Special Issue "Monitoring of Forest Ecological Environment based on Remote Sensing Technology"

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

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editor

Dr. Yasumasa Hirata
E-Mail Website
Guest Editor
Forestry and Forest Products Research Institute, Tsukuba, 305-8687, Japan
Interests: forest eclogy; forest ecosystems services; change detection; airborne LiDAR; forest mapping; forest structrue; indivudual tree detection

Special Issue Information

Dear Colleagues,

Forests play important roles in the production of timber and nontimber forest products, biodiversity conservation of genes, species, and ecosystems, mitigation of climate change, and so on, and they provide various benefits called ecosystem services to human life. However, due to the expansion of human activities and the impact of climate change, forests are rapidly decreasing, and the remaining forests are deteriorating. In order to control the progress of deforestation and forest degradation and to maximize forest ecosystem services, it is necessary to properly monitor forest ecological environments.

The progress of machine learning, including deep learning, and the spread of big data processing technology, such as Google Earth Engine, have made it possible to evaluate forest ecological environments and their changes widely and in detail. In addition, the forest condition can be monitored in detail by restoring the three-dimensional structure of the forest from drone photographs via SfM methods or airborne LiDAR. Moreover, terrestrial LiDAR can visualize the detailed situation in the forest.

In this Special Issue of Remote Sensing, I welcome original and innovative research papers focusing on monitoring of the forest ecological environment and its change from local to global scales based on novel remote sensing technology.

Dr. Yasumasa Hirata
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. 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 2000 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 monitoring
  • Change detection
  • Ecological environment
  • Ecosystem services
  • Time-series analysis
  • 3D structure of forest
  • Optical remote sensing
  • LiDAR remote sensing
  • SfM

Published Papers (1 paper)

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Open AccessLetter
Grouping-Based Time-Series Model for Monitoring of Fall Peak Coloration Dates Using Satellite Remote Sensing Data
Remote Sens. 2020, 12(2), 274; https://doi.org/10.3390/rs12020274 - 14 Jan 2020
Abstract
Accurate monitoring of plant phenology is vital to effective understanding and prediction of the response of vegetation ecosystems to climate change. Satellite remote sensing is extensively employed to monitor vegetation phenology. However, fall phenology, such as peak foliage coloration, is less well understood [...] Read more.
Accurate monitoring of plant phenology is vital to effective understanding and prediction of the response of vegetation ecosystems to climate change. Satellite remote sensing is extensively employed to monitor vegetation phenology. However, fall phenology, such as peak foliage coloration, is less well understood compared with spring phenological events, and is mainly determined using the vegetation index (VI) time-series. Each VI only emphasizes a single vegetation property. Thus, selecting suitable VIs and taking advantage of multiple spectral signatures to detect phenological events is challenging. In this study, a novel grouping-based time-series approach for satellite remote sensing was proposed, and a wide range of spectral wavelengths was considered to monitor the complex fall foliage coloration process with simultaneous changes in multiple vegetation properties. The spatial and temporal scale effects of satellite data were reduced to form a reliable remote sensing time-series, which was then divided into groups, namely pre-transition, transition and post-transition groups, to represent vegetation dynamics. The transition period of leaf coloration was correspondingly determined to divisions with the smallest intra-group and largest inter-group distances. Preliminary results using a time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2002 to 2013 at the Harvard Forest (spatial scale: ~3500 m; temporal scale: ~8 days) demonstrated that the method can accurately determine the coloration period (correlation coefficient: 0.88; mean absolute difference: 3.38 days), and that the peak coloration periods displayed a shifting trend to earlier dates. The grouping-based approach shows considerable potential in phenological monitoring using satellite time-series. Full article
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