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Special Issue "Characterizing of the Structure and the Species Composition of Forest by Using Multiple Remote Sensing Data Sources or Inventory Approaches"

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: 30 November 2019

Special Issue Editors

Guest Editor
Dr. Marcos Barrio-Anta

Department of Organisms and Systems Biology, GIS-Forest Research Group. Polytechnic School of Mieres, University of Oviedo, Asturias, Spain
Website | E-Mail
Interests: forest management and modelling, remote sensing, forest ecology, silviculture
Guest Editor
Dr. Carlos A. Lopez-Sanchez

Department of Organisms and Systems Biology, GIS-Forest Research Group. Polytechnic School of Mieres, University of Oviedo, Asturias, Spain
Website | E-Mail
Interests: remote sensing, forest management and modelling, biogeography and conservation, global change

Special Issue Information

Dear Colleagues,

The structure, species composition (occurrence and abundance) and productivity of forests and how they are explicit in time and space are key pieces of information for forest management.

Recent advances in remote sensing technologies allow us to capture large datasets on species-specific tree and stand attributes from multiple measurement systems. The new ways to analyze and process these datasets (e.g., novel machine learning algorithms) provide new insights necessary to generate spatially explicit information. This information has great value for nature conservationists as well as for forest managers that frequently require it to be displayed for large spatial extents.

In this Special Issue, the guest editors encourage the submission of current research that use data acquired with a variety of remote sensing technologies (airborne and terrestrial laser scanning (ALS/TLS), digital aerial photogrammetry (DAP), and high/very high spatial resolution (HSR/VHSR) satellite optical imagery) under different inventory approaches—the area-based approach (ABA) and the individual tree detection (ITD) approach—designed to characterize forest resource information for strategic, tactical, and operational planning. We would particularly welcome submissions on multi-sensor data fusion.

Dr. Marcos Barrio-Anta
Dr. Carlos A. Lopez-Sanchez
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. 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

  • Forest Inventory
  • Remote Sensing
  • Multispectral and Hyperspectral Imagery
  • Airborne and Terrestrial Laser Scanning
  • Tree Species Composition
  • Land Use and Land Cover (LULC)
  • Structural Diversity
  • Vertical Canopy Distributions
  • Area-Based Approach (ABA)
  • Individual Tree Detection (ITD)

Published Papers (2 papers)

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Research

Open AccessArticle Spatial Autocorrelation Analysis of Multi-Scale Damaged Vegetation in the Wenchuan Earthquake-Affected Area, Southwest China
Forests 2019, 10(2), 195; https://doi.org/10.3390/f10020195
Received: 17 January 2019 / Revised: 14 February 2019 / Accepted: 19 February 2019 / Published: 21 February 2019
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Abstract
Major earthquakes can cause serious vegetation destruction in affected areas. However, little is known about the spatial patterns of damaged vegetation and its influencing factors. Elucidating the main influencing factors and finding out the key vegetation type to reflect spatial patterns of damaged [...] Read more.
Major earthquakes can cause serious vegetation destruction in affected areas. However, little is known about the spatial patterns of damaged vegetation and its influencing factors. Elucidating the main influencing factors and finding out the key vegetation type to reflect spatial patterns of damaged vegetation are of great interest in order to improve the assessment of vegetation loss and the prediction of the spatial distribution of damaged vegetation caused by earthquakes. In this study, we used Moran’s I correlograms to study the spatial autocorrelation of damaged vegetation and its potential driving factors in the nine worst-hit Wenchuan earthquake-affected cities and counties. Both dependent and independent variables showed a positive spatial autocorrelation but with great differences at four aggregation levels (625 × 625 m, 1250 × 1250 m, 2500 × 2500 m, and 5000 × 5000 m). Shrubs can represent the characteristics of all damaged vegetation due to the significant linear relationship between their Moran’s I at the four aggregation levels. Clustering of similar high coverage of damaged vegetation occurred in the study area. The residuals of the standard linear regression model also show a significantly positive autocorrelation, indicating that the standard linear regression model cannot explain all the spatial patterns in damaged vegetation. Spatial autoregressive models without spatially autocorrelated residuals had the better goodness-of-fit to deal with damaged vegetation. The aggregation level 8 × 8 is a scale threshold for spatial autocorrelation. There are other environmental factors affecting vegetation destruction. Our study provides useful information for the countermeasures of vegetation protection and conservation, as well as the prediction of the spatial distribution of damaged vegetation, to improve vegetation restoration in earthquake-affected areas. Full article
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Open AccessArticle The NDVI-CV Method for Mapping Evergreen Trees in Complex Urban Areas Using Reconstructed Landsat 8 Time-Series Data
Forests 2019, 10(2), 139; https://doi.org/10.3390/f10020139
Received: 25 December 2018 / Revised: 2 February 2019 / Accepted: 5 February 2019 / Published: 8 February 2019
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Abstract
Evergreen trees play a significant role in urban ecological services, such as air purification, carbon and oxygen balance, and temperature and moisture regulation. Remote sensing represents an essential technology for obtaining spatiotemporal distribution data for evergreen trees in cities. However, highly developed subtropical [...] Read more.
Evergreen trees play a significant role in urban ecological services, such as air purification, carbon and oxygen balance, and temperature and moisture regulation. Remote sensing represents an essential technology for obtaining spatiotemporal distribution data for evergreen trees in cities. However, highly developed subtropical cities, such as Nanjing, China, have serious land fragmentation problems, which greatly increase the difficulty of extracting evergreen trees information and reduce the extraction precision of remote-sensing methods. This paper introduces a normalized difference vegetation index coefficient of variation (NDVI-CV) method to extract evergreen trees from remote-sensing data by combining the annual minimum normalized difference vegetation index (NDVIann-min) with the CV of a Landsat 8 time-series NDVI. To obtain an intra-annual, high-resolution time-series dataset, Landsat 8 cloud-free and partially cloud-free images over a three-year period were collected and reconstructed for the study area. Considering that the characteristic growth of evergreen trees remained nearly unchanged during the phenology cycle, NDVIann-min is the optimal phenological node to separate this information from that of other vegetation types. Furthermore, the CV of time-series NDVI considers all of the phenologically critical phases; therefore, the NDVI-CV method had higher extraction accuracy. As such, the approach presented herein represents a more practical and promising method based on reasonable NDVIann-min and CV thresholds to obtain spatial distribution data for evergreen trees. The experimental verification results indicated a comparable performance since the extraction accuracy of the model was over 85%, which met the classification accuracy requirements. In a cross-validation comparison with other evergreen trees’ extraction methods, the NDVI-CV method showed higher sensitivity and stability. Full article
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