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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 28181

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Prof. Dr. Jamal Jokar Arsanjani

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Guest Editor

Geoinformatics and Earth Observation Research Group, Department of Planning, Aalborg University Copenhagen, A.C. Meyers Vænge 15, DK-2450 Copenhagen, Denmark
Interests: citizen observations; earth observation; geocomputation; GEO-artificial intelligence; data quality; environmental monitoring and assessment
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Special Issue Information

Dear Colleagues,

Earth observation, citizen observation, and geospatial science/technologies have leveraged our understanding of our planet and the emerging concerns of our societies, including climate change. They have extensively empowered us to monitor our changing planet through comprehensive assessment of our land and marine resources using data-driven science. The United Nations (UN) Sustainable Development Goals (SDGs) provide us with a modular road map for moving toward sustainable use of our resources. The main objective of this Special Issue is gather research articles that can stimulate our efforts to develop state-of-the-art and cutting-edge approaches for achievement of the SDGs as well as monitoring their implementation. The Special Issue calls for original research contributions on topics including, but not limited, to:

Contribution of earth observation and citizen observations of SDGs;
Geospatial technologies, participatory approaches, and cloud-based services in supporting SDGs;
Open data and citizen science in service of the SDGs;
Impacts of climate change on SDGs implementation and achievement;
Artificial intelligence and computational approaches for the SDGs;
Data quality and uncertainty associated with measuring SDG indicators;
Visualization of the SDG indicators across space and time.

Please indicate which SDG goal is addressed by your study by including it in the title or abstract of your submission.

Prof. Jamal Jokar Arsanjani
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. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

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Published Papers (5 papers)

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16 pages, 2182 KiB

Open AccessArticle

Employing Machine Learning for Detection of Invasive Species using Sentinel-2 and AVIRIS Data: The Case of Kudzu in the United States

by Tobias Jensen, Frederik Seerup Hass, Mohammad Seam Akbar, Philip Holm Petersen and Jamal Jokar Arsanjani

Sustainability 2020, 12(9), 3544; https://doi.org/10.3390/su12093544 - 27 Apr 2020

Cited by 29 | Viewed by 5592

Abstract

Invasive plants are causing massive economic and environmental troubles for our societies worldwide. The aim of this study is to employ a set of machine learning classifiers for detecting invasive plant species using remote sensing data. The target species is Kudzu vine, which mostly grows in the south-eastern states of the US and quickly outcompetes other plants, making it a relevant and threatening species to consider. Our study area is Atlanta, Georgia and the surrounding area. Five different algorithms: Boosted Logistic Regression (BLR), Naive Bayes (NB), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM) were tested with the aim of testing their performance and identifying the most optimal one. Furthermore, the influence of temporal, spectral and spatial resolution in detecting Kudzu was also tested and reviewed. Our finding shows that random forest, neural network and support vector machine classifiers outperformed. While the achieved internal accuracies were about 97%, an external validation conducted over an expanded area of interest resulted in 79.5% accuracy. Furthermore, the study indicates that high accuracy classification can be achieved using multispectral Sentinel-2 imagery and can be improved while integrating with airborne visible/infrared imaging spectrometer (AVIRIS) hyperspectral data. Finally, this study indicates that dimensionality reduction methods such as principal component analysis (PCA) should be applied cautiously to the hyperspectral AVIRIS data to preserve its utility. The applied approach and the utilized set of methods can be of interest for detecting other kinds of invasive species as part of fulfilling UN sustainable development goals, particularly number 12: responsible consumption and production, 13: climate action, and 15: life on land. Full article

(This article belongs to the Special Issue Earth Observation, Citizen Observation, and Geospatial Technologies for Achieving and Monitoring the Sustainable Development Goals (SDGs))

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24 pages, 29909 KiB

Open AccessArticle

Input-Output Efficiency of Economic Growth: A Multielement System Perspective

by Lei Kang and Zhouying Song

Sustainability 2020, 12(11), 4624; https://doi.org/10.3390/su12114624 - 5 Jun 2020

Cited by 6 | Viewed by 5500

Abstract

Achieving sustainable and efficient economic development involves the pursuit of a model with low input, low emissions, and high yield. One approach to this is by considering input-output efficiency, which has been studied by many previous studies. However, existing literature mainly tend only to give an overall evaluation of regional input-output efficiency, which is unable to reveal the structure and components within the input-output system. This paper aims to overcome this problem by a systematic examination and measuring the resource efficiency, socio-economic efficiency, and environmental efficiency of separate subsystems using the Super-DEA model. The overall efficiency of 30 Chinese provinces from 2000 to 2015 is analyzed, along with each subsystem’s efficiency. The results show: (i) The overall input-output efficiency, resource efficiency, and socio-economic efficiency of the eastern region are relatively high. The efficiency of the northeastern region has performed poorly. Although the efficiency of the central and western regions is not high, their resource efficiency and socio-economic efficiency have risen in the last decade; (ii) Environmental efficiencies are markedly lower than the levels of the other two subsystems. Most western and northeastern provinces increased in rank, while most eastern and central provinces fell. (iii) Provinces can be divided into three categories, such as resource, socio-economic, and environmental efficiency-constrained provinces. Finally, we discuss the reasons driving the spatiotemporal pattern of China’s input-output efficiency and improvement policies. Full article

(This article belongs to the Special Issue Earth Observation, Citizen Observation, and Geospatial Technologies for Achieving and Monitoring the Sustainable Development Goals (SDGs))

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28 pages, 5807 KiB

Open AccessArticle

Machine Learning for Conservation Planning in a Changing Climate

by Ana Cristina Mosebo Fernandes, Rebeca Quintero Gonzalez, Marie Ann Lenihan-Clarke, Ezra Francis Leslie Trotter and Jamal Jokar Arsanjani

Sustainability 2020, 12(18), 7657; https://doi.org/10.3390/su12187657 - 16 Sep 2020

Cited by 12 | Viewed by 5442

Abstract

Wildlife species’ habitats throughout North America are subject to direct and indirect consequences of climate change. Vulnerability assessments for the Intermountain West regard wildlife and vegetation and their disturbance as two key resource areas in terms of ecosystems when considering climate change issues. Despite the adaptability potential of certain wildlife, increased temperature estimates of 1.67–2 °C by 2050 increase the likelihood and severity of droughts, floods, heatwaves and wildfires in Utah. As a consequence, resilient flora and fauna could be displaced. The aim of this study was to locate areas of habitat for an exemplary species, i.e., sage-grouse, based on current climate conditions and pinpoint areas of future habitat based on climate projections. The locations of wildlife were collected from Volunteered Geographic Information (VGI) observations in addition to normal temperature and precipitation, vegetation cover and other ecosystem-related data. Four machine learning algorithms were then used to locate the current sites of wildlife habitats and predict suitable future sites where wildlife would likely relocate to, dependent on the effects of climate change and based on a timeframe of scientifically backed temperature-increase estimates. Our findings show that Random Forest outperforms other competing models, with an accuracy of 0.897, and a sensitivity and specificity of 0.917 and 0.885, respectively, and has great potential in Species Distribution Modeling (SDM), which can provide useful insights into habitat predictions. Based on this model, our predictions show that sage-grouse habitats in Utah will continue to decrease over the coming years due to climate change, producing a highly fragmented habitat and causing a loss of close to 70% of their current habitat. Priority Areas of Conservation (PACs) and protected areas might be deemed insufficient to halt this habitat loss, and more effort should be put into maintaining connectivity between patches to ensure the movement and genetic diversity within the sage-grouse population. The underlying data-driven methodical approach of this study could be useful for environmentalists, researchers, decision-makers, and policymakers, among others. Full article

(This article belongs to the Special Issue Earth Observation, Citizen Observation, and Geospatial Technologies for Achieving and Monitoring the Sustainable Development Goals (SDGs))

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26 pages, 1167 KiB

Open AccessArticle

Using a DEA–AutoML Approach to Track SDG Achievements

by Bodin Singpai and Desheng Wu

Sustainability 2020, 12(23), 10124; https://doi.org/10.3390/su122310124 - 4 Dec 2020

Cited by 11 | Viewed by 3085

Abstract

Each country needs to monitor progress on their Sustainable Development Goals (SDGs) to develop strategies that meet the expectations of the United Nations. Data envelope analysis (DEA) can help identify best practices for SDGs by setting goals to compete against. Automated machine learning (AutoML) simplifies machine learning for researchers who need less time and manpower to predict future situations. This work introduces an integrative method that integrates DEA and AutoML to assess and predict performance in SDGs. There are two experiments with different data properties in their interval and correlation to demonstrate the approach. Three prediction targets are set to measure performance in the regression, classification, and multi-target regression algorithms. The back-propagation neural network (BPNN) is used to validate the outputs of the AutoML. As a result, AutoML can outperform BPNN for regression and classification prediction problems. Low standard deviation (SD) data result in poor prediction performance for the BPNN, but does not have a significant impact on AutoML. Highly correlated data result in a higher accuracy, but does not significantly affect the R-squared values between the actual and predicted values. This integrative approach can accurately predict the projected outputs, which can be used as national goals to transform an inefficient country into an efficient country. Full article

(This article belongs to the Special Issue Earth Observation, Citizen Observation, and Geospatial Technologies for Achieving and Monitoring the Sustainable Development Goals (SDGs))

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13 pages, 707 KiB

Open AccessArticle

Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers

by Papiya Debnath, Pankaj Chittora, Tulika Chakrabarti, Prasun Chakrabarti, Zbigniew Leonowicz, Michal Jasinski, Radomir Gono and Elżbieta Jasińska

Sustainability 2021, 13(2), 971; https://doi.org/10.3390/su13020971 - 19 Jan 2021

Cited by 32 | Viewed by 7255

Abstract

Earthquakes are one of the most overwhelming types of natural hazards. As a result, successfully handling the situation they create is crucial. Due to earthquakes, many lives can be lost, alongside devastating impacts to the economy. The ability to forecast earthquakes is one of the biggest issues in geoscience. Machine learning technology can play a vital role in the field of geoscience for forecasting earthquakes. We aim to develop a method for forecasting the magnitude range of earthquakes using machine learning classifier algorithms. Three different ranges have been categorized: fatal earthquake; moderate earthquake; and mild earthquake. In order to distinguish between these classifications, seven different machine learning classifier algorithms have been used for building the model. To train the model, six different datasets of India and regions nearby to India have been used. The Bayes Net, Random Tree, Simple Logistic, Random Forest, Logistic Model Tree (LMT), ZeroR and Logistic Regression algorithms have been applied to each dataset. All of the models have been developed using the Weka tool and the results have been noted. It was observed that Simple Logistic and LMT classifiers performed well in each case. Full article

(This article belongs to the Special Issue Earth Observation, Citizen Observation, and Geospatial Technologies for Achieving and Monitoring the Sustainable Development Goals (SDGs))

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