Special Issue "Plant Species and Functional Types Monitoring with Imaging Spectroscopy"

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

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Dr. Keely L. Roth
E-Mail Website
Guest Editor
Data Science, The Climate Corporation, San Francisco, CA, USA
Interests: hyperspectral; vegetation mapping; agriculture; plant ecology; functional traits
Dr. Erin Wetherley
E-Mail Website
Guest Editor
Geography Department, University of California, Santa Barbara, USA
Interests: plant ecology; urban systems; data fusion; climate modeling; hyperspectral

Special Issue Information

Dear Colleagues,

Vegetation is a critical barometer of ecological change, and making maps of plant species and functional types is valuable for monitoring landscapes, tracking climate change impacts, and understanding the effects of land disturbance or management. Increasing availability of imaging spectroscopy data, with its richness in spectral information, can be used to measure and map plant biophysical, phenological, and structural traits. This creates an opportunity for developing new techniques and applications to deliver on critical monitoring needs.

With this Special Issue, we shall collect state-of-the-art research that investigates using imaging spectroscopy to monitor plant species and functional types, with a particular emphasis on developing new techniques, examining cross-ecosystem applications, and exploring new dimensions of plant species and functional type monitoring.

Within these areas of emphasis, we invite authors to submit papers on a range of topics, including but not limited to species detection, classification, and derivation of functional traits, time-series analysis, comparative cross-biome studies, high-value monitoring applications (e.g., wildfire risk, agriculture, urban ecosystems), multiplatform or multiresolution investigations, and assimilation of imaging spectroscopy data for modeling of ecosystems.

Dr. Keely L. Roth
Dr. Erin Wetherley
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. 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 2400 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

  • Vegetation monitoring and mapping
  • Terrestrial ecosystems
  • UAV, airborne, and spaceborne imaging spectroscopy
  • Plant species and functional types
  • Ecosystem modeling
  • Hyperspectral

Published Papers (2 papers)

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Research

Open AccessArticle
Changes in the Greenness of Mountain Pine (Pinus mugo Turra) in the Subalpine Zone Related to the Winter Climate
Remote Sens. 2021, 13(9), 1788; https://doi.org/10.3390/rs13091788 - 04 May 2021
Viewed by 260
Abstract
In the current alteration of temperature and snow cover regimes, the impacts of winter climate have received considerably less attention than those of the vegetation period. In this study, we present the results demonstrating the influence of the winter climate conditions on the [...] Read more.
In the current alteration of temperature and snow cover regimes, the impacts of winter climate have received considerably less attention than those of the vegetation period. In this study, we present the results demonstrating the influence of the winter climate conditions on the Mountain pine (Pinus mugo Turra) communities in High Tatra Mts (Western Carpathians). The changes in greenness in 2000–2020 were represented by the inter-annual differences of satellite-derived Normalized Difference Vegetation Index (NDVI). The winter climate conditions were characterized by climate indices calculated from the temperature and snow cover data measured at Skalnaté Pleso Observatory (1778 m a.s.l.) over the period between 1941–2020. Areas with P. mugo were classified into two density classes and five altitudinal zones of occurrence. The partial correlation analyses, which controlled the influence of summer climate, indicated that winter warm spells (WWS) caused a significant decrease in the greenness of the P. mugo thickets growing in the dense class D2 (R = −0.47) and in the altitudinal zones A2 (1600–1700 m a.s.l.) and A3 (1700–1800 m a.s.l.) with R = −0.54 for each zone. The changes in greenness were related to the average snow depth (ASD) as well, particularly in the dense class D2 (R = 0.45) and in the altitudinal zone A2 (R = 0.50). Here, in the summers following winters with the incidence of WWS or low ASD, we found decreased greenness following the injury of P. mugo shrubs, but NDVI after winters with higher ASD indicated more greenness. At lower altitudes, injuries may result in the loss of competition capacity of P. mugo near the timberline, where taller mountain tree species can utilize the conditions of warmer climate for expansion. We also found a significant positive effect of warmer winter seasons in the sparse P. mugo thickets (D1) with R = 0.50 and at higher altitudes (R = 0.49 in A4—1800–1900 m a.s.l.; R = 0.53 in A5—1900–2000 m a.s.l.). The increased temperatures in December correlated significantly with the increase of the greenness in all P. mugo pixels (R = 0.47), with the most pronounced effect in the sparse class D1 (R = 0.57) and in altitudinal zones A4 (R = 0.63) and A5 (R = 0.44), creating advantageous conditions for the thermophilisation of the alpine zone by P. mugo. Full article
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Open AccessArticle
Mapping of Eucalyptus in Natura 2000 Areas Using Sentinel 2 Imagery and Artificial Neural Networks
Remote Sens. 2020, 12(14), 2176; https://doi.org/10.3390/rs12142176 - 08 Jul 2020
Cited by 3 | Viewed by 1524
Abstract
Plantations of fast-growing Eucalyptus trees have become a common sight in the western Iberian peninsula where they are planted to exploit their economic potential. Negative side-effects of large scale plantations including the invasive behavior of Eucalyptus trees outside of regular plantations have become [...] Read more.
Plantations of fast-growing Eucalyptus trees have become a common sight in the western Iberian peninsula where they are planted to exploit their economic potential. Negative side-effects of large scale plantations including the invasive behavior of Eucalyptus trees outside of regular plantations have become apparent. This study uses medium resolution, multi-spectral imagery of the Sentinel 2 satellites to map Eucalyptus across Portugal and parts of Spain with a focus on Natura 2000 areas inside Portugal, that are protected under the European birds and habitats directives. This method enables the detection of small incipient as well as mixed populations outside of regular plantations. Ground truth maps were compiled using field surveys as well as high resolution satellite imagery and were used to train Feedforward Neural Networks. These models predict Eucalyptus tree cover with a sensitivity of up to 75.7% as well as a specificity of up to 95.8%. The overall accuracy of the prediction is 92.5%. A qualitative assessment of Natura 2000 areas in Portugal has been performed and 15 areas have been found to be affected by Eucalyptus of which 9 are strongly affected. This study demonstrates the applicability of multi-spectral imagery for tree-species classification and invasive species control. It provides a probability-map of Eucalyptus tree cover for the western Iberian peninsula with 10 m spatial resolution and shows the need for monitoring of Eucalyptus in protected areas. Full article
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