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Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multicriteria

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Journal of Mountain Science 2017年第10期14卷 1919-1937页

作者： RAHMAN Md.Shahinoor AHMED Bayes di liping Center for Spatial Information Science and Systems George Mason University BUET-Japan Institute of disaster Prevention and Urban Safety Bangladesh University of Engineering and Technology (BUET) Institute for Risk and disaster Reduction Department of Earth SciencesUniversity College London (UCL) Department of disaster Science and Management Faculty of Earth and Environmental SciencesUniversity of Dhaka

Rainfall induced landslides are a common threat to the communities living on dangerous hillslopes in Chittagong Metropolitan Area, Bangladesh. Extreme population pressure, indiscriminate hill cutting, increased precipitation events due to global warming and associated unplanned urbanization in the hills are exaggerating landslide events. The aim of this article is to prepare a scientifically accurate landslide susceptibility map by combining landslide initiation and runout maps. Land cover, slope, soil permeability, surface geology, precipitation, aspect, and distance to hill cut, road cut, drainage and stream network factor maps were selected by conditional independence test. The locations of 56 landslides were collected by field surveying. A weight of evidence(Wo E) method was applied to calculate the positive(presence of landslides) and negative(absence of landslides) factor weights. A combination of analytical hierarchical process(AHP) and fuzzymembership standardization(weighs from 0 to 1) was applied for performing a spatial multi-criteria evaluation. Expert opinion guided the decision rule for AHP. The Flow-R tool that allows modeling landslide runout from the initiation sources was applied. The flow direction was calculated using the modified Holmgren's algorithm. The AHP landslide initiation and runout susceptibility maps were used to prepare a combined landslide susceptibility map. The relative operating characteristic curve was used for model validation purpose. The accuracy of Wo E, AHP, and combined susceptibility map was calculated 96%, 97%, and 98%, respectively.

关键词： Landslide susceptibility Landslide runout GIS Remote sensing Weights of evidence(Wo E) Analytical hierarchical process(AHP) Relative operating characteristic(ROC) Bangladesh

A capability matching and ontology reasoning method for high precision OGC web service discovery

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International Journal of digital Earth 2011年第6期4卷 449-470页

作者： Nengcheng Chen Zeqiang Chen Chuli Hu liping di State Key Lab for Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhan 430079China Center for Spatial Information Science and Systems George Mason UniversityFairfaxVA 22032USA

Finding the right spatially aware web service in a heterogeneous distributed environment using criteria such as service type,version,time,space,and scale has become a challenge in the integration of geospatial information services.A new method for retrieving Open Geospatial Consortium(OGC)Web Service(OWS)that deals with this challenge using page crawling,link detection,service capability matching,and ontology reasoning,is described in this *** major components are distributed OWS,the OWS search engine,the OWS ontology generator,the ontology-based OWS catalog service,and the ontology-based multi-protocol OWS *** results show that the execution time of this proposed method equals only 0.26 of that of Nutch’s *** addition,the precision is much ***,this proposed method can carry out complex OWS reasoning-based *** is being used successfully for the Antarctica multi-protocol OWS portal of the Geo-Information Web Service Portal of the Polar.

关键词： geospatial information service link detection capability matching OWL-S ontology reasoning

Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers

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Journal of Integrative Agriculture 2017年第2期16卷 312-323页

作者： Claire G. Boryan Zhengwei Yang Patrick Willis liping di Research and Development division USDA National Agricultural Statistics Service VA 22030 USA Center for Spatial Information Science and Systems George Mason University VA 22030 USA

Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008-2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota （SD） U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit （PSU） level based on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service （NASS） ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency （FSA） Common Land Unit （CLU） and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.

关键词： cropland data layer crop planting frequency data layers automated stratification crop specific stratification multi-crop stratification

Remote Sensing Based Rapid Assessment of Flood Crop Damage Using Novel disaster Vegetation Damage Index(DVdi)

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International Journal of disaster Risk Science 2021年第1期12卷 90-110页

作者： Md.Shahinoor Rahman liping di Eugene Yu Li Lin Zhiqi Yu Center for Spatial Information Science and Systems George Mason UniversityFairfaxVA 22030USA

Accurate crop-specific damage assessment immediately after flood events is crucial for grain pricing,food policy,and agricultural *** main goal of this research is to estimate the crop-specific damage that occurs immediately after flood events by using a newly developed disaster Vegetation Damage Index(DVdi).By incorporating the DVdi along with information on crop types and flood inundation extents,this research assessed crop damage for three case-study events:Iowa Severe Storms and Flooding(DR 4386),Nebraska Severe Storms and Flooding(DR 4387),and Texas Severe Storms and Flooding(DR 4272).Crop damage is assessed on a qualitative scale and reported at the county level for the selected flood cases in Iowa,Nebraska,and *** than half of flooded corn has experienced no damage,whereas 60%of affected soybean has a higher degree of loss in most of the selected counties in ***,a total of 350 ha of soybean has moderate to severe damage whereas corn has a negligible impact in Cuming,which is the most affected county in Nebraska.A total of 454 ha of corn are severely damaged in Anderson County,*** than 200 ha of alfalfa have moderate to severe damage in Navarro County,*** results of damage assessment are validated through the NDVI profile and yield loss in percentage.A linear relation is found between DVdi values and crop yield *** R2 value of 0.54 indicates the potentiality of DVdi for rapid crop damage *** results also indicate the association between DVdi class and crop yield loss.

关键词： Crop damage disaster vegetation damage index(DVdi) Flood inundation Rapid assessment Remote sensing

Metadata requirements analysis for the emerging Sensor Web

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International Journal of digital Earth 2009年第S01期2卷 3-17页

作者： liping di Karen L.Moe Genong(Eugene)Yu Center for Spatial Information Science and Systems(CSISS) George Mason University6301 Ivy LnSuite 620GreenbeltMD 20770USA Earth System Technology Office NASA/GSFC MS Code 407GreenbeltMD 20771USA

The Sensor Web has emerged from Earth Science research with the development of Web technology,to achieve process automation,sensor interoperation,and service *** promises require the discovery of the right sensor at the right time and the right location with the right ***,for sensor,platform,and data,are crucial for achieving such ***,analysis and practical use of these metadata reveals that the metadata and their associations are not applicable or suitable for the Sensor *** shortfalls are(1)the nonstandard metadata expression language;(2)the missing link between sensor and domain knowledge;(3)the insufficiency in the information for geographic locating and sensor tasking;and(4)the enhanced requirements on the quality,security,and ownership of both sensors and their sensed *** paper reviews the current standards that have metadata components for the sensor and its platform,especially those from ISO TC211,Open Geospatial Consortium Inc.,and The National Aeronautics and Space Administration Global Change Master directory.A recommendation on metadata that meets the requirement of crossmission sensor discovery in a pervasive Web environment is derived from *** recommendation addresses issues on language formalization,sensor geolocation,semantics,quality,and *** of the emerging semantic Web technology for enabling robust discovery of sensor are discussed.

关键词： Sensor Web geospatial standards metadata interoperation semantic web

Remote sensing for agricultural applications

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Journal of Integrative Agriculture 2017年第2期16卷 239-241页

作者： zhengwei yang wu wen-bin liping di berk ustundag research and development division usda national agricultural statistics service VA22030USA institute of agricultural resources and regional planning chinese academy of agricultural sciences Beijing 1 00081PR.China center for spatial information science and systems george mason university VA 22030USA agricultural and environmental informatics application&research center/faculty of informatics and computer engineering istanbul technical university Istanbul 34469Turkey

Agricultural geospatial information is critical for agricultural policy formulation and decision making, land use monitoring, agricultural sustainability, crop acreage and yield estimation, disaster assessment, bioenergy crop inventory, food security policy, environmental assessment, carbon accounting, and other research topics that are of vital importance to agricul- ture and economy. Remote sensing technology enables us to collect, process, and analyze remotely sensed data and to retrieve, synthesize, visualize valuable geospatial information for agriculture uses. Specifically, remote sensing technology empowers capability for large scale field level or regional assessment and monitoring of crop land cover,

关键词： Remote sensing for agricultural applications MOdiS RUSLE NDVI data

RF-CLASS： A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

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Journal of Integrative Agriculture 2017年第2期16卷 408-423页

作者： liping di Eugene G. Yu Lingjun Kang Ranjay Shrestha BAI Yu-qi Center for Spatial Information Science and Systems (CSISS) George Mason University VA 22030 USA Department of Geography and Geoinformation Science George Mason University VA 22030 USA Key Laboratory for Earth System Modelling Ministry of Education/Department of Earth System Science (DESS) Tsinghua University Beijing 100084 P.R. China

Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSdiS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment,

关键词： crop condition flooding crop damage time series MOdiS web service remote sensing decision-making

Geoscience model service integrated workflow for rainstorm waterlogging analysis

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International Journal of digital Earth 2021年第7期14卷 851-873页

作者： Xicheng Tan Jingguo Jiao Nengcheng Chen Fang Huang liping di Jinchuan Wang Zongyao Sha Jin Liu School of Remote Sensing and Information Engineering Wuhan UniversityWuhanPeople’s Republic of China The State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanPeople’s Republic of China School of Resources&Environment University of Electronic Science and Technology of ChinaChengduPeople’s Republic of China Center for Spatial Information Science and Systems George Mason UniversityFairfaxVAUSA School of Computer Science Wuhan UniversityWuhanPeople’s Republic of China

This paper proposed a geoscience model service integrated workflowbased rainstorm waterlogging analysis method to overcome the defects of conventional waterlogging analysis *** 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 *** 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 *** 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.

关键词： Geoscience model workflow waterlogging OGC geospatial service

Regression model to estimate flood impact on corn yield using MOdiS NDVI and USDA cropland data layer

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Journal of Integrative Agriculture 2017年第2期16卷 398-407页

作者： Ran jay Shrestha liping di Eugene G. Yu Lingjun Kang SHAO Yuan-zhen BAI Yu-qi Center for Spatial Information Science and Systems (CSISS) George Mason University VA 22030 USA Department of Geography and Geoinformation Science George Mason University VA 22030 USA State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430079 P. R. China Key Laboratory for Earth System Modelling Ministry of Education/Department of Earth System Science (DESS) Tsinghua University Beijing 100084 P.R.China

Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change, which requires accurate assessment to quantify the damages. Various remote sensing products and indices have been used in the past for this purpose. This paper utilizes the moderate resolution imaging spectroradiometer （MOdiS） weekly normalized difference vegetation index （NDVI） product to detect and further quantify flood damages on corn within the major corn producing states in the Midwest region of the US. County-level analyses were performed by taking weighted average of all pure corn pixels （〉90%） masked by the United States Department of Agriculture （USDA） Cropland Data Layer （CDL）. The NDVI-based time-series difference between flood years and normal year （median of years 2000-2014） was used to detect flood occur- rences. To further measure the impact of the flood on corn yield, regression analysis between change in NDVI and change in corn yield as independent and dependent variables respectively was performed for 30 different flooding events within growing seasons of the corn. With the R2 value of 0.85, the model indicates statistically significant linear relation between the NDVI and corn yield. Testing the predictability of the model with 10 new cases, the average relative error of the model was only 4.47%. Furthermore, small error （4.8%） of leave-one-out cross validation （LOOCV） along with smaller statistical error indicators （root mean square error （RMSE）, mean absolute error （MAE）, and mean bias error （MBE））, further validated the accuracy of the model. Utilizing the linear regression approach, change in NDVI during the growing season of corn appeared to be a good indicator to quantify the yield loss due to flood. Additionally, with the 250 m MOdiS-based NDVI, these yield losses can be estimated up to field level.

关键词： NDVI MOdiS agriculture corn yield remote sensing regression