2015 23rd International Conference on Geoinformatics最新文献

{"title":"Cotton growth monitoring and yield estimation based on assimilation of remote sensing data and crop growth model","authors":"Yepei Chen, X. Mei, Junyi Liu","doi":"10.1109/GEOINFORMATICS.2015.7378675","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2015.7378675","url":null,"abstract":"Predicting cotton growth and yield accurately is significantly important to farmland management and sustainable development of agriculture. Remote sensing and crop growth model both have its advantages in crop growth monitoring and yield estimation, however, they also have limitations in mechanism or acquisition of the input parameters. This study combines the satellite remote sensing data and crop growth models by using data assimilation technique. The research uses global optimization algorithm called shuffled complex evolution-University of Arizona (SCE-UA) to constantly inverse and correct the values of model input parameters with the leaf area index (LAI) as the combination point, selects decision support system for agrotrchnology transfer (DSSAT) to build growth model of cotton in Jianghan plain in the middle reaches of the Yangtze River. The results of the research show that the precision of simulation is effectively improved after cotton model is assimilated by remote sensing data.","PeriodicalId":371399,"journal":{"name":"2015 23rd International Conference on Geoinformatics","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125610993","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}

{"title":"Study on land use/cover in Yingkou City based on the Geographical Conditions Survey of China","authors":"Anqi Wang, Chao Xie","doi":"10.1109/GEOINFORMATICS.2015.7378599","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2015.7378599","url":null,"abstract":"Selecting the official land cover data from the government-led Geographical Conditions Survey of China and utilizing the method of global and local spatial autocorrelation based on grid processing, the authors analyzed Yingkou City's characteristics of land cover spatial autocorrelation pattern. The study suggested following conclusions: 1) The Global Moran's I index of all types of land cover in Yingkou City were greater than 0.35, whose confidence level was higher than 95%. The results showed that there are strong spatial autocorrelations among different types of land cover in this area. 2) The Local Moran's I Index showed that there are significant differences in various types of land cover's coverages and ranges of the spatial aggregation or anomaly. 3) Using the cross-variable correlation analysis, the authors discussed the relationships and distribution patterns of different land cover types. This research was helpful to reveal the correlation between the spatial autocorrelation of land cover and types of land cover. The study above was also suitable for large-scale regional application, and the results were significant important for the geographical conditions monitoring.","PeriodicalId":371399,"journal":{"name":"2015 23rd International Conference on Geoinformatics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130286713","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}

{"title":"Improving automatic target generation process for hyperspectral endmember extraction","authors":"Jee-Cheng Wu, Gwo-Chyang Tsuei, Cheng-Fu Feng","doi":"10.1109/GEOINFORMATICS.2015.7378711","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2015.7378711","url":null,"abstract":"Although many endmember extraction algorithms (EEAs) have been proposed, the accurate identification of endmembers is still a challenging task in spectral unmixing of hyperspectral imagery. One of the EEAs, automatic target generation process (ATGP), works by iterative orthogonal projections of the data then finding the largest magnitude vector of this projection, and it will stop until reaches a predefined number of endmembers. This paper proposes an updated version of ATGP by making improvements on two aspects of the method. First, spectral and spatial redundancies are removed, and only a group of candidate endmember pixels will be processed by ATGP. Second, after an endmember pixel is found using orthogonal projection, this pixel will be used to divide the group of candidate endmember pixels into a smaller group and a cluster using similarity measure. Furthermore, a threshold criterion is set to evaluate the quantity of the cluster, which avoids the found pixel is an interfering pixel. A comparative study and the obtained experimental results show that the improved ATGP algorithm not only reduces computational complexity but also provides better performance than the four well-known published algorithms.","PeriodicalId":371399,"journal":{"name":"2015 23rd International Conference on Geoinformatics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130743739","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}

{"title":"Self-adaptive Wi-Fi indoor positioning model","authors":"Ying Chen, Danhuai Guo, Wenjuan Cui, Jianhui Li","doi":"10.1109/GEOINFORMATICS.2015.7378593","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2015.7378593","url":null,"abstract":"Wi-Fi based indoor positioning, which is based on attenuation of Received Signal Strength Indicator (RSSI) is an emerging Location Based Service (LBS) technology. As positioning accuracy is sensitive to environmental factors, most of the existing algorithms based on experimental test perform badly without adaptation to dynamics of environment. In this paper, we propose an indoor positioning method by locating the representation of a cluster within similar environments. The K-Means algorithm is used to extract the similarities of the objects within the nearby area. To overcome the problem of parameter determination under the circumstances of lack of fingerprint and extra hardware, we proposed a Log-normal shadowing model (LNSM) with Artificial Neural Networks to estimate distance enabling the parameters to be dynamically adjusted according to the change of the environment. The experimental results of one day auto fair data demonstrate the performance of our method with a higher degree of accuracy than other methods.","PeriodicalId":371399,"journal":{"name":"2015 23rd International Conference on Geoinformatics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130966834","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}

{"title":"A comparative study on effects of spatial aggregation for GlobeLand30","authors":"Shiteng Tan, Zhu Xu, Peng Ti","doi":"10.1109/GEOINFORMATICS.2015.7378706","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2015.7378706","url":null,"abstract":"Global Land Cover 30m (GlobeLand30) can be usually used for environmental change studies, land resource management, sustainable development, and many other fields. However, this land cover dataset only provides a 30m resolution. For some cases, Ecology system and Climate Change, etc., data with coarser resolutions may still be needed. To solve this problem, the spatial aggregation of the catergories data is necessary. Current spatial aggregations approaches can generally divided into two classes, i.e. majority rule-based aggregation and random rule-based aggregation. This study aims to evaluate these two methods for the effective of the spatial aggregation for GlobeLand30 data with consideration of some measures, i.e. Cover type Proportion, Perimeter-Area Fractal Dimension (PAFRAC), Aggregation Index (AI), and Landscape Shape Index (LSI). The result demonstrated that random rule-based aggregation maintains land cover diversity and category proportion, but landscape pattern can lead to disaggregated which reflected from PLAND and AI indexs scalogram. In contrast, majority rule-based aggregation keeps spatial patterns better than random rules.","PeriodicalId":371399,"journal":{"name":"2015 23rd International Conference on Geoinformatics","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134509883","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}

{"title":"Analysis of land use and land cover change in Nadowli District, Ghana","authors":"L. Prosper, Qingfeng Guan","doi":"10.1109/GEOINFORMATICS.2015.7378647","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2015.7378647","url":null,"abstract":"There is a growing environmental concern and interest in land at the Nadowli District, Ghana since the influx of legal and illegal miners in the area. Analysis of these concerns requires the assessment of the Land use and land cover dynamics of the area. Geographic information systems and satellite remote sensing information are latest technologies in land-cover change assessment. Their strengths lie in providing insights into land-cover change properties through the use of spatio-temporal and multi spectral data. Landsat satellite imageries of three different time periods, i.e., 1990, 2000 and 2014 were used to quantify the land use and land cover changes in the area. Supervised classification using Maximum Likelihood technique was used resulting in the classes: Water, Open Savannah and Closed savannah woodlands, Agricultural/Fallow Land, Settlement and bare lands. A post-classification change detection method was employed and a LULC change matrix obtained. The study shows that between the years 1991 and 2000 the changes in the LULC changes were not as significant as in the years between 2000 and 2014. There was a decrease in Water and closed Savannah woodlands although Open Savannah has increased marginally. The felling of trees for fuel wood is also depleting the closed Savannah wood lot. Agriculture has increased especially along the Black Volta River. Settlements/bare areas may have decreased probably due to the clamp down of illegal mining activities and easy access to markets along the North Eastern part close to the District Capital, Nadowli.","PeriodicalId":371399,"journal":{"name":"2015 23rd International Conference on Geoinformatics","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127415974","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}

{"title":"HJ satellite based mapping technologies of land use products for emergency response of agricultural disasters","authors":"Chongyang Wang, Dan Li, Xia Zhou, Siyu Huang, W. Liu, Weiqi Chen, Shuisen Chen","doi":"10.1109/GEOINFORMATICS.2015.7378703","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2015.7378703","url":null,"abstract":"Accurate and reliable information on land use is a basis of agricultural disaster warning and emergency action. The natural disasters typhoon, cold disaster, drought, and so on, have great influences on agricultural production in Guangdong Province, China. Through literature analysis at home and abroad, it was pointed out that Chinese HJ satellites have become important sources of remote sensing images due to short imaging period and broad coverage (about 2 days and 700km). It also advances the free sharing of OLI data on the new generation of remote sensor of Landsat. Considering the severe orbit deviation and frequent phenology changes of agricultural land use, this paper described and laid emphasis on the necessity and perspective of developing multi-scale (1:500,000, 1:20,000, 1:50,000) agricultural land use products of Provincial-City-County within Guangdong through the usage of relatively fixed ground control point database and spectral library based advanced hierarchical classification technologies supported by HJ satellite data. Such a tendency of quick disaster emergency response for land use dynamic implies a technological focus on the usage of above-mentioned advanced HJ satellite technologies. Based on Landsat images, we also built the 11 united ground control points (40 points at most for whole Guangdong Province) and standardized the technology system of land use remote sensing mapping with the combination of spectral library-based hierarchy classification technology. Selected key technologies for enhancing land use production mapping efficiency and accuracy that involve Landsat OLI and HJ satellites are presented and discussed. The HJ satellite is an effective information source for land use dynamic mapping under emergency action such as disaster damage evaluation, which can provide a short imaging period with wide spatial coverage and enhance the ability of land use data acquirement from one to three or four times or so in one year. The building of relatively fixed ground control points increase the efficiency of province-level land use mapping from one week to two days. The proposed method is especially useful for the development of land use products in cloudy and rainy south China.","PeriodicalId":371399,"journal":{"name":"2015 23rd International Conference on Geoinformatics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115771090","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}

{"title":"Based on the support vector machine for LUCC research in Binchuan of Yunnan Province","authors":"Chao Yang, Jin-ling Wang","doi":"10.1109/GEOINFORMATICS.2015.7378618","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2015.7378618","url":null,"abstract":"Land use and cover change is the focus of environmental change research. We used the support vector machine classification method to extract the years 1995 Landsat TM, 2000 and 2005 LandsatETM+, and 2013 LandsatOLI four remote sensing data's LUCC types and evaluate the accuracy of extraction. Finally, use of land use transfer matrix system quantitatively described, simultaneous analysis of the area of LUCC spatial and temporal dynamic characteristics and the factors of driving force, in order to protect the valuable forest resources and continuing effective use land resources of Binchuan to provide a scientific basis for decision making. The result indicate that: the SVM classification method overall accuracy was 89.23, with a kappa coefficient greater than 0.7. From 1995 to 2013, rural and mining residential land generally increased, except 2000, and reaching the maximum in 2013, which is almost double than in 1995. This permits Binchuan, which for nearly 20 years, has always been committed to the city and rural development. The most obvious conversion land use types were unused land and cultivated land, with cultivated land showing a clear decreasing trend, and majority of conversions were to rural and mining residential land, and unused land mostly converted to grassland and rural and mining residential land. For woodland, it experienced an initial increase, then decrease, and then finally increased procedure. However the increase of woodland area is not large (it remained in stable condition), and this proved Binchuan has an emphasis on the protection of forest resources. The water area from 1995 to 2000 years showed a substantial reduction, but in the subsequent 10 years has rebounded. However water resources is still relatively scarce, suggesting the Government to strengthen the construction of water conservancy facilities and soil and water conservation and related work. The LUCC drivers of Binchuan are complex, but the human factor is the main driving factor, rapid population growth, and high-speed economic development are the fundamental factors that led to the massive building occupants, so there is a large number of rural and mining residential land.","PeriodicalId":371399,"journal":{"name":"2015 23rd International Conference on Geoinformatics","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116825745","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}

{"title":"Spatio-temporal Analysis of the Street Crime hotspots in faisalabad city of Pakistan","authors":"S. Khalid, Jie-chen Wang, M. Shakeel, Xia Nan","doi":"10.1109/GEOINFORMATICS.2015.7378693","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2015.7378693","url":null,"abstract":"The Geographic Information System has become an important and useful tool in the implementation of crime control and monitoring activities in crimes affected areas and it has the ability to examine spatial relationships of phenomena. The present study has been taken up for detecting the hotspots of street crimes and developing the crime control strategies in the Faisalabad city of Pakistan. The spatial patterns of urban street crimes were analyzed. The crime reports of 2012 were geocoded and the crime maps were prepared in ArcGIS 10. The strategic crime analysis was done in a series of meetings with police department and crime controlling strategies were built. The Compstat model with some modifications was followed for the accountability and performance management of the police department. Operational analysis was carried out for resource allocations and deployment. After implementing crime control strategies it has been observed that there was a remarkable reduction in the street crimes. The crime data of 2013 was plotted on map and the hotspot changing patterns were observed.","PeriodicalId":371399,"journal":{"name":"2015 23rd International Conference on Geoinformatics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117047311","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}

{"title":"Spatio-temporal similarity analysis strategy of SAR image time series for land development intensity monitoring","authors":"Yafei Wang, Dong Chen, Kan Zhou, Rui-feng Guo","doi":"10.1109/GEOINFORMATICS.2015.7378580","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2015.7378580","url":null,"abstract":"Land development intensity is one key indicator of Major Function Oriented Zoning (MFOZ). For land development intensity monitoring in a large area, SAR image time series with medium resolution provides an appropriate way, because of its large-scale and high-temporal frequency measurements. According to time-series characteristics of cultivated land and construction land pixels, a spatio-temporal similarity analysis strategy considering mixed pixels and noise is presented to extract change nodes and change pixels. This strategy mainly includes three components: (1) Construction of pixel-level SAR image time series; and (2) Iterative binary partition mean square error (MSE) model to ascertain change nodes; (3) Spatio-temporal similarity analysis based on pixel-level SAR image time series to determine the change range of cultivated land to construction land. Through the monitoring of conversion of cultivated land to construction land across multiple periods leveraging pixel-level SAR image time series in Chengdu, several conclusions can be drawn from this study. (1) This study has illuminated the utility of pixel-level SAR image time series for land development intensity monitoring, especially in those areas with cloud cover the majority of the time. SAR images are not affected by cloud cover and provide continuous time-series information. (2) The spatio-temporal similarity measure was able to effectively extract change nodes and change range of cultivated land to construction land. Generally, the correctness of 85.82% and completeness of 84.78% were achieved.","PeriodicalId":371399,"journal":{"name":"2015 23rd International Conference on Geoinformatics","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117249091","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}