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Special Issue "Monitoring Sustainable Development Goals"

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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).

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Special Issue Editors

Dr. Antonello Bonfante
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Guest Editor

Consiglio Nazionale delle Ricerche (CNR), Istituto per i sistemi Agricoli e Forestali del Mediterraneo, Via Patacca, 85 - 80056 Ercolano, Napoli, Italy
Interests: hydropedology; precision agriculture; crop adaptation to climate change
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Dr. Antonio Pepe
E-Mail Website

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Co-Guest Editor

National Research Council (CNR) of Italy—Institute for the Electromagnetic Sensing of the Environment (IREA), via Diocleziano 328, 80124 Napoli, Italy
Interests: remote Sensing; signal theory; environmental engineering; image processing; radar technologies; aerospace; data fusion; land use-land changes
Special Issues, Collections and Topics in MDPI journals

Dr. Anna Brook
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Co-Guest Editor

Spectroscopy and Remote Sensing Laboratory, Department of Geography and Environmental Studie, Faculty of Social Science, University of Haifa, Haifa 3498838, Israel
Interests: data fusion; image and signal processing; automation target recognition; sub-pixel detection; spectral models across NIR-MIR regions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The United Nations has proposed 17 sustainable development goals, which present a clear challenge to not only national governments but also a wide range of stakeholders. Every nation has to implement these goals to achieve the agreed targets by 2030. Most of these are focused on the biophysical management of environments while others more oriented to socio-economic focus. Still, in general, they are interconnected because to achieve goals with an ecosystem focus, we need to consider the socio-economic aspect.

For example, environmental sustainability depends on the actions of land users such as farmers and forest managers, but also urban developments have significant effects on local land use. The environmental changes are nowadays clearly detectable by using global Earth observation systems and remote sensing technologies. Every scientific discipline faces the challenge of acting upon these SDGs, and they have the responsibility to provide all stakeholders with information that allows them to make informed choices. In this context, we promote a special issue on "Monitoring Sustainable Development Goals" to support the achievement of SDGs and help the involved stakeholders. We encourage the submission of research papers that address the main topic of the special issue, including those based on the exploitation of geospatial data. Among other, we expect contributions that can cover multi-disciplinary aspects such as i) the preservation of the natural heritage, ii) the sustainable development, iii) the improvement of the social resilience to environmental changes, iv) the application and development of geospatial techniques for the monitoring of land-use/land-changes, v) the socio-economical impacts of global climate changes.

Dr. Antonello Bonfante
Dr. Anna Brook
Dr. Antonio Pepe
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 papers will be 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 2500 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

Sustainable development goals SDGs
remote sensing
agriculture
environmental monitoring
land evaluation

Published Papers (4 papers)

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Research

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Open AccessArticle

How the Small Object Detection via Machine Learning and UAS-Based Remote-Sensing Imagery Can Support the Achievement of SDG2: A Case Study of Vole Burrows

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Remote Sens. 2021, 13(16), 3191; https://doi.org/10.3390/rs13163191 - 12 Aug 2021

Cited by 1 | Viewed by 741

Abstract

Small mammals, and particularly rodents, are common inhabitants of farmlands, where they play key roles in the ecosystem, but when overabundant, they can be major pests, able to reduce crop production and farmers’ incomes, with tangible effects on the achievement of Sustainable Development Goals no 2 (SDG2, Zero Hunger) of the United Nations. Farmers do not currently have a standardized, accurate method of detecting the presence, abundance, and locations of rodents in their fields, and hence do not have environmentally efficient methods of rodent control able to promote sustainable agriculture oriented to reduce the environmental impacts of cultivation. New developments in unmanned aerial system (UAS) platforms and sensor technology facilitate cost-effective data collection through simultaneous multimodal data collection approaches at very high spatial resolutions in environmental and agricultural contexts. Object detection from remote-sensing images has been an active research topic over the last decade. With recent increases in computational resources and data availability, deep learning-based object detection methods are beginning to play an important role in advancing remote-sensing commercial and scientific applications. However, the performance of current detectors on various UAS-based datasets, including multimodal spatial and physical datasets, remains limited in terms of small object detection. In particular, the ability to quickly detect small objects from a large observed scene (at field scale) is still an open question. In this paper, we compare the efficiencies of applying one- and two-stage detector models to a single UAS-based image and a processed (via Pix4D mapper photogrammetric program) UAS-based orthophoto product to detect rodent burrows, for agriculture/environmental applications as to support farmer activities in the achievements of SDG2. Our results indicate that the use of multimodal data from low-cost UASs within a self-training YOLOv3 model can provide relatively accurate and robust detection for small objects (mAP of 0.86 and an F1-score of 93.39%), and can deliver valuable insights for field management with high spatial precision able to reduce the environmental costs of crop production in the direction of precision agriculture management. Full article

(This article belongs to the Special Issue Monitoring Sustainable Development Goals)

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Open AccessArticle

On the Characterization and Forecasting of Ground Displacements of Ocean-Reclaimed Lands

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Remote Sens. 2020, 12(18), 2971; https://doi.org/10.3390/rs12182971 - 12 Sep 2020

Cited by 4 | Viewed by 1153

Abstract

In this work, we study ground deformation of ocean-reclaimed platforms as retrieved from interferometric synthetic aperture radar (InSAR) analyses. We investigate, in particular, the suitability and accuracy of some time-dependent models used to characterize and foresee the present and future evolution of ground deformation of the coastal lands. Previous investigations, carried out by the authors of this paper and other scholars, related to the zone of the ocean-reclaimed lands of Shanghai, have already shown that ocean-reclaimed lands are subject to subside (i.e., the ground is subject to settling down due to soil consolidation and compression), and the temporal evolution of that deformation follows a certain predictable model. Specifically, two time-gapped SAR datasets composed of the images collected by the ENVISAT ASAR (ENV) from 2007 to 2010 and the COSMO-SkyMed (CSK) sensors, available from 2013 to 2016, were used to generate long-term ground displacement time-series using a proper time-dependent geotechnical model. In this work, we use a third SAR data set consisting of Radarsat-2 (RST-2) acquisitions collected from 2012 to 2016 to further corroborate the validity of that model. As a result, we verified with the new RST-2 data, partially covering the gap between the ENV and CSK acquisitions, that the adopted model fits the data and that the model is suitable to perform future projections. Furthermore, we extended these analyses to the area of Pearl River Delta (PRD) and the city of Shenzhen, China. Our study aims to investigate the suitability of different time-dependent ground deformation models relying on the different geophysical conditions in the two areas of Shanghai and Shenzhen, China. To this aim, three sets of SAR data, collected by the ENV platform (from both ascending and descending orbits) and the Sentinel-1A (S1A) sensor (on ascending orbits), were used to obtain the ground displacement time-series of the Shenzhen city and its surrounding region. Multi-orbit InSAR data products were also combined to discriminate the up–down (subsidence) ground deformation time-series of the coherent points, which are then used to estimate the parameters of the models adopted to foresee the future evolution of the land-reclaimed ground consolidation procedure. The exploitation of the obtained geospatial data and products are helpful for the continuous monitoring of coastal environments and the evaluation of the socio-economical impacts of human activities and global climate change. Full article

(This article belongs to the Special Issue Monitoring Sustainable Development Goals)

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Open AccessArticle

Geospatial Assessment of Water-Migration Scenarios in the Context of Sustainable Development Goals (SDGs) 6, 11, and 16

Nidhi Nagabhatla

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Rupal Brahmbhatt

Remote Sens. 2020, 12(9), 1376; https://doi.org/10.3390/rs12091376 - 27 Apr 2020

Cited by 4 | Viewed by 2870

Abstract

Communities and countries around the world are gearing up efforts to implement the 2030 Agenda goals and targets. In this paper, the water and migration scenarios are explained with a focus on Sustainable Development Goals (SDGs) 6 (water-related), 11 (urbanization), and 16 (peace and political stability). The study has two phases. The first phase illustrates the application of geospatial data and tools to assess the water-migration interlinkages (nexus) by employing a case study approach. Three case studies, Lake Chad, the Aral Sea region, and the Nile Delta, representing various geographic and socio-political settings, were selected to perform the multitemporal analysis. For this analysis, a mixed toolset framework that combined algorithmic functions of digital image processing, the Landsat sensor data, and applied a geographic information system (GIS) platform was adopted. How water-related events directly or indirectly trigger human migration is described using spatial indicators such as water spread and the extent of urban sprawl. Additionally, the geospatial outputs were analyzed in tandem with the climate variables such as temperature, precipitation data, and socio-economic variables such as population trends and migration patterns. Overall, the three case studies examined how water and climate crisis scenarios influence migration at a local and regional scale. The second phase showcases global-scale analysis based on the Global Conflict Risk Index (GCRI). This indicator reflects on the risks and conflicts with environmental, social, and political aspects and comments on the connection of these dimensions with migration. Together, the two phases of this paper provide an understanding ofthe interplay of water-related events on migration by applying the geospatial assessment and a proxy global index. Additionally, the paper reiterates that such an understanding can serve to establish facts and create evidence to inform sustainable development planning and decision making, particularly with regard to SDGs 6, 11, and 16. Targets such as 6.4 (managing water stress), 6.5 (transboundary challenges) and, 11.B (adaptation and resilience planning) can benefit from the knowledge generated by this geospatial exercise. For example, the high GCRI values for the African region speak to SDG targets 11.B (integrated policies/plans) and 16.7 (decision support systems for peaceful societies). Two key highlights from the synthesis: (a) migration and urbanization are closely interconnected, and (b) the impact of water and climate crisis is comparatively high for rural-urban migration due to the considerable dependence of rural communities on nature-based livelihoods. In conclusion, geospatial analysis is an important tool to study the interlinkages between water and migration. The paper presents a novel perspective toward widening the scope of remote sensing data and GIS toward the implementation of the SDG Agenda. Full article

(This article belongs to the Special Issue Monitoring Sustainable Development Goals)

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Open AccessTechnical Note

Machine Learning Comparison and Parameter Setting Methods for the Detection of Dump Sites for Construction and Demolition Waste Using the Google Earth Engine

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Remote Sens. 2021, 13(4), 787; https://doi.org/10.3390/rs13040787 - 21 Feb 2021

Cited by 2 | Viewed by 1064

Abstract

Machine learning has been successfully used for object recognition within images. Due to the complexity of the spectrum and texture of construction and demolition waste (C&DW), it is difficult to construct an automatic identification method for C&DW based on machine learning and remote sensing data sources. Machine learning includes many types of algorithms; however, different algorithms and parameters have different identification effects on C&DW. Exploring the optimal method for automatic remote sensing identification of C&DW is an important approach for the intelligent supervision of C&DW. This study investigates the megacity of Beijing, which is facing high risk of C&DW pollution. To improve the classification accuracy of C&DW, buildings, vegetation, water, and crops were selected as comparative training samples based on the Google Earth Engine (GEE), and Sentinel-2 was used as the data source. Three classification methods of typical machine learning algorithms (classification and regression trees (CART), random forest (RF), and support vector machine (SVM)) were selected to classify the C&DW from remote sensing images. Using empirical methods, the experimental trial method, and the grid search method, the optimal parameterization scheme of the three classification methods was studied to determine the optimal method of remote sensing identification of C&DW based on machine learning. Through accuracy evaluation and ground verification, the overall recognition accuracies of CART, RF, and SVM for C&DW were 73.12%, 98.05%, and 85.62%, respectively, under the optimal parameterization scheme determined in this study. Among these algorithms, RF was a better C&DW identification method than were CART and SVM when the number of decision trees was 50. This study explores the robust machine learning method for automatic remote sensing identification of C&DW and provides a scientific basis for intelligent supervision and resource utilization of C&DW. Full article

(This article belongs to the Special Issue Monitoring Sustainable Development Goals)

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