combination with random forest (RF) classification in extracting the main HS and LiDAR features needed to map selected Natura 2000 grasslands along Polish lowland river valleys, in particular alluvial meadows 6440, lowland hay meadows 6510, and xeric and calcareous grasslands 6120. We developed an automated RFE-RF system capable to combine the potentials of both techniques and applied it to multiple acquisitions. Several LiDAR-based products and different spectral indices (SI) were computed and used as input in the system, with the aim of shedding light on the best-to-use features. Results showed a remarkable increase in classification accuracy when LiDAR and SI products are added to the HS dataset, strengthening in particular the importance of employing LiDAR in combination with HS. Using only the 24 optimal features selection generalized over the three study areas, strongly linked to the highly heterogeneous characteristics of the habitats and landscapes investigated, it was possible to achieve rather high classification results (K around 0.7–0.77 and habitats F1 accuracy around 0.8–0.85), indicating that the selected Natura 2000 meadows and dry grasslands habitats can be automatically mapped by airborne HS and LiDAR data. Similar approaches might be considered for future monitoring activities in the context of habitats protection and conservation. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing for Biodiversity Mapping)

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33 pages, 12246 KiB

Open AccessArticle

Monitoring of Canopy Stress Symptoms in New Zealand Kauri Trees Analysed with AISA Hyperspectral Data

by Jane J. Meiforth, Henning Buddenbaum, Joachim Hill and James Shepherd

Remote Sens. 2020, 12(6), 926; https://doi.org/10.3390/rs12060926 - 13 Mar 2020

Cited by 15 | Viewed by 5875

Abstract

The endemic New Zealand kauri trees (Agathis australis) are under threat by the deadly kauri dieback disease (Phytophthora agathidicida (PA)). This study aimed to identify spectral index combinations for characterising visible stress symptoms in the kauri canopy. The analysis is based on an aerial AISA hyperspectral image mosaic and 1258 reference crowns in three study sites in the Waitakere Ranges west of Auckland. A field-based assessment scheme for canopy stress symptoms (classes 1–5) was further optimised for use with RGB aerial images. A combination of four indices with six bands in the spectral range 450–1205 nm resulted in a correlation of 0.93 (mean absolute error 0.27, RMSE 0.48) for all crown sizes. Comparable results were achieved with five indices in the 450–970 nm region. A Random Forest (RF) regression gave the most accurate predictions while a M5P regression tree performed nearly as well and a linear regression resulted in slightly lower correlations. Normalised Difference Vegetation Indices (NDVI) in the near-infrared / red spectral range were the most important index combinations, followed by indices with bands in the near-infrared spectral range from 800 to 1205 nm. A test on different crown sizes revealed that stress symptoms in smaller crowns with denser foliage are best described in combination with pigment-sensitive indices that include bands in the green and blue spectral range. A stratified approach with individual models for pre-segmented low and high forest stands improved the overall performance. The regression models were also tested in a pixel-based analysis. A manual interpretation of the resulting raster map with stress symptom patterns observed in aerial imagery indicated a good match. With bandwidths of 10 nm and a maximum number of six bands, the selected index combinations can be used for large-area monitoring on an airborne multispectral sensor. This study establishes the base for a cost-efficient, objective monitoring method for stress symptoms in kauri canopies, suitable to cover large forest areas with an airborne multispectral sensor. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing for Biodiversity Mapping)

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16 pages, 1312 KiB

Open AccessArticle

Retrieving Foliar Traits of Quercus garryana var. garryana across a Modified Landscape Using Leaf Spectroscopy and LiDAR

by Paul W. Hacker, Nicholas C. Coops, Philip A. Townsend and Zhihui Wang

Remote Sens. 2020, 12(1), 26; https://doi.org/10.3390/rs12010026 - 19 Dec 2019

Cited by 3 | Viewed by 3610

Abstract

Understanding the ecological effects of human activities on an ecosystem is integral to the implementation of conservation management plans. The plasticity of plant functional traits presents an opportunity to examine the capacity for intraspecific functional trait variations to be indicators of anthropogenic landscape modifications. The presence of intraspecific trait variation would indicate that plants of a single species could to be used to evaluate and map functional diversity, a common metric used to measure biodiversity. This study uses leaf spectroscopy, light detection and ranging (LiDAR) and partial least squares regression (PLSR) to examine the intraspecific variation of functional traits in a population of 40 Quercus garryana experiencing varying levels of anthropogenic influence at the site level (<0.3 km²) in Duncan, B.C., Canada. These individuals vary in their spatial relationship to roads, agricultural land use change and an encroaching Coastal Douglas-fir forest. A total of 14 functional traits were estimated using pre-determined PLSR coefficients from a multi-species dataset. LiDAR data for each tree and were organized into functional categories based on their influence of plant lifeform, leaf growth or leaf structure. Principal components analysis was performed on each functional category to determine the relative influence of each trait. Results show that leaf growth and lifeform functional trait categories express significant variation in relation to three anthropogenic landscape modifications, while traits associated to leaf structure only varied between land use types (p = 0.05). Diameter at breast height (DBH), mass-based chlorophyll and leaf mass per area (LMA) showed the strongest variation across treatments. These findings support the hypothesis that trait variation exists in small populations of the same species and illustrate that spectroscopy can be used to indirectly sense land use via the leaf functional traits of a single tree species. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing for Biodiversity Mapping)

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