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Special Issue "Hyperspectral Remote Sensing of Forest and Trees outside Forests Ecosystems"

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

Deadline for manuscript submissions: closed (1 September 2018)

Special Issue Editors

Guest Editor
Dr. Mathieu Fauvel

University of Toulouse, INP-ENSAT, UMR 1201 DYNAFOR, F-31326 Castanet Tolosan, France
Website | E-Mail
Interests: Signal and Image processing; pattern recognition; remote sensing; kernel methods
Guest Editor
Dr. David Sheeren

University of Toulouse, INP-ENSAT, UMR 1201 DYNAFOR, F-31326 Castanet Tolosan, France
Website | E-Mail
Interests: remote sensing of biodiversity, multitemporal and hyperspectral analysis, forest ecosystems, landscape ecology
Guest Editor
Dr. Jean-Baptiste Féret

UMR TETIS (IRSTEA, CNRS, CRAD, AgroParisTech), F-34000 Montpellier, France
Website | E-Mail
Interests: imaging spectroscopy; high-spatial-resolution optical satellite imagery; radiative transfer modeling; leaf chemistry; tropical ecology; biodiversity
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

Special Issue Information

Dear Colleagues,

Forests are one of the major ecosystems of the earth, supporting a wide diversity of plants and animals and a significant number of services to humankind. At the same time, trees outside forests (TOF), including rural forestry, agroforestry and urban trees, also contribute to the global carbon cycle and climate regulation. They also represent a non-negligible component of the tree biomass in the national forest inventories, in addition to their important ecological and cultural values. Thus, monitoring current state of forest ecosystems and TOF with their changes is of prime importance for public policy and land management.

Hyperspectral remote sensing and imaging spectroscopy offers a unique way to characterize forests and TOF as the fine spectral detail allows to characterize the canopy chemistry and structure. Current airborne imagery and future hyperspectral missions such as EnMAP (Environmental Mapping and Analysis Program), PRISMA (Precursore IperSpettrale della Missione Applicativa), HYPXIM (HYPerspectral X Imagery) or HyspIRI (Hyperspectral Infrared Imager) open new opportunities for the analysis of these ecosystems at broad scale. Enhanced ability of hyperspectral data for the analysis of complex systems comes along with increased difficulties for data processing due to the very high dimensionality and size, requiring specific developments in order to fully exploit these images. Characterizing TOF in terms of structure and composition is also challenging because of their high diversity and heterogeneity, combined with limited spatial extent.

This Special Issue on “Hyperspectral Remote Sensing of Forest and Trees outside Forests Ecosystems” aims to publish original research works based on hyperspectral data to improve forest and TOF ecosystems monitoring. It includes (but is not limited to): taxonomic and functional biodiversity assessment, biochemical parameter estimation, mapping of ecosystem services, and multi-source data fusion (hyperspectral with LiDAR, time series).

Dr. Mathieu Fauvel
Dr. David Sheeren
Dr. Jean-Baptiste Feret
Dr. Ben Somers
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 monthly 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

  • Forest ecosystems
  • Trees outside forests in urban and agro-ecosystems
  • Essential biodiversity variables
  • Ecosystem services
  • Hyperspectral analysis
  • Multitemporal hyperspectral processing
  • Estimation of biochemical parameters
  • Machine learning
  • Data fusion
  • Feature extraction
  • Functional analysis
  • Spectral unmixing

Published Papers (3 papers)

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Research

Open AccessFeature PaperArticle Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data
Remote Sens. 2018, 10(8), 1218; https://doi.org/10.3390/rs10081218
Received: 25 June 2018 / Revised: 24 July 2018 / Accepted: 29 July 2018 / Published: 3 August 2018
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Abstract
Knowledge of the distribution of tree species within a forest is key for multiple economic and ecological applications. This information is traditionally acquired through time-consuming and thereby expensive field work. Our study evaluates the suitability of a visible to near-infrared (VNIR) hyperspectral dataset
[...] Read more.
Knowledge of the distribution of tree species within a forest is key for multiple economic and ecological applications. This information is traditionally acquired through time-consuming and thereby expensive field work. Our study evaluates the suitability of a visible to near-infrared (VNIR) hyperspectral dataset with a spatial resolution of 0.4 m for the classification of 13 tree species (8 broadleaf, 5 coniferous) on an individual tree crown level in the UNESCO Biosphere Reserve ‘Wienerwald’, a temperate Austrian forest. The study also assesses the automation potential for the delineation of tree crowns using a mean shift segmentation algorithm in order to permit model application over large areas. Object-based Random Forest classification was carried out on variables that were derived from 699 manually delineated as well as automatically segmented reference trees. The models were trained separately for two strata: small and/or conifer stands and high broadleaf forests. The two strata were delineated beforehand using CHM-based tree height and NDVI. The predictor variables encompassed spectral reflectance, vegetation indices, textural metrics and principal components. After feature selection, the overall classification accuracy (OA) of the classification based on manual delineations of the 13 tree species was 91.7% (Cohen’s kappa (κ) = 0.909). The highest user’s and producer’s accuracies were most frequently obtained for Weymouth pine and Scots Pine, while European ash was most often associated with the lowest accuracies. The classification that was based on mean shift segmentation yielded similarly good results (OA = 89.4% κ = 0.883). Based on the automatically segmented trees, the Random Forest models were also applied to the whole study site (1050 ha). The resulting tree map of the study area confirmed a high abundance of European beech (58%) with smaller amounts of oak (6%) and Scots pine (5%). We conclude that highly accurate tree species classifications can be obtained from hyperspectral data covering the visible and near-infrared parts of the electromagnetic spectrum. Our results also indicate a high automation potential of the method, as the results from the automatically segmented tree crowns were similar to those that were obtained for the manually delineated tree crowns. Full article
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Open AccessArticle Tree Species Classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) Using Artificial Neural Networks and APEX Hyperspectral Images
Remote Sens. 2018, 10(7), 1111; https://doi.org/10.3390/rs10071111
Received: 29 May 2018 / Revised: 27 June 2018 / Accepted: 10 July 2018 / Published: 12 July 2018
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Abstract
Knowledge of tree species composition is obligatory in forest management. Accurate tree species maps allow for detailed analysis of a forest ecosystem and its interactions with the environment. The research presented here focused on developing methods of tree species identification using aerial hyperspectral
[...] Read more.
Knowledge of tree species composition is obligatory in forest management. Accurate tree species maps allow for detailed analysis of a forest ecosystem and its interactions with the environment. The research presented here focused on developing methods of tree species identification using aerial hyperspectral data. The research area is located in Southwestern Poland and covers the Karkonoski National Park (KNP), which was significantly damaged by acid rain and pest infestation in the 1980s. High-resolution (3.35 m) Airborne Prism Experiment (APEX) hyperspectral images (288 spectral bands in the range of 413 to 2440 nm) were used as a basis for tree species classification. Beech (Fagus sylvatica), birch (Betula pendula), alder (Alnus incana), larch (Larix decidua), pine (Pinus sylvestris), and spruce (Picea abies) were classified. The classification algorithm used was feed-forward multilayered perceptron (MLP) with a single hidden layer. To simulate such a network, we used the R programming environment and the nnet package. To provide more accurate measurement of accuracy, iterative accuracy assessment was performed. The final tree species maps cover the whole area of KNP; a median overall accuracy (OA) of 87% was achieved, with median producer accuracy (PA) for all classes exceeding 68%. The best-classified classes were spruce, beech, and birch, with median producer accuracy of 93%, 88% and 83%, respectively. The pine class achieved the lowest median producer and user accuracies (68% and 75%, respectively). The results show great potential for the use of hyperspectral data as a tool for identifying tree species locations in diverse mountainous forest. Full article
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Open AccessArticle Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models
Remote Sens. 2018, 10(1), 89; https://doi.org/10.3390/rs10010089
Received: 22 November 2017 / Revised: 23 December 2017 / Accepted: 8 January 2018 / Published: 11 January 2018
Cited by 2 | PDF Full-text (15802 KB) | HTML Full-text | XML Full-text
Abstract
Mangroves are one of the most important coastal wetland ecosystems, and the compositions and distributions of mangrove species are essential for conservation and restoration efforts. Many studies have explored this topic using remote sensing images that were obtained by satellite-borne and airborne sensors,
[...] Read more.
Mangroves are one of the most important coastal wetland ecosystems, and the compositions and distributions of mangrove species are essential for conservation and restoration efforts. Many studies have explored this topic using remote sensing images that were obtained by satellite-borne and airborne sensors, which are known to be efficient for monitoring the mangrove ecosystem. With improvements in carrier platforms and sensor technology, unmanned aerial vehicles (UAVs) with high-resolution hyperspectral images in both spectral and spatial domains have been used to monitor crops, forests, and other landscapes of interest. This study aims to classify mangrove species on Qi’ao Island using object-based image analysis techniques based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (UHD 185) onboard a UAV platform. First, the image objects were obtained by segmenting the UAV hyperspectral image and the UAV-derived digital surface model (DSM) data. Second, spectral features, textural features, and vegetation indices (VIs) were extracted from the UAV hyperspectral image, and the UAV-derived DSM data were used to extract height information. Third, the classification and regression tree (CART) method was used to selection bands, and the correlation-based feature selection (CFS) algorithm was employed for feature reduction. Finally, the objects were classified into different mangrove species and other land covers based on their spectral and spatial characteristic differences. The classification results showed that when considering the three features (spectral features, textural features, and hyperspectral VIs), the overall classification accuracies of the two classifiers used in this paper, i.e., k-nearest neighbor (KNN) and support vector machine (SVM), were 76.12% (Kappa = 0.73) and 82.39% (Kappa = 0.801), respectively. After incorporating tree height into the classification features, the accuracy of species classification increased, and the overall classification accuracies of KNN and SVM reached 82.09% (Kappa = 0.797) and 88.66% (Kappa = 0.871), respectively. It is clear that SVM outperformed KNN for mangrove species classification. These results also suggest that height information is effective for discriminating mangrove species with similar spectral signatures, but different heights. In addition, the classification accuracy and performance of SVM can be further improved by feature reduction. The overall results provided evidence for the effectiveness and potential of UAV hyperspectral data for mangrove species identification. Full article
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