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Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 59474

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Special Issue Editor

Dr. Ronald C. Estoque

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Guest Editor

Center for Biodiversity and Climate Change, Forestry and Forest Products Research Institute (FFPRI), Tsukuba 305-8687, Ibaraki, Japan
Interests: sustainability science; land change science; forest transition theory; forest monitoring; sustainable forest management; ecosystem services; climate change; GIScience and remote sensing
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Special Issue Information

Dear Colleagues,

The formulation of the 17 Sustainable Development Goals (SDGs) is a major leap towards humankind’s quest for sustainability. The SDGs now collectively serve as the platform for global development—a platform which now helps to guide current actions and shape visions for a sustainable future. There is a need to track the spatiotemporal dynamics of progress towards the SDGs in particular and sustainability in general, not only at the global and national scales, but also at the subnational and landscape levels. The advances in geospatial technologies (GIS and remote sensing), including the increasing availability of geospatial data, can help in this regard.

This Special Issue will bring together novel contributions on the assessment of sustainability over space and time. Contributions that highlight or explore the role or potential contribution of geospatial (GIS and remote sensing) data, tools, and techniques in the assessment of sustainability over space and time are very much welcome. Contributions that do not necessarily employ geospatial data, tools, and techniques but consider the space and time dimensions of sustainability are also very much welcome.

Contributions can be in the form of:

Articles;
Reviews;
Perspectives and Insights.

Dr. Ronald C. Estoque
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. 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

Sustainability
Sustainability assessment
Sustainable development
Sustainable development goals
SDGs
Landscape sustainability
Urban sustainability
GIScience
GIS
Remote sensing
Earth observations
Spatiotemporal analysis
Spatial thinking
Geospatial data
Scale
Space–time
Sustainability indicators
SDG indicators
Land change
Land use/land cover, etc.

Published Papers (12 papers)

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Editorial

Jump to: Research, Review

4 pages, 190 KiB

Open AccessEditorial

Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing

by Ronald C. Estoque

Remote Sens. 2023, 15(11), 2764; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112764 - 26 May 2023

Cited by 1 | Viewed by 991

Abstract

Sustainability is a critical global challenge that requires comprehensive assessments of environmental, social, and economic indicators [...] Full article

(This article belongs to the Special Issue Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing)

Research

Jump to: Editorial, Review

20 pages, 51152 KiB

Open AccessArticle

Tropical Dry Forest Dynamics Explained by Topographic and Anthropogenic Factors: A Case Study in Mexico

by Yan Gao, Jonathan V. Solórzano, Ronald C. Estoque and Shiro Tsuyuzaki

Remote Sens. 2023, 15(5), 1471; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051471 - 06 Mar 2023

Cited by 1 | Viewed by 1728

Abstract

Tropical dry forest is one of the most threatened ecosystems, and it is disappearing at an alarming rate. Shifting cultivation is commonly cited as a driver of tropical dry forest loss, although it helps to maintain the forest coverage but with less density. We investigated tropical dry forest dynamics and their contributing factors to find out if there is an equilibrium between these two processes. We classified multi-temporal Sentinel-2A images with machine learning algorithms and used a logistic regression model to associate topographic, anthropogenic, and land tenure variables as plausible factors in the dynamics. We carried out an accuracy assessment of the detected changes in loss and gain considering the imbalance in area proportion between the change classes and the persistence classes. We estimated a 1.4% annual loss rate and a 0.7% annual gain rate in tropical dry forest and found that the topographic variable of slope and the anthropogenic variable of distance to roads helped explain the occurrence probability of both tropical forest loss and tropical forest gain. Since the area estimation yielded a wide confidence interval for both tropical forest loss and gain despite the measures that we took to counterbalance the disproportion in areas, we cannot conclude that the loss process was more intense than the gain process, but rather that there was an equilibrium in tropical dry forest dynamics under the influence of shifting cultivation. Full article

(This article belongs to the Special Issue Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing)

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32 pages, 21306 KiB

Open AccessArticle

Assessment of Intra-Urban Heat Island in a Densely Populated City Using Remote Sensing: A Case Study for Manila City

by Mark Angelo Purio, Tetsunobu Yoshitake and Mengu Cho

Remote Sens. 2022, 14(21), 5573; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215573 - 04 Nov 2022

Cited by 6 | Viewed by 5893

Abstract

Changes in the environment occur in cities due to increased urbanization and population growth. Sustainable Development Goal (SDG) 11 is intrinsically linked to the environment, one facet of which is the need for universal access to secure, inclusive, and accessible green and public places. As urban heat islands (UHI) have the potential to negatively influence cities and their residents, existing resources and data must be used to identify and quantify these effects. To address this, we present the use of satellite-derived (2013–2022) and meteorological data (2014–2020) to assess intra-urban heat islands in Manila City, Philippines. The assessment includes (a) understanding the temporal variability of air temperature measurements and outdoor thermal comfort based on meteorological data, (b) comparative and correlative analysis between common Land-Use Land Cover indicators (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-up Index (NDBI)) and Land Surface Temperature (LST), (c) spatial and temporal analysis of LST using spatial statistics techniques, and (d) generation of an intra-urban heat island (IUHI) map with a recommended class of action using a suitability analysis model. Finally, the areas that need intervention are compared to the affected population, and suggestions to enhance the thermal characteristics of the city and mitigate the effects of UHI are established. Full article

(This article belongs to the Special Issue Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing)

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19 pages, 6068 KiB

Open AccessArticle

Assessing Spatiotemporal Changes of SDG Indicators at the Neighborhood Level in Guilin, China: A Geospatial Big Data Approach

by Liying Han, Linlin Lu, Junyu Lu, Xintong Liu, Shuangcheng Zhang, Ke Luo, Dan He, Penglong Wang, Huadong Guo and Qingting Li

Remote Sens. 2022, 14(19), 4985; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194985 - 07 Oct 2022

Cited by 8 | Viewed by 1900

Abstract

Due to the challenges in data acquisition, especially for developing countries and at local levels, spatiotemporal evaluation for SDG11 indicators was still lacking. The availability of big data and earth observation technology can play an important role to facilitate the monitoring of urban sustainable development. Taking Guilin, a sustainable development agenda innovation demonstration area in China as a case study, we developed an assessment framework for SDG indicators 11.2.1, 11.3.1, and 11.7.1 at the neighborhood level using high-resolution (HR) satellite images, gridded population data, and other geospatial big data (e.g., road network and point of interest data). The findings showed that the proportion of the population with convenient access to public transport in the functional urban area gradually improved from 42% in 2013 to 52% in 2020. The increase in built-up land was much faster than the increase in population. The areal proportion of public open space decreased from 56% in 2013 to 24% in 2020, and the proportion of the population within the 400 m service areas of open public space decreased from 73% to 59%. The township-level results indicated that low-density land sprawling should be strictly managed, and open space and transportation facilities should be improved in the three fast-growing towns, Lingui, Lingchuan, and Dingjiang. The evaluation results of this study confirmed the applicability of SDG11 indicators to neighborhood-level assessment and local urban governance and planning practices. The evaluation framework of the SDG11 indicators based on HR satellite images and geospatial big data showed great promise to apply to other cities for targeted planning and assessment. Full article

(This article belongs to the Special Issue Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing)

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23 pages, 9592 KiB

Open AccessArticle

Sustainable Development of Life Service Resources: A New Framework Based on GIScience and Spatial Justice

by Ze Xu, Lu Niu, Zhengfeng Zhang, Jing Huang, Zhaodi Lu, Yufan Huang, Yangyang Wen, Chu Li and Xiaokun Gu

Remote Sens. 2022, 14(9), 2031; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092031 - 23 Apr 2022

Cited by 5 | Viewed by 2283

Abstract

The sustainable development goals (SDGs) reflect the pursuit of achieving spatial justice. Both SDG 1.4 and SDG 11.1 reflect a concern for urban services. Life service resources, which are the new concept proposed by the Chinese government, also call for sustainable development path. However, few studies have focused on the realization of spatial justice in life service resources. This paper proposes a two-level, four-step analysis framework composed of quantity, structure, pattern, and coupling coordination to perceive the spatial justice of life service resources. Based on remote sensing technology and geographic information science, this paper acquires and analyses multi-source data including population density, building outlines, point of interests, subway lines, etc. Furthermore, the case study in downtown Beijing found the following: (1) The total life service resources are extensive and varying in type; (2) regional differences are evident and low-level equilibrium and high-level priority development coexist; (3) life service resources are concentrated in contiguous and multi-centre clusters with a greater north–south than east–west difference; (4) the overall level of life service resources is low, specifically for “high in the centre and low in the periphery” and “high in the east and low in the west”. Future management should consider narrowing the development gap and formulating industry development plans to improve spatial justice. Finally, the comparison between Beijing and London and more cities in the future needs to consider the urban development stage, population density, and other aspects. Full article

(This article belongs to the Special Issue Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing)

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22 pages, 10467 KiB

Open AccessArticle

Understanding the Relationship between China’s Eco-Environmental Quality and Urbanization Using Multisource Remote Sensing Data

by Dong Xu, Jie Cheng, Shen Xu, Jing Geng, Feng Yang, He Fang, Jinfeng Xu, Sheng Wang, Yubai Wang, Jincai Huang, Rui Zhang, Manqing Liu and Haixing Li

Remote Sens. 2022, 14(1), 198; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010198 - 02 Jan 2022

Cited by 21 | Viewed by 3290

Abstract

The rapid development of urbanization and population growth in China has posed a major threat to the green sustainable development of the ecological environment. However, the impact of urbanization on the eco-environmental quality (EEQ) in China remains to be developed. Understanding their interactive coupling mechanism is of great significance to achieve the urban sustainable development goals. By using multi-source remote sensing data and the coupling coordination degree model (CCDM), we intended to answer the question “What are the temporal and spatial characteristics of urbanization and EEQ in China on the pixel scale during 2000–2013, and what is the coupling mechanism between the urbanization and the EEQ?”. To answer these questions, we explored the coupling mechanism between urbanization and the EEQ in China with a combined mathematical and graphics model. The results show that the urbanization and the coupling coordination degree (CCD) of the whole region continually increased from 2000 to 2013, especially in the three major urban agglomerations, with a spatial distribution pattern that was “high in the east and low in the west”. Most importantly, from 2000 to 2013, the CCD type of cities in China gradually evolved from uncoordinated cities to coordinated cities. Additionally, the decisive factor affecting the CCD from 2000 to 2013 was the development of urbanization, and the degree at which urbanization had an impact on CCD was about 8.4 times larger than that of the EEQ. At the same time, the rapid urbanization that has occurred in some areas has led to a significant decline in the EEQ, thus indicating that China needs to increase its protection of the ecological environment while pursuing social and economic development in the future. This study makes up for the deficiencies in the existing literature and investigates the long-term coupling of the EEQ and urbanization in China, thereby providing a new research perspective for the sustainable development of China and even the world in the future. Full article

(This article belongs to the Special Issue Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing)

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23 pages, 7101 KiB

Open AccessArticle

Assessment of Urban Ecological Quality and Spatial Heterogeneity Based on Remote Sensing: A Case Study of the Rapid Urbanization of Wuhan City

by Jingye Li, Jian Gong, Jean-Michel Guldmann and Jianxin Yang

Remote Sens. 2021, 13(21), 4440; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214440 - 04 Nov 2021

Cited by 25 | Viewed by 3486

Abstract

Rapid urbanization significantly affects the productivity of the terrestrial ecosystem and the foundation of regional ecosystem services, thereby detrimentally influencing the ecological environment and urban ecological security. The United Nations’ Sustainable Development Goals (SDGs) also require accurate and timely assessments of where people live in order to develop, implement and monitor sustainable development policies. Sustainable development also emphasizes the process of protecting the ecological environment for future generations while maintaining the current needs of mankind. We propose a comprehensive evaluation method for urban ecological quality (UEQ) using Landsat TM/ETM+/OLI/TIRS images to extract remote sensing information representing four ecological elements, namely humidity, greenness, heat and dryness. An improved comprehensive remote sensing ecological index (IRSEI) evaluation model is constructed by combining the entropy weight method and principal component analysis. This modeling is applied to the city of Wuhan, China, from 1995 to 2020. Spatial autocorrelation analysis was conducted on the geographic clusters of the IRSEI. The results show that (1) from 1995 to 2015, the mean IRSEI of Wuhan city decreased from 0.60 to 0.47, indicating that environmental deterioration overwhelmed improvements; (2) the global Moran’s I for IRSEI ranged from 0.535 to 0.592 from 1995 to 2020, indicating significant heterogeneity in its spatial distribution, highlighting that high and low clusters gradually developed at the edge of the city and at the city center, respectively; (3) the high clusters are mainly distributed in the Huangpi and Jiangxia districts, and the low clusters at the city center, which exhibits a dense population and intense human activity. This paper uses remote sensing index methods to evaluate UEQ as a scientific theoretical basis for the improvement of UEQ, the control of UEQ and the formulation of urban sustainable development strategies in the future. Our results show that the UEQ method is a low-cost, feasible and simple technique that can be used for territorial spatial control and spatiotemporal urban sustainable development. Full article

(This article belongs to the Special Issue Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing)

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26 pages, 16067 KiB

Open AccessFeature PaperArticle

Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning

by Haoming Zhuang, Xiaoping Liu, Yuchao Yan, Jinpei Ou, Jialyu He and Changjiang Wu

Remote Sens. 2021, 13(17), 3533; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173533 - 06 Sep 2021

Cited by 12 | Viewed by 2958

Abstract

Fine knowledge of the spatiotemporal distribution of the population is fundamental in a wide range of fields, including resource management, disaster response, public health, and urban planning. The United Nations’ Sustainable Development Goals also require the accurate and timely assessment of where people live to formulate, implement, and monitor sustainable development policies. However, due to the lack of appropriate auxiliary datasets and effective methodological frameworks, there are rarely continuous multi-temporal gridded population data over a long historical period to aid in our understanding of the spatiotemporal evolution of the population. In this study, we developed a framework integrating a ResNet-N deep learning architecture, considering neighborhood effects with a vast number of Landsat-5 images from Google Earth Engine for population mapping, to overcome both the data and methodology obstacles associated with rapid multi-temporal population mapping over a long historical period at a large scale. Using this proposed framework in China, we mapped fine-scale multi-temporal gridded population data (1 km × 1 km) of China for the 1985–2010 period with a 5-year interval. The produced multi-temporal population data were validated with available census data and achieved comparable performance. By analyzing the multi-temporal population grids, we revealed the spatiotemporal evolution of population distribution from 1985 to 2010 in China with the characteristic of concentration of the population in big cities and the contraction of small- and medium-sized cities. The framework proposed in this study demonstrates the feasibility of mapping multi-temporal gridded population distribution at a large scale over a long period in a timely and low-cost manner, which is particularly useful in low-income and data-poor areas. Full article

(This article belongs to the Special Issue Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing)

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20 pages, 7224 KiB

Open AccessFeature PaperArticle

Impacts of Urbanization on the Muthurajawela Marsh and Negombo Lagoon, Sri Lanka: Implications for Landscape Planning towards a Sustainable Urban Wetland Ecosystem

by Darshana Athukorala, Ronald C. Estoque, Yuji Murayama and Bunkei Matsushita

Remote Sens. 2021, 13(2), 316; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020316 - 18 Jan 2021

Cited by 36 | Viewed by 9248

Abstract

Urban wetland ecosystems (UWEs) play important social and ecological roles but are often adversely affected by urban landscape transformations. Spatio-temporal analyses to gain insights into the trajectories of landscape changes in these ecosystems are needed for better landscape planning towards sustainable UWEs. In this study, we examined the impacts of urbanization on the Muthurajawela Marsh and Negombo Lagoon (MMNL), an important UWE in Sri Lanka that provides valuable ecosystem services. We used remote sensing data to detect changes in the land use/cover (LUC) of the MMNL over a two-decade period (1997–2017) and spatial metrics to characterize changes in landscape composition and configuration. The results revealed that the spatial and socio-economic elements of rapid urbanization of the MMNL had been the main driver of transformation of its natural environment over the past 20 years. This is indicated by a substantial expansion of settlements (+68%) and a considerable decrease of marshland and mangrove cover (−41% and −21%, respectively). A statistical analysis revealed a significant relationship between the change in population density and the loss of wetland due to settlement expansion at the Grama Niladhari division level (n = 99) (where wetland includes marshland, mangrove, and water) (1997–2007: R² = 0.435, p = 0.000; 2007–2017: R² = 0.343, p = 0.000). The findings also revealed that most of the observed LUC changes occurred in areas close to roads and growth nodes (viz. Negombo, Ja-Ela, Wattala, and Katana), which resulted in both landscape fragmentation and infill urban expansion. We conclude that, in order to ensure the sustainability of the MMNL, there is an urgent need for forward-looking landscape and urban planning to promote environmentally conscious urban development in the area which is a highly valuable UWE. Full article

(This article belongs to the Special Issue Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing)

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15 pages, 5671 KiB

Open AccessArticle

Remotely Sensed Urban Surface Ecological Index (RSUSEI): An Analytical Framework for Assessing the Surface Ecological Status in Urban Environments

by Mohammad Karimi Firozjaei, Solmaz Fathololoumi, Qihao Weng, Majid Kiavarz and Seyed Kazem Alavipanah

Remote Sens. 2020, 12(12), 2029; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122029 - 24 Jun 2020

Cited by 45 | Viewed by 3577

Abstract

Urban Surface Ecological Status (USES) reflects the structure and function of an urban ecosystem. USES is influenced by the surface biophysical, biochemical, and biological properties. The assessment and modeling of USES is crucial for sustainability assessment in support of achieving sustainable development goals such as sustainable cities and communities. The objective of this study is to present a new analytical framework for assessing the USES. This analytical framework is centered on a new index, Remotely Sensed Urban Surface Ecological index (RSUSEI). In this study, RSUSEI is used to assess the USES of six selected cities in the U.S.A. To this end, Landsat 8 images, water vapor products, and the National Land Cover Database (NLCD) land cover and imperviousness datasets are downloaded for use. Firstly, Land Surface Temperature (LST), Wetness, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Soil Index (NDSI) are derived by remote sensing methods. Then, RSUSEI is developed by the combination of NDVI, NDSI, Wetness, LST, and Impervious Surface Cover (ISC) with Principal Components Analysis (PCA). Next, the spatial variations of USES across the cities are evaluated and compared. Finally, the association degree of each parameter in the USES modeling is investigated. Results show that the spatial variability of LST, ISC, NDVI, NDSI, and Wetness is heterogeneous within and between cities. The mean (standard deviation) value of RSUSEI for Minneapolis, Dallas, Phoenix, Los Angeles, Chicago and Seattle yielded 0.58 (0.16), 0.54 (0.17), 0.47 (0.19), 0.63 (0.21), 0.50 (0.17), and 0.44 (0.19), respectively. For all the cities, PC1 included more than 93% of the surface information, which is contributed by greenness, moisture, dryness, heat, and imperviousness. The highest and lowest mean values of RSUSEI are found in “Developed, High intensity” (0.76) and “Developed, Open Space” (0.35) lands, respectively. The mean correlation coefficient between RSUSEI and LST, ISC, NDVI, NDSI, and Wetness, is 0.47, 0.97, −0.31, 0.17, and −0.27, respectively. The statistical significance of these correlations is confirmed at 95% confidence level. These results suggest that the association degree of ISC in USES modeling is the highest, despite the differences in land cover and biophysical characteristics in the cities. RSUSEI could be very useful in modeling and comparing USES across cities with different geographical, climatic, environmental, and biophysical conditions and can also be used for assessing urban sustainability over space and time. Full article

(This article belongs to the Special Issue Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing)

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Review

Jump to: Editorial, Research

29 pages, 7848 KiB

Open AccessReview

Remote Sensing for International Peace and Security: Its Role and Implications

by Ram Avtar, Asma Kouser, Ashwani Kumar, Deepak Singh, Prakhar Misra, Ankita Gupta, Ali P. Yunus, Pankaj Kumar, Brian Alan Johnson, Rajarshi Dasgupta, Netrananda Sahu and Andi Besse Rimba

Remote Sens. 2021, 13(3), 439; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030439 - 27 Jan 2021

Cited by 19 | Viewed by 8648

Abstract

Remote sensing technology has seen a massive rise in popularity over the last two decades, becoming an integral part of our lives. Space-based satellite technologies facilitated access to the inaccessible terrains, helped humanitarian teams, support complex emergencies, and contributed to monitoring and verifying conflict zones. The scoping phase of this review investigated the utility of the role of remote sensing application to complement international peace and security activities owing to their ability to provide objective near real-time insights at the ground level. The first part of this review looks into the major research concepts and implementation of remote sensing-based techniques for international peace and security applications and presented a meta-analysis on how advanced sensor capabilities can support various aspects of peace and security. With key examples, we demonstrated how this technology assemblage enacts multiple versions of peace and security: for refugee relief operations, in armed conflicts monitoring, tracking acts of genocide, providing evidence in courts of law, and assessing contravention in human rights. The second part of this review anticipates future challenges that can hinder the applicative capabilities of remote sensing in peace and security. Varying types of sensors pose discrepancies in image classifications and issues like cost, resolution, and difficulty of ground-truth in conflict areas. With emerging technologies and sufficient secondary resources available, remote sensing plays a vital operational tool in conflict-affected areas by supporting an extensive diversity in public policy actions for peacekeeping processes. Full article

(This article belongs to the Special Issue Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing)

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22 pages, 7384 KiB

Open AccessReview

A Review of the Sustainability Concept and the State of SDG Monitoring Using Remote Sensing

by Ronald C. Estoque

Remote Sens. 2020, 12(11), 1770; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111770 - 31 May 2020

Cited by 79 | Viewed by 12718

Abstract

The formulation of the 17 sustainable development goals (SDGs) was a major leap forward in humankind’s quest for a sustainable future, which likely began in the 17th century, when declining forest resources in Europe led to proposals for the re-establishment and conservation of forests, a strategy that embodies the great idea that the current generation bears responsibility for future generations. Global progress toward SDG fulfillment is monitored by 231 unique social-ecological indicators spread across 169 targets, and remote sensing (RS) provides Earth observation data, directly or indirectly, for 30 (18%) of these indicators. Unfortunately, the UN Global Sustainable Development Report 2019—The Future is Now: Science for Achieving Sustainable Development concluded that, despite initial efforts, the world is not yet on track for achieving most of the SDG targets. Meanwhile, through the EO4SDG initiative by the Group on Earth Observations, the full potential of RS for SDG monitoring is now being explored at a global scale. As of April 2020, preliminary statistical data were available for 21 (70%) of the 30 RS-based SDG indicators, according to the Global SDG Indicators Database. Ten (33%) of the RS-based SDG indicators have also been included in the SDG Index and Dashboards found in the Sustainable Development Report 2019—Transformations to Achieve the Sustainable Development Goals. These statistics, however, do not necessarily reflect the actual status and availability of raw and processed geospatial data for the RS-based indicators, which remains an important issue. Nevertheless, various initiatives have been started to address the need for open access data. RS data can also help in the development of other potentially relevant complementary indicators or sub-indicators. By doing so, they can help meet one of the current challenges of SDG monitoring, which is how best to operationalize the SDG indicators. Full article

(This article belongs to the Special Issue Assessing Sustainability over Space and Time: The Emerging Roles of GIScience and Remote Sensing)

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