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Remote Sensing of Ecosystem Functions: Advances for Ecosystems Conservation Status...
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Remote Sensing of Ecosystem Functions: Advances for Ecosystems Conservation Status Assessment

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 6503

Special Issue Editors

Dr. Paola Mairota

E-Mail Website
Guest Editor
Dipartimento di Scienze Agro Ambientali e Territoriali, Università degli Studi di Bari Aldo Moro, Bari, Italy
Interests: biodiversity and conservation; climate change; forest ecology; forest management; habitat modelling; landscape ecology; remote sensing
Prof. Dr. Javier Cabello

E-Mail Website
Guest Editor
Andalusian Center for the Assessment and Monitoring of Global Change, University of Almería, 04120 Almería, Spain
Interests: arid zones ecology; conservation biodiversity; ecosystem functions and services; remote sensing; translational ecology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Domingo Alcaraz-Segura

E-Mail Website
Guest Editor
Inter-university Institute for Earth System Research, Department of Botany, University of Granada, Granada, Spain
Interests: remote-sensing based assessment of land-use change on ecosystem functioning and services; development of monitoring and warning systems for changes in the functioning of socio-ecosystems; geographical priorities in biodiversity conservation; botany and vegetation science
Special Issues, Collections and Topics in MDPI journals
Dr. Ana Sofia Vaz

E-Mail Website
Guest Editor
CIBIO-INBIO, Research Centre in Biodiversity and Genetic Resources, InBIO - Research Network in Biodiversity and Evolutionary Biology, Associate Laboratory, University of Porto, Porto, Portugal
Interests: remote sensing of biological invasions; monitoring systems for evaluating changes in ecosystem services; social and ecological approaches to support ecosystem management and planning

Special Issue Information

Dear Colleagues,

The Convention on Biological Diversity (CBD 2014) and the 2030 Agenda for Sustainable Development (UNDP 2015) advocate the assessment of ecosystem conservation status. Biodiversity conservation, both inside and outside protected areas, is imperative due to its intrinsic and extrinsic importance for Earth system equilibrium and human wellbeing.

Remote sensing technologies are usually applied in the context of the IUCN Red List of Ecosystems and the EU Habitats Directive for the quantification of spatial indicators (e.g., geographical distribution, extent) and their trends. Despite the recognized great potential and advantages offered by these technologies, their application in the same context to functional indicators (e.g., vegetation phenology, defoliation, habitat quality, water availability, shade provided to the ground) and corresponding proxies is so far underrepresented.

This Special Issue will comprise a selection of research papers reporting scientific advances in the application of cost-effective, integrated and transferable remote sensing based frameworks to ecosystem function assessment, within the perspectives of enhancing monitoring and reporting capacities of managing authorities.

Dr. Paola Mairota
Prof. Javier Cabello
Prof. Domingo Alcaraz-Segura
Dr. Ana Sofia Vaz
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 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

  • Conservation status
  • Risk of collapse
  • Ecosystem functions

Published Papers (2 papers)

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Research

19 pages, 19674 KiB  
Article
Protection Effect and Vacancy of the Ecological Protection Redline: A Case Study in Guangdong–Hong Kong–Macao Greater Bay Area, China
by Xiuming Wang, Youyue Wen, Xucheng Liu, Ding Wen, Yingxian Long, Peng Zhao, Piao Liu and Jenny Zhong
Remote Sens. 2021, 13(24), 5171; https://doi.org/10.3390/rs13245171 - 20 Dec 2021
Cited by 10 | Viewed by 3421
Abstract
The Ecological Protection Redline (EPR) is an innovative measure implemented in China to maintain the structural stability and functional security of the ecosystem. By prohibiting large-scale urban and industrial construction activities, EPR is regarded as the “lifeline” to ensure national ecological security. It [...] Read more.
The Ecological Protection Redline (EPR) is an innovative measure implemented in China to maintain the structural stability and functional security of the ecosystem. By prohibiting large-scale urban and industrial construction activities, EPR is regarded as the “lifeline” to ensure national ecological security. It is of great practical significance to scientifically evaluate the protection effect of EPR and identify the protection vacancies. However, current research has focused only on the protection effects of the EPR on ecosystem services (ESs), and the protection effect of the EPR on ecological connectivity remains poorly understood. Based on an evaluation of ES importance, the circuit model, and hotspot analysis, this paper identified the ecological security pattern in Guangdong–Hong Kong–Macao Greater Bay Area (GBA), analyzed the role of EPR in maintaining ES and ecological connectivity, and identified protection gaps. The results were as follows: (1) The ecological sources were mainly distributed in mountainous areas of the GBA. The ecological sources and ecological corridors constitute a circular ecological shelter surrounding the urban agglomeration of the GBA. (2) The EPR effectively protected water conservation, soil conservation, and biodiversity maintenance services, but the protection efficiency of carbon sequestration service and ecological connectivity were low. In particularly, EPR failed to continuously protect regional large-scale ecological corridors and some important stepping stones. (3) The protection gaps of carbon sequestration service and ecological connectivity in the study area reached 1099.80 km2 and 2175.77 km2, respectively, mainly distributed in Qingyuan, Yunfu, and Huizhou. In future EPR adjustments, important areas for carbon sequestration service and ecological connectivity maintenance should be included. This study provides a comprehensive understanding of the protection effects of EPR on ecological structure and function, and it has produced significant insights into improvements of the EPR policy. In addition, this paper proposes that the scope of resistance surface should be extended, which would improve the rationality of the ecological corridor simulation. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystem Functions: Advances for Ecosystems Conservation Status Assessment)
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21 pages, 6472 KiB  
Article
Assessment of Poplar Looper (Apocheima cinerarius Erschoff) Infestation on Euphrates (Populus euphratica) Using Time-Series MODIS NDVI Data Based on the Wavelet Transform and Discriminant Analysis
by Tiecheng Huang, Xiaojuan Ding, Xuan Zhu, Shujiang Chen, Mengyu Chen, Xiang Jia, Fengbing Lai and Xiaoli Zhang
Remote Sens. 2021, 13(12), 2345; https://doi.org/10.3390/rs13122345 - 15 Jun 2021
Cited by 7 | Viewed by 2172
Abstract
Poplar looper (Apocheima cinerarius Erschoff) is a destructive insect infesting Euphrates or desert poplars (Populus euphratica) in Xinjiang, China. Since the late 1950s, it has been plaguing desert poplars in the Tarim Basin in Xinjiang and caused widespread damages. This [...] Read more.
Poplar looper (Apocheima cinerarius Erschoff) is a destructive insect infesting Euphrates or desert poplars (Populus euphratica) in Xinjiang, China. Since the late 1950s, it has been plaguing desert poplars in the Tarim Basin in Xinjiang and caused widespread damages. This paper presents an approach to the detection of poplar looper infestations on desert poplars and the assessment of the severity of the infestations using time-series MODIS NDVI data via the wavelet transform and discriminant analysis, using the middle and lower reaches of the Yerqiang River as a case study. We first applied the wavelet transform to the NDVI time series data in the period of 2009–2014 for the study area, which decomposed the data into a representation that shows detailed NDVI changes and trends as a function of time. This representation captures both intra- and inter-annual changes in the data, some of which characterise transient events. The decomposed components were then used to filter out details of the changes to create a smoothed NDVI time series that represent the phenology of healthy desert poplars. Next the subset of the original NDVI time series spanning the time period when the pest was active was extracted and added to the smoothed time series to generate a blended time series. The wavelet transform was applied again to decompose the blended time series to enhance and identify the changes in the data that may represent the signals of the pest infestations. Based on the amplitude of the enhanced pest infestation signals, a predictive model was developed via discriminant analysis to detect the pest infestation and assess its severity. The predictive model achieved a severity classification accuracy of 91.7% and 94.37% accuracy in detecting the time of the outbreak. The methodology presented in this paper provides a fast, precise, and practical method for monitoring pest outbreak in dense desert poplar forests, which can be used to support the surveillance and control of poplar looper infestations on desert poplars. It is of great significance to the conservation of the desert ecological environment. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystem Functions: Advances for Ecosystems Conservation Status Assessment)
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