Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Remote Sensing Applications for Earth Observation and Global Change Detection
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing Applications for Earth Observation and Global Change Detection

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 July 2021) | Viewed by 8656

Special Issue Editors

Dr. Monica Rivas Casado

E-Mail Website
Guest Editor
School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK430AL, UK
Interests: surface water flooding; standardised monitoring approaches; systems engineering; disruptive technologies; climate change; extreme events
Special Issues, Collections and Topics in MDPI journals
Dr. Yadira Bajon Fernandez

E-Mail Website
Guest Editor
School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK430AL, UK
Interests: sludge treatment; wastewater treatment; waste management; biosolids; environmental engineering; anaerobic digestion; gas to liquid mass transfer; biorefineries
Dr. Marco Palma

E-Mail
Guest Editor
1. Dipartimento di Scienze della Vita e dell’Ambiente, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy
2. Habitats Edge Ltd 39 High Street, Bedford MK416AG, UK
Interests: underwater photogrammetry; marine habitat monitoring and restoration; environmental accounting; taxonomy; innovative technologies

Special Issue Information

Dear Colleagues,

Future changes in population growth, climate patterns and water demand will translate into a vast array of environmental and agricultural challenges, ranging from sustainable food production to increased frequency and intensity of extreme events such as droughts or floods. Global change is expected to affect the planet at different scales, thus requiring innovative environment solutions to address a combination of overarching challenges. Remote sensing techniques for Earth Observation have systematically informed management decisions and provided a well-tested ground for Global Change monitoring and detection. The increased resolution and accuracy of new remote sensing products could enable the detection of proxy variable alerting of Global Change patterns. This in turn will provide increased capacity in the mitigation and response to such changes. It is envisaged that advances in remote sensing methods will further increase our monitoring and overall understanding of Global Change drivers, extent and impact. This includes real-time data processing, advances in robotics and autonomous systems, machine learning algorithms and data fusion techniques, amongst others. This Special Issue aims to collate manuscripts displaying advanced applications of remote sensing techniques for Earth Observation within the context of Global Change detection. Of special interest as those manuscripts covering the use of remote sensing techniques to alleviate the effects of Global Change.

Subtopics

  • Extreme events; monitoring and assessment
  • Development of Global Change mitigation measures (pre-, during, post-change)
  • Integration of multiple remote sensing methods
  • Upscaling and downscaling of remote sensing methodologies
  • Global Change detection at local and regional scale
  • Identification of proxy variables for Global Change detection
  • Machine learning techniques for Global Change mitigation
  • Specific case studies

Dr. Monica Rivas Casado
Dr. Yadira Bajon Fernandez
Dr. Marco Palma
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.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 11328 KiB  
Article
Detection of Flood Damage in Urban Residential Areas Using Object-Oriented UAV Image Analysis Coupled with Tree-Based Classifiers
by Joanna Zawadzka, Ian Truckell, Abdou Khouakhi and Mónica Rivas Casado
Remote Sens. 2021, 13(19), 3913; https://doi.org/10.3390/rs13193913 - 30 Sep 2021
Cited by 1 | Viewed by 2214
Abstract
Timely clearing-up interventions are essential for effective recovery of flood-damaged housing, however, time-consuming door-to-door inspections for insurance purposes need to take place before major repairs can be done to adequately assess the losses caused by flooding. With the increased probability of flooding, there [...] Read more.
Timely clearing-up interventions are essential for effective recovery of flood-damaged housing, however, time-consuming door-to-door inspections for insurance purposes need to take place before major repairs can be done to adequately assess the losses caused by flooding. With the increased probability of flooding, there is a heightened need for rapid flood damage assessment methods. High resolution imagery captured by unmanned aerial vehicles (UAVs) offers an opportunity for accelerating the time needed for inspections, either through visual interpretation or automated image classification. In this study, object-oriented image segmentation coupled with tree-based classifiers was implemented on a 10 cm resolution RGB orthoimage, captured over the English town of Cockermouth a week after a flood triggered by storm Desmond, to automatically detect debris associated with damages predominantly to residential housing. Random forests algorithm achieved a good level of overall accuracy of 74%, with debris being correctly classified at the rate of 58%, and performing well for small debris (67%) and skips (64%). The method was successful at depicting brightly-colored debris, however, was prone to misclassifications with brightly-colored vehicles. Consequently, in the current stage, the methodology could be used to facilitate visual interpretation of UAV images. Methods to improve accuracy have been identified and discussed. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Earth Observation and Global Change Detection)
►▼ Show Figures

Graphical abstract

15 pages, 1776 KiB  
Article
Vegetation Dynamics and Climatological Drivers in Ethiopia at the Turn of the Century
by Carly Muir, Jane Southworth, Reza Khatami, Hannah Herrero and Berkay Akyapı
Remote Sens. 2021, 13(16), 3267; https://doi.org/10.3390/rs13163267 - 18 Aug 2021
Cited by 11 | Viewed by 3005
Abstract
Global change, particularly climate change, poses a risk of altering vegetation composition and health. The consequences manifest throughout Earth’s system as a change in ecosystem services and socioecological stability. It is therefore critical that vegetation dynamics are monitored to establish baseline conditions and [...] Read more.
Global change, particularly climate change, poses a risk of altering vegetation composition and health. The consequences manifest throughout Earth’s system as a change in ecosystem services and socioecological stability. It is therefore critical that vegetation dynamics are monitored to establish baseline conditions and detect shifts. Africa is at high risk of environmental change, yet evaluation of the link between climate and vegetation is still needed for some regions. This work expands on more frequent local and multinational scale studies of vegetation trends by quantifying directional persistence (DP) at a national scale for Ethiopia, based on the normalized difference vegetation index (NDVI) between 2000 and 2016. The DP metric determines cumulative change in vegetation greenness and has been applied to studies of ecological stability and health. Secondary analysis utilizing panel regression methodologies is carried out to measure the effect of climate on NDVI. Models are developed to consider spatial dependence by including fixed effects and spatial weights. Results indicate widespread cumulative declines in NDVI, with the greatest change during the dry season and concentrated in northern Ethiopia. Regression analyses suggest significant control from climatic variables. However, temperature has a larger effect on NDVI, which contrasts with findings of some previous studies. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Earth Observation and Global Change Detection)
►▼ Show Figures

Graphical abstract

19 pages, 7307 KiB  
Article
GBRT-Based Estimation of Terrestrial Latent Heat Flux in the Haihe River Basin from Satellite and Reanalysis Datasets
by Lu Wang, Yuhu Zhang, Yunjun Yao, Zhiqiang Xiao, Ke Shang, Xiaozheng Guo, Junming Yang, Shuhui Xue and Jie Wang
Remote Sens. 2021, 13(6), 1054; https://doi.org/10.3390/rs13061054 - 10 Mar 2021
Cited by 16 | Viewed by 2218
Abstract
An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is fundamental and crucial for the rational utilization of water resources in the Haihe River Basin (HRB). However, the sparsity of flux observation sites hinders the accurate characterization of spatiotemporal LE [...] Read more.
An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is fundamental and crucial for the rational utilization of water resources in the Haihe River Basin (HRB). However, the sparsity of flux observation sites hinders the accurate characterization of spatiotemporal LE patterns over the HRB. In this study, we estimated the daily LE across the HRB using the gradient boosting regression tree (GBRT) from global land surface satellite NDVI data, reanalysis data and eddy covariance data. Compared with the random forests (RF) and extra tree regressor (ETR) methods, the GBRT obtains the best results, with R2 = 0.86 and root mean square error (RMSE = 18.1 W/m2. Then, we applied the GBRT algorithm to map the average annual terrestrial LE of the HRB from 2016 to 2018 with a spatial resolution of 0.05°. When compared with the Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) LE products, the difference between the terrestrial LE estimated by the GBRT algorithm and the GLASS and MODIS products was less than 20 W/m2 in most areas; thus, the GBRT algorithm was reliable and reasonable for estimating the long-term LE estimation over the HRB. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Earth Observation and Global Change Detection)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop