Special Issue "Biodiversity Study by Remote Sensing"

A special issue of Diversity (ISSN 1424-2818).

Deadline for manuscript submissions: closed (30 September 2016).

Special Issue Editor

Special Issue Information

Dear Colleagues,

Quantifying biodiversity is a challenging task, overall considering impervious areas. In some cases, field sampling might need an efficient way to be planned. In this view, remote sensing is a valuable tool for efficiently planning field sampling for biodiversity data gathering.
Additionally, remote sensing represents a powerful tool for predicting biodiversity spots and species community turnover, based on a number of different sensors/images at different spatial and spectral resolutions.
The aim of this Special Issue is to focus on theoretical and empirical research to face biodiversity-related issues, based on remote sensing data.
We encourage researchers to send their manuscript on the following topics:

- the theory behind the use of remote sensing for biodiversity study
- macroecology and remote sensing
- remote sensing for the improvement of the efficiency of sampling design
- empirical evidence on the relationship between field and remote sensing information on biodiversity
- species distribution modeling
- species invasions, tracked by remote sensing and remotely sensed variables

Dr. Duccio Rocchini
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. Diversity 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

  • biodiversity
  • macroecology
  • remote sensing
  • species distribution modeling
  • sensors
  • spatial resolution
  • spectral resolution
  • statistics

Published Papers (1 paper)

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Research

Article
Estimating Invasion Success by Non-Native Trees in a National Park Combining WorldView-2 Very High Resolution Satellite Data and Species Distribution Models
Diversity 2017, 9(1), 6; https://doi.org/10.3390/d9010006 - 18 Jan 2017
Cited by 10 | Viewed by 3032
Abstract
Invasion by non-native tree species is an environmental and societal challenge requiring predictive tools to assess invasion dynamics. The frequent scale mismatch between such tools and on-ground conservation is currently limiting invasion management. This study aimed to reduce these scale mismatches, assess the [...] Read more.
Invasion by non-native tree species is an environmental and societal challenge requiring predictive tools to assess invasion dynamics. The frequent scale mismatch between such tools and on-ground conservation is currently limiting invasion management. This study aimed to reduce these scale mismatches, assess the success of non-native tree invasion and determine the environmental factors associated to it. A hierarchical scaling approach combining species distribution models (SDMs) and satellite mapping at very high resolution (VHR) was developed to assess invasion by Acacia dealbata in Peneda-Gerês National Park, the only national park in Portugal. SDMs were first used to predict the climatically suitable areas for A. dealdata and satellite mapping with the random-forests classifier was then applied to WorldView-2 very-high resolution imagery to determine whether A. dealdata had actually colonized the predicted areas (invasion success). Environmental attributes (topographic, disturbance and canopy-related) differing between invaded and non-invaded vegetated areas were then analyzed. The SDM results indicated that most (67%) of the study area was climatically suitable for A. dealbata invasion. The onset of invasion was documented to 1905 and satellite mapping highlighted that 12.6% of study area was colonized. However, this species had only colonized 62.5% of the maximum potential range, although was registered within 55.6% of grid cells that were considerable unsuitable. Across these areas, the specific success rate of invasion was mostly below 40%, indicating that A. dealbata invasion was not dominant and effective management may still be possible. Environmental attributes related to topography (slope), canopy (normalized difference vegetation index (ndvi), land surface albedo) and disturbance (historical burnt area) differed between invaded and non-invaded vegetated area, suggesting that landscape attributes may alter at specific locations with Acacia invasion. Fine-scale spatial-explicit estimation of invasion success combining SDM predictions with VHR invasion mapping allowed the scale mismatch between predictions of invasion dynamics and on-ground conservation decision making for invasion management to be reduced. Locations with greater potential to suppress invasions could also be defined. Uncertainty in the invasion mapping needs to be accounted for in the interpretation of the results. Full article
(This article belongs to the Special Issue Biodiversity Study by Remote Sensing)
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