Int. J. Appl. Geospat. Res.最新文献_第8页

Modeling Subalpine and Upper Montane Forest-Climate Interactions in Colorado: A Comparative Study Using GIS 模拟科罗拉多州亚高山和高山森林-气候相互作用:利用GIS的比较研究

Int. J. Appl. Geospat. Res. Pub Date : 2014-10-01 DOI: 10.4018/IJAGR.2014100102

S. Jennings, E. Billmeyer

引用次数: 3

Simplified Toolbar to Accelerate Repeated Tasks (START) for ArcGIS: Optimizing Workflows in Humanitarian Demining 简化工具栏加速重复任务(START)为ArcGIS:优化人道主义排雷工作流程

Int. J. Appl. Geospat. Res. Pub Date : 2014-10-01 DOI: 10.4018/ijagr.2014100106

Pierre Lacroix, Pablo de Roulet, Nicolas Ray

引用次数: 4

Monitoring Urban Sprawl and Sustainable Urban Development Using the Moran Index: A Case Study of Stellenbosch, South Africa 利用Moran指数监测城市扩张与可持续城市发展——以南非Stellenbosch为例

Int. J. Appl. Geospat. Res. Pub Date : 2014-07-01 DOI: 10.4018/IJAGR.2014070101

W. Musakwa, A. Niekerk

引用次数: 24

Land Classification Research: A Retrospective and Agenda 土地分类研究:回顾与议程

Int. J. Appl. Geospat. Res. Pub Date : 2014-07-01 DOI: 10.4018/ijagr.2014070106

Michael N. DeMers

{"title":"Land Classification Research: A Retrospective and Agenda","authors":"Michael N. DeMers","doi":"10.4018/ijagr.2014070106","DOIUrl":"https://doi.org/10.4018/ijagr.2014070106","url":null,"abstract":"Land classification is so central to geography that its use, and the use of its derivative and corresponding products, is seldom even questioned. Since its earliest implementations land classification has adapted to changes in geographic scale and in the nature of the categorical systematics upon which it is based. Land classification has changed in its techniques and in how it adapts to technological changes, particularly those related to remote sensing and geographic information systems. The adaptation of land classification to digital pixel-based classification spawned a wide range of land classification error analysis techniques. These techniques do not easily transfer to non-pixel based classification error analysis as recent research on rapid land assessment methodologies and land change error analysis has shown. This disparity suggests a need to reevaluate the very nature of land classification research. To begin such an evaluation, this lecture provides a retrospective on the roots of land classification research, examines some of the milestones of that research, and describes the divergent paths such research has taken. It examines the importance of land classification in these times of ever decreasing global resources, and reviews its potential legal, social, and economic implications. Based on this retrospective, this paper advances the need for geographic researchers to envision land classification not only as a set of techniques, but more generally to focus on systematic geography in all its facets as a research agenda in its own right.","PeriodicalId":368300,"journal":{"name":"Int. J. Appl. Geospat. Res.","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127265815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Impact of Training Set Size on Object-Based Land Cover Classification: A Comparison of Three Classifiers 训练集大小对基于目标的土地覆盖分类的影响:三种分类器的比较

Int. J. Appl. Geospat. Res. Pub Date : 2014-07-01 DOI: 10.4018/IJAGR.2014070104

G. Myburgh, A. Niekerk

{"title":"Impact of Training Set Size on Object-Based Land Cover Classification: A Comparison of Three Classifiers","authors":"G. Myburgh, A. Niekerk","doi":"10.4018/IJAGR.2014070104","DOIUrl":"https://doi.org/10.4018/IJAGR.2014070104","url":null,"abstract":"Supervised classifiers are commonly employed in remote sensing to extract land cover information, but various factors affect their accuracy. The number of available training samples, in particular, is known to have a significant impact on classification accuracies. Obtaining a sufficient number of samples is, however, not always practical. The support vector machine (SVM) is a supervised classifier known to perform well with limited training samples and has been compared favourably to other classifiers for various problems in pixel-based land cover classification. Very little research on training-sample size and classifier performance has been done in a geographical object-based image analysis (GEOBIA) environment. This paper compares the performance of SVM, nearest neighbour (NN) and maximum likelihood (ML) classifiers in a GEOBIA environment, with a focus on the influence of training-set size. Training-set sizes ranging from 4-20 per land cover class were tested. Classification tree analysis (CTA) was used for feature selection. The results indicate that the performance of all the classifiers improved significantly as the size of the training set increased. The ML classifier performed poorly when few (<10 per class) training samples were used and the NN classifier performed poorly compared to SVM throughout the experiment. SVM was the superior classifier for all training-set sizes although ML achieved competitive results for sets of 12 or more training areas per class.","PeriodicalId":368300,"journal":{"name":"Int. J. Appl. Geospat. Res.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123949486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

Compute-Efficient Geo-Localization of Targets from UAV Videos: Real-Time Processing in Unknown Territory 无人机视频中目标的高效地理定位:未知区域的实时处理

Int. J. Appl. Geospat. Res. Pub Date : 2014-07-01 DOI: 10.4018/ijagr.2014070103

Deendayal Kushwaha, Sridhar Janagam, N. Trivedi

{"title":"Compute-Efficient Geo-Localization of Targets from UAV Videos: Real-Time Processing in Unknown Territory","authors":"Deendayal Kushwaha, Sridhar Janagam, N. Trivedi","doi":"10.4018/ijagr.2014070103","DOIUrl":"https://doi.org/10.4018/ijagr.2014070103","url":null,"abstract":"Unmanned Air Vehicles (UAVs) have crucial roles to play in traditional warfare, asymmetric conflicts, and also civilian applications such as search and rescue operations. Though satellites provide extensive coverage and capabilities crucial to many remote sensing tasks, UAVs have distinct edge over satellites in dynamic situations due to shorter revisit times and desired area/time coverage. The course, speed and altitude of a UAV can be dynamically altered, details of an activity of interest monitored by loitering over the area as desired. A fundamental requirement in most UAV operations is to find geo-coordinates of an object in the captured image. Most small, low-cost UAVs use low-cost, less accurate sensors. Matching with pre-registered images may not be possible in areas with low details or in emergency situations where terrain may have undergone severe sudden changes. In these situations that demand near real-time results and wider coverage, it is often enough to provide approximate results as long as bounds on accuracies can be established. Even when image registration is possible, it can benefit from these bounds to reduce search space thereby saving execution time. The prime contributions of this paper are computation of location of target anywhere in the image even at larger slant ranges, optimized algorithm to compute terrain elevation at target point, and use of visual simulation tool to validate the model. Analysis from simulation and results from real UAV flights are presented.","PeriodicalId":368300,"journal":{"name":"Int. J. Appl. Geospat. Res.","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114697388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Geospatial Analysis of Neighborhood Characteristics and Access to Fresh Produce: The Role of Farmers' Markets and Roadside Farm Stands 邻里特征与新鲜农产品获取的地理空间分析:农贸市场和路边农场摊位的作用

Int. J. Appl. Geospat. Res. Pub Date : 2014-07-01 DOI: 10.4018/ijagr.2014070105

Y. Ogneva-Himmelberger, Fei Meng

引用次数: 4

The Impact of Data Time Span on Forecast Accuracy through Calibrating the SLEUTH Urban Growth Model 数据时间跨度对SLEUTH城市增长模型预测精度的影响

Int. J. Appl. Geospat. Res. Pub Date : 2014-07-01 DOI: 10.4018/IJAGR.2014070102

R. Peiman, K. Clarke

{"title":"The Impact of Data Time Span on Forecast Accuracy through Calibrating the SLEUTH Urban Growth Model","authors":"R. Peiman, K. Clarke","doi":"10.4018/IJAGR.2014070102","DOIUrl":"https://doi.org/10.4018/IJAGR.2014070102","url":null,"abstract":"Does the spacing of time intervals used for model input data have an impact on the model's subsequent calibration and so projections of land use change and urban growth? This study evaluated the performance of the SLEUTH urban growth and land use change model through two independent model calibrations with different temporal extents (1972 to 2006 vs. 2000 to 2006) for the historical Italian cities of Pisa Province and their surroundings. The goal in performing two calibrations was to investigate the sensitivity of SLEUTH forecasts to longer or shorter calibration timelines, that is does calibrating the model over a longer time period produce better model fits and therefore forecasts? The best fit parameters from each calibration were then used in forecasting urban growth in the area up to the year 2027. The authors findings show that the spatial growth estimated by the model was strongly influenced by the physical landscape and road networks. The forecast outputs over 100 Monte Carlo trials reflect the start of newly formed detached settlements towards and along existing roads, i.e., classic urban sprawl. The authors conclude that the short term calibration was a better model fit compared to the long term calibration. Nevertheless, the absolute preference for the short-term calibration over long-term implies that time-sensitivity in calibration remains a challenge for SLEUTH applications.","PeriodicalId":368300,"journal":{"name":"Int. J. Appl. Geospat. Res.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132467661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Geographic Information System Effects on Policing Efficacy: An Evaluation of Empirical Assessments 地理信息系统对警务效能的影响:一项实证评估

Int. J. Appl. Geospat. Res. Pub Date : 2014-04-01 DOI: 10.4018/ijagr.2014040103

Yan Zhang, Larry Hoover, Jihong Zhao

引用次数: 9

Online Flood Information System: REST-Based Web Service 在线洪水信息系统:基于rest的Web服务

Int. J. Appl. Geospat. Res. Pub Date : 2014-04-01 DOI: 10.4018/ijagr.2014040101

Xiannian Chen, X. Ye, M. Carroll, Yingru Li

引用次数: 4