《国际数字地球学报》(International Journal of Digital Earth)是国际数字地球学会依托中国科学院空天信息创新研究院主办的学术刊物。《学报》于2008年3月创刊,目前已被12个大型国际期刊检索机构收录。2020年影响因子为3.097,在全球50个地理类期刊中名列第17位,在30个遥感类期刊中排名第14位。在2019 Scopus CiteScore 引用分数榜中,《学报》在地球与行星科学类187个期刊中排名第11位。
《学报》以传播数字地球理念为宗旨,致力于数字地球学术交流,促进数字地球技术发展,推动数字地球在经济和社会可持续发展中的应用,并将在全球气候变化、自然灾害防治与响应、新能源探测、农业与食品安全和城市规划管理等方面发挥重要作用。该刊得到国内外科学界同行的广泛认可与高度肯定,成为同领域的主流学术期刊。
Normalization of VIIRS DNB images for improved estimation of socioeconomic indicatorsDuc Chuc Man, Hirakawa Tsubasa & Hiromichi Fukui
Pages: 540-554
Published online: 28 Nov 2020
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 (R2) 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.https://doi.org/10.1080/17538947.2020.1849438
Sea ice conditions and navigability throuth the Northeast Passage in the past 40 years based on remote-sensing dataMiao Yu, Peng Lu, Zhiyuan Li, Zhijun Li, Qingkai Wang, Xiaowei Cao & Xiaodong ChenPublished online: 14 Dec 2020Sea 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 (Tair) 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 Tair on SIC, quantified by ΔSIC/ΔTair 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.https://doi.org/10.1080/17538947.2020.1860144
Ensemble machine learning models based on Reduced Error Pruning Tree for prediction of rainfall-inducded landslides
Binh Thai Pham, Abolfazl Jaafari, Trung Nguyen-Thoi, Tran Van Phong, Huu Duy Nguyen,Neelima Satyam, Md Masroor,Sufia Rehman,Haroon Sajjad, Mehebub Sahana,Hiep Van Le & Indra Prakash
Published online: 15 Dec 2020In 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.
https://doi.org/10.1080/17538947.2020.1860145
Antarctic-wide annual ice flow maps from Landsat 8 imagery between 2013 and 2019Qiang Shen, Hansheng Wang, C.K.Shum,Liming Jiang,Houtse Hsu,Fan Gao &Yingli ZhaoPages: 597-618
Published online: 28 Dec 2020Ice 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.https://doi.org/10.1080/17538947.2020.1862317
Dense 3D surface reconstruction of large-scale streetscape from vehicle-borne imagery and LiDARXiaohu Lin, Bisheng Yang,Fuhong Wang, Jianping Li & Xiqi WangPublished online: 17 Dec 2020Accurate 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. VGI to update or enrich authoritative LU data and potentially LULC data more generally.
https://doi.org/10.1080/17538947.2020.1862318
Validation of Landsat land surface temperature product in the conterminous United States using in situ measurements from SURFRAD,ARM,and NDBC sitesSi-Bo Duan, Zhao-Liang Li, Wei Zhao, Penghai Wu, Cheng Huang, Xiao-Jing Han, Maofang Gao,Pei Leng & Guofei Shang
Pages: 640-660
Published online: 28 Dec 2020
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.
https://doi.org/10.1080/17538947.2020.1862319
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