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Advanced Remote Sensing Methods for 3D Vegetation Mapping and Characterization

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 October 2015) | Viewed by 65090

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Special Issue Editors

Dr. Marco Heurich

E-Mail Website1 Website2
Guest Editor

1. Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park, Freyunger Str. 2, 94481 Grafenau, Germany
2.Chair of Wildlife Ecology and Management, University of Freiburg, Tennenbacher Straße 4, 79106 Freiburg, Germany
Interests: lidar applications in forest ecology and management; remote sensing in wildlife ecology; essential biodiversity variables
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Peter Krzystek

E-Mail Website
Guest Editor

Department of Geoinformatics, University of Applied Sciences - Munich, Karlstraße 6, D-80333 Munich, Germany
Interests: LiDAR; remote sensing; forestry; image analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yong Pang

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Guest Editor

Chinese Academy of Forestry China
Interests: Lidar backscatter modeling from forests; integration of multi-source/sensor data for forest parameters retrieval

Dr. Cheng Wang

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Guest Editor

Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Interests: lidar remote sensing; vegetation structure; ICESat; Forest mapping; biomass estimation

Dr. Wei Yao

E-Mail Website
Guest Editor

Department of Geoinformatics, University of Applied Sciences - Munich, Karlstraße 6, D-80333 Munich, Germany
Interests: phtotogrammetry; remote sensing; LiDAR; forestry; image analysis

Special Issue Information

Dear colleagues,

In recent years, remote sensing techniques have emerged as a powerful and operational tool for producing large-area forest inventory data at a fine spatial resolution. In particular, with the fast development of 3D active systems (like laser scanning or SAR), the accurate monitoring and characterization of volumetric structures of vegetation can be made possible for wall to wall sampling based regional or national forest inventories. Because the spatial extent and resolution size of a given sensor are inversely related, so far, large-area forest inventories that are based on remote sensing often rely on integrating multiple sources of data, which have different levels of detail in a multi-phase sampling framework. For example, LiDAR samples may be used in combination with multi-/hyperspectral imagery to facilitate stratification and enable the extension of attributes (e.g., height) across large areas. On the other hand, forest structural parameters, such as tree species and vitality, can be better determined by fusing spectral information with point cloud data that describes 3D tree geometry. Compared to forested areas, urban forests were given less attention (due to limited data acquisition conditions). Detailed information concerning urban vegetation is vital for establishing 3D city models and is an integral part of urban environmental planning procedures. Such information plays a key role in regulating local urban climate and soil erosion, and acts as an important design element for ensuring better visibility in urban traffic.

Recent advances in active sensing technology have rendered new remote platforms that can provide higher signal penetration ability against volume-scattering objects as well as backscattering properties for illuminated object surfaces. These developments will certainly produce a series of exciting research findings and new applications for natural resource assessment in the forthcoming years. Moreover, it is currently an open research question (in both the remote sensing and forestry communities) how persistent and precise mapping of terrestrial ecosystems can be obtained in complex and dynamic environments.

This Special Issue will present insights into whole processing pipeline for remote sensing based precision forestry. In this pipeline, different sensor and field data are processed and evaluated together to drive important parameters (such as the pattern and extent of volumetric forest structures, species compositions, and the physiological conditions of single trees), so as to deliver a multi-scale quantitative representation of terrestrial ecosystems.

The Special Issue, “Advanced Remote Sensing Methods for Vegetation Mapping and Characterization”, aims to pave the way for the precise and intelligent monitoring of vegetation and closely related objects, from advanced 3D/4D remote sensing data, for sustainable natural resource management. This Special Issue will focus on advanced remote sensing methods for vegetation mapping and relevant object modeling; some topics to be discussed are listed non-exhaustively:

Extraction and reconstruction of 3D tree models for forest inventory
Efficient and precise retrieval of multi-scale forest structural parameters
Mapping and characterization of vegetation areas in urban areas
Intelligent approaches for classifying and detecting vegetation and related objects
Feature derivation and selection of image spectral and point cloud data concerning the environment
Integration of sensor data acquired on multiple platforms
New platforms and technologies for data acquisition for volumetric object modelling
Carbon stock estimation and multi-temporal analysis using remote sensing
Ecology protection and biodiversity analysis using 3D remote sensing
Geo-statistical analysis and Geo-visualization for nature resource mapping

Prof. Dr. Peter Krzystek
Dr. Wei Yao
Prof. Dr. Yong Pang
Dr. Marco Heurich
Prof. Dr. Cheng Wang
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

3d vegetation mapping
point cloud processing
species conservation and biodiversity
lidar
sar
hyperspectral imaging
sensor fusion
aerial and satellite imagery dense matching
object detection, classification and modeling
forest inventory

Published Papers (6 papers)

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Research

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3909 KiB

Open AccessArticle

Mapping Aboveground Biomass using Texture Indices from Aerial Photos in a Temperate Forest of Northeastern China

by Shili Meng, Yong Pang, Zhongjun Zhang, Wen Jia and Zengyuan Li

Remote Sens. 2016, 8(3), 230; https://doi.org/10.3390/rs8030230 - 11 Mar 2016

Cited by 31 | Viewed by 6917

Abstract

Optical remote sensing data have been considered to display signal saturation phenomena in regions of high aboveground biomass (AGB) and multi-storied forest canopies. However, some recent studies using texture indices derived from optical remote sensing data via the Fourier-based textural ordination (FOTO) approach have provided promising results without saturation problems for some tropical forests, which tend to underestimate AGB predictions. This study was applied to the temperate mixed forest of the Liangshui National Nature Reserve in Northeastern China and demonstrated the capability of FOTO texture indices to obtain a higher prediction quality of forest AGB. Based on high spatial resolution aerial photos (1.0 m spatial resolution) acquired in September 2009, the relationship between FOTO texture indices and field-derived biomass measurements was calibrated using a support vector regression (SVR) algorithm. Ten-fold cross-validation was used to construct a robust prediction model, which avoided the over-fitting problem. By further comparison the performance of the model estimates for greater coverage, the predicted results were compared with a reference biomass map derived from LiDAR metrics. This study showed that the FOTO indices accounted for 88.3% of the variance in ground-based AGB; the root mean square error (RMSE) was 34.35 t/ha, and RMSE normalized by the mean value of the estimates was 22.31%. This novel texture-based method has great potential for forest AGB estimation in other temperate regions. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Methods for 3D Vegetation Mapping and Characterization)

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1552 KiB

Open AccessArticle

Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level

by Roope Näsi, Eija Honkavaara, Päivi Lyytikäinen-Saarenmaa, Minna Blomqvist, Paula Litkey, Teemu Hakala, Niko Viljanen, Tuula Kantola, Topi Tanhuanpää and Markus Holopainen

Remote Sens. 2015, 7(11), 15467-15493; https://doi.org/10.3390/rs71115467 - 18 Nov 2015

Cited by 299 | Viewed by 20999

Abstract

Low-cost, miniaturized hyperspectral imaging technology is becoming available for small unmanned aerial vehicle (UAV) platforms. This technology can be efficient in carrying out small-area inspections of anomalous reflectance characteristics of trees at a very high level of detail. Increased frequency and intensity of insect induced forest disturbance has established a new demand for effective methods suitable in mapping and monitoring tasks. In this investigation, a novel miniaturized hyperspectral frame imaging sensor operating in the wavelength range of 500–900 nm was used to identify mature Norway spruce (Picea abies L. Karst.) trees suffering from infestation, representing a different outbreak phase, by the European spruce bark beetle (Ips typographus L.). We developed a new processing method for analyzing spectral characteristic for high spatial resolution photogrammetric and hyperspectral images in forested environments, as well as for identifying individual anomalous trees. The dense point clouds, measured using image matching, enabled detection of single trees with an accuracy of 74.7%. We classified the trees into classes of healthy, infested and dead, and the results were promising. The best results for the overall accuracy were 76% (Cohen’s kappa 0.60), when using three color classes (healthy, infested, dead). For two color classes (healthy, dead), the best overall accuracy was 90% (kappa 0.80). The survey methodology based on high-resolution hyperspectral imaging will be of a high practical value for forest health management, indicating a status of bark beetle outbreak in time. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Methods for 3D Vegetation Mapping and Characterization)

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2001 KiB

Open AccessArticle

The Extraction of Vegetation Points from LiDAR Using 3D Fractal Dimension Analyses

by Haiquan Yang, Wenlong Chen, Tianlu Qian, Dingtao Shen and Jiechen Wang

Remote Sens. 2015, 7(8), 10815-10831; https://doi.org/10.3390/rs70810815 - 21 Aug 2015

Cited by 26 | Viewed by 8999

Abstract

Light Detection and Ranging (LiDAR), a high-precision technique used for acquiring three-dimensional (3D) surface information, is widely used to study surface vegetation information. Moreover, the extraction of a vegetation point set from the LiDAR point cloud is a basic starting-point for vegetation information analysis, and an important part of its further processing. To extract the vegetation point set completely and to describe the different spatial morphological characteristics of various features in a LiDAR point cloud, we have used 3D fractal dimensions. We discovered that every feature has its own distinctive 3D fractal dimension interval. Based on the 3D fractal dimensions of tall trees, we propose a new method for the extraction of vegetation using airborne LiDAR. According to this method, target features can be distinguished based on their morphological characteristics. The non-ground points acquired by filtering are processed by region growing segmentation and the morphological characteristics are evaluated by 3D fractal dimensions to determine the features required for the determination of the point set for tall trees. Avon, New York, USA was selected as the study area to test the method and the result proves the method’s efficiency. Thus, this approach is feasible. Additionally, the method uses the 3D coordinate properties of the LiDAR point cloud and does not require additional information, such as return intensity, giving it a larger scope of application. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Methods for 3D Vegetation Mapping and Characterization)

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4679 KiB

Open AccessArticle

aTrunk—An ALS-Based Trunk Detection Algorithm

by Sebastian Lamprecht, Johannes Stoffels, Sandra Dotzler, Erik Haß and Thomas Udelhoven

Remote Sens. 2015, 7(8), 9975-9997; https://doi.org/10.3390/rs70809975 - 05 Aug 2015

Cited by 16 | Viewed by 8148

Abstract

This paper presents a rapid multi-return ALS-based (Airborne Laser Scanning) tree trunk detection approach. The multi-core Divide & Conquer algorithm uses a CBH (Crown Base Height) estimation and 3D-clustering approach to isolate points associated with single trunks. For each trunk, a principal-component-based linear model is fitted, while a deterministic modification of LO-RANSAC is used to identify an optimal model. The algorithm returns a vector-based model for each identified trunk while parameters like the ground position, zenith orientation, azimuth orientation and length of the trunk are provided. The algorithm performed well for a study area of 109 trees (about 2/3 Norway Spruce and 1/3 European Beech), with a point density of 7.6 points per m², while a detection rate of about 75% and an overall accuracy of 84% were reached. Compared to crown-based tree detection methods, the aTrunk approach has the advantages of a high reliability (5% commission error) and its high tree positioning accuracy (0.59m average difference and 0.78m RMSE). The usage of overlapping segments with parametrizable size allows a seamless detection of the tree trunks. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Methods for 3D Vegetation Mapping and Characterization)

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2355 KiB

Open AccessArticle

End-to-End Simulation for a Forest-Dedicated Full-Waveform Lidar Onboard a Satellite Initialized from Airborne Ultraviolet Lidar Experiments

by Xiaoxia Shang and Patrick Chazette

Remote Sens. 2015, 7(5), 5222-5255; https://doi.org/10.3390/rs70505222 - 27 Apr 2015

Cited by 9 | Viewed by 8703

Abstract

In order to study forests at the global scale, a detailed link budget for a lidar system onboard satellite is presented. It is based on an original approach coupling airborne lidar observations and an end-to-end simulator. The simulator is initialized by airborne lidar measurements performed over temperate and tropical forests on the French territory, representing a wide range of forests ecosystems. Considering two complementary wavelengths of 355 and 1064 nm, the end-to-end simulator computes the performance of spaceborne lidar systems for different orbits. The analysis is based on forest structural (tree top height, quadratic mean canopy height) and optical (forest optical thickness) parameters. Although an ultraviolet lidar appears to be a good candidate for airborne measurements, our results show that the limited energy is not favorable for spaceborne missions with such a wavelength. A near infrared wavelength at 1064 nm is preferable, requiring ~100 mJ laser emitted energy, which is in agreement with current and future spaceborne missions involving a lidar. We find that the signal-to-noise ratio at the ground level to extract both the structural and optical parameters of forests must be larger than 10. Hence, considering the presence of clouds and aerosols in the atmosphere and assuming a stationary forest, a good detection probability of 99% can be reached when 4 or 5 satellite revisits are considered for a lidar system onboard the ISS or ICESat, respectively. This concerns ~90% of forest covers observed from the lidar, which have an optical thickness less than 3. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Methods for 3D Vegetation Mapping and Characterization)

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Other

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12809 KiB

Open AccessTechnical Note

LiCHy: The CAF’s LiDAR, CCD and Hyperspectral Integrated Airborne Observation System

by Yong Pang, Zengyuan Li, Hongbo Ju, Hao Lu, Wen Jia, Lin Si, Ying Guo, Qingwang Liu, Shiming Li, Luxia Liu, Binbin Xie, Bingxiang Tan and Yuanyong Dian

Remote Sens. 2016, 8(5), 398; https://doi.org/10.3390/rs8050398 - 13 May 2016

Cited by 97 | Viewed by 10206

Abstract

We describe the design, implementation and performance of a novel airborne system, which integrates commercial waveform LiDAR, CCD (Charge-Coupled Device) camera and hyperspectral sensors into a common platform system. CAF’s (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System is a unique system that permits simultaneous measurements of vegetation vertical structure, horizontal pattern, and foliar spectra from different view angles at very high spatial resolution (~1 m) on a wide range of airborne platforms. The horizontal geo-location accuracy of LiDAR and CCD is about 0.5 m, with LiDAR vertical resolution and accuracy 0.15 m and 0.3 m, respectively. The geo-location accuracy of hyperspectral image is within 2 pixels for nadir view observations and 5–7 pixels for large off-nadir observations of 55° with multi-angle modular when comparing to LiDAR product. The complementary nature of LiCHy’s sensors makes it an effective and comprehensive system for forest inventory, change detection, biodiversity monitoring, carbon accounting and ecosystem service evaluation. The LiCHy system has acquired more than 8000 km² of data over typical forests across China. These data are being used to investigate potential LiDAR and optical remote sensing applications in forest management, forest carbon accounting, biodiversity evaluation, and to aid in the development of similar satellite configurations. This paper describes the integration of the LiCHy system, the instrument performance and data processing workflow. We also demonstrate LiCHy’s data characteristics, current coverage, and potential vegetation applications. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Methods for 3D Vegetation Mapping and Characterization)

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