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International Journal of Digital Earth pp 1-14; doi:10.1080/17538947.2021.1952323

Abstract:
As an important facilitator in e-government and society in general, Open SDI merits an assessment of its characteristics and the monitoring of its development. The aim of the study was the proposal of the SDI openness assessment approach based on existing openness assessment frameworks, as well as the presentation of the Polish Spatial Data Infrastructure (PSDI) development towards openness. The results indicated that ten geodetic and cartographic databases fulfilled ten out of eleven criteria of data openness, according to the methodological assumptions, and reached a 3-star level of openness. The need for further development of the infrastructure towards sharing public administration data is recognized, as well as non-governmental data that meet the open data criteria, thus contributing to the openness of the SDI. The proposed assessment method, referenced to a five-level data openness system and providing clear scoring benchmarking for assessing SDI openness, may be used for comparative analysis of SDI openness in different countries, including EU Member States that draw on the experience of the implementation of the INSPIRE Directive.
, , Yong Zhang, Shuqing Zhang, Xiaohui Ding,
Published: 8 July 2021
by 10.1080
International Journal of Digital Earth pp 1-19; doi:10.1080/17538947.2021.1950853

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Fengyuan Zhang, , Ming Wang, Zihuan Wang, Shuo Zhang, Songshan Yue, Yongning Wen, Guonian Lü
Published: 6 July 2021
by 10.1080
International Journal of Digital Earth pp 1-23; doi:10.1080/17538947.2021.1949400

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Gaoxiang Zhou, , Xiangnan Liu
Published: 5 July 2021
by 10.1080
International Journal of Digital Earth pp 1-16; doi:10.1080/17538947.2021.1949399

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, , , Zhenkun Lei
Published: 5 July 2021
by 10.1080
International Journal of Digital Earth pp 1-23; doi:10.1080/17538947.2021.1946178

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Kaifang Shi, Jingwei Shen, Yizhen Wu, Shirao Liu,
International Journal of Digital Earth pp 1-14; doi:10.1080/17538947.2021.1946605

Abstract:
Exploring carbon dioxide (CO2) emissions from human activities is essential for urban energy conservation and resource management. Remotely sensed nighttime lights from the Suomi NPP-VIIRS provide spatial consistency in and a low-cost way of revealing CO2 emissions. Although many researches have documented the feasibility of the Suomi NPP-VIIRS data for assessing CO2 emissions, few have systematically revealed the ability of nighttime lights for evaluating CO2 emissions from different industries, such as service industry CO2 emissions (SC), traffic CO2 emissions (TC), and secondary industry CO2 emissions (IC). Here, China was selected as the experimental subject, and we comprehensively explored the relationships between the nighttime lights and SC, TC, and IC, and investigated the factors mediating these relationships. We found that without considering other factors, the nighttime lights only revealed up to 51.2% of TC, followed by 41.7% of IC and 22.7% of SC. When controlling for city characteristic variables, the models showed that there were positive correlations between the Suomi NPP-VIIRS data and SC, IC, and TC, and that nighttime lights have an Inverted-U relationship with SC. The Suomi NPP-VIIRS data are more suitable for revealing SC, TC, and IC in medium-sized and large-sized cities than in small-sized cities and megacities.
, Weiwei Wang, Liming Du, Zhongjun Zhang, Xiaojun Liang, Yongning Li, Zuyuan Wang
International Journal of Digital Earth pp 1-25; doi:10.1080/17538947.2021.1943018

Abstract:
The spectral clustering method has notable advantages in segmentation. But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging (LiDAR) point cloud data. We proposed the Nyström-based spectral clustering (NSC) algorithm to decrease the computational burden. This novel NSC method showed accurate and rapid in individual tree segmentation using point cloud data. The K-nearest neighbour-based sampling (KNNS) was proposed for the Nyström approximation of voxels to improve the efficiency. The NSC algorithm showed good performance for 32 plots in China and Europe. The overall matching rate and extraction rate of proposed algorithm reached 69% and 103%. For all trees located by Global Navigation Satellite System (GNSS) calibrated tape-measures, the tree height regression of the matching results showed an value of 0.88 and a relative root mean square error (RMSE) of 5.97%. For all trees located by GNSS calibrated total-station measures, the values were 0.89 and 4.49%. The method also showed good performance in a benchmark dataset with an improvement of 7% for the average matching rate. The results demonstrate that the proposed NSC algorithm provides an accurate individual tree segmentation and parameter estimation using airborne LiDAR point cloud data.
Yunbo Lu, , Bo Hu, Ming Zhang, Wenmin Qin, Jiaojiao Zhou, Minghui Tao
International Journal of Digital Earth pp 1-21; doi:10.1080/17538947.2021.1946179

Abstract:
Land surface albedo (LSA) is an important parameter in surface energy balance and global climate change. It has been used in the fields of energy budgets, climate dynamics, and land surface processes. To apply satellite LSA products more widely, the product accuracy needs to be evaluated at different scales and under atmospheric and surface conditions. This study validates and analyzes the errors of the LSA datasets from the Global LAnd Surface Satellites (GLASS) product, the European Space Agency’s Earth Observation Envelope Programme (GlobAlbedo), the Quality Assurance for Essential Climate Variables (QA4ECV) project, the Gap-filled Snow-free Bidirectional Reflectance Distribution Function (BRDF) parameters product (MCD43GF), and the Satellite Application Facility on Climate Monitoring (CM SAF) Albedo dataset from the AVHRR data (CLARA-SAL) against the Chinese Ecosystem Research Network (CERN) measurements at different spatiotemporal scales over China from 2005 to 2015. The results show that LSA estimated by GLASS agrees well with the CERN measurements on a continental scale. The GLASS product is characterized by a correlation coefficient of 0.80, a root-mean-square error of 0.09, and a mean absolute error of 0.06. The consistency between GLASS, GlobAlbedo, and CLARA-SAL is slightly lower over the regions with high aerosol optical depth (AOD) (e.g. Sichuan Basin, northern China) and high cloud cover compared with that in regions with lower AOD and low cloud cover. The estimation errors are related to varying atmospheric and surface conditions and increase with increasing AOD and cloud cover and decreasing enhanced vegetation index. Therefore, algorithms under complex atmospheric and surface conditions (e.g. high AOD, sparse vegetation) should be optimized to improve the accuracy of LSA products.
Dayu Yu, , Fan Ye, Chongcheng Chen
International Journal of Digital Earth pp 1-19; doi:10.1080/17538947.2021.1945151

Abstract:
It is of great significance for disaster prevention and mitigation to carry out disaster simulations for dam failure accidents in advance, but at present, there are few professional systems for disaster simulations of tailings dams. In this paper, we focused on the construction of a virtual geographic environment (VGE) system that provides an effective tool for visualizing the dam-break process of a tailings pond. The dam-break numerical model of the tailings dam based on computational fluid dynamics (CFD) was integrated into the VGE system. The infrastructure of the VGE was supported by a 3-D geographic information system (GIS) with a user-friendly interface for the initiation, visualization, and analysis of the dynamic process of tailings dam failure. Key technologies, including the integration of numerical models, rendering of large-scale scenes, and optimizations of disaster simulation and visualization, were discussed in detail. In the prototype system, information on the run-out path, travel distance, etc. can be obtained to visually describe the flow motion released by two dam failure cases. The simulation results showed that the VGE can be used for the multidimensional, dynamic and interactive visualization of dam-break disasters, and can also be useful for assessing the risk associated with tailings dams.
Evan J. Coopersmith, , Patrick J. Starks, David D. Bosch, Chandra Holifield Collins, Mark Seyfried, Stan Livingston, John Prueger
International Journal of Digital Earth pp 1-12; doi:10.1080/17538947.2021.1943550

Abstract:
The U.S. Department of Agriculture’s Agricultural Research Service (USDA-ARS) maintains seven in situ soil moisture networks throughout the continental United States, some since 2002. These networks are crucial for understanding the spatial and temporal extent of droughts in their historical context, parameterization of hydrologic models, and local agricultural decision support. However, the estimates from these networks are dependent upon their ability to provide reliable soil moisture information at a large scale. It is also not known how many network stations are sufficient to monitor watershed scale dynamics. Therefore, the objectives of this research were to: (1) determine how temporally stable these networks are, including the relationships between various sensors on a year-to-year and seasonal basis, and (2) attempt to determine how many sensors are required, within a network, to approximate the full network average. Using data from seven in situ, it is concluded that approximately 12 soil moisture sensors are sufficient in most environments, presuming their locations are distributed to capture the hydrologic heterogeneity of the watershed. It is possible to install a temporary network containing a suitable number of sensors for an appropriate length of time, glean stable relationships between locations, and retain these insights moving forward with fewer sensor resources.
Shanshan Feng,
International Journal of Digital Earth pp 1-27; doi:10.1080/17538947.2021.1936227

Abstract:
Many efforts have been devoted to extracting impervious surfaces based on different methods from multiple spatial resolution images. Differences in extraction methods and spatial resolutions are significant and have led to discrepant performances in terms of the impervious surface extraction accuracy. However, which extraction method is more suitable for which kind of spatial resolution image in practice is poorly understood. This study systematically compared the performances of 12 methods of impervious surface extraction for four spatial resolution images (i.e. Landsat 8 [30 m], Sentinel-2A [20 m], Sentinel-2A [10 m], and Gaofen-2 [4 m]) in three testing areas. The results indicated that for the medium-spatial resolutions of 30 and 20 m, the support vector machine (SVM) method was considered as the optimal classification method with the highest accuracy of impervious surface extraction. For the high-spatial resolutions of 10 and 4 m, the object based image analysis (OBIA) method obtained the highest accuracy of the impervious surface distribution. Furthermore, the perpendicular impervious surface index (PISI) outperformed the other indices in obtaining the impervious surface distribution, with the highest accuracy for four spatial resolution images. These comprehensive assessments can provide a valuable guidance for future impervious surface extraction from different spatial resolutions.
Jing Tian, , Xuanze Zhang
International Journal of Digital Earth pp 1-19; doi:10.1080/17538947.2021.1937352

Abstract:
Over the recent decades, the increase of atmospheric carbon dioxide (CO2) concentration has caused large effects on the earth system. However, few studies have paid attention to the effects of heterogeneity of CO2 on the biosphere and the hydrosphere. Using a coupled diagnostic biophysical model (PML-V2) and comparing three heterogeneous CO2 datasets (GOSAT, CMIP6 and CarbonTracker) against a baseline homogeneous data (ESRL), this study investigated the effects of heterogeneous CO2 on gross primary production (GPP), actual evapotranspiration (ET) and water use efficiency (WUE) across the global. The results show that among the three heterogeneity CO2, CarbonTracker produced the highest CO2 concentration and showed the largest difference in ET (−6% to 2%), GPP (−2% to 5%) and WUE (4% to 11%) compared to those from the baseline. The most effects of the CO2 heterogeneity occurred in summer. Russia was identified as a vulnerable region with prominent decrease in GPP and an increase in ET due to CO2 heterogeneity. An obvious increase in GPP and a decrease in ET appeared in the Amazon rainforest, the Congo rainforest, and eastern Asia. On global scale, the effects of the CO2 heterogeneity on ET/GPP/WUE were not significant.
Jun Geng, , , J. M. Chen, Yongguang Zhang, Weiliang Fan, Lili Tu, Jianwei Huang, Shaoteng Wang, Lichen Xu, et al.
International Journal of Digital Earth pp 1-19; doi:10.1080/17538947.2021.1936226

Abstract:
Comparison and validation of canopy reflectance (CR) models are two important steps to ensure their reliability. Pure forest plantations are an ideal type of forest for validating CR models because of their simple background and the low variance in the crown structures which are usually assumed to be identical in most CR models. A Geometric Optical Model for Forest Plantations (GOFP) was compared using dataset in two radiation transfer model intercomparison exercise (RAMI) stands and validated using in situ dataset of detailed optical and structural data of two forest plantations in the Saihanba Forestry Center, China. The results show that (1) the tree distributions in stands described by the hypergeometric model in GOFP show good consistencies with the dataset in the two RAMI stands and measurements from the two Saihanba forest stands; and (2) the CRs simulated with GOFP are also compared well in the two RAMI stands and validated with measurements collected with unmanned aerial vehicles in the two Saihanba stands. GOFP shows a better consistency with the CR measurements than those from CR models for natual forestsbecause the tree distribution in forest plantations is described more reasonably in GOFP.
, , , , Luciano Shozo Shiratsuchi, , Larissa Pereira Ribeiro Teodoro, Auana Vicente Tiago, Guilherme Fernando Capristo-Silva
International Journal of Digital Earth pp 1-27; doi:10.1080/17538947.2021.1923841

Abstract:
The aims of this study were: i) to compare no-till areas in two municipalities located in different regions of Brazil, along with the influence on CO2Flux and GPP, and ii) to verify the difference between environmental factors followed by the trends of these variables regarding future scenarios (ARIMA time-series model number). The study was carried out in two areas with different latitudes in the municipalities of Sinop-MT and Passo Fundo-RS, both in Brazil. A time series of 19 years was performed with data acquired by remote sensing from the following satellites: i) Landsat-8 (OLI and TIRS), and ii) TERRA/AQUA (MODIS). The results propound that the spectro-temporal variables are directly influenced by soil management and agricultural practices over the observation time, with a satisfactory correlation in future predictions of the variables for the next ten years, in which presented that the variation of GPP and albedo values for the two study sites would gradually increase until 2028 and the temperature remained constant between the range of its seasonality, and CO2Flux tends to decrease in its seasonality, indicating a higher CO2 absorption.
, Zhiyu Li, Fengyuan Zhang, Daniel P. Ames, , E. James Nelson, Rohit Khattar
International Journal of Digital Earth pp 1-20; doi:10.1080/17538947.2021.1925758

Abstract:
As researchers globally work towards a fully digital representation of the earth and its processes – i.e. a true Digital Earth – the need grows for software and systems to link disparate computer simulation models of various parts of the earth in a reliable and highly functional way. Web services have been demonstrated as an effective way to share and reuse models as they enable communication and interoperation among applications via the Internet. However, even using well-designed software tools, it remains a daunting process to publish heterogeneous environmental models as web services and provide long-term maintenance in response to changing computational environments. We present an approach that enables environmental models to be published as long-term functional web services on the same platform regardless of execution mode, programming language, and computational environment conflicts. The approach adopts the OpenGMS Wrapper System (OGMS-WS) for service publishing and Docker containers for model isolation. A streamflow prediction service is developed using this approach and is applied to analyze historical streamflow trends in Bangladesh. We demonstrate that this approach can lower the barrier to deploying heterogeneous environmental models as long-term functional web services, contributing to the development of a Digital Earth.
Shangshu Cai, , Shuangna Jin, Jie Shao, Linyuan Li, Sisi Yu, Guangjian Yan
International Journal of Digital Earth pp 1-16; doi:10.1080/17538947.2021.1921862

Abstract:
Accurate and rapid estimation of canopy cover (CC) is crucial for many ecological and environmental models and for forest management. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represent a promising tool for CC estimation due to their high mobility, low cost, and high point density. However, the CC values from UAV-LiDAR point clouds may be underestimated due to the presence of large quantities of within-crown gaps. To alleviate the negative effects of within-crown gaps, we proposed a pit-free CHM-based method for estimating CC, in which a cloth simulation method was used to fill the within-crown gaps. To evaluate the effect of CC values and within-crown gap proportions on the proposed method, the performance of the proposed method was tested on 18 samples with different CC values (40−70%) and 6 samples with different within-crown gap proportions (10−60%). The results showed that the CC accuracy of the proposed method was higher than that of the method without filling within-crown gaps (R2 = 0.99 vs 0.98; RMSE = 1.49% vs 2.2%). The proposed method was insensitive to within-crown gap proportions, although the CC accuracy decreased slightly with the increase in within-crown gap proportions.
, Venkataraman Lakshmi
International Journal of Digital Earth pp 1-22; doi:10.1080/17538947.2021.1914759

Abstract:
Long-term droughts significantly impact surface and groundwater resources in India, however, observed changes in major river basins have not been well explored. Here we use Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) at three different time scales (24, 48, and 60 months) to identify long-term droughts in India for the observed record of 1951–2015. Drought characteristics (extent, events, frequency, and intensity) are analyzed for different river basins in India. Increasing trend in the areal extent of droughts is observed in two methods with three time scales in the maximum area (63.66%) in India. We use the data from the Gravity Recovery and Climate Experiment (GRACE) to estimate the changes in the terrestrial water storage (TWS) during the period 2002–2015. We identify that major long-term droughts in India occurred from 1966 to 1969, 1972, 1986–1987, and 2002–2004. The all-India average TWS shows a negative trend from 2002 to 2015 with prominent decline in north Indian river basins and positive trend in south Indian river basins. SPI and SPEI at longer time scales are positively associated with TWS indicating the adverse impacts of droughts on surface and groundwater resources in such a populated region.
Shahin Mohammadi, Fatemeh Balouei, Khadijeh Haji, ,
International Journal of Digital Earth pp 1-21; doi:10.1080/17538947.2021.1919230

Abstract:
The aim of this study was the spatial and temporal monitoring of soil erosion patterns in Iran. The G2 model was used, as an appropriate tool to provide the required month-time step country-wide soil loss maps and statistical outputs. The input parameters were derived with field surveys and remote sensing imagery (MODIS, SRTM, SPOT, and Sentinel-2 scenes). An innovation for G2 was the consideration of the snow cover effect on the rainfall erosivity. It was also the first time G2 was used to map such a big country like Iran on a country scale. The mean annual soil erosion in Iran was found to be 16.5 t ha−1, which is equal to about 2.7 billion tons of soil loss. In spatial terms, the highest soil loss values were found in the north, west and southwest part of Iran, in the steep slopes in Alborz and Zagros mountains. In temporal terms, the highest and lowest monthly erosion values of 3.2 and 0.09 t ha−1 correspond to January and July, respectively.
International Journal of Digital Earth pp 1-15; doi:10.1080/17538947.2021.1915396

Abstract:
Evacuation is an effective and commonly taken strategy to minimize death and injuries from an incoming hurricane. For decades, interdisciplinary research has contributed to a better understanding of evacuation behavior. Evacuation destination choice modeling is an essential step for hurricane evacuation transportation planning. Multiple factors are identified associated with evacuation destination choices, in which long-term social factors have been found essential, yet neglected, in most studies due to difficulty in data collection. This study utilized long-term human movement records retrieved from Twitter to (1) reinforce the importance of social factors in evacuation destination choices, (2) quantify individual-level familiarity measurement and its relationship with an individual’s destination choice, (3) develop a big data approach for aggregated county-level social distance measurement, and (4) demonstrate how gravity models can be improved by including both social distance and physical distance for evacuation destination choice modeling.
International Journal of Digital Earth pp 1-19; doi:10.1080/17538947.2021.1913522

Abstract:
Seamless navigation has attracted lots of attention and many methods have been reported in the literature or made available as commercial applications. The process of navigation can be interpreted as a continuous movement of 3D objects from one unoccupied 3D indoor/outdoor space to another. From a technical perspective, a 3D navigation model is one of the critical components that should be available to perform successful navigation. A major approach to build a unified navigation model to support seamless path computation is linking indoor navigation networks to outdoor road/street-based networks. Because of different sources of indoor and outdoor navigation networks, the major approach fails to build up true seamless navigation models. With regards to this, we propose a unified 3D space-based navigation model (U3DSNM). The presented model ensures all types of spaces for navigation (indoor, semi-indoor, semi-outdoor, and outdoor) have the same representation, management methods, and network derivation approach, thereby building up unified navigation networks to support seamless navigation paths planning. The model can be linked to the international standards (data models) that are also based on spaces, such as IndoorGML and the on-going version of CityGML 3.0. Three navigation path planning cases show the feasibility of U3DSNM.
Yuqi Bai,
International Journal of Digital Earth pp 1-16; doi:10.1080/17538947.2021.1907463

Abstract:
Metadata information and catalogue services are major ways of making satellite images findable and accessible. Spatio-temporal indexing is the key to ensuring efficient searches. Because spatial information and temporal information are usually independently maintained and indexed, the image retrieval process has to include two search steps: a spatial query and a temporal query. As most Earth Observation satellites are specially designed to have repeating sun-synchronous orbits (RSSO), this type of satellite data has a close correlation between its spatial coverage and temporal coverage information. In this paper, an integrated spatio-temporal indexing mechanism is proposed for RSSO satellites. The spatio-temporal Look-Up Table (st-LUT) that serves as the index reflects the coupled correlation between the spatial and temporal coverage information within one orbit revisiting cycle. Image retrieval algorithms are designed based on the st-LUT. In this study, Landsat 8 data are used to demonstrate the proposed indexing mechanism and search algorithms. Because this new method only focuses on the changes in the spatial and temporal information over time in one orbit revisiting cycle, the search space is limited to the constant level. This method provides an effective RSSO satellite data retrieval capability for dealing with the challenges of rapidly growing data volumes. GRAPHICAL ABSTRACT
Peng Guo, , Jiancheng Shi, Hongxin Xu, Xiuwei Li, Shengda Niu
International Journal of Digital Earth pp 1-21; doi:10.1080/17538947.2021.1907461

Abstract:
The Terrestrial Water Resources Satellite (TWRS) campaign is a planned Chinese candidate satellite mission, and a one-dimensional synthetic aperture technology will be used, resulting in variant incidence angles for collecting synchronous active-passive observations at L-band, which would make brightness temperature (Tb) downscaling especially challenging when aiming to improve the spatial resolution of soil moisture measurements. In this study, two active-passive Tb downscaling algorithms, the time-series regression (TSR) and spectral analysis (SA) algorithms, are assessed comprehensively based on airborne experimental datasets. The results with data collected during the Soil Moisture Experiment 2002 (SMEX02) showed that both approaches could provide a reliable downscaled Tb at the same incidence angle. Based on the ground and airborne active-passive observations under variant incidence angles from the Soil Moisture Experiment in the Luan River (SMELR) it can be shown that the linear relationship between Tb and σ is still robust under the case of variant incidence angles, and Tb (both h- and v-pol) is better correlated to σvv for most cases than σhh. Both downscaling approaches can be applied to active-passive observations under varying incidence angles. Moreover, SA method performed better than the TSR method according to the lower RMSE values and higher correlation.
International Journal of Digital Earth pp 1-22; doi:10.1080/17538947.2021.1907462

Abstract:
Algal blooms are a frequent subject in scientific discussions and are the focus of many recent studies, mainly due to their adverse effect on society. Given the lack of ground truth data and the need to develop tools for their detection and monitoring, this research proposes a novel method to automate detection. Concepts derived from multi-temporal image series processing, spectral indices and classification with One-class Support Vector Machine (OC-SVM) are used in this proposal. Imagery from multi-spectral sensors on Landsat-8 and MODIS were acquired through the Google Earth Engine API (GEE API). In order to evaluate our method, two bloom detection case studies (Lake Erie (USA) and Lake Taihu (China)) were performed. Comparisons were made with methods based on spectral index thresholds. Also, to demonstrate the performance of the OC-SVM classifier compared to other machine learning methods, the proposal was adapted to be used with a Random Forest (RF) classifier, having its results added to the analysis. In situ measurements show that the proposed method delivers highly accurate results compared to spectral index thresholding approaches. However, a drawback of the proposal refers to its higher computational cost. The application of the new method to a real-world bloom case is demonstrated.
Liu Yang, , , Zigeng Niu, Rui Yao, Deqing Yu, Chang’An Li, Qiuhua He
International Journal of Digital Earth pp 1-26; doi:10.1080/17538947.2021.1907464

Abstract:
Identifying the spatiotemporal dynamics of the water body in Dongting Lake, the second largest freshwater lake in China, is crucial for water resource management. In this study, the variations of the water body were comprehensively analyzed based on remote sensing images and in situ measurements from 2000 to 2019. Four breakpoint detection approaches were integrated to analyze the change trends and explore the related driving forces behind the changes. The results showed that significant intra– and inter–annual fluctuations of the water body were found from 2000 to 2019. The water area and volume decreased at rates of 1.26 km2/a and 16.65 × 106 m3/a, respectively. During the entire study period, the outflow at Chenglingji station (CLJ), the inflow from three outlets of the Yangtze River (Inflow2), and the inundation conditions during the last period (Arealag) made the largest relative contributions to the water area variation (around 25%, 27% and 24%, respectively). A breakpoint was detected around 2004, corresponding to the operation period of the Three Gorges Dam (TGD). The regulation of TGD profoundly affected the hydrological characteristics at the three outlets and CLJ, and may have indirectly caused the water area to expand by 2.41 km2/a during the dry seasons between 2004 and 2019. These results provide valuable insight into how natural and anthropogenic factors affect water body variation and may offer a practical reference for the local government to adjust management strategies.
, M. T. Durand, Z. Courville, B. J. Vander Jagt, N. P. Molotch, S. A. Margulis, E. J. Kim, M. Schneebeli, C. Mätzler
International Journal of Digital Earth pp 1-21; doi:10.1080/17538947.2021.1902006

Abstract:
Reliable microstructure measurement of snow is a requirement for microwave radiative transfer model validation. Snow specific surface area (SSA) can be measured using stereological methods, in which snow samples are cast in the field and photographed in the laboratory. Processing stereology photographs manually by counting intersections of test cycloids with air–ice boundaries reduces the problems in binary segmentation. This paper is a case study to evaluate the repeatability of the manually stereology interpretation by two independent research groups. We further assessed how uncertainty in snow SSA influences simulated brightness temperature (TB) driven by the Microwave Emission Model of Layered Snowpacks (MEMLS), and how stereology compares to Near Infrared (NIR) camera and hand lens. Data was obtained from two alpine snow profiles from Steamboat Springs, Colorado. Results showed that stereological SSA values measured by two groups are highly consistent, and the ground radiometer measured TB at 19 and 37 GHz was successfully predicted (RMSE<3.8 K); simulations using NIR SSA and hand-lens geometric grain size (Dg) measurements have larger errors. This conclusion was not sensitive to uncertainty in the free parameters of TB modeling.
, Marlène Villanova-Oliver
International Journal of Digital Earth pp 1-25; doi:10.1080/17538947.2021.1900937

Abstract:
While ontological modelling and Semantic Web technologies are sometimes used to describe knowledge domains with a spatial component, there is still a lack of semantics to describe how to present this knowledge geovisually to the end user and how to automatize the process. In this paper, we first present vocabularies to describe at a high level the elements that make up a geovisualization. We then propose a method that describes at a semantic level how to obtain a geovisualization from an existing data model. This method is based on our vocabularies and on a set of semantic rules encoding rich and complex operations on data. This leads to the derivation of ontological knowledge, ready to be exploited to automate the creation of a geovisualization. The method is implemented in a framework that uses Semantic Web technologies. The singularity and the strength of our proposal is that it enables to describe a geovisualization through a RDF specification file, which once loaded in our system makes the geovisualization directly available for use from a Web browser. This result is obtained by extending a priori an application data model with ad hoc geovisualization semantics features and rules.
International Journal of Digital Earth pp 1-22; doi:10.1080/17538947.2021.1900938

Abstract:
Grassland fires are a serious problem in Victoria, Australia due to large quantity of dry grass. Grassland curing degree (GCD) measures the dryness of the grass and is an important factor for assessing grassland fire danger. Grassland curing maps (GCMs) display the spatial distribution of GCDs, but the quality of GCMs varies depending on the spatial resolution of the observing satellite remote sensing system. The higher the spatial resolution, the finer the GCD details and more spatial variations the GCM can reveal. In this study, GCD calculation algorithm named MapVictoria based on MODIS data is tested for Landsat 8 Sentinel 2; GCMs generated from these three satellites are contrasted by their GCD differences, defined here as inter-satellite variability (ISV). ISV is used to identify areas where higher resolution satellite GCMs should be used. Results show that spatial resolution difference (ΔSR), seasonality and geographical locations affect the magnitude of the ISV. Based on these findings, this paper provides recommendations to decision makers on where and when to use which satellite for grassland observations.
, Jingguo Jiao, Nengcheng Chen, , Liping Di, Jinchuan Wang, Zongyao Sha, Jin Liu
International Journal of Digital Earth pp 1-23; doi:10.1080/17538947.2021.1898686

Abstract:
This paper proposed a geoscience model service integrated workflow-based rainstorm waterlogging analysis method to overcome the defects of conventional waterlogging analysis systems. In this research, we studied a general OGC WPS service invoking strategy, an automatic asynchronous invoking mechanism of WPS services in the BPEL workflow, and a distributed waterlogging analysis services integrated workflow to realize the reconstruction of a waterlogging analysis model based on the proposed method. The proposed method can make use of the flexible adjustment capability of the workflow and not only overcomes the inherent defects of conventional geoscience analysis methods but also realizes the integration and calculation of distributed geospatial data, models and computing resources automatically. The method has better construction convenience, execution reliability, extensibility and intelligence potential than a conventional method and has important value for dealing with more natural disasters and environmental challenges.
Fenglin Tian, Qing Mao, Yazhen Zhang,
International Journal of Digital Earth, Volume 14, pp 766-788; doi:10.1080/17538947.2021.1886355

Abstract:
In this paper, we present a novel ocean visualization framework, which focuses on analyzing multidimensional and spatiotemporal ocean data. GPU-based visualization methods are explored to effectively visualize ocean data. An improved ray casting algorithm for heterogeneous multisection ocean volume data is presented. A two-layer spherical shell is taken as the ocean data proxy geometry, which enables oceanographers to obtain a real geographic background based on global terrain. An efficient ray sampling technique including an adaptive sampling technique and a preintegrated transfer function is proposed to achieve high-effectiveness and high-efficiency rendering. Moreover, an interactive transfer function is also designed to analyze the 3D structure of ocean temperature and salinity anomaly phenomena. Based on the framework, an integrated visualization system called i4Ocean is created. The visualization of ocean temperature and salinity anomalies extracted interactively by the transfer function is demonstrated.
Xiaoqi Zhang, Zi-Ke Zhang, Wenbo Wang, Donglin Hou, Jiajing Xu, , Shengwen Li
International Journal of Digital Earth, Volume 14, pp 401-423; doi:10.1080/17538947.2021.1888326

Abstract:
The pandemic of COVID-19 witnessed a massive infodemic with the public being bombarded with vast quantities of information. The spreading of neutral and highly accurate reports can guide the public to self-protect and reduce the pandemic. Mis- and dis-information would intrigue panic and high exposure risk to epidemic. Although the infodemic has attracted attentions from the academia, it is still not known to what degree and in which direction the information flows contribute to the COVID-19 pandemic. To fill the gap, we apply network reconstruction techniques to rebuild the hidden multiplex network of information and COVID-19 spreading by which we aim at quantifying the interaction between the propagation of information and the spatial outbreak of COVID-19, and delineate between the positive and negative impact of information on the pandemic. By differentiating the types of media that participated in the information process, we find that in the early stage of COVID-19 pandemic, infodemic does play a critical role to amplify the risk of virus outbreak in China and the risk is even larger for those highly developed regions. Compared to the old-fashion media, the new mobile platforms impose a greater risk to reinforce the positive feedback between infodemic and COVID-19 pandemic.
International Journal of Digital Earth pp 1-15; doi:10.1080/17538947.2021.1889056

Abstract:
Cloud computing enables performing computations and analysis tasks and sharing services in web-based computer centres instead of local desktop systems. One of the most used areas of cloud computing is geographic information systems (GIS) applications. Although Desktop GIS products are still used in the community frequently, Web GIS and Cloud GIS applications have drawn attention and have become more efficient for users. In this study, a serverless Cloud GIS framework is implemented for the land valuation platform. In order to store, analyse, and share geospatial data, the Aurora Serverless PostgreSQL database is created on Amazon Web Services (AWS). While adopting Aurora Serverless PostgreSQL as a database management system, a simple point in polygon analysis conducted to compare the performances with Amazon Relational Database Service (RDS) instance. Results showed that the serverless database responded to the query faster and scaled up during high workload to decrease latency. Hence, parcel vector data, which conveys ownership information and land values attributes, is shared directly from the PostGIS database as vector tiles. Besides S3 and AWS Lambda services are used for storing and disseminating raster-based land value map tiles. To visualize all shared data and maps through a web browser, open source web mapping library Mapbox GL JS is used.
International Journal of Digital Earth, Volume 14, pp 789-805; doi:10.1080/17538947.2021.1886356

Abstract:
Spatial prediction of any geographic phenomenon can be an intractable problem. Predicting sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data sources. We present an innovative approach that combines data in a Discrete Global Grid System (DGGS) and uses machine learning for analysis. A DGGS provides a structured input for multiple types of spatial data, consistent over multiple scales. This data framework facilitates the training of an Artificial Neural Network (ANN) to map and predict a phenomenon. Spatial lag regression models (SLRM) are used to evaluate and rank the outputs of the ANN. In our case study, we predict hate crimes in the USA. Hate crimes get attention from mass media and the scientific community, but data on such events is sparse. We trained the ANN with data ingested in the DGGS based on a 50% sample of hate crimes as identified by the Southern Poverty Law Center (SPLC). Our spatial prediction is up to 78% accurate and verified at the state level against the independent FBI hate crime statistics with a fit of 80%. The derived risk maps are a guide to action for policy makers and law enforcement.
, , , Xinyue Ye, , Jiajia Zhang,
International Journal of Digital Earth, Volume 14, pp 424-442; doi:10.1080/17538947.2021.1886358

Abstract:
This study reveals the human mobility from various sources and the luxury nature of social distancing in the U.S during the COVID-19 pandemic by highlighting the disparities in mobility dynamics from lower-income and upper-income counties. We collect, process, and compute mobility data from four different sources. We further design a Responsive Index ( R I ) based on the time series of mobility change percentages to quantify the general degree of mobility-based responsiveness to COVID-19 at the U.S. county level. We find statistically significant positive correlations in the R I between either two data sources, revealing their general similarity, albeit with varying Pearson’s r coefficients. Despite the similarity, however, mobility from each source presents unique and even contrasting characteristics, in part demonstrating the multifaceted nature of human mobility. The results suggest that counties with higher income tend to react more aggressively in terms of reducing more mobility in response to the COVID-19 pandemic. Most states present a positive difference in R I between their upper-income and lower-income counties, where diverging patterns in time series of mobility changes percentages can be found. The findings shed light on not only the characteristics of multi-source mobility data but also the mobility patterns in tandem with the economic disparity.
Lin Fu, , Weilian Li, Qing Zhu, BingLi Xu, Yakun Xie, Yunhao Zhang, Ya Hu, Jingtao Lu, Pei Dang, et al.
International Journal of Digital Earth pp 1-15; doi:10.1080/17538947.2021.1886359

Abstract:
The visualization of flood disasters in virtual reality (VR) scenes is useful for the representation and sharing of disaster knowledge and can effectively improve users’ cognitive efficiency in comprehending disaster information. However, the existing VR methods of visualizing flood disaster scenes have some shortcomings, such as low rendering efficiency and poor user experience. In this paper, a tunnel vision optimization method for VR flood scenes based on Gaussian blur is proposed. The key techniques are studied, such as region of interest (ROI) calculation and tunnel vision optimization considering the characteristics of the human visual system. A prototype system has been developed and used to carry out an experimental case analysis. The experimental results show that the number of triangles drawn in a flood VR scene is reduced by approximately 30%–40% using this method and that the average frame rate is stable at approximately 90 frames per second (fps), significantly improving the efficiency of scene rendering and reducing motion sickness.
Ruixiong Kou, , Zhen Dong, Fuxun Liang, Shuwen Yang
International Journal of Digital Earth pp 1-14; doi:10.1080/17538947.2021.1886357

Abstract:
The satellite visibility number of GNSS is an important indicator for evaluating its availability for positioning and navigation. In urban areas, urban canyons cause serious satellite signals block, resulting in positioning uncertainty. Many studies used 3D city models to evaluate the visible satellites in some areas at a certain time. Nevertheless, this kind of method is difficult to apply because 3D models are not widely available. This paper thus proposes an easy method to evaluate the visibility of satellites with widely available street view panoramic imagery and GNSS ephemeris. The proposed method utilizes the locations of street view panoramic imagery and the associated GNSS ephemeris to calculate the visible satellite number at different times. Hence, the visible satellite number at a specific time can be mapped. Moreover, the visibility of satellites can be predicted according to its orbit parameters. To evaluate the effectiveness of the proposed method, Wuhan and Shanghai were taken to map post-event, real-time and forecast GNSS visibility. The experiments demonstrated that the proposed method provides a light weighted and easy to use solution to map the spatio-temporal visibility of satellites in urban areas, which is an important reference for GNSS stations layout and positioning qualities evaluation.
International Journal of Digital Earth, Volume 14, pp 736-765; doi:10.1080/17538947.2021.1875062

Abstract:
There are different processes in 3D urban land administration in which spatial analysis plays an underpinning role. Among 3D data models, Industry Foundation Class (IFC) provides the potential capabilities for modelling legal and physical dimensions of urban properties. However, performing spatial analysis using IFC files cannot address the on-demand spatial analysis requirements of 3D urban land administration. In response to this limitation, 3D data needs to be stored in a spatial database to enable spatial analyses required by different stakeholders. Therefore, in this paper, by considering spatial analysis requirements in 3D-enabled urban land administration, an IFC-based database schema is designed. Moreover, a methodology for transforming Building Information Modelling (BIM) data into the proposed schema is provided. This methodology includes seven steps: designing the architectural model and adding legal data, georeferencing, IFC data validation and cleaning, mapping process, database data validation and cleaning, spatial analysis, and visualisation. To demonstrate the feasibility of the proposed database, three datasets are implemented in the database. Moreover, a new method for modelling legal spaces with oblique structures and two applications of spatial analysis in 3D urban land administration are provided.
Xuehui Pi, Lian Feng, , Junguo Liu, Xingxing Kuang, Kun Shi, Wei Qi, , Jing Tang
International Journal of Digital Earth, Volume 14, pp 714-735; doi:10.1080/17538947.2021.1872722

Abstract:
As essential parts of the unique ecosystem of Tibetan Plateau (TP), the sizes and associated physical properties of alpine lakes have long been investigated. However, little is known about one of the most critical biogeochemical properties, i.e. the Chlorophyll-a (Chl-a) concentrations. Here, for the first time, we presented a comprehensive investigation of the temporal–spatial variations in Chl-a in 82 lakes (>50 km2) across the entire TP region, based on MODIS observations in the period of 2003–2017. The results showed that the 82 lakes exhibited an average long-term mean Chl-a of 3.3 ± 4.3 mg m−3, with high Chl-a lakes concentrated in the eastern and southern inner TP basin and northeastern parts of the TP. An interannual trend analysis revealed that lakes exhibiting (significantly) decreasing Chl-a trends and (significantly) increasing Chl-a trends were comparable in numbers but differed in distribution patterns. A correlation analysis indicated that at least 70% of the interannual variability in Chl-a values of lakes was significantly correlated with one of the four environmental factors (wind speed, ice cover duration, lake water surface temperature and surface runoff) and lake size. In addition, glacier meltwater tended to reduce lake Chl-a while salinity levels showed minor influences.
International Journal of Digital Earth, Volume 14, pp 696-713; doi:10.1080/17538947.2020.1868585

Abstract:
In order to facilitate and coordinate spatial data sharing and exchange, many organisations have developed spatial data infrastructures (SDIs). SDI governance plays a pivotal role in the development and evolution of an SDI, but as SDIs are complex adaptive systems, governing is a challenge. This research therefore proposes a complexity perspective to SDI governance by exploring the use of agent-based modelling to simulate and examine SDI governance interactions. In this agent-based simulation, we examine interactions between SDI stakeholders, data availability and the effects of different governance styles (hierarchical, network and laissez-faire governance) and budget policies. The simulation shows that it is possible to mimic SDI governance dynamics through agent-based modelling. By running different scenarios, it appears that a network approach is more successful compared to a hierarchical or laissez-faire approach. Expert validation shows that overall the results of the simulation are credible and insightful, although improvements can be made to make the model more realistic. With agent-based modelling, SDI governance becomes more tangible and visible, which facilitates discussion and understanding. Agent-based modelling therefore appears to be a helpful new approach in a better understanding of the complexities and dynamics of SDI governance.
Guiyan Han, Fenglin Tian, Chunyong Ma,
International Journal of Digital Earth, Volume 14, pp 464-479; doi:10.1080/17538947.2020.1842523

Abstract:
The symmetrical circular shape of mesoscale eddies has been widely used in their scientific researches. Recently, an elliptical average eddy shape has been confirmed for eddies in the global ocean using multi-satellite altimeter data. As a regional extension of a previous study on the geometry of global eddies, a mean eddy shape in the South China Sea (SCS) has been derived by averaging a large number of orientational eddy boundaries. The mean shape is approximately a mathematic ellipse with a semimajor axis of 101.3 km and a semiminor axis of 61.3 km. Its size is larger than the global one. The principal eddy orientation in the SCS is 74°/254° (nearly northeast-southwest), different from that of eddies in the global ocean (171°/351°, nearly east–west). Composite analyses of chlorophyll (CHL) concentrations and sea surface temperature anomalies (SSTA) indicate a dipole structure for circular eddies in the non-rotated coordinate system. While a monopole structure for elliptical eddies in the eddy-centric coordinate system is obtained. The results demonstrate that the elliptical shape of eddies affects oceanographical variables. The findings provide a new approach for exploring the role of air–sea interactions on oceanic eddies.
Xianghan Sun, Jianqiang Liu, Jianru Wang, , Qu Zhou, Jian Li
International Journal of Digital Earth, Volume 14, pp 443-463; doi:10.1080/17538947.2020.1868584

Abstract:
During the COVID-19 epidemic in Wuhan, China, a series of measures were implemented by the government to prevent the spread of disease, including the lockdown policy and construction of emergency hospitals. To estimate the impact of these measures on aquatic environments, turbidity of lakes in Wuhan was dynamically monitored by integrating multi-sensor satellite observations. Calibrated against field measurements, empirical turbidity models were developed with high accuracy (R 2 = 0.77, RMSE = 3.13 NTU). Time series of lake turbidity during COVID-19 were then retrieved, and possible factors for the turbidity change were discussed, including meteorological conditions and human activities. Results demonstrated that (1) the mean turbidity showed a 24.9% decline from 33.4 NTU to 25.1 NTU after the lockdown in Wuhan, which dropped 16.0% compared to that in the previous year. This decline might be related to the sharp reduction in human activities after the lockdown; (2) no obvious turbidity disturbance was observed in the lakes around emergency hospitals during their construction, and the lakes remained stable after the operation of hospitals. The method of integrating multi-sensor satellite observations used in this study shows great performance in term of temporal resolution for dynamic monitoring of inland water.
International Journal of Digital Earth, Volume 14, pp 678-695; doi:10.1080/17538947.2020.1865467

Abstract:
With growing pressures on marine ecosystems and on marine space, an increasingly needed strategy to optimise the use of marine space is to co-locate synergic marine human uses in close spatial–temporal proximity while separating conflicting marine human uses. The ArcMap toolbox SEANERGY is a new, cross-sectoral spatial decision support tool (DST) that enables maritime spatial planners to consider synergies and conflicts between marine uses to support assessments of co-location options. Cross-sectoral approaches are important to reach more integrative maritime spatial planning (MSP) processes. As this article demonstrates through a Baltic Sea analysis, SEANERGY presents a cross-sectoral use catalogue for MSP through enabling the tool users to answer important specific questions to spatially and/or numerically weight potential synergies/conflicts between marine uses. The article discusses to what degree such a cross-sectoral perspective can support integrative MSP processes. While MSP integrative challenges still exist, SEANERGY enables MSP processes to move towards developing shared goals and initiate discussions built on best available knowledge regarding potential use-use synergies and use-use conflicts for whole sea basins at once.
Qiang Li, , Youhua Ran, Min Feng, Yanyun Nian, Meibao Tan, Xi Chen
International Journal of Digital Earth, Volume 14, pp 661-677; doi:10.1080/17538947.2020.1865466

Abstract:
The Aral Sea crisis is considered one of the most severe ecological tragedies from the 1960s in Central Asia. The reasons for this crisis, especially in the twenty-first century, are still scientific disputes. This study investigated the relationship between land cover change in the Aral Sea related basins and the Aral Sea crisis from 2000 to 2020 by employing the GlobeLand30 dataset with 30 m resolution. Results showed that the cultivated land in the Aral Sea basin increased by 2,291 km2, and 75.4% of it occurred in the region of Karakum Canal, the largest water conservancy project for irrigation in the world. The water surface area of reservoirs increased by 1,183.5 km2 during the same period. Coincident with this change, the Aral Sea further shrank from 26,280.8 km2 in 2000 to 9,285.2 km2 in 2020, mainly occurred in the first decade of the twenty-first century. These imply that the Aral Sea crisis is persistent in the twenty-first century and is likely driven by water competition among different regions within the basin for agricultural irrigation. Strengthening the coordination and cooperation of cross-boundary water resource management is still the most important management strategy choice to address the crisis from a broader perspective.
Si-Bo Duan, , Wei Zhao, Penghai Wu, Cheng Huang, Xiao-Jing Han, Maofang Gao, Pei Leng, Guofei Shang
International Journal of Digital Earth, Volume 14, pp 640-660; doi:10.1080/17538947.2020.1862319

Abstract:
Since 1982, Landsat series of satellite sensors continuously acquired thermal infrared images of the Earth’s land surface. In this study, Landsat 5, 7, and 8 land surface temperature (LST) products in the conterminous United States from 2009 to 2019 were validated using in situ measurements collected at 6 SURFRAD (Surface Radiation Budget Network) sites, 6 ARM (Atmospheric Radiation Measurement) sites, and 9 NDBC (National Data Buoy Center) sites. The results indicate that a relatively consistent performance among Landsat 5, 7, and 8 LST products is obtained for most sites due to the consistent LST retrieval algorithm in conjunction with the same atmospheric compensation and land surface emissivity (LSE) correction methods for Landsat 5, 7, and 8 sensors. Large bias and root mean square error (RMSE) of Landsat LST product are obtained at some vegetated sites due to incorrect LSE estimation where LSE is invariant with the increasing of normalized difference vegetation index (NDVI). Except for the sites with incorrect LSE estimation, a mean bias (RMSE) of the differences between Landsat LST and in situ LST is 1.0 K (2.1 K) over snow-free land surfaces, −1.1 K (1.6 K) over snow surfaces, and −0.3 K (1.1 K) over water surfaces.
, Hansheng Wang, C. K. Shum, Liming Jiang, Houtse Hsu, Fan Gao, Yingli Zhao
International Journal of Digital Earth, Volume 14, pp 597-618; doi:10.1080/17538947.2020.1862317

Abstract:
Ice velocity constitutes a key parameter for quantifying ice-sheet discharge rates and is thus crucial for improving the coupled models of the Antarctic ice sheet towards accurately predict its contribution to future global sea-level rise. However, in Antarctica, high-resolution and continuous ice velocity estimates remain elusive, which is key to unravel Antarctica’s present-day ice mass balance processes. Here, we present a suite of newly estimated Antarctic-wide, annually-sampled ice velocity products at 105-m grid-spacing observed by Landsat 8 optical images data. We first describe a procedure that can automatically calibrate and integrate ice displacement maps to generate Antarctic-wide seamless ice velocity products. The annual ice velocity mosaics are assembled using a total of 250,000 displacement maps inferred from more than 80,000 Landsat 8 images acquired between December 2013 and April 2019. The new annual Antarctic ice velocity data product exhibits an improved quantification of near-decadal Antarctic-wide ice flow, and an opportunity to investigate ice dynamics at a higher spatial resolution and annual sampling, as compared to existing data products. Validation studies confirmed improved accuracy and consistency of this new data product, when compared with independently estimated optical and radar ice velocity data products, as well as in situ data.
Xiaohu Lin, , Fuhong Wang, Jianping Li, Xiqi Wang
International Journal of Digital Earth, Volume 14, pp 619-639; doi:10.1080/17538947.2020.1862318

Abstract:
Accurate and efficient three-dimensional (3D) streetscape reconstruction is the fundamental ability for an exploration vehicle to navigate safely and perform high-level tasks. Recently, remarkable progress has been made in streetscape reconstruction with visual images and light detection and ranging (LiDAR), but they have difficulties either in scaling and reconstructing large-scale outdoors or in efficient processing. To address these issues, this paper proposed an automatic method for incremental dense reconstruction of large-scale 3D streetscapes from coarse to fine at near real time. Firstly, the pose of vehicle is estimated by visual and laser odometry (VLO) and the state-of-the-art pyramid stereo matching network (PSMNet) is introduced to estimate depth information. Then, incremental dense 3D streetscape reconstruction is conducted by key-frame selection and coarse registration with local optimization. Finally, redundant and noise points are removed through multiple filtering, resulting good quality of dense reconstruction. Comprehensive experiments were undertaken to check the visual effect, trajectory pose error and multi-scale model to model cloud comparison (M3C2) based on reference trajectories and reconstructions provided by the state-of-the-art method, showing the precision, recall and F-score of sampling core points (SCPs) are over 80.42%, 71.68% and 77.19%, respectively, which verified the proposed method.
Binh Thai Pham, , , Tran Van Phong, Huu Duy Nguyen, Neelima Satyam, Masroor, Sufia Rehman, Haroon Sajjad, Mehebub Sahana, et al.
International Journal of Digital Earth, Volume 14, pp 575-596; doi:10.1080/17538947.2020.1860145

Abstract:
In this paper, we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree (REPT) as a base classifier with the Bagging (B), Decorate (D), and Random Subspace (RSS) ensemble learning techniques for spatial prediction of rainfall-induced landslides in the Uttarkashi district, located in the Himalayan range, India. To do so, a total of 103 historical landslide events were linked to twelve conditioning factors for generating training and validation datasets. Root Mean Square Error (RMSE) and Area Under the receiver operating characteristic Curve (AUC) were used to evaluate the training and validation performances of the models. The results showed that the single REPT model and its derived ensembles provided a satisfactory accuracy for the prediction of landslides. The D-REPT model with RMSE = 0.351 and AUC = 0.907 was identified as the most accurate model, followed by RSS-REPT (RMSE = 0.353 and AUC = 0.898), B-REPT (RMSE = 0.396 and AUC = 0.876), and the single REPT model (RMSE = 0.398 and AUC = 0.836), respectively. The prominent ensemble models proposed and verified in this study provide engineers and modelers with insights for development of more advanced predictive models for different landslide-susceptible areas around the world.
Miao Yu, , Zhiyuan Li, Zhijun Li, Qingkai Wang, , Xiaodong Chen
International Journal of Digital Earth, Volume 14, pp 555-574; doi:10.1080/17538947.2020.1860144

Abstract:
Sea ice conditions and navigability along four typical routes of the Northeast Passage (NEP) are analysed using remote-sensing data from 1979 to 2019. The influence of air temperature (T air) and surface wind on the sea ice concentration (SIC) and the navigability of routes is determined. It is found that the annually averaged SICs of the different routes have decreased over the past 41 years. The fastest rate of decrease occurred in the Kara Sea (∼−1% per year), while the slowest rates of decrease occurred in the Laptev/East Siberian Sea (∼−0.42% per year). The number of navigable days for the Kara Sea has become ∼1–2 months longer than the Laptev/East Siberian Sea route as a result. The effect of T air on SIC, quantified by ΔSIC/ΔT air in the routes through the eastern Kara Sea and Laptev/East Siberian Sea in 2010s was ∼−0.04/°C, two to three times that seen during the 1980s. Air temperature is becoming a significant driving force of melting ice in these routes. Surface winds are also a crucial factor for the navigability of the Vilkitsky Strait and Long Strait, as they drive ice drift, and affect the navigability of the Kara Strait by introducing warm air.
International Journal of Digital Earth, Volume 13; doi:10.1080/17538947.2020.1841412

, Hirakawa Tsubasa, Hiromichi Fukui
International Journal of Digital Earth, Volume 14, pp 540-554; doi:10.1080/17538947.2020.1849438

Abstract:
Monthly Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) composite data are widely used in research, such as estimations of socioeconomic parameters. However, some surface conditions affect the VIIRS DNB radiance, which may create some estimation bias in certain regions. In this paper, we propose a novel normalization algorithm for VIIRS DNB monthly composite data. The aim is to normalize VIIRS radiance, collected under different surface conditions, to a reference point, so that the bias is reduced. The algorithm is based on the utilization of stable lit pixels as a reference and a nonlinear regression algorithm, to match un-normalized data to the reference data. Experimental results show that the algorithm could improve correlation (R 2) between the total sum of nightlights (TOL), electric power consumption (EPC), and gross domestic product (GDP) at both a global and local scale. The algorithm could significantly diminish the seasonal component of un-normalized nightlights radiance caused by snow. The intensified nightlights radiance in sandy regions could also be reduced to a more reasonable range in comparison with other regions. Visual inspection shows that the brightness of snow-affected and sandy regions was strongly reduced after undergoing normalization.
Yuting Chen, , Ling Xiao, Qi Jing, Lan You, Yulin Ding, Mingyuan Hu, Adam Thomas Devlin
International Journal of Digital Earth, Volume 14, pp 510-539; doi:10.1080/17538947.2020.1849439

Abstract:
Obal change refers to changes in the relationship between humans and nature. It is desirable to actively integrate human social activities into the unified framework of global change so that their mutual relations and functional mechanisms can be understood. This complicated issue necessitates an appropriate method allowing domain experts to collaboratively contribute their knowledge to geoscientific research. Also, an efficient approach to optimize experimentation is of great importance. The reproducibility of research methods and results needs to be improved to boost the sharing of geographic knowledge and resources. This paper proposes a versioned geoscientific workflow and characterizes its full lifecycle using Virtual Geographic Environments, intending to facilitate and improve research related to the interactions between global change and human activities. The geoscientific workflow management is realized using the concept of version management, making geographic simulation methods and computational results easily reproducible and extendable. The sharing and reuse of geographic knowledge in various forms are archived through version management of geoscientific workflows. A versatile prototype system is implemented which enables the visual modeling of geoscientific workflows, the interactive optimization and collaborative evaluation of geoscientific workflows at runtime, the multi-dimensional dynamic visualization of geo-workflow outputs, and role-based access control for data security.
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