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Earth Observations for Sustainable Development Goals
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Earth Observations for Sustainable Development Goals

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

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 61477

Special Issue Editors

Dr. Joan Masó

E-Mail Website
Guest Editor
CREAF—Centre for Ecological Research and Forestry Applications, 08193 Barcelona, Spain
Interests: remote sensing; land cover; sustainable development; citizen science
Special Issues, Collections and Topics in MDPI journals
Dr. Ivette Serral

E-Mail Website
Guest Editor
Centre for Ecological Research and Forestry Applications, 08193 Barcelona, Spain
Interests: GIS; remote sensing; standars; environmental management
Special Issues, Collections and Topics in MDPI journals
Dr. Alaitz Zabala Torres

E-Mail Website
Guest Editor
Departament de Geografia, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain
Interests: data; metadata; web semantics; remote sensing; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Earth observation (EO) provides extensive data, from radar to optical sensors, and from satellite (RS) to airborne. Spatial coverage and revisiting the periods of observations are significantly increasing with new sensors and platforms, allowing for observing the same area from huge and diverse spatial, spectral, and temporal perspectives, with a large range of thematic applications. In turn, there are hundreds of multi-lateral environment agreements addressing societal and economic development. In 2015, the United Nations approved the Sustainable Development Goals (SDGs), which define a safe operating space for humanity through 17 goals articulated in 169 targets and 240 indicators to measure progress.

EO has been proven to be a valuable source for Earth monitoring. However, some studies suggest that the current indicator framework is biased to socioeconomic variables, and only a few of them can be inferred by EO only. It is clear that currently, the intersection between SDG and EO has some limitations. To what extent? The following Special Issue aims to shed some light on aspects, including but not limited to, the following:

  1. How can EO contribute to calculate SDG indicators?
  2. How can EO be used to increase granularity (spatial resolution) of UN statistics?
  3. How EO detects EVs useful to create indicators?
  4. How can EO be used to understand the natural mechanism that affect sustainability (e.g., ecosystem services)?
  5. How can EO be used to detect and characterize the extension of human activities (e.g., pollution, human settlements, etc)?
  6. How SDGs offer a useful framework to show gaps in current remote sensing constellations?
  7. Propose other indicators that could be better extracted from RS. 

You may choose our Joint Special Issue in Geomatics.

Dr. Joan Masó
Dr. Ivette Serral
Dr. Alaitz Zabala Torres
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

  • Sustainability
  • Earth observation
  • Monitoring
  • Indications
  • Modelling
  • Pressures

Published Papers (13 papers)

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Editorial

Jump to: Research, Review

4 pages, 188 KiB  
Editorial
Earth Observations for Sustainable Development Goals
by Joan Maso, Alaitz Zabala and Ivette Serral
Remote Sens. 2023, 15(10), 2570; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15102570 - 15 May 2023
Cited by 1 | Viewed by 1688
Abstract
In 2015, the United Nations adopted the 17 Sustainable Development Goals (SDGs), aiming at ending poverty, protecting the planet, and ensuring peace and prosperity [...] Full article
(This article belongs to the Special Issue Earth Observations for Sustainable Development Goals)

Research

Jump to: Editorial, Review

15 pages, 1691 KiB  
Article
G-reqs, a New Model Proposal for Capturing and Managing In Situ Data Requirements: First Results in the Context of the Group on Earth Observations
by Joan Maso, Alba Brobia, Marie-Francoise Voidrot, Alaitz Zabala and Ivette Serral
Remote Sens. 2023, 15(6), 1589; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15061589 - 15 Mar 2023
Cited by 2 | Viewed by 1654
Abstract
In the field of Earth observation, the importance of in situ data was recognized by the Group on Earth Observations (GEO) in the Canberra Declaration in 2019. The GEO community focuses on three global priority engagement areas: the United Nations 2030 Agenda for [...] Read more.
In the field of Earth observation, the importance of in situ data was recognized by the Group on Earth Observations (GEO) in the Canberra Declaration in 2019. The GEO community focuses on three global priority engagement areas: the United Nations 2030 Agenda for Sustainable Development, the Paris Agreement, and the Sendai Framework for Disaster Risk Reduction. While efforts have been made by GEO to open and disseminate in situ data, GEO did not have a general way to capture in situ data user requirements and drive the data provider efforts to meet the goals of its three global priorities. We present a requirements data model that first formalizes the collection of user requirements motivated by user-driven needs. Then, the user requirements can be grouped by essential variable and an analysis can derive product requirements and parameters for new or existing products. The work was inspired by thematic initiatives, such as OSCAR, from WMO, OSAAP (formerly COURL and NOSA) from NOAA, and the Copernicus In Situ Component Information System. The presented solution focuses on requirements for all applications of Earth observation in situ data. We present initial developments and testing of the data model and discuss the steps that GEO should take to implement a requirements database that is connected to actual data in the GEOSS platform and propose some recommendations on how to articulate it. Full article
(This article belongs to the Special Issue Earth Observations for Sustainable Development Goals)
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Graphical abstract

29 pages, 6926 KiB  
Article
Localizing SDG 11.6.2 via Earth Observation, Modelling Applications, and Harmonised City Definitions: Policy Implications on Addressing Air Pollution
by Jennifer Bailey, Martin Otto Paul Ramacher, Orestis Speyer, Eleni Athanasopoulou, Matthias Karl and Evangelos Gerasopoulos
Remote Sens. 2023, 15(4), 1082; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15041082 - 16 Feb 2023
Cited by 5 | Viewed by 6451
Abstract
While Earth observation (EO) increasingly provides a multitude of solutions to address environmental issues and sustainability from the city to global scale, their operational integration into the Sustainable Development Goals (SDG) framework is still falling behind. Within this framework, SDG Indicator 11.6.2 asks [...] Read more.
While Earth observation (EO) increasingly provides a multitude of solutions to address environmental issues and sustainability from the city to global scale, their operational integration into the Sustainable Development Goals (SDG) framework is still falling behind. Within this framework, SDG Indicator 11.6.2 asks countries to report the “annual mean levels of fine particulate matter (PM2.5) in cities (population-weighted)”. The official United Nations (UN) methodology entails aggregation into a single, national level value derived from regulatory air quality monitoring networks, which are non-existent or sparse in many countries. EO, including, but not limited to remote sensing, brings forth novel monitoring methods to estimate SDG Indicator 11.6.2 alongside more traditional ones, and allows for comparability and scalability in the face of varying city definitions and monitoring capacities which impact the validity and usefulness of such an indicator. Pursuing a more harmonised global approach, the H2020 SMURBS/ERA-PLANET project provides two EO-driven approaches to deliver the indicator on a more granular level across Europe. The first approach provides both city and national values for SDG Indicator 11.6.2 through exploiting the Copernicus Atmospheric Monitoring Service reanalysis data (0.1° resolution and incorporating in situ and remote sensing data) for PM2.5 values. The SDG Indicator 11.6.2 values are calculated using two objective city definitions—“functional urban area” and “urban centre”—that follow the UN sanctioned Degree of Urbanization concept, and then compared with official indicator values. In the second approach, a high-resolution city-scale chemical transport model ingests satellite-derived data and calculates SDG Indicator 11.6.2 at intra-urban scales. Both novel approaches to calculating SDG Indicator 11.6.2 using EO enable exploration of air pollution hotspots that drive the indicator as well as actual population exposure within cities, which can influence funding allocation and intervention implementation. The approaches are introduced, and their results frame a discussion around interesting policy implications, all with the aim to help move the dial beyond solely reporting on SDGs to designing the pathways to achieve the overarching targets. Full article
(This article belongs to the Special Issue Earth Observations for Sustainable Development Goals)
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33 pages, 22554 KiB  
Article
Assessing the Use of Sentinel-2 Data for Spatio-Temporal Upscaling of Flux Tower Gross Primary Productivity Measurements
by Anna Spinosa, Mario Alberto Fuentes-Monjaraz and Ghada El Serafy
Remote Sens. 2023, 15(3), 562; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030562 - 17 Jan 2023
Cited by 1 | Viewed by 3346
Abstract
The conservation, restoration and sustainable use of wetlands is the target of several international agreements, among which are the Sustainable Development Goals (SDGs). Earth Observation (EO) technologies can assist national authorities in monitoring activities and the environmental status of wetlands to achieve these [...] Read more.
The conservation, restoration and sustainable use of wetlands is the target of several international agreements, among which are the Sustainable Development Goals (SDGs). Earth Observation (EO) technologies can assist national authorities in monitoring activities and the environmental status of wetlands to achieve these targets. In this study, we assess the capabilities of the Sentinel-2 instrument to model Gross Primary Productivity (GPP) as a proxy for the monitoring of ecosystem health. To estimate the spatial and temporal variation of GPP, we develop an empirical model correlating in situ measurements of GPP, eight Sentinel-2 derived vegetation indexes (VIs), and different environmental drivers of GPP. The model automatically performs an interdependency analysis and selects the model with the highest accuracy and statistical significance. Additionally, the model is upscaled across larger areas and monthly maps of GPP are produced. The study methodology is applied in a marsh ecosystem located in Doñana National Park, Spain. In this application, a combination of the red-edge chlorophyll index (CLr) and rainfall data results in the highest correlation with in situ measurements of GPP and is used for the model formulation. This yields a coefficient of determination (R2) of 0.93, Mean Absolute Error (MAE) equal to 0.52 gC m−2 day−1, Root Mean Squared Error (RMSE) equal to 0.63 gC m−2 day−1, and significance level p < 0.05. The model outputs are compared with the MODIS GPP global product (MOD17) for reference; an enhancement of the estimation of GPP is found in the applied methodology. Full article
(This article belongs to the Special Issue Earth Observations for Sustainable Development Goals)
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40 pages, 16165 KiB  
Article
Air Quality Improvement Following COVID-19 Lockdown Measures and Projected Benefits for Environmental Health
by Yuei-An Liou, Trong-Hoang Vo, Kim-Anh Nguyen and James P. Terry
Remote Sens. 2023, 15(2), 530; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020530 - 16 Jan 2023
Cited by 7 | Viewed by 2299
Abstract
Many regions worldwide suffer from heavy air pollution caused by particulate matter (PM2.5) and nitrogen dioxide (NO2), resulting in a huge annual disease burden and significant welfare costs. Following the outbreak of the COVID-19 global pandemic, enforced curfews and [...] Read more.
Many regions worldwide suffer from heavy air pollution caused by particulate matter (PM2.5) and nitrogen dioxide (NO2), resulting in a huge annual disease burden and significant welfare costs. Following the outbreak of the COVID-19 global pandemic, enforced curfews and restrictions on human mobility (so-called periods of ‘lockdown’) have become important measures to control the spread of the virus. This study aims to investigate the improvement in air quality following COVID-19 lockdown measures and the projected benefits for environmental health. China was chosen as a case study. The work projects annual premature deaths and welfare costs by integrating PM2.5 and NO2 pollutant measurements derived from satellite imagery (MODIS instruments on Terra and Aqua, and TROPOMI on Sentinel-5P) with census data archived by the Organization for Economic Co-operation and Development (OECD). A 91-day timeframe centred on the initial lockdown date of 23 January 2020 was investigated. To perform the projections, OECD data on five variables from 1990 to 2019 (mean population exposure to ambient PM2.5, premature deaths, welfare costs, gross domestic product and population) were used as training data to run the Autoregressive Integrated Moving Average (ARIMA) and multiple regression models. The analysis of the satellite imagery revealed that across the regions of Beijing, Hebei, Shandong, Henan, Xi’an, Shanghai and Hubei, the average concentrations of PM2.5 decreased by 6.2, 30.7, 14.1, 20.7, 29.3, 5.5 and 17.3%, while the NO2 decreased by 45.5, 54.7, 60.5, 58.7, 63.6, 50.5 and 66.5%, respectively, during the period of lockdown restrictions in 2020, as compared with the equivalent period in 2019. Such improvements in air quality were found to be beneficial, reducing in 2020 both the number of premature deaths by approximately 97,390 and welfare costs by over USD 74 billion. Full article
(This article belongs to the Special Issue Earth Observations for Sustainable Development Goals)
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Graphical abstract

17 pages, 2358 KiB  
Article
Assessing the Applications of Earth Observation Data for Monitoring Artisanal and Small-Scale Gold Mining (ASGM) in Developing Countries
by Abdul-Wadood Moomen, Pierre Lacroix, Antonio Benvenuti, Marion Planque, Thomas Piller, Kenneth Davis, Manoela Miranda, Elsy Ibrahim and Gregory Giuliani
Remote Sens. 2022, 14(13), 2971; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14132971 - 21 Jun 2022
Cited by 5 | Viewed by 3383
Abstract
This paper discusses opportunities to use remote sensing (RS) technologies in addressing the persistent global challenges related to the artisanal and small-scale gold mining (ASGM) sector. The paper uses a systematic literature review to identify, analyze, and synthesize various uses of RS on [...] Read more.
This paper discusses opportunities to use remote sensing (RS) technologies in addressing the persistent global challenges related to the artisanal and small-scale gold mining (ASGM) sector. The paper uses a systematic literature review to identify, analyze, and synthesize various uses of RS on the detection and monitoring of ASGM activities across the globe. The study covers the use of spaceborne sensors and available opportunities for data access and processing and emphasizes the important role that freely-available data has played in understanding ASGM activities. It discusses applications and opportunities offered in assessing the geospatial and temporal characteristics of ASGM and its impacts on the surrounding environment. Furthermore, it examines different indicators for the detection of ASGM in the landscape. Finally, technological capabilities described in the study are illustrated with case studies in the Democratic Republic of Congo and in Colombia using cloud computing with the Open Data Cube. The case studies demonstrate the identification and quantification of impacts of ASGM activities on land degradation and water turbidity in remote areas and results are dissiminated using the MapX platform. This facilitates policy development, implementation, and evaluation in the ASGM context. Full article
(This article belongs to the Special Issue Earth Observations for Sustainable Development Goals)
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Graphical abstract

17 pages, 2976 KiB  
Article
Swin-Transformer-Enabled YOLOv5 with Attention Mechanism for Small Object Detection on Satellite Images
by Hang Gong, Tingkui Mu, Qiuxia Li, Haishan Dai, Chunlai Li, Zhiping He, Wenjing Wang, Feng Han, Abudusalamu Tuniyazi, Haoyang Li, Xuechan Lang, Zhiyuan Li and Bin Wang
Remote Sens. 2022, 14(12), 2861; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122861 - 15 Jun 2022
Cited by 78 | Viewed by 15737
Abstract
Object detection has made tremendous progress in natural images over the last decade. However, the results are hardly satisfactory when the natural image object detection algorithm is directly applied to satellite images. This is due to the intrinsic differences in the scale and [...] Read more.
Object detection has made tremendous progress in natural images over the last decade. However, the results are hardly satisfactory when the natural image object detection algorithm is directly applied to satellite images. This is due to the intrinsic differences in the scale and orientation of objects generated by the bird’s-eye perspective of satellite photographs. Moreover, the background of satellite images is complex and the object area is small; as a result, small objects tend to be missing due to the challenge of feature extraction. Dense objects overlap and occlusion also affects the detection performance. Although the self-attention mechanism was introduced to detect small objects, the computational complexity increased with the image’s resolution. We modified the general one-stage detector YOLOv5 to adapt the satellite images to resolve the above problems. First, new feature fusion layers and a prediction head are added from the shallow layer for small object detection for the first time because it can maximally preserve the feature information. Second, the original convolutional prediction heads are replaced with Swin Transformer Prediction Heads (SPHs) for the first time. SPH represents an advanced self-attention mechanism whose shifted window design can reduce the computational complexity to linearity. Finally, Normalization-based Attention Modules (NAMs) are integrated into YOLOv5 to improve attention performance in a normalized way. The improved YOLOv5 is termed SPH-YOLOv5. It is evaluated on the NWPU-VHR10 dataset and DOTA dataset, which are widely used for satellite image object detection evaluations. Compared with the basal YOLOv5, SPH-YOLOv5 improves the mean Average Precision (mAP) by 0.071 on the DOTA dataset. Full article
(This article belongs to the Special Issue Earth Observations for Sustainable Development Goals)
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Graphical abstract

22 pages, 9625 KiB  
Article
Earth-Observation-Based Services for National Reporting of the Sustainable Development Goal Indicators—Three Showcases in Bulgaria
by Adelina Aleksieva-Petrova, Irena Mladenova, Katya Dimitrova, Kamen Iliev, Atanas Georgiev and Anna Dyankova
Remote Sens. 2022, 14(11), 2597; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112597 - 28 May 2022
Cited by 3 | Viewed by 1812
Abstract
Earth Observation (EO) is used to monitor and assess the status of, and changes in, the natural and manmade environment via remote sensing technologies, usually involving satellites carrying imaging devices. EO applications provide important inputs to governments in planning, implementing, and monitoring the [...] Read more.
Earth Observation (EO) is used to monitor and assess the status of, and changes in, the natural and manmade environment via remote sensing technologies, usually involving satellites carrying imaging devices. EO applications provide important inputs to governments in planning, implementing, and monitoring the progress of the 2030 Agenda for Sustainable Development. Along with other countries, Bulgaria has committed to all 17 Sustainable Development Goals (SDGs) and reflected them in its strategic documents. EO is one of the priority technologies for the development of the Bulgarian space sector. This paper analyzes how EO data could significantly help Bulgarian authorities in achieving and monitoring the progress of the SDG targets based on three specific EO monitoring pilot projects’ results (showcases) focused more on the policy management approach than scientific achievement. The first project showed the opportunities of EO data for integration of a national (local) geospatial database with the existing international networks for monitoring natural disasters and accidents. The second demonstrated the time series usage of EO data for water quality monitoring. The third project integrated remote sensing data from EO and in situ measurements with ancillaries’ data to provide phenology status and crop production forecast in a common geospatial database with the aim to support the Bulgarian agriculture sector modernization. Full article
(This article belongs to the Special Issue Earth Observations for Sustainable Development Goals)
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Graphical abstract

20 pages, 8492 KiB  
Article
Severe Biomass-Burning Aerosol Pollution during the 2019 Amazon Wildfire and Its Direct Radiative-Forcing Impact: A Space Perspective from MODIS Retrievals
by Shuyun Yuan, Fangwen Bao, Xiaochuan Zhang and Ying Li
Remote Sens. 2022, 14(9), 2080; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092080 - 26 Apr 2022
Cited by 6 | Viewed by 2305
Abstract
An extreme biomass burning event occurred in the Amazonian rainforest from July through September 2019 due to the extensive wildfires used to clear the land, which allowed for more significant forest burning than previously occurred. In this study, we reclustered the clear-sky ambient [...] Read more.
An extreme biomass burning event occurred in the Amazonian rainforest from July through September 2019 due to the extensive wildfires used to clear the land, which allowed for more significant forest burning than previously occurred. In this study, we reclustered the clear-sky ambient aerosols to adapt the black carbon (BC) aerosol retrieval algorithm to Amazonia. This not only isolated the volumetric fraction of BC (fbc) from moderate-resolution imaging spectroradiometer (MODIS) aerosol data, but also facilitated the use of aerosol mixing and scattering models to estimate the absorption properties of smoke plumes. The retrieved MODIS aerosol dataset provided a space perspective on characterizing the aerosol changes and trends of the 2019 pollution event. A very high aerosol optical depth (AOD) was found to affect the source areas continuously, with higher and thus stronger aerosol absorption. These pollutants also affected the atmosphere downwind due to the transport of air masses. In addition, properties of aerosols emitted from the 2019 Amazonian wildfire events visualized a significant year-to-year enhancement, with the averaged AOD at 550 nm increased by 150%. A 200% increase in the aerosol-absorption optical depth (AAOD) at 550 nm was recognized due to the low single-scattering albedo (SSA) caused by the explosive BC emissions during the pollution peak. Further simulations of aerosol radiative forcing (ARF) showed that the biomass-burning aerosols emitted during the extreme Amazonian wildfires event in 2019 forced a significant change in the radiative balance, which not only produced greater heating of the atmospheric column through strong absorption of BC, but also reduced the radiation reaching the top-of-atmosphere (TOA) and surface level. The negative radiative forcing at the TOA and surface level, as well as the positive radiative forcing in the atmosphere, were elevated by ~30% across the whole of South America compared to 2018. These radiative effects of the absorbing aerosol could have the ability to accelerate the deterioration cycle of drought and fire over the Amazonian rainforest. Full article
(This article belongs to the Special Issue Earth Observations for Sustainable Development Goals)
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34 pages, 33923 KiB  
Article
Improvement of a Dasymetric Method for Implementing Sustainable Development Goal 11 Indicators at an Intra-Urban Scale
by Mariella Aquilino, Maria Adamo, Palma Blonda, Angela Barbanente and Cristina Tarantino
Remote Sens. 2021, 13(14), 2835; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142835 - 19 Jul 2021
Cited by 9 | Viewed by 3452
Abstract
Local and Regional Authorities require indicators at the intra-urban scale to design adequate policies to foster the achievement of the objectives of Sustainable Development Goal (SDG) 11. Updated high-resolution population density and settlement maps are the basic input products for such indicators and [...] Read more.
Local and Regional Authorities require indicators at the intra-urban scale to design adequate policies to foster the achievement of the objectives of Sustainable Development Goal (SDG) 11. Updated high-resolution population density and settlement maps are the basic input products for such indicators and their sub-indicators. When provided at the intra-urban scale, these essential variables can facilitate the extraction of population flows, including both local and regular migrant components. This paper discusses a modification of the dasymetric method implemented in our previous work, aimed at improving the population density estimation. The novelties of our paper include the introduction of building height information and site-specific weight values for population density correction. Based on the proposed improvements, selected indicators/sub-indicators of four SDG 11 targets were updated or newly implemented. The output density map error values are provided in terms of the mean absolute error, root mean square error and mean absolute percentage indicators. The values obtained (i.e., 2.3 and 4.1 people, and 8.6%, respectively) were lower than those of the previous dasymetric method. The findings suggest that the new methodology can provide updated information about population fluxes and processes occurring over the period 2011–2020 in the study site—Bari city in southern Italy. Full article
(This article belongs to the Special Issue Earth Observations for Sustainable Development Goals)
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23 pages, 38891 KiB  
Article
Integrating Remote Sensing and a Markov-FLUS Model to Simulate Future Land Use Changes in Hokkaido, Japan
by Zhanzhuo Chen, Min Huang, Daoye Zhu and Orhan Altan
Remote Sens. 2021, 13(13), 2621; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132621 - 03 Jul 2021
Cited by 64 | Viewed by 5853
Abstract
As the second largest island in Japan, Hokkaido provides precious land resources for the Japanese people. Meanwhile, as the food base of Japan, the gradual decrease of the agricultural population and more intensive agricultural practices on Hokkaido have led its arable land use [...] Read more.
As the second largest island in Japan, Hokkaido provides precious land resources for the Japanese people. Meanwhile, as the food base of Japan, the gradual decrease of the agricultural population and more intensive agricultural practices on Hokkaido have led its arable land use to change year by year, which has also caused changes to the whole land use pattern of the entire island of Hokkaido. To realize the sustainable use of land resources in Hokkaido, past and future changes in land use patterns must be investigated, and target-based land use planning suggestions should be given on this basis. This study uses remote sensing and GIS technology to analyze the temporal and spatial changes of land use in Hokkaido during the past two decades. The types of land use include cultivated land, forest, waterbody, construction, grassland, and others, by using the satellite images of the Landsat images in 2000, 2010, and 2019 to achieve this goal to make classification. In addition, this study used the coupled Markov-FLUS model to simulate and analyze the land use changes in three different scenarios in Hokkaido in the next 20 years. Scenario-based situational analysis shows that the cultivated land in Hokkaido will drop by about 25% in 2040 under the natural development scenario (ND), while the cultivated land area in Hokkaido will remain basically unchanged in cultivated land protection scenario (CP). In forest protection scenario (FP), the area of forest in Hokkaido will increase by 1580.8 km2. It is believed that the findings reveal that the forest land in Hokkaido has been well protected in the past and will be protected well in the next 20 years. However, in land use planning for future, Hokkaido government and enterprises should pay more attention to the protection of cultivated land. Full article
(This article belongs to the Special Issue Earth Observations for Sustainable Development Goals)
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Review

Jump to: Editorial, Research

28 pages, 4787 KiB  
Review
The Arctic Amplification and Its Impact: A Synthesis through Satellite Observations
by Igor Esau, Lasse H. Pettersson, Mathilde Cancet, Bertrand Chapron, Alexander Chernokulsky, Craig Donlon, Oleg Sizov, Andrei Soromotin and Johnny A. Johannesen
Remote Sens. 2023, 15(5), 1354; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051354 - 28 Feb 2023
Cited by 9 | Viewed by 3604
Abstract
Arctic climate change has already resulted in amplified and accelerated regional warming, or the Arctic amplification. Satellite observations have captured this climate phenomenon in its development and in sufficient spatial details. As such, these observations have been—and still are—indispensable for monitoring of the [...] Read more.
Arctic climate change has already resulted in amplified and accelerated regional warming, or the Arctic amplification. Satellite observations have captured this climate phenomenon in its development and in sufficient spatial details. As such, these observations have been—and still are—indispensable for monitoring of the amplification in this remote and inhospitable region, which is sparsely covered with ground observations. This study synthesizes the key contributions of satellite observations into an understanding and characterization of the amplification. The study reveals that the satellites were able to capture a number of important environmental transitions in the region that both precede and follow the emergence of the apparent amplification. Among those transitions, we find a rapid decline in the multiyear sea ice and subsequent changes in the surface radiation balance. Satellites have witnessed the impact of the amplification on phytoplankton and vegetation productivity as well as on human activity and infrastructure. Satellite missions of the European Space Agency (ESA) are increasingly contributing to amplification monitoring and assessment. The ESA Climate Change Initiative has become an essential provider of long-term climatic-quality remote-sensing data products for essential climate variables. Still, such synthesis has found that additional efforts are needed to improve cross-sensor calibrations and retrieval algorithms and to reduce uncertainties. As the amplification is set to continue into the 21st century, a new generation of satellite instruments with improved revisiting time and spectral and spatial resolutions are in high demand in both research and stakeholders’ communities. Full article
(This article belongs to the Special Issue Earth Observations for Sustainable Development Goals)
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30 pages, 1879 KiB  
Review
A Systematic Review on the Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Health Attributes, Indicators, and Measures
by Irini Soubry, Thuy Doan, Thuan Chu and Xulin Guo
Remote Sens. 2021, 13(16), 3262; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163262 - 18 Aug 2021
Cited by 32 | Viewed by 7152
Abstract
It is important to protect forest and grassland ecosystems because they are ecologically rich and provide numerous ecosystem services. Upscaling monitoring from local to global scale is imperative in reaching this goal. The SDG Agenda does not include indicators that directly quantify ecosystem [...] Read more.
It is important to protect forest and grassland ecosystems because they are ecologically rich and provide numerous ecosystem services. Upscaling monitoring from local to global scale is imperative in reaching this goal. The SDG Agenda does not include indicators that directly quantify ecosystem health. Remote sensing and Geographic Information Systems (GIS) can bridge the gap for large-scale ecosystem health assessment. We systematically reviewed field-based and remote-based measures of ecosystem health for forests and grasslands, identified the most important ones and provided an overview on remote sensing and GIS-based measures. We included 163 English language studies within terrestrial non-tropical biomes and used a pre-defined classification system to extract ecological stressors and attributes, collected corresponding indicators, measures, and proxy values. We found that the main ecological attributes of each ecosystem contribute differently in the literature, and that almost half of the examined studies used remote sensing to estimate indicators. The major stressor for forests was “climate change”, followed by “insect infestation”; for grasslands it was “grazing”, followed by “climate change”. “Biotic interactions, composition, and structure” was the most important ecological attribute for both ecosystems. “Fire disturbance” was the second most important for forests, while for grasslands it was “soil chemistry and structure”. Less than a fifth of studies used vegetation indices; NDVI was the most common. There are monitoring inconsistencies from the broad range of indicators and measures. Therefore, we recommend a standardized field, GIS, and remote sensing-based approach to monitor ecosystem health and integrity and facilitate land managers and policy-makers. Full article
(This article belongs to the Special Issue Earth Observations for Sustainable Development Goals)
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