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Special Issue "Remote Sensing for Biodiversity, Ecology and Conservation"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (12 February 2019)

Special Issue Editors

Guest Editor
Dr. Jens Oldeland

Biocentre Klein Flottbek and Botanical Garden, University of Hamburg, Ohnhorststr. 18, 22609 Hamburg, Germany
Website | E-Mail
Interests: biodiversity; vegetation ecology; remote sensing; hyperspectral; UAV; spectroscopy; invasion biology; statistics; ecological modelling
Guest Editor
Dr. Ben Somers

Division Forest, Nature & Landscape, KU Leuven, B-3001 Leuven, Belgium
Website | E-Mail
Interests: remote sensing; spectroscopy; image processing; vegetation; biodiversity; landscape ecology; ecosystem dynamics
Guest Editor
Prof. Duccio Rocchini

Center Agriculture Food Environment, University of Trento, Via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy
Centre for Integrative Biology, University of Trento, Via Sommarive, 14, 38123 Povo (TN), Italy
Department of Biodiversity and Molecular Ecology, Fondazione Edmund Mach, Research and Innovation Centre, Via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy
Website | E-Mail
Interests: biodiversity estimate; ecological informatics; remote sensing; species distribution modelling

Special Issue Information

Dear Colleagues,

Biodiversity is a multifaceted issue and, thus, can comprise multiple taxa from all possible biomes, and can further be separated into functional and phlyogenetic diversity. Human-induced global change is increasingly threatening biodiversity in all its forms. Thus, the loss of biodiversity has to be tackled now, and the scientific community should provide answers on how to reach a zero-net loss scenario. Remote sensing offers the tools for monitoring and mapping the Earth’s surface at different spatio-temporal scales, while biologists provide knowledge on the Earth’s biota, its ecology, and how to safeguard it. Therefore, this Special Issue on "Remote Sensing for Biodiversity, Ecology and Conservation" calls for manuscripts that demonstrate successful combinations of both disciplines. We welcome recent technological and/or methodological innovations in mapping, monitoring or measuring biodiversity, or detecting changes in states thereof. In particular, real-world applications and best practice examples showing how existing conservation strategies, e.g., the European NATURA 2000 network, can benefit from remotely-sensed information. For example, drones and (very) high spatial-resolution satellites, e.g., World View 3, Sentinel 2, are seen as game changers in the interface of ecology and remote sensing because of their high temporal and spatial flexibility and, thus, seem ideal for supporting nature conservation or studying the ecology and biodiversity of terrestrial ecosystems. In addition to terrestrial ecosystems, developments in the realm of marine remote sensing and ecology are also welcome. All types of original research contributions will be considered.

Dr. Jens Oldeland
Prof. Dr. Ben Somers
Prof. Dr. Duccio Rocchini
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 papers will be 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 1800 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

  1. Remote sensing of Taxonomic, Functional and Phylogenetic Biodiversity
  2. Multi-scale Remote Sensing
  3. Technological Innovation in the interface between ecology and remote sensing
  4. Methodological approaches to link ecological field data and remote sensing data
  5. Real World Applications Conservation (e.g. NATURA 2000)
  6. Supporting Biodiversity Monitoring from Space
  7. Exploiting multitemporal satellite data
  8. Mapping and Monitoring land degradation or land use change
  9. Theories and hypotheses linking remote sensing and ecology

Published Papers (11 papers)

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Research

Open AccessArticle
Looking for Ticks from Space: Using Remotely Sensed Spectral Diversity to Assess Amblyomma and Hyalomma Tick Abundance
Remote Sens. 2019, 11(7), 770; https://doi.org/10.3390/rs11070770
Received: 12 February 2019 / Revised: 15 March 2019 / Accepted: 27 March 2019 / Published: 30 March 2019
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Abstract
Landscape heterogeneity, as measured by the spectral diversity of satellite imagery, has the potential to provide information on the resources available within the movement capacity range of arthropod vectors, and to help predict vector abundance. The Spectral Variation Hypothesis states that higher spectral [...] Read more.
Landscape heterogeneity, as measured by the spectral diversity of satellite imagery, has the potential to provide information on the resources available within the movement capacity range of arthropod vectors, and to help predict vector abundance. The Spectral Variation Hypothesis states that higher spectral diversity is positively related to a higher number of ecological niches present in the landscape, allowing more species to coexist regardless of the taxonomic group considered. Investigating the landscape heterogeneity as a proxy of the resources available to vectors may be relevant for complex and continuous agro-forest mosaics of small farmlands and degraded forests, where land cover classification is often imprecise. In this study, we hypothesized that larger spectral diversity would be associated with higher tick abundance due to the potentially higher number of hosts in heterogeneous landscapes. Specifically, we tested whether spectral diversity indices could represent heterogeneous landscapes, and if so, whether they explain Amblyomma and Hyalomma tick abundance in Benin and inform on their habitat preferences. Benin is a West-African country characterized by a mosaic landscape of farmland and degraded forests. Our results showed that both NDVI-derived and spectral predictors are highly collinear, with NDVI-derived predictors related to vegetated land cover classes and spectral predictors correlated to mosaic landscapes. Amblyomma abundance was not related to the predictors considered. Hyalomma abundance showed positive relationships to spectral diversity indices and negative relationships to NDVI-derived-ones. Though taxa dependent, our approach showed moderate performance in terms of goodness of fit (ca. 13–20% R2), which is a promising result considering the sampling and scale limitations. Spectral diversity indices coupled with classical SRS vegetation indices could be a complementary approach for providing further ecological aspects in the field of disease biogeography. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
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Open AccessArticle
Application of UAV-Based Methodology for Census of an Endangered Plant Species in a Fragile Habitat
Remote Sens. 2019, 11(6), 719; https://doi.org/10.3390/rs11060719
Received: 11 February 2019 / Revised: 16 March 2019 / Accepted: 21 March 2019 / Published: 26 March 2019
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Abstract
Accurate census is essential for endangered plant management, yet lack of resources may make complete on-the-ground census difficult to achieve. Accessibility, especially for species in fragile habitats, is an added constraint. We examined the feasibility of using UAV (unmanned aerial vehicle, drone)-based imagery [...] Read more.
Accurate census is essential for endangered plant management, yet lack of resources may make complete on-the-ground census difficult to achieve. Accessibility, especially for species in fragile habitats, is an added constraint. We examined the feasibility of using UAV (unmanned aerial vehicle, drone)-based imagery for census of an endangered plant species, Arctomecon humilis (dwarf bear-poppy), an herbaceous perennial gypsophile endemic of the Mojave Desert, USA. Using UAV technology, we captured imagery at both 50-m altitude (census) and 15-m altitude (validation) at two populations, White Dome (325 ha) and Red Bluffs (166 ha). The imagery was processed into orthomosaics that averaged 2.32 cm ground sampling distance (GSD) for 50-m imagery and 0.73 cm GSD for 15-m imagery. Putative poppy plants were marked in the 50-m imagery according to predefined criteria. We then used the 15-m imagery from each area to verify the identification accuracy of marked plants. Visual evaluation of the 50-m imagery resulted in errors of both commission and omission, mainly caused by failure to accurately identify or detect small poppies (<10 cm diameter). Higher-resolution 30-m altitude imagery (1.19 cm GSD) greatly reduced errors of commission. Habitat classification demonstrated that poppy density variation was closely tied to soil surface color. This study showed that drone imagery can potentially be used to census rare plant species with distinctive morphology in open habitats and understand their spatial distribution. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
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Open AccessArticle
High-Resolution Mapping of Redwood (Sequoia sempervirens) Distributions in Three Californian Forests
Remote Sens. 2019, 11(3), 351; https://doi.org/10.3390/rs11030351
Received: 14 December 2018 / Revised: 2 February 2019 / Accepted: 4 February 2019 / Published: 10 February 2019
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Abstract
High-resolution maps of redwood distributions could enable strategic land management to satisfy diverse conservation goals, but the currently-available maps of redwood distributions are low in spatial resolution and biotic detail. Classification of airborne imaging spectroscopy data provides a potential avenue for mapping redwoods [...] Read more.
High-resolution maps of redwood distributions could enable strategic land management to satisfy diverse conservation goals, but the currently-available maps of redwood distributions are low in spatial resolution and biotic detail. Classification of airborne imaging spectroscopy data provides a potential avenue for mapping redwoods over large areas and with high confidence. We used airborne imaging spectroscopy data collected over three redwood forests by the Carnegie Airborne Observatory, in combination with field training data and application of a gradient boosted regression tree (GBRT) machine learning algorithm, to map the distribution of redwoods at 2-m spatial resolution. Training data collected from the three sites showed that redwoods have spectral signatures distinct from the other common tree species found in redwood forests. We optimized a gradient boosted regression model for high performance and computational efficiency, and the resulting model was demonstrably accurate (81–98% true positive rate and 90–98% overall accuracy) in mapping redwoods in each of the study sites. The resulting maps showed marked variation in redwood abundance (0–70%) within a 1 square kilometer aggregation block, which match the spatial resolution of currently-available redwood distribution maps. Our resulting high-resolution mapping approach will facilitate improved research, conservation, and management of redwood trees in California. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
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Open AccessArticle
Spatial–Temporal Variation of ANPP and Rain-Use Efficiency Along a Precipitation Gradient on Changtang Plateau, Tibet
Remote Sens. 2019, 11(3), 325; https://doi.org/10.3390/rs11030325
Received: 28 November 2018 / Revised: 1 February 2019 / Accepted: 1 February 2019 / Published: 6 February 2019
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Abstract
Aboveground net primary productivity (ANPP) and rain-use efficiency (RUE) are important indicators in assessing the response of ecosystems to climate change. In this paper, the Changtang Plateau in the Tibetan Autonomous Region was selected as the study area to analyze the spatial and [...] Read more.
Aboveground net primary productivity (ANPP) and rain-use efficiency (RUE) are important indicators in assessing the response of ecosystems to climate change. In this paper, the Changtang Plateau in the Tibetan Autonomous Region was selected as the study area to analyze the spatial and temporal changes of ANPP and RUE in grassland communities and their response to climate change. The results showed the following:(1) The spatial pattern of ANPP was closely related to rainfall on the Changtang Plateau. The average ANPP over the past 15 years increased gradually from the arid west to the humid east. A consistent pattern was exhibited in different grassland types and climate zones. (2) The RUE was higher at the east and west edges of the Changtang Plateau, especially in the arid west, but was lower in the center. From the perspective of different climatic zones, the average RUE in the southern Tibetan semiarid climate zone and the Ngari arid climate zone was significantly higher than that in other climate zones. However, the average RUE in different grassland types only varied from 0.07 to 0.09 g·m−2·mm−1. The spatial variation in RUE was more distinct in different climatic zones than in different grassland types. (3) Climate change influenced the interannual variation of ANPP and RUE, but the response of ANPP to rainfall showed a significant lag. The interannual change in RUE was negatively correlated with changes in precipitation. (4) In general, a greater area showed a significant increase rather than a decrease in ANPP on the Changtang Plateau, which meant that the grassland condition is improving. The temporal variation patterns of ANPP and RUE in different climate zones were consistent with the overall patterns on the Changtang Plateau, while the variation was not significant in different grassland types. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
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Open AccessArticle
Identification of Linear Vegetation Elements in a Rural Landscape Using LiDAR Point Clouds
Remote Sens. 2019, 11(3), 292; https://doi.org/10.3390/rs11030292
Received: 21 January 2019 / Accepted: 29 January 2019 / Published: 1 February 2019
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Abstract
Modernization of agricultural land use across Europe is responsible for a substantial decline of linear vegetation elements such as tree lines, hedgerows, riparian vegetation, and green lanes. These linear objects have an important function for biodiversity, e.g., as ecological corridors and local habitats [...] Read more.
Modernization of agricultural land use across Europe is responsible for a substantial decline of linear vegetation elements such as tree lines, hedgerows, riparian vegetation, and green lanes. These linear objects have an important function for biodiversity, e.g., as ecological corridors and local habitats for many animal and plant species. Knowledge on their spatial distribution is therefore essential to support conservation strategies and regional planning in rural landscapes but detailed inventories of such linear objects are often lacking. Here, we propose a method to detect linear vegetation elements in agricultural landscapes using classification and segmentation of high-resolution Light Detection and Ranging (LiDAR) point data. To quantify the 3D structure of vegetation, we applied point cloud analysis to identify point-based and neighborhood-based features. As a preprocessing step, we removed planar surfaces such as grassland, bare soil, and water bodies from the point cloud using a feature that describes to what extent the points are scattered in the local neighborhood. We then applied a random forest classifier to separate the remaining points into vegetation and other. Subsequently, a rectangularity-based region growing algorithm allowed to segment the vegetation points into 2D rectangular objects, which were then classified into linear objects based on their elongatedness. We evaluated the accuracy of the linear objects against a manually delineated validation set. The results showed high user’s (0.80), producer’s (0.85), and total accuracies (0.90). These findings are a promising step towards testing our method in other regions and for upscaling it to broad spatial extents. This would allow producing detailed inventories of linear vegetation elements at regional and continental scales in support of biodiversity conservation and regional planning in agricultural and other rural landscapes. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
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Open AccessArticle
Mapping Foliar Nutrition Using WorldView-3 and WorldView-2 to Assess Koala Habitat Suitability
Remote Sens. 2019, 11(3), 215; https://doi.org/10.3390/rs11030215
Received: 16 December 2018 / Revised: 14 January 2019 / Accepted: 17 January 2019 / Published: 22 January 2019
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Abstract
Conservation planning and population assessment for widely-distributed, but vulnerable, arboreal folivore species demands cost-effective mapping of habitat suitability over large areas. This study tested whether multispectral data from WorldView-3 could be used to estimate and map foliar digestible nitrogen (DigN), a nutritional measure [...] Read more.
Conservation planning and population assessment for widely-distributed, but vulnerable, arboreal folivore species demands cost-effective mapping of habitat suitability over large areas. This study tested whether multispectral data from WorldView-3 could be used to estimate and map foliar digestible nitrogen (DigN), a nutritional measure superior to total nitrogen for tannin-rich foliage for the koala (Phascolarctos cinereus). We acquired two WorldView-3 images (November 2015) and collected leaf samples from Eucalyptus woodlands in semi-arid eastern Australia. Linear regression indicated the normalized difference index using bands “Coastal” and “NIR1” best estimated DigN concentration (% dry matter, R2 = 0.70, RMSE = 0.19%). Foliar DigN concentration was mapped for multi-species Eucalyptus open woodlands across two landscapes using this index. This mapping method was tested on a WorldView-2 image (October 2012) with associated koala tracking data (August 2010 to November 2011) from a different landscape of the study region. Quantile regression showed significant positive relationship between estimated DigN and occurrence of koalas at 0.999 quantile (R2 = 0.63). This study reports the first attempt to use a multispectral satellite-derived spectral index for mapping foliar DigN at a landscape-scale (100s km2). The mapping method can potentially be incorporated in mapping and monitoring koala habitat suitability for conservation management. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
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Open AccessArticle
Remotely Sensed Single Tree Data Enable the Determination of Habitat Thresholds for the Three-Toed Woodpecker (Picoides tridactylus)
Remote Sens. 2018, 10(12), 1972; https://doi.org/10.3390/rs10121972
Received: 29 October 2018 / Revised: 22 November 2018 / Accepted: 26 November 2018 / Published: 6 December 2018
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Abstract
Forest biodiversity conservation requires precise, area-wide information on the abundance and distribution of key habitat structures at multiple spatial scales. We combined airborne laser scanning (ALS) data with color-infrared (CIR) aerial imagery for identifying individual tree characteristics and quantifying multi-scale habitat requirements using [...] Read more.
Forest biodiversity conservation requires precise, area-wide information on the abundance and distribution of key habitat structures at multiple spatial scales. We combined airborne laser scanning (ALS) data with color-infrared (CIR) aerial imagery for identifying individual tree characteristics and quantifying multi-scale habitat requirements using the example of the three-toed woodpecker (Picoides tridactylus) (TTW) in the Bavarian Forest National Park (Germany). This bird, a keystone species of boreal and mountainous forests, is highly reliant on bark beetles dwelling in dead or dying trees. While previous studies showed a positive relationship between the TTW presence and the amount of deadwood as a limiting resource, we hypothesized a unimodal response with a negative effect of very high deadwood amounts and tested for effects of substrate quality. Based on 104 woodpecker presence or absence locations, habitat selection was modelled at four spatial scales reflecting different woodpecker home range sizes. The abundance of standing dead trees was the most important predictor, with an increase in the probability of TTW occurrence up to a threshold of 44–50 dead trees per hectare, followed by a decrease in the probability of occurrence. A positive relationship with the deadwood crown size indicated the importance of fresh deadwood. Remote sensing data allowed both an area-wide prediction of species occurrence and the derivation of ecological threshold values for deadwood quality and quantity for more informed conservation management. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
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Open AccessFeature PaperArticle
Normalized Difference Vegetation Vigour Index: A New Remote Sensing Approach to Biodiversity Monitoring in Oil Polluted Regions
Remote Sens. 2018, 10(6), 897; https://doi.org/10.3390/rs10060897
Received: 1 May 2018 / Revised: 1 June 2018 / Accepted: 5 June 2018 / Published: 7 June 2018
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Abstract
Biodiversity loss remains a global challenge despite international commitment to the United Nations Convention on Biodiversity. Biodiversity monitoring methods are often limited in their geographical coverage or thematic content. Furthermore, remote sensing-based integrated monitoring methods mostly attempt to determine species diversity from habitat [...] Read more.
Biodiversity loss remains a global challenge despite international commitment to the United Nations Convention on Biodiversity. Biodiversity monitoring methods are often limited in their geographical coverage or thematic content. Furthermore, remote sensing-based integrated monitoring methods mostly attempt to determine species diversity from habitat heterogeneity somewhat reflected in the spectral diversity of the image used. Up to date, there has been no standardized method for monitoring biodiversity against the backdrop of ecosystem or environmental pressures. This study presents a new method for monitoring the impact of oil pollution an environmental pressure on biodiversity at regional scale and presents a case study in the Niger delta region of Nigeria. It integrates satellite remote sensing and field data to develop a set of spectral metrics for biodiversity monitoring. Using vascular plants of various lifeforms observed on polluted and unpolluted (control) locations, as surrogates for biodiversity, the normalized difference vegetation vigour index (NDVVI) variants were estimated from Hyperion wavelengths sensitive to petroleum hydrocarbons and evaluated for potential use in biodiversity monitoring schemes. The NDVVI ranges from 0 to 1 and stems from the presupposition that increasing chlorophyll absorption in the green vegetation can be used as a predictor to model vascular plant species diversity. The performances of NDVVI variants were compared to traditional narrowband vegetation indices (NBVIs). The results show strong links between vascular plant species diversity and primary productivity of vegetation quantified by the chlorophyll content, vegetation vigour and abundance. An NDVVI-based model gave much more accurate predictions of species diversity than traditional NBVIs (R-squared and prediction square error (PSE) respectively for Shannon’s diversity = 0.54 and 0.69 for NDVVIs and 0.14 and 0.9 for NBVIs). We conclude that NDVVI is a superior remote sensing index for monitoring biodiversity indicators in oil-polluted areas than traditional NBVIs. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
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Open AccessArticle
Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data
Remote Sens. 2018, 10(5), 708; https://doi.org/10.3390/rs10050708
Received: 19 March 2018 / Revised: 28 April 2018 / Accepted: 3 May 2018 / Published: 4 May 2018
Cited by 1 | PDF Full-text (2331 KB) | HTML Full-text | XML Full-text
Abstract
How to effectively combine remote sensing data with the eddy covariance (EC) technique to accurately quantify gross primary production (GPP) in coastal wetlands has been a challenge and is also important and necessary for carbon (C) budgets assessment and climate change studies at [...] Read more.
How to effectively combine remote sensing data with the eddy covariance (EC) technique to accurately quantify gross primary production (GPP) in coastal wetlands has been a challenge and is also important and necessary for carbon (C) budgets assessment and climate change studies at larger scales. In this study, a satellite-based Vegetation Photosynthesis Model (VPM) combined with EC measurement and Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to evaluate the phenological characteristics and the biophysical performance of MODIS-based vegetation indices (VIs) and the feasibility of the model for simulating GPP of coastal wetland ecosystems. The results showed that greenness-related and water-related VIs can better identify the green-up and the senescence phases of coastal wetland vegetation, corresponds well with the C uptake period and the phenological patterns that were delineated by GPP from EC tower (GPPEC). Temperature can explain most of the seasonal variation in VIs and GPPEC fluxes. Both enhanced vegetation index (EVI) and water-sensitive land surface water index (LSWI) have a higher predictive power for simulating GPP in this coastal wetland. The comparisons between modeled GPP (GPPVPM) and GPPEC indicated that VPM model can commendably simulate the trajectories of the seasonal dynamics of GPPEC fluxes in terms of patterns and magnitudes, explaining about 85% of GPPEC changes over the study years (p < 0.0001). The results also demonstrate the potential of satellite-driven VPM model for modeling C uptake at large spatial and temporal scales in coastal wetlands, which can provide valuable production data for the assessment of global wetland C sink/source. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
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Open AccessArticle
Classification of High-Mountain Vegetation Communities within a Diverse Giant Mountains Ecosystem Using Airborne APEX Hyperspectral Imagery
Remote Sens. 2018, 10(4), 570; https://doi.org/10.3390/rs10040570
Received: 15 February 2018 / Revised: 28 March 2018 / Accepted: 3 April 2018 / Published: 7 April 2018
Cited by 3 | PDF Full-text (5095 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Mapping plant communities is a difficult and time consuming endeavor. Methods relying on field surveys deliver high quality data but are usually limited to relatively small areas. In this paper we apply airborne hyperspectral data to vegetation mapping in remote and hard to [...] Read more.
Mapping plant communities is a difficult and time consuming endeavor. Methods relying on field surveys deliver high quality data but are usually limited to relatively small areas. In this paper we apply airborne hyperspectral data to vegetation mapping in remote and hard to reach areas. We classified 22 vegetation communities in the Giant Mountains on 3.12-m Airborne Prism Experiment (APEX) hyperspectral images, registered in 288 spectral bands (10 September 2012). As the classification algorithm, Support Vector Machines (SVM) was used. APEX data were corrected geometrically and atmospherically, and three dimensionality reduction methods were performed to select the best dataset. As reference we used a non-forest vegetation map containing vegetation communities of Polish Karkonosze National Park from 2002, orthophotomap and field surveys data from 2013 to 2014. We obtained the post-classification maps of 22 vegetation communities, lakes and areas without any vegetation. Iterative accuracy assessment repeated 100 times was used to obtain the most objective results for individual communities. The median value of overall accuracy (OA) was 84%. Fourteen out of twenty-four classes were classified of more than 80% of producer accuracy (PA) and sixteen out of twenty-four of user accuracy (UA). APEX data and SVM with the use of iterative accuracy assessment are useful for the mountain communities classification. This can support both Polish and Czech national parks management by giving the information about diversity of communities in the whole transboundary area, helping with identification especially in changing environment caused by humans. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
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Open AccessArticle
Mapping Wild Leek through the Forest Canopy Using a UAV
Remote Sens. 2018, 10(1), 70; https://doi.org/10.3390/rs10010070
Received: 30 October 2017 / Revised: 2 January 2018 / Accepted: 3 January 2018 / Published: 6 January 2018
Cited by 5 | PDF Full-text (3612 KB) | HTML Full-text | XML Full-text
Abstract
Wild leek, an endangered plant species of Eastern North America, grows on forest floors and greens up to approximately three weeks before the trees it is typically found under, temporarily allowing it to be observed through the canopy by remote sensing instruments. This [...] Read more.
Wild leek, an endangered plant species of Eastern North America, grows on forest floors and greens up to approximately three weeks before the trees it is typically found under, temporarily allowing it to be observed through the canopy by remote sensing instruments. This paper explores the accuracy with which wild leek can be mapped with a low-flying UAV. Nadir video imagery was obtained using a commercial UAV during the spring of 2017 in Gatineau Park, Quebec. Point clouds were generated from the video frames with the Structure-from-Motion framework, and a multiscale curvature classification was used to separate points on the ground, where wild leek grows, from above-ground points belonging to the forest canopy. Five-cm resolution orthomosaics were created from the ground points, and a threshold value of 0.350 for the green chromatic coordinate (GCC) was applied to delineate wild leek from wood, leaves, and other plants on the forest floor, with an F1-score of 0.69 and 0.76 for two different areas. The GCC index was most effective in delineating bigger patches, and therefore often misclassified patches smaller than 30 cm in diameter. Although short flight times and long data processing times are presently technical challenges to upscaling, the low cost and high accuracy of UAV imagery provides a promising method for monitoring the spatial distribution of this endangered species. Full article
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
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