Pub Date : 2021-08-28DOI: 10.1080/20964471.2021.1948677
H. Fukui, Duc Chuc Man, A. Phan
ABSTRACT The 17 Sustainable Development Goals present clear directions toward the green transformation being sought by the global community. The SDGs are an integrated framework, with a complex network of interlinkages between the goals, targets and indicators, and they pose wicked problems to society. Consequently, measuring progress and achievements with the SDGs requires the integration of various spatio-temporal datasets from different domains and the synthesis of disciplines to describe a system of systems. The Group on Earth Observations has developed the concept of Essential Variables to describe systems across Societal Benefit Areas that are applicable for this purpose. Digital Earth is a virtual representation of the planet, potentially encompassing all its systems and life forms, including human societies. Designed as a multi-dimensional, multi-scale, multi-temporal, and multi-layer information facility, Digital Earth is a valuable platform that can contribute to the achievement of the SDGs and a green transformation. To that end, a set of Essential SDGs Variables (ESDGVs) for the platform are proposed and cases of implementation and use are introduced.
{"title":"Digital Earth: A platform for the SDGs and green transformation at the global and local level, employing essential SDGs variables","authors":"H. Fukui, Duc Chuc Man, A. Phan","doi":"10.1080/20964471.2021.1948677","DOIUrl":"https://doi.org/10.1080/20964471.2021.1948677","url":null,"abstract":"ABSTRACT The 17 Sustainable Development Goals present clear directions toward the green transformation being sought by the global community. The SDGs are an integrated framework, with a complex network of interlinkages between the goals, targets and indicators, and they pose wicked problems to society. Consequently, measuring progress and achievements with the SDGs requires the integration of various spatio-temporal datasets from different domains and the synthesis of disciplines to describe a system of systems. The Group on Earth Observations has developed the concept of Essential Variables to describe systems across Societal Benefit Areas that are applicable for this purpose. Digital Earth is a virtual representation of the planet, potentially encompassing all its systems and life forms, including human societies. Designed as a multi-dimensional, multi-scale, multi-temporal, and multi-layer information facility, Digital Earth is a valuable platform that can contribute to the achievement of the SDGs and a green transformation. To that end, a set of Essential SDGs Variables (ESDGVs) for the platform are proposed and cases of implementation and use are introduced.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87533238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-20DOI: 10.1080/20964471.2021.1901359
Li Wang, Yuanhuizi He, Yuelin Zhang, Lei Wang, Huicong Jia, Quan Zhou, Bo Yu, Mei-mei Zhang, Zhengyang Lin, Fang Chen
ABSTRACT In this study, based on the EM-DAT (The Emergency Events Database) database, disaster assessment for the “Belt and Road” region was carried out in relation to the indicator of the Sustainable Development Goals (SDGs) agenda launched in 2015. A new method for diagnosing trends in the indicators based on the Theil-Sen median method is proposed. In addition, using the data available in the EM-DAT, an overview of disaster records is used to quantify disasters for a total of 73 countries. The disaster trends for the period 2015‒2019 were found to demonstrate the following. (1) As a result of geological and climate conditions, Asia and Africa are high-risk disaster areas and disasters have caused considerable economic losses and affected the populations in developing and underdeveloped countries in these regions. (2) The clear positive value of found for China reflects the country’s encouraging achievements in disaster prevention and mitigation. (3) The value of was observed to be increasing in South Asia, northwest Africa and South Africa, with the increase in India and Mauritania being the most serious. The new method proposed in this paper allows the real trend in the indicator in various countries to be derived and provides critical intelligence support for international disaster risk reduction plans and sustainable development goals.
{"title":"Disaster assessment for the “Belt and Road” region based on SDG landmarks","authors":"Li Wang, Yuanhuizi He, Yuelin Zhang, Lei Wang, Huicong Jia, Quan Zhou, Bo Yu, Mei-mei Zhang, Zhengyang Lin, Fang Chen","doi":"10.1080/20964471.2021.1901359","DOIUrl":"https://doi.org/10.1080/20964471.2021.1901359","url":null,"abstract":"ABSTRACT In this study, based on the EM-DAT (The Emergency Events Database) database, disaster assessment for the “Belt and Road” region was carried out in relation to the indicator of the Sustainable Development Goals (SDGs) agenda launched in 2015. A new method for diagnosing trends in the indicators based on the Theil-Sen median method is proposed. In addition, using the data available in the EM-DAT, an overview of disaster records is used to quantify disasters for a total of 73 countries. The disaster trends for the period 2015‒2019 were found to demonstrate the following. (1) As a result of geological and climate conditions, Asia and Africa are high-risk disaster areas and disasters have caused considerable economic losses and affected the populations in developing and underdeveloped countries in these regions. (2) The clear positive value of found for China reflects the country’s encouraging achievements in disaster prevention and mitigation. (3) The value of was observed to be increasing in South Asia, northwest Africa and South Africa, with the increase in India and Mauritania being the most serious. The new method proposed in this paper allows the real trend in the indicator in various countries to be derived and provides critical intelligence support for international disaster risk reduction plans and sustainable development goals.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83574346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT Following the accelerated development of urbanization and industrialization, atmospheric particulate matter has become a significant threat to public health globally. Environmental health studies usually use the mass concentration of fine particles (PM2.5) as a base data to predict the health risks of particulate exposure. However, PM2.5 data from ground monitoring stations in China has not been provided until January 2013 by the Ministry of Environmental Protection of China. Hence, an alternative dataset of PM2.5 spatiotemporal distributions extending to years earlier than 2013 is urgently needed, which is of great significance to atmospheric environment assessment and pollution prevention and control. Atmospheric aerosol products by the moderate-resolution imaging spectroradiometer (MODIS) have been released since 2000, which provides the possibility to reconstruct historical PM2.5. However, most current methods do not have the ability to estimate PM2.5 mass concentration independently of ground observations. The PM2.5 mass concentration data set produced by PM2.5 remote sensing (PMRS) model based on physical processes does not depend on the ground observations, and also is not affected by the uncertainty of model emission sources or the completeness of chemical reaction mechanism. These ensure that the point-by-point validation for PM2.5 mass concentration data is more convincing, and the dataset can also be further used for model assimilation and artificial intelligence training to improve their predictions. In this study, we calculate the monthly PM2.5 mass concentration near the ground over land of China using aerosol inversion products (aerosol optical depth and fine-mode fraction) of MODIS and meteorological data (boundary layer height & relative humidity) provided by the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) data set. The results show that, in China, 6 pollution centers mainly concentrated in the central and eastern regions. The highest PM2.5 mass concentration occurred in winter, whereas the pollution range was larger in summer. There are 63.4% of validation sites with biases within ±20 μg m−3, and the expected error is as ±(15 μg m−3 + 30%) enveloped by the monthly mean PM2.5 mass concentrations. The monthly PM2.5 is stored as NETCDF format, with a spatial resolution of 1°×1°. The published data is available in http://www.dx.doi.org/10.11922/sciencedb.j00076.00061.
{"title":"A satellite-derived, ground-measurement-independent monthly PM2.5 mass concentration dataset over China during 2000–2015","authors":"Ying Zhang, Zhengqiang Li, Yuanyuan Wei, Zongren Peng","doi":"10.1080/20964471.2021.1918908","DOIUrl":"https://doi.org/10.1080/20964471.2021.1918908","url":null,"abstract":"ABSTRACT Following the accelerated development of urbanization and industrialization, atmospheric particulate matter has become a significant threat to public health globally. Environmental health studies usually use the mass concentration of fine particles (PM2.5) as a base data to predict the health risks of particulate exposure. However, PM2.5 data from ground monitoring stations in China has not been provided until January 2013 by the Ministry of Environmental Protection of China. Hence, an alternative dataset of PM2.5 spatiotemporal distributions extending to years earlier than 2013 is urgently needed, which is of great significance to atmospheric environment assessment and pollution prevention and control. Atmospheric aerosol products by the moderate-resolution imaging spectroradiometer (MODIS) have been released since 2000, which provides the possibility to reconstruct historical PM2.5. However, most current methods do not have the ability to estimate PM2.5 mass concentration independently of ground observations. The PM2.5 mass concentration data set produced by PM2.5 remote sensing (PMRS) model based on physical processes does not depend on the ground observations, and also is not affected by the uncertainty of model emission sources or the completeness of chemical reaction mechanism. These ensure that the point-by-point validation for PM2.5 mass concentration data is more convincing, and the dataset can also be further used for model assimilation and artificial intelligence training to improve their predictions. In this study, we calculate the monthly PM2.5 mass concentration near the ground over land of China using aerosol inversion products (aerosol optical depth and fine-mode fraction) of MODIS and meteorological data (boundary layer height & relative humidity) provided by the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) data set. The results show that, in China, 6 pollution centers mainly concentrated in the central and eastern regions. The highest PM2.5 mass concentration occurred in winter, whereas the pollution range was larger in summer. There are 63.4% of validation sites with biases within ±20 μg m−3, and the expected error is as ±(15 μg m−3 + 30%) enveloped by the monthly mean PM2.5 mass concentrations. The monthly PM2.5 is stored as NETCDF format, with a spatial resolution of 1°×1°. The published data is available in http://www.dx.doi.org/10.11922/sciencedb.j00076.00061.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80293993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-29DOI: 10.1080/20964471.2021.1914400
M. Nabil, Miao Zhang, Bingfang Wu, José Bofana, Abdelrazek Elnashar
ABSTRACT Despite its essential importance to various spatial agriculture and environmental applications, the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote-sensing products. Each of the African regions has its unique physical and environmental limiting factors to accurate cropland mapping, which leads to high spatial discrepancies among remote sensing cropland products. Since no dataset could cope with all limitations, multiple datasets initially derived from various remote sensing sensors and classification techniques must be integrated into a more accurate cropland product than individual layers. Here, in the current study, four cropland products, produced initially from multiple sensors (e.g. Landsat-8 OLI, Sentinel-2 MSI, and PROBA–V) to cover the period (2015–2017), were integrated based on their cropland mapping accuracy to build a more accurate cropland layer. The four cropland layers’ accuracy was assessed at Agro-ecological zones units via an intensive reference dataset (17,592 samples). The most accurate cropland layer was then identified for each zone to construct the final cropland mask at 30 m resolution for the nominal year of 2016 over Africa. As a result, the new layer was produced in higher cropland mapping accuracy (overall accuracy = 91.64% and cropland’s F-score = 0.75). The layer mapped the African cropland area as 282 Mha (9.38% of the Continent area). Compared to earlier cropland synergy layers, the constructed cropland mask showed a considerable improvement in its spatial resolution (30 m instead of 250 m), mapping quality, and closeness to official statistics (R2 = 0.853 and RMSE = 2.85 Mha). The final layer can be downloaded as described under the “Data Availability Statement” section.
{"title":"Constructing a 30m African Cropland Layer for 2016 by Integrating Multiple Remote sensing, crowdsourced, and Auxiliary Datasets","authors":"M. Nabil, Miao Zhang, Bingfang Wu, José Bofana, Abdelrazek Elnashar","doi":"10.1080/20964471.2021.1914400","DOIUrl":"https://doi.org/10.1080/20964471.2021.1914400","url":null,"abstract":"ABSTRACT Despite its essential importance to various spatial agriculture and environmental applications, the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote-sensing products. Each of the African regions has its unique physical and environmental limiting factors to accurate cropland mapping, which leads to high spatial discrepancies among remote sensing cropland products. Since no dataset could cope with all limitations, multiple datasets initially derived from various remote sensing sensors and classification techniques must be integrated into a more accurate cropland product than individual layers. Here, in the current study, four cropland products, produced initially from multiple sensors (e.g. Landsat-8 OLI, Sentinel-2 MSI, and PROBA–V) to cover the period (2015–2017), were integrated based on their cropland mapping accuracy to build a more accurate cropland layer. The four cropland layers’ accuracy was assessed at Agro-ecological zones units via an intensive reference dataset (17,592 samples). The most accurate cropland layer was then identified for each zone to construct the final cropland mask at 30 m resolution for the nominal year of 2016 over Africa. As a result, the new layer was produced in higher cropland mapping accuracy (overall accuracy = 91.64% and cropland’s F-score = 0.75). The layer mapped the African cropland area as 282 Mha (9.38% of the Continent area). Compared to earlier cropland synergy layers, the constructed cropland mask showed a considerable improvement in its spatial resolution (30 m instead of 250 m), mapping quality, and closeness to official statistics (R2 = 0.853 and RMSE = 2.85 Mha). The final layer can be downloaded as described under the “Data Availability Statement” section.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89063817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-27DOI: 10.1080/20964471.2021.1918909
Naichen Xing, Wenjiang Huang, H. Ye, Yingying Dong, Weiping Kong, Yu Ren, Qiaoyun Xie
ABSTRACT Leaf area index (LAI) and canopy chlorophyll density (CCD) are key indicators of crop growth status. In this study, we compared several vegetation indices and their red-edge modified counterparts to evaluate the optimal red-edge bands and the best vegetation index at different growth stages. The indices were calculated with Sentinel-2 MSI data and hyperspectral data. Their performances were validated against ground measurements using R2, RMSE, and bias. The results suggest that indices computed with hyperspectral data exhibited higher R2 than multispectral data at the late jointing stage, head emergence stage, and filling stage. Furthermore, red-edge modified indices outperformed the traditional indices for both data genres. Inversion models indicated that the indices with short red-edge wavelengths showed better estimation at the early jointing and milk development stage, while indices with long red-edge wavelength estimate the sought variables better at the middle three stages. The results were consistent with the red-edge inflection point shift at different growth stages. The best indices for Sentinel-2 LAI retrieval, Sentinel-2 CCD retrieval, hyperspectral LAI retrieval, and hyperspectral CCD retrieval at five growth stages were determined in the research. These results are beneficial to crop trait monitoring by providing references for crop biophysical and biochemical parameters retrieval.
{"title":"Remote sensing retrieval of winter wheat leaf area index and canopy chlorophyll density at different growth stages","authors":"Naichen Xing, Wenjiang Huang, H. Ye, Yingying Dong, Weiping Kong, Yu Ren, Qiaoyun Xie","doi":"10.1080/20964471.2021.1918909","DOIUrl":"https://doi.org/10.1080/20964471.2021.1918909","url":null,"abstract":"ABSTRACT Leaf area index (LAI) and canopy chlorophyll density (CCD) are key indicators of crop growth status. In this study, we compared several vegetation indices and their red-edge modified counterparts to evaluate the optimal red-edge bands and the best vegetation index at different growth stages. The indices were calculated with Sentinel-2 MSI data and hyperspectral data. Their performances were validated against ground measurements using R2, RMSE, and bias. The results suggest that indices computed with hyperspectral data exhibited higher R2 than multispectral data at the late jointing stage, head emergence stage, and filling stage. Furthermore, red-edge modified indices outperformed the traditional indices for both data genres. Inversion models indicated that the indices with short red-edge wavelengths showed better estimation at the early jointing and milk development stage, while indices with long red-edge wavelength estimate the sought variables better at the middle three stages. The results were consistent with the red-edge inflection point shift at different growth stages. The best indices for Sentinel-2 LAI retrieval, Sentinel-2 CCD retrieval, hyperspectral LAI retrieval, and hyperspectral CCD retrieval at five growth stages were determined in the research. These results are beneficial to crop trait monitoring by providing references for crop biophysical and biochemical parameters retrieval.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80896542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-27DOI: 10.1080/20964471.2021.1941571
Jinhu Bian, Ainong Li, Xi Nan, G. Lei, Zhengjia Zhang
ABSTRACT Mountains are undergoing widespread changes caused by human activities and climate change. Given the importance of mountains, the protection and sustainable development of mountain ecosystems have been listed as the goals of the United Nations 2030 Sustainable Development Agenda. As one of the indicators, the Mountain Green Cover Index (MGCI) datasets can provide consistent and comparable status of green vegetation in mountainous areas, which can support the mapping of heterogeneous mountain ecosystem health and monitoring changes over time. The production of explicitly high-spatial-resolution MGCI datasets is therefore urgently needed to support the protection measures at subnational and multitemporal scales. In this paper, the MGCI datasets with 500-meter spatial resolutions, covering the economic corridors of the Belt and Road Initiative (BRI), were developed for 2010 to 2019 based on all available Landsat-8 data and the Google Earth Engine cloud computing platform. The validation of green vegetation cover with the ground-truth samples indicated that the datasets can achieve an overall accuracy of 94.06%, with well-detailed spatial and temporal variations. The archived datasets include the MGCI of each BRI economic corridor, matched to a geospatial layer denoting the economic corridor boundaries. The essential information of the datasets and their limitations, along with the production flow, were described in this paper. The published geospatial datasets are available at http://www.doi.org/10.11922/sciencedb.1005.
由于人类活动和气候变化，山区正在发生广泛的变化。鉴于山区的重要性，山区生态系统的保护和可持续发展已被列为联合国2030年可持续发展议程的目标。山地绿化指数(Mountain Green Cover Index, MGCI)数据集作为指标之一，能够提供山区绿色植被的一致性和可比性状况，为异质山地生态系统健康制图和监测变化提供支持。因此，迫切需要制作明确的高空间分辨率MGCI数据集，以支持次国家和多时间尺度的保护措施。本文基于所有可用的Landsat-8数据和谷歌Earth Engine云计算平台，开发了2010 - 2019年覆盖“一带一路”经济走廊的500米空间分辨率MGCI数据集。地面真值样本对绿色植被覆盖度的验证表明，数据集的总体精度为94.06%，具有较详细的时空变化。存档的数据集包括每个“一带一路”经济走廊的MGCI，并与表示经济走廊边界的地理空间层相匹配。本文描述了数据集的基本信息和它们的局限性，以及生产流程。已发布的地理空间数据集可在http://www.doi.org/10.11922/sciencedb.1005上获得。
{"title":"Dataset of the mountain green cover index (SDG15.4.2) over the economic corridors of the Belt and Road Initiative for 2010-2019","authors":"Jinhu Bian, Ainong Li, Xi Nan, G. Lei, Zhengjia Zhang","doi":"10.1080/20964471.2021.1941571","DOIUrl":"https://doi.org/10.1080/20964471.2021.1941571","url":null,"abstract":"ABSTRACT Mountains are undergoing widespread changes caused by human activities and climate change. Given the importance of mountains, the protection and sustainable development of mountain ecosystems have been listed as the goals of the United Nations 2030 Sustainable Development Agenda. As one of the indicators, the Mountain Green Cover Index (MGCI) datasets can provide consistent and comparable status of green vegetation in mountainous areas, which can support the mapping of heterogeneous mountain ecosystem health and monitoring changes over time. The production of explicitly high-spatial-resolution MGCI datasets is therefore urgently needed to support the protection measures at subnational and multitemporal scales. In this paper, the MGCI datasets with 500-meter spatial resolutions, covering the economic corridors of the Belt and Road Initiative (BRI), were developed for 2010 to 2019 based on all available Landsat-8 data and the Google Earth Engine cloud computing platform. The validation of green vegetation cover with the ground-truth samples indicated that the datasets can achieve an overall accuracy of 94.06%, with well-detailed spatial and temporal variations. The archived datasets include the MGCI of each BRI economic corridor, matched to a geospatial layer denoting the economic corridor boundaries. The essential information of the datasets and their limitations, along with the production flow, were described in this paper. The published geospatial datasets are available at http://www.doi.org/10.11922/sciencedb.1005.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87222569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-27DOI: 10.1080/20964471.2021.1923231
Christos Polykretis, D. Alexakis, M. Grillakis, A. Agapiou, B. Cuca, Nikos Papadopoulos, Apostolos Sarris
ABSTRACT Among the environmental threats, the intensification of natural hazards, such as soil erosion may threaten the integrity and value of cultural heritage sites. In this framework, the present study’s main objective was to identify archaeological sites susceptible by soil erosion, taking the case study of Chania prefecture in Crete Island. Remotely sensed and other available geospatial datasets were analyzed in a GIS-based empirical model, namely Unit Stream Power Erosion and Deposition (USPED), to estimate the average annual soil loss and deposition rates due to water-induced erosion in the study area. The resultant erosion map was then intersected with the locations and surrounding zones of the known archaeological sites for identifying the sites and the portions of their vicinity being at risk. The results revealed that Chania prefecture and its cultural heritage are significantly affected by both soil loss and deposition processes. Between the two processes, soil loss was found to be more intensive, influencing a larger part of the prefecture (especially to the west) as well as a higher amount of archaeological sites. The extreme and high soil loss classes were also detected to cover the most considerable portion of the sites’ surrounding area. The identification of the archaeological sites being most exposed to soil erosion hazard can constitute a basis for cultural heritage managers in order to take preventive preservation measures and develop specific risk mitigation strategies.
{"title":"Assessment of water-induced soil erosion as a threat to cultural heritage sites: the case of Chania prefecture, Crete Island, Greece","authors":"Christos Polykretis, D. Alexakis, M. Grillakis, A. Agapiou, B. Cuca, Nikos Papadopoulos, Apostolos Sarris","doi":"10.1080/20964471.2021.1923231","DOIUrl":"https://doi.org/10.1080/20964471.2021.1923231","url":null,"abstract":"ABSTRACT Among the environmental threats, the intensification of natural hazards, such as soil erosion may threaten the integrity and value of cultural heritage sites. In this framework, the present study’s main objective was to identify archaeological sites susceptible by soil erosion, taking the case study of Chania prefecture in Crete Island. Remotely sensed and other available geospatial datasets were analyzed in a GIS-based empirical model, namely Unit Stream Power Erosion and Deposition (USPED), to estimate the average annual soil loss and deposition rates due to water-induced erosion in the study area. The resultant erosion map was then intersected with the locations and surrounding zones of the known archaeological sites for identifying the sites and the portions of their vicinity being at risk. The results revealed that Chania prefecture and its cultural heritage are significantly affected by both soil loss and deposition processes. Between the two processes, soil loss was found to be more intensive, influencing a larger part of the prefecture (especially to the west) as well as a higher amount of archaeological sites. The extreme and high soil loss classes were also detected to cover the most considerable portion of the sites’ surrounding area. The identification of the archaeological sites being most exposed to soil erosion hazard can constitute a basis for cultural heritage managers in order to take preventive preservation measures and develop specific risk mitigation strategies.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81408395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-03DOI: 10.1080/20964471.2021.1936943
M. Kulmala, A. Lintunen, Ilona Ylivinkka, Janne Mukkala, Rosa Rantanen, J. Kujansuu, T. Petäjä, H. Lappalainen
ABSTRACT Big open data comprising comprehensive, long-term atmospheric and ecosystem in-situ observations will give us tools to meet global grand challenges and to contribute towards sustainable development. United Nations’ Sustainable Development Goals (UN SDGs) provide framework for the process. We present synthesis on how Station for Measuring Earth Surface–Atmosphere Relations (SMEAR) observation network can contribute to UN SDGs. We describe SMEAR II flagship station in Hyytiälä, Finland. With more than 1200 variables measured in an integrated manner, we can understand interactions and feedbacks between biosphere and atmosphere. This contributes towards understanding impacts of climate change to natural ecosystems and feedbacks from ecosystems to climate. The benefits of SMEAR concept are highlighted through outreach project in Eastern Lapland utilizing SMEAR I observations from Värriö research station. In contrast to boreal environment, SMEAR concept was also deployed in Beijing. We underline the benefits of comprehensive observations to gain novel insights into complex interactions between densely populated urban environment and atmosphere. Such observations enable work towards solving air quality problems and improve the quality of life inside megacities. The network of comprehensive stations with various measurements will enable science-based decision making and support sustainable development by providing long-term view on spatio-temporal trends on atmospheric composition and ecosystem parameters.
{"title":"Atmospheric and ecosystem big data providing key contributions in reaching United Nations’ Sustainable Development Goals","authors":"M. Kulmala, A. Lintunen, Ilona Ylivinkka, Janne Mukkala, Rosa Rantanen, J. Kujansuu, T. Petäjä, H. Lappalainen","doi":"10.1080/20964471.2021.1936943","DOIUrl":"https://doi.org/10.1080/20964471.2021.1936943","url":null,"abstract":"ABSTRACT Big open data comprising comprehensive, long-term atmospheric and ecosystem in-situ observations will give us tools to meet global grand challenges and to contribute towards sustainable development. United Nations’ Sustainable Development Goals (UN SDGs) provide framework for the process. We present synthesis on how Station for Measuring Earth Surface–Atmosphere Relations (SMEAR) observation network can contribute to UN SDGs. We describe SMEAR II flagship station in Hyytiälä, Finland. With more than 1200 variables measured in an integrated manner, we can understand interactions and feedbacks between biosphere and atmosphere. This contributes towards understanding impacts of climate change to natural ecosystems and feedbacks from ecosystems to climate. The benefits of SMEAR concept are highlighted through outreach project in Eastern Lapland utilizing SMEAR I observations from Värriö research station. In contrast to boreal environment, SMEAR concept was also deployed in Beijing. We underline the benefits of comprehensive observations to gain novel insights into complex interactions between densely populated urban environment and atmosphere. Such observations enable work towards solving air quality problems and improve the quality of life inside megacities. The network of comprehensive stations with various measurements will enable science-based decision making and support sustainable development by providing long-term view on spatio-temporal trends on atmospheric composition and ecosystem parameters.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81246983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-03DOI: 10.1080/20964471.2021.1946290
D. Castle, P. Hebert, E. Clare, I. Hogg, C. Tremblay
ABSTRACT Global biodiversity is in crises. Recognition of the scale and pace of biodiversity loss is leading to rapid technological development in biodiversity science to identify species, their interactions, and ecosystem dynamics. National and international policy developments to stimulate mitigation and remediation actions are escalating to meet the biodiversity crises. They can take advantage of biosurveillance “big data” as evidence for more sweeping and impactful policy measures. The critical factor is translating biosurveillance data into the value-based frameworks underpinning new policy measures. An approach to this integration process, using natural capital accounting frameworks is developed.
{"title":"Capturing the value of biosurveillance “big data” through natural capital accounting","authors":"D. Castle, P. Hebert, E. Clare, I. Hogg, C. Tremblay","doi":"10.1080/20964471.2021.1946290","DOIUrl":"https://doi.org/10.1080/20964471.2021.1946290","url":null,"abstract":"ABSTRACT Global biodiversity is in crises. Recognition of the scale and pace of biodiversity loss is leading to rapid technological development in biodiversity science to identify species, their interactions, and ecosystem dynamics. National and international policy developments to stimulate mitigation and remediation actions are escalating to meet the biodiversity crises. They can take advantage of biosurveillance “big data” as evidence for more sweeping and impactful policy measures. The critical factor is translating biosurveillance data into the value-based frameworks underpinning new policy measures. An approach to this integration process, using natural capital accounting frameworks is developed.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91005932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-03DOI: 10.1080/20964471.2021.1940733
S. Mihály, Gábor Remetey-Fülöpp, D. Kristóf, A. Czinkóczky, Tamás Palya, L. Pásztor, Pál Rudan, G. Szabó, L. Zentai
ABSTRACT To support the monitoring and reporting processes during implementation of the Sustainable Development Goals, well-developed, commonly recognized Earth observations and geospatial data, methods, innovations, committed professionals, and strong sustainability policies are necessary. This article informs the readers on the Earth observation and geoinformation developments and innovations, and on the engagement of profession, academy and governance to support implementation of the Sustainable Development Goals in Hungary. Description, analyses and critical assessments are given on the elements selected from Hungarian sustainable-oriented Earth observation and geospatial novelties: (a) Working Group for Sustainable Development mission and national sustainability-policy, (b) international partnerships, domestic activities and achievements, (c) status of the professional education, (d) spatial databases and services to support implementation of the sustainable development, (e) a case study on the internationally recognized soil geoinformation system, (f) national Earth Observation Information System and perspectives of its applications for monitoring the sustainability. The article conclusion strongly advises the Hungarian realization of (a) institutionalization of the Earth observation and geospatial tools and capacity for sustainable development, (b) their use in integration with statistical data, (c) establishment of national spatial information infrastructure and (d) development and spreading of the use of big data.
{"title":"Earth observation and geospatial big data management and engagement of stakeholders in Hungary to support the SDGs","authors":"S. Mihály, Gábor Remetey-Fülöpp, D. Kristóf, A. Czinkóczky, Tamás Palya, L. Pásztor, Pál Rudan, G. Szabó, L. Zentai","doi":"10.1080/20964471.2021.1940733","DOIUrl":"https://doi.org/10.1080/20964471.2021.1940733","url":null,"abstract":"ABSTRACT To support the monitoring and reporting processes during implementation of the Sustainable Development Goals, well-developed, commonly recognized Earth observations and geospatial data, methods, innovations, committed professionals, and strong sustainability policies are necessary. This article informs the readers on the Earth observation and geoinformation developments and innovations, and on the engagement of profession, academy and governance to support implementation of the Sustainable Development Goals in Hungary. Description, analyses and critical assessments are given on the elements selected from Hungarian sustainable-oriented Earth observation and geospatial novelties: (a) Working Group for Sustainable Development mission and national sustainability-policy, (b) international partnerships, domestic activities and achievements, (c) status of the professional education, (d) spatial databases and services to support implementation of the sustainable development, (e) a case study on the internationally recognized soil geoinformation system, (f) national Earth Observation Information System and perspectives of its applications for monitoring the sustainability. The article conclusion strongly advises the Hungarian realization of (a) institutionalization of the Earth observation and geospatial tools and capacity for sustainable development, (b) their use in integration with statistical data, (c) establishment of national spatial information infrastructure and (d) development and spreading of the use of big data.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81923007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：