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Hyperspectral Remote Sensing of Forest and Trees outside Forests Ecosystems

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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) | Viewed by 45967

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Special Issue Editors

Dr. Mathieu Fauvel

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Guest Editor

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

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Guest Editor

DYNAFOR Lab., University of Toulouse, INRA, F-31326 Castanet Tolosan, France
Interests: remote sensing of biodiversity; machine learning for earth observation; time series; hyperspectral imagery; forest ecosystems; landscape ecology
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Dr. Jean-Baptiste Féret

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Guest Editor

TETIS Lab., IRSTEA, F-3400 Montpellier, France
Interests: remote sensing of vegetation; biodiversity mapping; vegetation biophysical properties; imaging spectroscopy; tropical ecosystems; physical modeling; leaf traits
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Dr. Ben Somers

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Guest Editor

Division Forest, Nature & Landscape, KU Leuven, B-3001 Leuven, Belgium
Interests: remote sensing; spectroscopy; image processing; vegetation; biodiversity; landscape ecology; ecosystem dynamics
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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 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

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 (5 papers)

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Research

19 pages, 4692 KiB

Open AccessArticle

Improving the Reliability of Mixture Tuned Matched Filtering Remote Sensing Classification Results Using Supervised Learning Algorithms and Cross-Validation

by Devin Routh, Lindsi Seegmiller, Charlie Bettigole, Catherine Kuhn, Chadwick D. Oliver and Henry B. Glick

Remote Sens. 2018, 10(11), 1675; https://doi.org/10.3390/rs10111675 - 23 Oct 2018

Cited by 17 | Viewed by 6162

Abstract

Mixture tuned matched filtering (MTMF) image classification capitalizes on the increasing spectral and spatial resolutions of available hyperspectral image data to identify the presence, and potentially the abundance, of a given cover type or endmember. Previous studies using MTMF have relied on extensive user input to obtain a reliable classification. In this study, we expand the traditional MTMF classification by using a selection of supervised learning algorithms with rigorous cross-validation. Our approach removes the need for subjective user input to finalize the classification, ultimately enhancing replicability and reliability of the results. We illustrate this approach with an MTMF classification case study focused on leafy spurge (Euphorbia esula), an invasive forb in Western North America, using free 30-m hyperspectral data from the National Aeronautics and Space Administration’s (NASA) Hyperion sensor. Our protocol shows for our data, a potential overall accuracy inflation between 18.4% and 30.8% without cross-validation and according to the supervised learning algorithm used. We propose this new protocol as a final step for the MTMF classification algorithm and suggest future researchers report a greater suite of accuracy statistics to affirm their classifications’ underlying efficacies. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Forest and Trees outside Forests Ecosystems)

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22 pages, 2865 KiB

Open AccessArticle

Ultra-Light Aircraft-Based Hyperspectral and Colour-Infrared Imaging to Identify Deciduous Tree Species in an Urban Environment

by Gintautas Mozgeris, Vytautė Juodkienė, Donatas Jonikavičius, Lina Straigytė, Sébastien Gadal and Walid Ouerghemmi

Remote Sens. 2018, 10(10), 1668; https://doi.org/10.3390/rs10101668 - 22 Oct 2018

Cited by 20 | Viewed by 5812

Abstract

One may consider the application of remote sensing as a trade-off between the imaging platforms, sensors, and data gathering and processing techniques. This study addresses the potential of hyperspectral imaging using ultra-light aircraft for vegetation species mapping in an urban environment, exploring both the engineering and scientific aspects related to imaging platform design and image classification methods. An imaging system based on simultaneous use of Rikola frame format hyperspectral and Nikon D800E adopted colour infrared cameras installed onboard a Bekas X32 manned ultra-light aircraft is introduced. Two test imaging flight missions were conducted in July of 2015 and September of 2016 over a 4000 ha area in Kaunas City, Lithuania. Sixteen and 64 spectral bands in 2015 and 2016, respectively, in a spectral range of 500–900 nm were recorded with colour infrared images. Three research questions were explored assessing the identification of six deciduous tree species: (1) Pre-treatment of spectral features for classification, (2) testing five conventional machine learning classifiers, and (3) fusion of hyperspectral and colour infrared images. Classification performance was assessed by applying leave-one-out cross-validation at the individual crown level and using as a reference at least 100 field inventoried trees for each species. The best-performing classification algorithm—multilayer perceptron, using all spectral properties extracted from the hyperspectral images—resulted in a moderate classification accuracy. The overall classification accuracy was 63%, Cohen’s Kappa was 0.54, and the species-specific classification accuracies were in the range of 51–72%. Hyperspectral images resulted in significantly better tree species classification ability than the colour infrared images and simultaneous use of spectral properties extracted from hyperspectral and colour infrared images improved slightly the accuracy over the 2015 image. Even though classifications using hyperspectral data cubes of 64 bands resulted in relatively larger accuracies than with 16 bands, classification error matrices were not statistically different. Alternative imaging platforms (like an unmanned aerial vehicle and a Cessna 172 aircraft) and settings of the flights were discussed using simulated imaging projects assuming the same study area and field of application. Ultra-light aircraft-based hyperspectral and colour-infrared imaging was considered to be a technically and economically sound solution for urban green space inventories to facilitate tree mapping, characterization, and monitoring. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Forest and Trees outside Forests Ecosystems)

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29 pages, 8890 KiB

Open AccessFeature PaperArticle

Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data

by Julia Maschler, Clement Atzberger and Markus Immitzer

Remote Sens. 2018, 10(8), 1218; https://doi.org/10.3390/rs10081218 - 03 Aug 2018

Cited by 122 | Viewed by 11067

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 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

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Forest and Trees outside Forests Ecosystems)

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18 pages, 5400 KiB

Open AccessArticle

Tree Species Classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) Using Artificial Neural Networks and APEX Hyperspectral Images

by Edwin Raczko and Bogdan Zagajewski

Remote Sens. 2018, 10(7), 1111; https://doi.org/10.3390/rs10071111 - 12 Jul 2018

Cited by 21 | Viewed by 5544

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 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

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Forest and Trees outside Forests Ecosystems)

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20 pages, 15802 KiB

Open AccessArticle

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

by Jingjing Cao, Wanchun Leng, Kai Liu, Lin Liu, Zhi He and Yuanhui Zhu

Remote Sens. 2018, 10(1), 89; https://doi.org/10.3390/rs10010089 - 11 Jan 2018

Cited by 202 | Viewed by 16262

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, 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

(This article belongs to the Special Issue Hyperspectral Remote Sensing of Forest and Trees outside Forests Ecosystems)

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