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Special Issue "New Application based on Advanced Remote Sensing Data in Forests and Wood Land Areas"

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Quantitative Methods and Remote Sensing".

Deadline for manuscript submissions: closed (28 February 2018).

Special Issue Editors

Guest Editor
Prof. Dr. Barbara Koch

Chair of Remote Sensing and Landscape Information Systems University of Freiburg, Tennenbacherstr. 4, D-79106 Freiburg, Germany
Website | E-Mail
Interests: Rmote Sensing, spatial modelling, process modelling based on geodata, forest inventory, biodiversity, landuse change, environmental impact assessment
Guest Editor
PD Dr. Hooman Latifi

1. Dept of Photogrammerty and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, No. 1346 Valiasr Str., Mirdamad Crossing, Postal Code 19967-15433 Tehran, Iran
2. Dept. of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, Oswald-Kuelpe-Weg 86, 97074 Wuerzburg, Germany
Website 1 | Website 2 | E-Mail
Interests: remote sensing-assisted monitoring of forest structure, phenology and health; small-scale forest inventory; spatial statistics; modelling and optimization; LiDAR; UAV and their point cloud processing for forest inventory

Special Issue Information

Dear Colleagues,

Remote sensing data gained increasing interest for different environmental applications. In forestry, remote sensing has for long tradition and foresters were one of the first civil users of remote sensing data. Already, in the 19th century, the use of aerial photographs for forest planning was documented. Within the last twenty years, sensor technology has developed rapidly and, together with new flight platforms, a number of new applications in forestry have opened up. Especially, new developments with hyperspectral cameras and laser technology allow applications that would not have been feasible 10 years ago. Together with these new sensor technology developments, new flight platforms are also available, and offer a link between remote sensing data of different resolution from terrestrial, to unmanned aerial vehicles, to airborne flight platforms to satellite platforms. Along with these developments, a large amount of satellite systems for Earth observation have been launched and provide continuous data from local to global applications. The wealth of technological developments and data led new algorithms and software allowing, not only new mapping options, but also to closely analyse change processes and provide risk forecasts. This Special Issue shall collate the newest findings for remote sensing applications in forestry. It shall comprise examples of practical applications in forestry, as well as first research results. Especially, integrative approaches, using different sources of information will be a focus, because this is of high importance for remote sensing applications in forestry.

Prof. Dr. Barbara Koch
Dr. Hooman Latifi
Guest Editor

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. Forests 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

  • multi-sensoral applications

  • flight platforms

  • hyperspectral

  • laser

  • high performance satellite system

  • risk assessment

  • biodiversity assessment

  • tree type detection

  • change detection

Published Papers (9 papers)

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Research

Open AccessArticle
Automatic Detection of Single Trees in Airborne Laser Scanning Data through Gradient Orientation Clustering
Forests 2018, 9(6), 291; https://doi.org/10.3390/f9060291
Received: 20 February 2018 / Revised: 17 May 2018 / Accepted: 19 May 2018 / Published: 24 May 2018
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Abstract
Currently, existing methods for single-tree detection based on airborne laser scanning (ALS) data usually require some thresholds and parameters to be set manually. Manually setting threshold or parameters is laborious and time-consuming, and for dense forests, the high commission and omission rate make [...] Read more.
Currently, existing methods for single-tree detection based on airborne laser scanning (ALS) data usually require some thresholds and parameters to be set manually. Manually setting threshold or parameters is laborious and time-consuming, and for dense forests, the high commission and omission rate make most existing single-tree detection techniques inefficient. As a solution to these problems, this paper proposed an automatic single-tree detection method in ALS data through gradient orientation clustering (GOC). In this method, the rasterized Canopy Height Model (CHM) was derived from ALS data using surface interpolation. Then, potential trees were assumed as approximate conical shapes and extracted based on the GOC. Finally, trees were identified from the potential trees based on the compactness of the crown shape. This method used the gradient orientation information of rasterized CHM, thus increasing the generalization of single-tree detection method. In order to verify the validity and practicability of the proposed method, twelve 1256 m2 circular study plots of different forest types were selected from the benchmark dataset (NEWFOR), and the results from nine different methods were presented and compared for these study plots. Among nine methods, the proposed method had the highest root mean square matching score (RMS_M = 43). Moreover, the proposed method had excellent detection (M > 47) in both single-layer coniferous and single-layered mixed stands. Full article
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Open AccessArticle
A Generalized Lidar-Based Model for Predicting the Merchantable Volume of Balsam Fir of Sites Located along a Bioclimatic Gradient in Quebec, Canada
Forests 2018, 9(4), 166; https://doi.org/10.3390/f9040166
Received: 22 February 2018 / Revised: 19 March 2018 / Accepted: 21 March 2018 / Published: 24 March 2018
Cited by 1 | PDF Full-text (4326 KB) | HTML Full-text | XML Full-text
Abstract
Lidar-based models rely on an optimal relationship between the field and the lidar data for accurate predictions of forest attributes. This relationship may be altered by the variability in the stand growth conditions or by the temporal discrepancy between the field inventory and [...] Read more.
Lidar-based models rely on an optimal relationship between the field and the lidar data for accurate predictions of forest attributes. This relationship may be altered by the variability in the stand growth conditions or by the temporal discrepancy between the field inventory and the lidar survey. In this study, we used lidar data to predict the timber merchantable volume (MV) of five sites located along a bioclimatic gradient of temperature and elevation. The temporal discrepancies were up to three years. We adjusted a random canopy height coefficient (accounting for the variability amongst sites), and a growth function (accounting for the growth during the temporal discrepancy), to the predictive model. The MV could be predicted with a pseudo-R2 of 0.86 and a residual standard deviation of 24.3 m3 ha−1. The average biases between the field-measured and the predicted MVs were small. The variability of MV predictions was related to the bioclimatic gradient. Fixed-effect models that included a bioclimatic variable provided similar prediction accuracies. This study suggests that the variability amongst sites, the occurrence of a bioclimatic gradient and temporal discrepancies are essential in building a generalized lidar-based model for timber volume. Full article
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Open AccessArticle
Estimation of Total Biomass in Aleppo Pine Forest Stands Applying Parametric and Nonparametric Methods to Low-Density Airborne Laser Scanning Data
Forests 2018, 9(4), 158; https://doi.org/10.3390/f9040158
Received: 31 January 2018 / Revised: 12 March 2018 / Accepted: 19 March 2018 / Published: 21 March 2018
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Abstract
The account of total biomass can assist with the evaluation of climate regulation policies from local to global scales. This study estimates total biomass (TB), including tree and shrub biomass fractions, in Pinus halepensis Miller forest stands located in the Aragon Region (Spain) [...] Read more.
The account of total biomass can assist with the evaluation of climate regulation policies from local to global scales. This study estimates total biomass (TB), including tree and shrub biomass fractions, in Pinus halepensis Miller forest stands located in the Aragon Region (Spain) using Airborne Laser Scanning (ALS) data and fieldwork. A comparison of five selection methods and five regression models was performed to relate the TB, estimated in 83 field plots through allometric equations, to several independent variables extracted from ALS point cloud. A height threshold was used to include returns above 0.2 m when calculating ALS variables. The sample was divided into training and test sets composed of 62 and 21 plots, respectively. The model with the lower root mean square error (15.14 tons/ha) after validation was the multiple linear regression model including three ALS variables: the 25th percentile of the return heights, the variance, and the percentage of first returns above the mean. This study confirms the usefulness of low-density ALS data to accurately estimate total biomass, and thus better assess the availability of biomass and carbon content in a Mediterranean Aleppo pine forest. Full article
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Open AccessArticle
Shifts in Forest Structure in Northwest Montana from 1972 to 2015 Using the Landsat Archive from Multispectral Scanner to Operational Land Imager
Forests 2018, 9(4), 157; https://doi.org/10.3390/f9040157
Received: 15 February 2018 / Revised: 15 March 2018 / Accepted: 19 March 2018 / Published: 21 March 2018
Cited by 6 | PDF Full-text (4154 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
There is a pressing need to map changes in forest structure from the earliest time period possible given forest management policies and accelerated disturbances from climate change. The availability of Landsat data from over four decades helps researchers study an ecologically meaningful length [...] Read more.
There is a pressing need to map changes in forest structure from the earliest time period possible given forest management policies and accelerated disturbances from climate change. The availability of Landsat data from over four decades helps researchers study an ecologically meaningful length of time. Forest structure is most often mapped utilizing lidar data, however these data are prohibitively expensive and cover a narrow temporal window relative to the Landsat archive. Here we describe a technique to use the entire length of the Landsat archive from Multispectral Scanner to Operational Land Imager (M2O) to produce three novel outcomes: (1) we used the M2O dataset and standard change vector analysis methods to classify annual forest structure in northwestern Montana from 1972 to 2015, (2) we improved the accuracy of each yearly forest structure classification by applying temporal continuity rules to the whole time series, with final accuracies ranging from 97% to 68% respectively for two and six-category classifications, and (3) we demonstrated the importance of pre-1984 Landsat data for long-term change studies. As the Landsat program continues to acquire Earth imagery into the foreseeable future, time series analyses that aid in classifying forest structure accurately will be key to the success of any land management changes in the future. Full article
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Open AccessArticle
Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion
Forests 2018, 9(3), 119; https://doi.org/10.3390/f9030119
Received: 1 February 2018 / Revised: 27 February 2018 / Accepted: 1 March 2018 / Published: 5 March 2018
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Abstract
The vast extent and inaccessibility of boreal forest ecosystems are barriers to routine monitoring of forest structure and composition. In this research, we bridge the scale gap between intensive but sparse plot measurements and extensive remote sensing studies by collecting forest inventory variables [...] Read more.
The vast extent and inaccessibility of boreal forest ecosystems are barriers to routine monitoring of forest structure and composition. In this research, we bridge the scale gap between intensive but sparse plot measurements and extensive remote sensing studies by collecting forest inventory variables at the plot scale using an unmanned aerial vehicle (UAV) and a structure from motion (SfM) approach. At 20 Forest Inventory and Analysis (FIA) subplots in interior Alaska, we acquired overlapping imagery and generated dense, 3D, RGB (red, green, blue) point clouds. We used these data to model forest type at the individual crown scale as well as subplot-scale tree density (TD), basal area (BA), and aboveground biomass (AGB). We achieved 85% cross-validation accuracy for five species at the crown level. Classification accuracy was maximized using three variables representing crown height, form, and color. Consistent with previous UAV-based studies, SfM point cloud data generated robust models of TD (r2 = 0.91), BA (r2 = 0.79), and AGB (r2 = 0.92), using a mix of plot- and crown-scale information. Precise estimation of TD required either segment counts or species information to differentiate black spruce from mixed white spruce plots. The accuracy of species-specific estimates of TD, BA, and AGB at the plot scale was somewhat variable, ranging from accurate estimates of black spruce TD (+/−1%) and aspen BA (−2%) to misallocation of aspen AGB (+118%) and white spruce AGB (−50%). These results convey the potential utility of SfM data for forest type discrimination in FIA plots and the remaining challenges to develop classification approaches for species-specific estimates at the plot scale that are more robust to segmentation error. Full article
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Open AccessArticle
Hyperspectral Analysis of Pine Wilt Disease to Determine an Optimal Detection Index
Forests 2018, 9(3), 115; https://doi.org/10.3390/f9030115
Received: 30 January 2018 / Revised: 26 February 2018 / Accepted: 27 February 2018 / Published: 3 March 2018
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Abstract
Bursaphelenchus xylophilus, the pine wood nematode (PWN) which causes pine wilt disease, is currently a serious problem in East Asia, including in Japan, Korea, and China. This paper investigates the hyperspectral analysis of pine wilt disease to determine the optimal detection indices [...] Read more.
Bursaphelenchus xylophilus, the pine wood nematode (PWN) which causes pine wilt disease, is currently a serious problem in East Asia, including in Japan, Korea, and China. This paper investigates the hyperspectral analysis of pine wilt disease to determine the optimal detection indices for measuring changes in the spectral reflectance characteristics and leaf reflectance in the Pinus thunbergii (black pine) forest on Geoje Island, South Korea. In the present study, we collected the leaf reflectance spectra of pine trees infected with pine wilt disease using a hyperspectrometer. We used 10 existing vegetation indices (based on hyperspectral data) and introduced the green-red spectral area index (GRSAI). We made comparisons between non-infected and infected trees over time. A t-test was then performed to find the most appropriate index for detecting pine wilt disease-infected pine trees. Our main result is that, in most of the infected trees, the reflectance changed in the red and mid-infrared wavelengths within two months after pine wilt infection. The vegetation atmospherically resistant index (VARI), vegetation index green (VIgreen), normalized wilt index (NWI), and GRSAI indices detected pine wilt disease infection faster than the other indices used. Importantly, the GRSAI results showed less variability than the results of the other indices. This optimal index for detecting pine wilt disease is generated by combining red and green wavelength bands. These results are expected to be useful in the early detection of pine wilt disease-infected trees. Full article
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Open AccessArticle
Inventory of Close-to-Nature Forests Based on the Combination of Airborne LiDAR Data and Aerial Multispectral Images Using a Single-Tree Approach
Forests 2017, 8(12), 467; https://doi.org/10.3390/f8120467
Received: 15 October 2017 / Revised: 21 November 2017 / Accepted: 27 November 2017 / Published: 28 November 2017
Cited by 1 | PDF Full-text (4015 KB) | HTML Full-text | XML Full-text
Abstract
This study is concerned with the assessment of application possibilities for remote sensing data within a forest inventory in close-to-nature forests. A combination of discrete airborne laser scanning data and multispectral aerial images separately evaluated main tree and forest stand characteristics (i.e., the [...] Read more.
This study is concerned with the assessment of application possibilities for remote sensing data within a forest inventory in close-to-nature forests. A combination of discrete airborne laser scanning data and multispectral aerial images separately evaluated main tree and forest stand characteristics (i.e., the number of trees, mean height and diameter, tree species, tree height, tree diameter, and tree volume). We used eCognition software (Trimble GeoSpatial, Munich, Germany) for tree species classification and reFLex software (National Forest Centre, Zvolen, Slovakia) for individual tree detection as well as for forest inventory attribute estimations. The accuracy assessment was conducted at the ProSilva demo site Smolnícka Osada (Eastern Slovakia, Central Europe), which has been under selective management for more than 60 years. The remote sensing data were taken using a scanner (Leica ALS70-CM) and camera (Leica RCD30) from an average height of 1034 m, and the ground reference data contained the measured positions and dimensions of 1151 trees in 45 plots distributed across the region. This approach identified 73% of overstory and 28% of understory trees. Tree species classification within overstory trees resulted in an overall accuracy slightly greater than 65%. We also found that the mean difference between the remote-based results and ground data was −0.3% for tree height, 1.1% for tree diameter, and 1.9% for stem volume. At the stand level, the mean difference reached values of 0.4%, 17.9%, and −21.4% for mean height, mean diameter, and growing stock, respectively. Full article
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Open AccessArticle
Exploring the Regional Potential of Lignocellulosic Biomass for an Emerging Bio-Based Economy: A Case Study from Southwest Germany
Forests 2017, 8(11), 449; https://doi.org/10.3390/f8110449
Received: 23 October 2017 / Revised: 13 November 2017 / Accepted: 14 November 2017 / Published: 17 November 2017
Cited by 1 | PDF Full-text (2195 KB) | HTML Full-text | XML Full-text
Abstract
The globally emerging concepts and strategies for a “bioeconomy” rely on the vision of a sustainable bio-based substitution process. Fossil fuels are scarce and their use contributes to global warming. To replace them in the value chains, it is essential to gain knowledge [...] Read more.
The globally emerging concepts and strategies for a “bioeconomy” rely on the vision of a sustainable bio-based substitution process. Fossil fuels are scarce and their use contributes to global warming. To replace them in the value chains, it is essential to gain knowledge about quantities and spatial distributions of renewable resources. Decision makers specifically require knowledge-based models for rational development choices. In this paper, we demonstrate such an approach using remote sensing-derived maps that represent the potential available biomass of forests and trees outside forests (TOF). The maps were combined with infrastructure data, transport costs and wood pricing to calculate the potentially available biomass for a regional bioeconomy in the federal state of Baden-Württemberg in Southwest Germany. We estimated the spatially explicit regional supply of biomass using routable data in a GIS environment, and created an approach to find the most suitable positions for biomass conversion facilities by minimizing transport distances and biomass costs. The approach resulted in the theoretical, regional supply of woody biomass with transport distances between 10 and 50 km. For a more realistic assessment, we subsequently applied several restrictions and assumptions, compiled different scenarios, optimised transport distances and identified wood assortments. Our analysis demonstrated that a regional bioeconomy using only local primary lignocellulosic biomass is possible. There would be, however, strong competition with traditional wood-processing sectors, mainly thermal utilisation and pulp and paper production. Finally, suitable positions for conversion facilities in Baden-Württemberg were determined for each of the six most plausible scenarios. This case study demonstrates the value of remote sensing and GIS techniques for a flexible, expandable and upgradable spatially explicit decision model. Full article
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Open AccessArticle
Treefall Gap Mapping Using Sentinel-2 Images
Forests 2017, 8(11), 426; https://doi.org/10.3390/f8110426
Received: 27 September 2017 / Revised: 25 October 2017 / Accepted: 3 November 2017 / Published: 7 November 2017
Cited by 2 | PDF Full-text (6087 KB) | HTML Full-text | XML Full-text
Abstract
Proper knowledge about resources in forest management is fundamental. One of the most important parameters of forests is their size or spatial extension. By determining the area of treefall gaps inside the compartments, a more accurate yield can be calculated and the scheduling [...] Read more.
Proper knowledge about resources in forest management is fundamental. One of the most important parameters of forests is their size or spatial extension. By determining the area of treefall gaps inside the compartments, a more accurate yield can be calculated and the scheduling of forestry operations could be planned better. Several field- and remote sensing-based approaches are in use for mapping but they provide only static measurements at high cost. The Earth Observation satellite mission Sentinel-2 was put in orbit as part of the Copernicus programme. With the 10-m resolution bands, it is possible to observe small-scale forestry operations like treefall gaps. The spatial extension of these gaps is often less than 200 m2, thus their detection can only be done on sub-pixel level. Due to the higher temporal resolution of Sentinel-2, multiple observations are available in a year; therefore, a time series evaluation is possible. The modelling of illumination can increase the accuracy of classification in mountainous areas. The method was tested on three deciduous forest sites in the Börzsöny Mountains in Hungary. The area evaluation produced less than 10% overestimation with the best possible solutions on the sites. The presented work shows a low-cost method for mapping treefall gaps which delivers annual information about the gap area in a deciduous forest. Full article
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