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Remote Sensing
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Remote Sensing Approaches to Biogeographical Applications
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Remote Sensing Approaches to Biogeographical Applications

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

Deadline for manuscript submissions: closed (30 July 2020) | Viewed by 31283

Special Issue Editor

Dr. Jennifer Jensen

E-Mail Website
Guest Editor
Geography, Texas State University, 601 University Drive, San Marcos, TX 78666, USA
Interests: remote sensing; vegetation mapping and monitoring; habitat modeling; landscape ecology; natural resource management; land cover–land use change; lidar

Special Issue Information

Dear Colleagues,

Characterizing the historic, current, and projected distributions of species and ecosystems are critical components toward understanding biogeographical patterns and processes. Remote sensing makes a vital contribution to our improved understanding of ecological phenomena at local, regional, and global scales.
Given the broad relevance of biogeography in landscape ecology, habitat mapping and restoration, ecological indicators, and conservation planning, this Special Issue serves as an outlet for articles covering but not limited to:

  • Spatiotemporal mapping of species distribution;
  • Remote sensing of ecological indicators or processes;
  • Time series analysis of ecosystems and landscape change;
  • Cross-disciplinary approaches that use remote sensing to characterize biogeographical patterns.

Dr. Jennifer Jensen
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 submissions that pass pre-check are 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 2700 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

  • Biogeography
  • Landscape ecology
  • Ecological processes
  • Ecological indicators
  • Spatiotemporal analysis
  • Habitat mapping and restoration
  • Conservation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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22 pages, 9666 KiB  
Article
Forest Degradation Assessment Based on Trend Analysis of MODIS-Leaf Area Index: A Case Study in Mexico
by Yunuen Reygadas, Jennifer L. R. Jensen and Gretchen G. Moisen
Remote Sens. 2019, 11(21), 2503; https://doi.org/10.3390/rs11212503 - 25 Oct 2019
Cited by 13 | Viewed by 5508
Abstract
: Assessing forest degradation has been a challenging task due to the generally slow-changing nature of the process, which demands long periods of observation and high frequency of records. This research contributes to efforts aimed at detecting forest degradation by analyzing the trend [...] Read more.
: Assessing forest degradation has been a challenging task due to the generally slow-changing nature of the process, which demands long periods of observation and high frequency of records. This research contributes to efforts aimed at detecting forest degradation by analyzing the trend component of the time series of Leaf Area Index (LAI) collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) over Central Mexico from 2002 to 2017. The analysis of the trend component is proposed to overcome the challenge of identifying very subtle and gradual changes that can be undetected if only the raw time series is examined. Additionally, the use of LAI as an alternative to other widely used indexes (e.g., Normalize Difference Vegetation Index and Enhanced Vegetation Index) facilitates consideration of the structural changes evident from degradation though not necessarily observable with spectral indices. Overall, results indicate that 52% of the study area has experienced positive trends of vegetation change (i.e., increasing LAI), 37% has remained unchanged, and 11% exhibits some level of forest degradation. Particularly, the algorithm estimated that 0.6% (385 km2) is highly degraded, 5.3% (3406 km2) moderately degraded, and 5.1% (3245 km2) slightly degraded. Most of the moderate and highly degraded areas are distributed over the east side of the study area and evergreen broadleaf appears to be the most affected forest type. Model validation resulted an accuracy of 63%. Some actions to improve this accuracy are suggested, but also a different approach to validate this type of study is suggested as an area of opportunity for future research. Full article
(This article belongs to the Special Issue Remote Sensing Approaches to Biogeographical Applications)
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Review

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31 pages, 3075 KiB  
Review
A Review of the Applications of Remote Sensing in Fire Ecology
by David M. Szpakowski and Jennifer L. R. Jensen
Remote Sens. 2019, 11(22), 2638; https://doi.org/10.3390/rs11222638 - 12 Nov 2019
Cited by 192 | Viewed by 24931
Abstract
Wildfire plays an important role in ecosystem dynamics, land management, and global processes. Understanding the dynamics associated with wildfire, such as risks, spatial distribution, and effects is important for developing a clear understanding of its ecological influences. Remote sensing technologies provide a means [...] Read more.
Wildfire plays an important role in ecosystem dynamics, land management, and global processes. Understanding the dynamics associated with wildfire, such as risks, spatial distribution, and effects is important for developing a clear understanding of its ecological influences. Remote sensing technologies provide a means to study fire ecology at multiple scales using an efficient and quantitative method. This paper provides a broad review of the applications of remote sensing techniques in fire ecology. Remote sensing applications related to fire risk mapping, fuel mapping, active fire detection, burned area estimates, burn severity assessment, and post-fire vegetation recovery monitoring are discussed. Emphasis is given to the roles of multispectral sensors, lidar, and emerging UAS technologies in mapping, analyzing, and monitoring various environmental properties related to fire activity. Examples of current and past research are provided, and future research trends are discussed. In general, remote sensing technologies provide a low-cost, multi-temporal means for conducting local, regional, and global-scale fire ecology research, and current research is rapidly evolving with the introduction of new technologies and techniques which are increasing accuracy and efficiency. Future research is anticipated to continue to build upon emerging technologies, improve current methods, and integrate novel approaches to analysis and classification. Full article
(This article belongs to the Special Issue Remote Sensing Approaches to Biogeographical Applications)
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