Earth Science Informatics

Sun, Ziheng; Di, Liping; Hao, Haosheng; Wu, Xiaoqing; Tong, Daniel; Zhang, Chen; Virgei, Cora; Fang, Hui; Yu, Eugene; Tan, Xicheng; Yue, Peng; Lin, Li

doi: 10.1007/s12145-017-0308-4pmid: N/A

Feeding multisource Earth observation (EO) data into Earth science models (ESM) remains a daunting challenge. This paper presents a service-oriented approach as an alternative solution. It uses geospatial web services to process the EO data and geoprocessing workflow for automation. Different from existing approaches, it takes advantage of virtual data products (VDP) to release modelers from intensive data processing. It can directly connect ESMs to public EO sources via Cyberinfrastructure. A prototype called CyberConnector is implemented. CyberConnector supports intuitive building of VDP, automatic execution of workflows and effortless retrieval of model-ready input files. We used it to stream multiple datasets to several ESMs including finite-volume coastal ocean model (FVCOM) and cloud-resolving model (CRM). The results show that CyberConnector can truly benefit modelers on time saving and effort minimizing.

Li, Wenliang; Wu, Changshan; Choi, Woonsup

doi: 10.1007/s12145-017-0312-8pmid: N/A

Urban impervious surfaces are considered as key indicator of urbanization intensity and environmental quality. Due to their significant impact on surface runoff, flood frequency, and water quality, impervious surfaces have been identified as an important indicator for examining the hydrological impact of urbanization. The amount and distribution of impervious surfaces have been estimated using remote sensing and geographic information system (GIS) techniques. Little research, however, has been conducted to predict future impervious surface distributions. To address this problem, we developed an integrated residential/commercial growth and impervious surface distribution model to predict urban impervious surface distribution. Taking Milwaukee River Basin, Wisconsin as a case study, we simulated future residential and commercial developments using a CA model. Further, we developed a linear regression model to predict impervious surface distributions in residential and commercial land uses. Analysis of results suggests that the proposed model performs significantly better than the traditional approaches.

Boulila, Wadii; Farah, Imed; Hussain, Amir

doi: 10.1007/s12145-017-0313-7pmid: N/A

Applications of remote sensing (RS) data cover several fields such as: cartography, surveillance, land-use planning, archaeology, environmental studies, resources management, etc. However, the amount of RS data has grown considerably due to the increase of aerial and satellite sensors. With this continuous increase, the necessity of having automated tools for the interpretation and analysis of RS big data is clearly obvious. The manual interpretation becomes a time consuming and expensive task. In this paper, a novel tool for interpreting and analyzing RS big data is described. The proposed system allows knowledge gathering for decision support in RS fields. It helps users easily make decisions in many fields related to RS by providing descriptive, predictive and prescriptive analytics. The paper outlines the design and development of a framework based on three steps: RS data acquisition, modeling, and analysis & interpretation. The performance of the proposed system has been demonstrated through three models: clustering, decision tree and association rules. Results show that the proposed tool can provide efficient decision support (descriptive and predictive) which can be adapted to several RS users’ requests. Additionally, assessing these results show good performances of the developed tool.

Dong, Luan; Zheng, Fengling; Chang, Hongxia; Yan, Qin

doi: 10.1007/s12145-017-0317-3pmid: N/A

Digitized topographic maps normally have to go through the geometric calibration before practical utilization. Nowadays, the reference points for the calibration are still manually assigned. Corner points (graticule intersections) in a map are usually good candidates in favor of the reference points. This paper proposes an algorithm for automatically locating the corner points in the electronic topographic maps by detecting the specific rectangle objects in the map corners. It assigns the probabilities to each row and column in the region of interests (RoIs) to provide information regarding the location of the objects. In order to facilitate the object detection with high level visual features, the deep neural networks (DNNs) are employed in the proposed algorithm. For the object proposal, the sliding window scheme is adopted. The experimental results indicate that the proposed approach outperforms the conventional bounding-box regression method in both detection and localization accuracy. For the proposed algorithm, the average F1 score in the object detection is 0.91, which is 12% higher than the conventional model. The mean Euclidean distance between the predicted corner points and the ground-truth by the proposed algorithm is 2.22 pixels, 35.8% lower compared with the regression based model.

Davarpanah, A.; Babaie, H.; Dai, D.

doi: 10.1007/s12145-017-0315-5pmid: N/A

The sequence of eruption, spatial pattern, and spatio-temporal relationships among the Neogene-Quaternary rhyolitic and basaltic lava along the Snake River Plain (SRP) in Idaho are analyzed applying the spatial methods of global and local Moran’s I, standard deviational ellipse, and Ripley’s K-function. The results of the analyses by the Moran’s I and K-function methods indicate a higher spatial autocorrelation, hence clustering, of rhyolitic lava compared to the more dispersed basaltic lava in each center of eruption along the SRP. The clustered nature of rhyolitic lava around each caldera either reflects the original spread and large thickness of the rhyolitic lava, or the absence of younger cover strata or lava like the distribution of rhyolite in the present caldera at the Yellowstone National Park. The standard deviational ellipses (SDEs) of the lavas indicate that younger basaltic lava that erupted from newer calderas overlapped older rhyolitic and basaltic lava as the position of the Yellowstone hotspot progressively migrated to the northeast along the SRP. The less eccentric SDEs of rhyolitic lava in each caldera probably reflect the original caldera-scale spread of viscous felsic lava, compared to the more eccentric and larger SDEs of basaltic lava which represent basalt’s wider and more directed spread due to its higher fluidity and ability to flow longer distances along the trend of the SRP. The alignment of the long axes of the lava SDEs with the trend of the Eastern SRP and the trend of systematic spatial overlap of older lava by successively younger basaltic lava corroborate the previously reported migration of the centers of eruption along the ESRP as the Yellowstone hotspot migrated to the northeast.

Salgado, Cesar; Zortea, Maciel; Scharcanski, Jacob

doi: 10.1007/s12145-017-0318-2pmid: N/A

Changing atmospheric conditions often result in a data distribution shift in remote sensing images for different dates and locations making it difficult to discriminate between various classes of interest. To alleviate this data shift issue, we introduce a novel supervised classification framework, called Classify-Normalize-Classify (CNC). The proposed scheme uses a two classifier approach where the first classifier performs a rough segmentation of the class of interest (COI) in the input image. Then, the median signal of the estimated COI regions is subtracted from all image pixels values to normalize them. Finally, the second classifier is applied to the normalized image to produce the refined COI segmentation. The proposed methodology was tested to detect deforestation using bitemporal Landsat 8 OLI images over the Amazon rainforest. The CNC framework compared favorably to benchmark masks of the PRODES program and state-of-the-art classifiers run on surface reflectance images provided by USGS.

Sewnet, Amare; Abebe, Gebeyehu

doi: 10.1007/s12145-017-0323-5pmid: N/A

Information on use/land cover change is important for planners and decision makers to implement sustainable use and management of resources. This study was intended to assess the land use land cover (LULC) change in the Koga watershed. The MSS of 1973, TM images of 1986, 1995 and 2011 were used together with survey and demographic data to detect the drivers of land cover changes. The result revealed that a remarkable LULC change occurred in the study area for the past thirty eight years. The area of cultivated and settlement has increased by 7054.6 ha, while, grass and bush lands decreased by 4846.5 and 3376 ha respectively. Wetland also declined from 580.2 ha to 68.3 ha. The growing demand for cultivable land and fuel wood were the major causes to the deterioration of grass and bush lands. Hence, the appropriate land use policy should be employed to sustain available resource in the watershed.

Bai, Luyi; Cao, Xingru; Jia, Weijia

doi: 10.1007/s12145-017-0322-6pmid: N/A

The problem of modeling and operating spatiotemporal data has received a great deal of interest, due to its various applications in the real world such as GIS and sensor database. A wide range of work covering spatial data, temporal data and spatiotemporal data assumes that the data is known, accurate and complete. But in reality, information is often imprecise and imperfect. In addition, traditional data models which are investigating in the context of traditional database suffer from some inadequacy of necessary semantics such as inability to handle imprecise and uncertain information. Consequently, the advent of XML, which has the advantages of simplicity, readability and extensibility, seems to provide an opportunity for modeling and operating uncertain spatiotemporal data. Hence, the new problem that emerges is how to model and operate uncertain spatiotemporal data in XML. Therefore, in this paper, we establish an uncertain spatiotemporal data model based on XML. Then, on the basis of the model we provide a set of algebraic operations for capturing and handling uncertain spatiotemporal data. By employing algebraic operations, we demonstrate how to translate queries expressed in XQuery to our algebra. A translation example shows that our algebraic operations are full of expressive power and illustrates that our algebra can be applied to general data. Apart from this, we also propose a set of equivalence rules to optimize the process of query and give an example to show how the optimization approach works.

Ramadhani, Sheilla; Bennett, R.; Nex, F.

doi: 10.1007/s12145-017-0314-6pmid: N/A

Accelerating the process of land registration is a key issue in emerging countries like Indonesia. However, existing methods – typically based around terrestrial measurements for surveying and mapping activities – are slow-paced and entail decades more work to ensure completion. Increasing demand for information about land tenure and use inspires the development of fit-for-purpose approaches. Emerging technologies such as Unmanned Aircraft Vehicles (UAVs) are potentially a worthy alternative for enabling swifter, cheaper – yet still appropriately accurate – land data acquisition. This research seeks to evaluate the use of UAV technology for cadastral boundary data acquisition processes in Indonesia. After a comprehensive review of requisites for cadastral boundary data survey and UAV regulation in Indonesia, various requirement elements were derived to design a UAV-image based approach. The designed approach utilizes UAV derived imagery capture and participatory boundary mapping methods for the boundary delineation. By comparison with the existing terrestrial method, and evaluating against fit-for-purpose criteria, the UAV based image approach showed a higher perceived involvement of the community and` indicated an improvement in cost efficiency, decreased duration, resulted in a high accuracy UAV ortho-photo, and adequate spatial descriptions of boundaries.

Liu, Pudong; Shi, Runhe; Gao, Wei

doi: 10.1007/s12145-017-0319-1pmid: N/A

Estimating leaf chlorophyll contents through leaf reflectance spectra is efficient and nondestructive, but the actual dataset always based on a single or a few kinds of specific species, has a limitation and instability for a common use. To address this problem, a combination of multiple spectral indices and a model simulated dataset are proposed in this paper. Six spectral indices are selected, including Blue Green Index (BGI), Photochemical Reflectance Index (PRI_5), Triangle Vegetation Index (TVI), Chlorophyll Absorption Ratio Index (CARI), Carotenoid Reflectance Index (CRI) and the green peak reflectance (R525). Both stepwise linear regression (SLR) and back-propagation artificial neural network (ANN) are used to combine the six spectral indices for the estimation of chlorophyll content (Cab). In addition, to overcome the limitation of actual dataset, a “big data” is applied by a within-leaf radiation transfer model (PROSPECT) to generate a large number of simulated samples with varying biochemical and biophysical parameters. 30% of the simulated dataset (SIM30) and an experimental dataset are used for validation. Compared with linear regression method, NN yields better result with R2 = 0.96 and RMSE = 5.80ug.cm−2 for Cab if validated by SIM30, while R2 = 0.95 and RMSE = 6.39ug.cm−2 for SLR. NN also gives satisfactory result with R2 = 0.80 and RMSE = 5.93ug.cm−2 for Cab if validated by LOPEX dataset, however, the SLR only gets 0.72 of R2 and 12.20ug.cm−2 of RMSE. The results indicate that integrating multiple spectral indices can improve the Cab estimating accuracy with a better stability in different kind of species and the model simulated dataset can make up the shortfall of actual measured dataset.