Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Remote Sensing of Anthropic Impact on the Environment
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 of Anthropic Impact on the Environment

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 12640

Special Issue Editors

Dr. Donato Amitrano

E-Mail Website
Guest Editor
Surrey Space Centre, University of Surrey, Guildford GU2 7XH, UK
Interests: remote sensing; SAR; Earth observation; electromagnetic scattering, computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Cicala

E-Mail Website
Guest Editor
CIRA, Italian Aerospace Research Center, via Maiorise s.n., 81043 Capua, Italy
Interests: remote sensing applications; multi-sensor data fusion; multi-platform data fusion; image processing; computer vision; machine learning; geo-processing; UAV data processing

Special Issue Information

Dear Colleagues,

Climate changes and the rapid increase of the population are nowadays the most important threats for the Earth ecosystem. The increasing demand of goods and resources, especially from developing countries, is putting under pressure the biosphere and the hydrosphere within environments already fragile. In most of the cases, classic in situ monitoring is not effective as it is not able to provide the synoptic picture necessary to public and private decision makers for large-scale actions either for planning activities or for rapid response in emergency situations.

In this context, remote sensing technologies have gradually been an indispensable approach to environmental monitoring. The availability of several sensors imaging the Earth surface at different scales and wavelength is significantly boosting the development of new applications and methodologies, which can benefit of the possibility given by the synergic exploitation of multi-source data.

The objective of this Special Issue is to highlight the most recent advances in remote sensing for the monitoring of the impacts of human activities on the natural landscape. Contributions are expected on (but not limited to) the following topics:

  • Monitoring of natural resources, like forests and water, and temporal tracking of their changes;
  • Monitoring of urban agglomerates and of the effects of urbanization on the natural landscape;
  • Detection and recognition of environmental issues of anthropic origin.

The proposed solutions are expected to be based on:

  • Multi-temporal and/or multi-source satellite image processing;
  • Remote sensing data assimilation within mathematical problem-specific models;
  • Emerging proximal sensing technologies, like remotely piloted aerial systems.

Papers must be original contributions, not previously published or submitted elsewhere. Submissions based on previously published or submitted conference papers may be considered provided that they are significantly improved and extended.

We look forward to your participation in this Special Issue.

Dr. Donato Amitrano
Dr. Luca Cicala
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

  • remote sensing
  • multi-sensor data fusion
  • multi-temporal
  • natural resources
  • urban areas
  • UAV remote sensing
  • environmental monitoring
  • anthropic impact

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (4 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

Jump to: Other

15 pages, 15179 KiB  
Article
The Deep Convolutional Neural Network Role in the Autonomous Navigation of Mobile Robots (SROBO)
by Shabnam Sadeghi Esfahlani, Alireza Sanaei, Mohammad Ghorabian and Hassan Shirvani
Remote Sens. 2022, 14(14), 3324; https://doi.org/10.3390/rs14143324 - 10 Jul 2022
Cited by 16 | Viewed by 3107
Abstract
The ability to navigate unstructured environments is an essential task for intelligent systems. Autonomous navigation by ground vehicles requires developing an internal representation of space, trained by recognizable landmarks, robust visual processing, computer vision and image processing. A mobile robot needs a platform [...] Read more.
The ability to navigate unstructured environments is an essential task for intelligent systems. Autonomous navigation by ground vehicles requires developing an internal representation of space, trained by recognizable landmarks, robust visual processing, computer vision and image processing. A mobile robot needs a platform enabling it to operate in an environment autonomously, recognize the objects, and avoid obstacles in its path. In this study, an open-source ground robot called SROBO was designed to accurately identify its position and navigate certain areas using a deep convolutional neural network and transfer learning. The framework uses an RGB-D MYNTEYE camera, a 2D laser scanner and inertial measurement units (IMU) operating through an embedded system capable of deep learning. The real-time decision-making process and experiments were conducted while the onboard signal processing and image capturing system enabled continuous information analysis. State-of-the-art Real-Time Graph-Based SLAM (RTAB-Map) was adopted to create a map of indoor environments while benefiting from deep convolutional neural network (Deep-CNN) capability. Enforcing Deep-CNN improved the performance quality of the RTAB-Map SLAM. The proposed setting equipped the robot with more insight into its surroundings. The robustness of the SROBO increased by 35% using the proposed system compared to the conventional RTAB-Map SLAM. Full article
(This article belongs to the Special Issue Remote Sensing of Anthropic Impact on the Environment)
►▼ Show Figures

Figure 1

27 pages, 19421 KiB  
Article
Analysis of Vegetative Cover Vulnerability in Rohingya Refugee Camps of Bangladesh Utilizing Landsat and Per Capita Greening Area (PCGA) Datasets
by Md Fazlul Karim and Xiang Zhang
Remote Sens. 2021, 13(23), 4922; https://doi.org/10.3390/rs13234922 - 3 Dec 2021
Cited by 7 | Viewed by 3831
Abstract
The vegetative cover in and surrounding the Rohingya refugee camps in Ukhiya-Teknaf is highly vulnerable since millions of refugees moved into the area, which led to severe environmental degradation. In this research, we used a supervised image classification technique to quantify the vegetative [...] Read more.
The vegetative cover in and surrounding the Rohingya refugee camps in Ukhiya-Teknaf is highly vulnerable since millions of refugees moved into the area, which led to severe environmental degradation. In this research, we used a supervised image classification technique to quantify the vegetative cover changes both in Ukhiya-Teknaf and thirty-four refugee camps in three time-steps: one pre-refugee crisis (January 2017), and two post-refugee crisis (March 2018, and February 2019), in order to identify the factors behind the decline in vegetative cover. The vegetative cover vulnerability of the thirty-four refugee camps was assessed using the Per Capita Greening Area (PCGA) datasets and K-means classification techniques. The satellite-based monitoring result affirms a massive loss of vegetative cover, approximately 5482.2 hectares (14%), in Ukhiya-Teknaf and 1502.56 hectares (79.57%) among the thirty-four refugee camps, between 2017 and 2019. K-means classification revealed that the vegetative cover in about 82% of the refugee camps is highly vulnerable. In the end, a recommendation as to establishing the studied region as an ecological park is proposed and some guidelines discussed. This could protect and reserve forests from further deforestation in the area, and foster future discussion among policymakers and researchers. Full article
(This article belongs to the Special Issue Remote Sensing of Anthropic Impact on the Environment)
►▼ Show Figures

Graphical abstract

22 pages, 31713 KiB  
Article
Prediction of Erosion-Prone Areas in the Catchments of Big Lowland Rivers: Implementation of Maximum Entropy Modelling—Using the Example of the Lower Vistula River (Poland)
by Marta Brzezińska, Dawid Szatten and Zygmunt Babiński
Remote Sens. 2021, 13(23), 4775; https://doi.org/10.3390/rs13234775 - 25 Nov 2021
Cited by 6 | Viewed by 2278
Abstract
It is common knowledge that erosion depends on environmental factors modified by human activity. Erosion within a catchment area can be defined by local lithological, morphometric, hydrological features, etc., and land cover, with spatial distribution described by means of remote sensing tools. The [...] Read more.
It is common knowledge that erosion depends on environmental factors modified by human activity. Erosion within a catchment area can be defined by local lithological, morphometric, hydrological features, etc., and land cover, with spatial distribution described by means of remote sensing tools. The study relied on spatial data for the catchment of the Lower Vistula—the biggest river in Poland. GIS (SAGA, QGIS) tools were used to designate the spatial distribution of independent environmental variables that determined the process of erosion according to land cover types within the Lower Vistula catchment (Corine Land Cover). In addition, soil loss in the catchment area was calculated using the USLE model (Universal Soil Loss Equation). The spatial data was used to determine the predictive power of variables for the process of erosion by applying the maximum entropy model (MaxEnt) commonly used in fields of science unrelated to fluvial hydrology. The results of the study pointed directly to environmental features strongly connected with the process of erosion, identifying areas susceptible to intensified erosion, and in addition positively verified by USLE. This testifies to the correct selection of the proposed method, which is a strong point of the presented study. The proposed interdisciplinary approach to predict erosion within the catchment area (MaxEnt), widely supported by GIS tools, will allow the identification of environmental pressures to support the decision-making process in erosion-prone areas. Full article
(This article belongs to the Special Issue Remote Sensing of Anthropic Impact on the Environment)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research

15 pages, 2411 KiB  
Technical Note
Forest Aboveground Biomass Estimation Using Machine Learning Ensembles: Active Learning Strategies for Model Transfer and Field Sampling Reduction
by Donato Amitrano, Giovanni Giacco, Stefano Marrone, Antonio Elia Pascarella, Mattia Rigiroli and Carlo Sansone
Remote Sens. 2023, 15(21), 5138; https://doi.org/10.3390/rs15215138 - 27 Oct 2023
Cited by 3 | Viewed by 1832
Abstract
Biomass is a crucial indicator of the carbon sequestration capacity of a vegetation ecosystem. Its dynamic is of interest because it impacts on the carbon cycle, which plays an important role in the global climate and its changes. This work presents a novel [...] Read more.
Biomass is a crucial indicator of the carbon sequestration capacity of a vegetation ecosystem. Its dynamic is of interest because it impacts on the carbon cycle, which plays an important role in the global climate and its changes. This work presents a novel technique, able to transfer a calibrated regression model between different areas by exploiting an active learning methodology and using Shannon’s entropy as a discriminator for sample selection. Model calibration is performed based on a reference area for which an extended ground truth is available and implemented via regression bootstrap. Then, re-calibration samples for model transfer are selected through active learning, allowing for choosing a limited number of points to be investigated for training data collection. Different sampling strategies and regression techniques have been tested to demonstrate that a significant reduction in the number of calibration samples does not affect the estimation performance. The proposed workflow has been tested on a dataset concerning Finnish forests. Experimental results show that the joint exploitation of regression ensembles and active learning dramatically reduces the amount of field sampling, providing aboveground biomass estimates comparable to those obtained using literature techniques, which need extended training sets to build reliable predictions. Full article
(This article belongs to the Special Issue Remote Sensing of Anthropic Impact on the Environment)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop