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Remote Sensing in Support of Environmental Governance

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 4176

Special Issue Editor


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Guest Editor
Department of Engineering, University of Palermo, Palermo, Italy
Interests: remote sensing in land and marine applications such as precision farming; soil-vegetation system monitoring; water quality; and sea surface currents

Special Issue Information

Dear Colleagues,

over the last half-century, remote sensing has proven to be a valuable source of information with special regard to the environment monitoring. Several governments financed the Earth Observation (EO) research (e.g. the EU COPERNICUS program) to setup sophisticated network of satellites and ground-based remote sensing systems aiming to collect information leading to a more complete ‘Earth knowledge’. In the last decades, the scientific community exploited these networks to development of science and the provision of operational services. National and international authorities pursued EO because of its capability to provide synoptically repeated information over large areas (including unreachable areas).

The added value of the remote sensing inspection is nowadays becoming strictly mandatory because of the need of governments to tackle against the increasing anthropic pressure which is threating several environmental ecosystems. In this framework, governments are requiring innovative tools aiming decision maker to deploy effective political ecology and environmental policy actions that advocates sustainability. Indeed, by taking into account of the insights offered by satellite EO decision-makers on environmental policies can adopt tailored strategies to deal with these new challenges including those related to the climate change.

This special issue seeks to collect manuscripts focused in remote sensing applications supporting the environmental governance in several thematic areas including, among others, air, biodiversity, oceans and coasts, land and soil management, water consumption and food production optimization.

Dr. Fulvio Capodici
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

  • remote sensing
  • sustainability
  • resources conservation
  • environmental monitoring and assessment
  • environmental policy
  • pollution
  • resilience
  • environmental compliance
  • climate change

Published Papers (1 paper)

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Research

21 pages, 9529 KiB  
Article
Analyzing Ecological Vulnerability and Vegetation Phenology Response Using NDVI Time Series Data and the BFAST Algorithm
by Jiani Ma, Chao Zhang, Hao Guo, Wanling Chen, Wenju Yun, Lulu Gao and Huan Wang
Remote Sens. 2020, 12(20), 3371; https://doi.org/10.3390/rs12203371 - 15 Oct 2020
Cited by 19 | Viewed by 3752
Abstract
Identifying ecologically vulnerable areas and understanding the responses of phenology to negative changes in vegetation growth are important bases for ecological restoration. However, identifying ecologically vulnerable areas is difficult because it requires high spatial resolution and dense temporal resolution data over a long [...] Read more.
Identifying ecologically vulnerable areas and understanding the responses of phenology to negative changes in vegetation growth are important bases for ecological restoration. However, identifying ecologically vulnerable areas is difficult because it requires high spatial resolution and dense temporal resolution data over a long time period. In this study, a novel method is presented to identify ecologically vulnerable areas based on the normalized difference vegetation index (NDVI) time series from MOD09A1. Here, ecologically vulnerable areas are defined as those that experienced negative changes frequently and greatly in vegetation growth after the disturbances during 2000–2018. The number and magnitude of negative changes detected by the Breaks for Additive Season and Trend (BFAST) algorithm based on the NDVI time-series data were combined to identify ecologically vulnerable areas. TIMESAT was then used to extract the phenology metrics from an NDVI time series dataset to characterize the vegetation responses due to the abrupt negative changes detected by the BFAST algorithm. Focus was given to Jilin Province, a region of China known to be ecologically vulnerable because of frequent drought. The results showed that 13.52% of the study area, mostly in Jilin Province, is ecologically vulnerable. The vulnerability of trees is the lowest, while that of sparse vegetation is the highest. The response of phenology is such that the relative amount of vegetation biomass and length of the growing period were decreased by negative changes in growth for dense vegetation types. The present research results will be useful for the protection of environments being disturbed by regional ecological restoration. Full article
(This article belongs to the Special Issue Remote Sensing in Support of Environmental Governance)
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