Special Issue "Earth Observation, Citizen Observation, and Geospatial Technologies for Achieving and Monitoring the Sustainable Development Goals (SDGs)"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Human Geography and Social Sustainability".

Deadline for manuscript submissions: 30 December 2020.

Special Issue Editor

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

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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)

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Open AccessArticle
Machine Learning for Conservation Planning in a Changing Climate
Sustainability 2020, 12(18), 7657; https://doi.org/10.3390/su12187657 - 16 Sep 2020
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. [...] Read more.
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
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Open AccessArticle
Employing Machine Learning for Detection of Invasive Species using Sentinel-2 and AVIRIS Data: The Case of Kudzu in the United States
Sustainability 2020, 12(9), 3544; https://doi.org/10.3390/su12093544 - 27 Apr 2020
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 [...] Read more.
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
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Open AccessArticle
Input-Output Efficiency of Economic Growth: A Multielement System Perspective
Sustainability 2020, 12(11), 4624; https://doi.org/10.3390/su12114624 - 05 Jun 2020
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 [...] Read more.
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
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