Classification of Vegetation Coverage of Wetland Landscape based on Remote Sensing in the Coastal Area of Jiangsu Province

The Vegetation Coverage Estimation Model (VCEM) was established based on the classification of coastal wetlands by remote sensing technology and the improved dimidiate pixel model as well as the Normalized difference vegetation index (NDVI).The VCEM was used to calculate vegetation coverage (Fc) and grade level for different wetland types in the study area of Yancheng,Jiangsu Province.The relationship between wetland vegetation coverage and their types was analyzed further by overlaying the coverage classification map and wetland landscape map.The results show that the zonal distribution of plant community of the coastal wetland was notable.And the vegetation coverage level varies with different types of wetland.The reed marshes tend to grow at the high vegetation coverage zone,which account for 67.45% of the total area of reed marshes,and the medium vegetation coverage zone account for 23.61%,and the low vegetation coverage zone only account for 8.94%.Most the Suaeda salsa marshes were in the medium vegetation coverage zone,which account for 81.14% of its total area,and 3.36% and 16.06% were distributed at the high vegetation coverage zone and the low vegetation coverage zone respectively.Most of the Spartina alterniflora marshes were distributed at the high vegetation coverage zone,accounting of 83.47% of its total area,and only 15.07% at the medium vegetation coverage zone,1.74% at the low vegetation coverage zone.In addition,most high vegetation coverage areas of wetlands were distributed at the middle location of each vegetation zone,and the Transition zone for each wetland type was usually with lower vegetation coverage level.     

Aircraft Auto-detection in Domestic High Resolution Remote SensingImages Using Deep-learning

It is of great significance to automatically detect aircrafts from remote sensing imagery to get their locations.However,due to aircraft posture variance,complicated background and incomplete outlines,it is challenging to achieve a high aircraft detection accuracy.Traditional aircraft detection methods are usually based on hand\|crafted features and machine learning based classifiers,which is not robust enough for the translation and rotation variations.To tackle the above issues,this paper introduces deep convolutional neural network and the strategy of transfer learning to detect aircrafts from Chinses domestic satellite remote sensing images.Specifically,this paper first constructs an aircraft sample database,which consists aircrafts of different sizes and poses.Afterwards,YOLO V2 trained with natural images is utilized as the detection model and is further fine\|tuned with aircraft samples to increase the robustness and performance.Experiments were done on the Shanghai Pudong airport from Chinese GF\|2 remote sensing data.Experimental results showed a good performance with a recall of 92.25% and a precision of 94.93%.It is indicated that deep learning together with model transfer can get a high aircraft detection accuracy with limited training samples.The method in this paper can be generalized to other land object detection problems which shows a good promotional value.     

A Remote Sensing Classification Methods of Coastal Wetlands Considering the Distance to Main Water

Coastal wetlands is complex,"different objects with the same spectrum" is serious in the remote sensing image,so the classification accuracy only based on spectral information is low.For this issue,based on the coastal zone wetland's spatial distribution rule,this paper established two kinds of distance layers,distance to coastline layer and distance to river layer,which applied maximum likelihood method and decision tree method,and developed a coastal wetland remote sensing information extraction methods,taking Sheyang County,Jiangsu Province for example.The developed methods highly improved the classification accuracy with the overall classification accuracy of 81.5%,and Kappa of 0.79.The maximum likelihood supervised classification method classification accuracy was lower with overall classification accuracy of 62.3%,and Kappa of 0.60.     

A Review of Lake Dynamic Change Researchbased on Remote Sensing

The shrink and expansion of lakes can reflect the regional changes in climate and environment. It has an important significance for further research of the climate change and the sustainable development.The rapid development of remote sensing technology has provided technical support for the dynamic change to real-time monitoring of lakes.This paper discussed the selection of data source,the delineation of lake water,the lake variation trends and causes.Then made a systematic summary of the current situation and progress in the studies on lake change and predicted the trends of lake change research in the future.     

Tectonic Interpretation and Analysis based on Multi-source Remote Sensing Data：A Case Study of JitaiRiver in Chayi，Tibet

Linear Structures and Ring Structures are of great important to distinguish and analyze faults,folds and magmatic emplacement on the surface.Extracting linear structures from multi-source remote sensing data with the approach of Human-Computer-Interaction can understand the overall and individual geometrical characteristics of Linear Structures and Ring Structures objectively and comprehensively.Taking Jitai river as an example,three sets of Linear Structures with characteristic of clustering and abundant Ring Structures were extracted in working area based on remote sensing data from Google Earth,Landsat 8/OLI,ASTER GDEM and high-resolution DEM.The results of the analysis show that the working area is in a dextral shear zone with northwest direction and the southwest structure of Jitai River is still in a relatively active stage,which may be an unstable area of the engineering geology and the prone areas of geological disasters.     

A Radiant Topographical Correction Model of Remote SensingImageries by Considering Micro-terrain

The radiant topographical correction is an important preprocessing for the interpretation of remote sensing imageries.A radiant topographical correction model is proposed with high-resolution DEM data by considering micro-terrain.The model simulates the effects of the micro-terrain units on pixels’ radiance.These micro-terrain units in a single pixel are used to describe topographical non-Lambertian reflectance characteristics based on V-slope illumination model.The experiments using several images have proved that the results of model that was applied in this study are better than that of the conventional correction methods.     

APPlication of SPace Remote Sensing to ExPloration for the Carlin一tyPe Gold DePosites in QianGui Area

研究了黔桂地区卡林型金矿的宕矿石光谱特征，TM波段比值与岩矿石化学成分的相关性，并在此基础上进行了图像处理，提取与金矿化有关的性变信息，取得了满意的结果。     

Data Assimilation Experiment ofPoor Quality of RemoteSensing Image in Critical Phenology

In the process of data assimilation,influenced by the water vapor and cloud cover in the study area,the qualities of remote sensing images are poor in the key crop phenological phases.This will cause not to get the perfect remote sensing images for a long time.So we try to solve this problem by using an improved EnKF method to assimilate the WOFOST crop growth model and the terrible quality of remote sensing images to forecast the maize’s yield in the Red Star Farm in Heilongjiang province.In order to improve the accuracy of simulated time series curve of the LAI and yield production results,the consideration on quality evaluation of the remote sensing images is introduced by using expansion coefficient and adjustable factor.The results shows based on the improved EnKF method,time series curve of the LAI keeps a normal tendency of LAI rather than negative fluctuations,and it also avoids the serrated fluctuation to a certain extent.In addition,compared with the original EnKF method,in the field level R2 can increased to 0.67 from 0.59,RMSE is reduced to 92.23 kg/hm2 from 240.57 kg/hm2 and in the farm level R2 can increased to 0.61 from 0.52,RMSE is reduced to 122.44 kg/hm2 from 310.94 kg/hm2 between simulated yield and measured yield.     

IBI and CA Model based Extraction of Urban Built\|up Areaand Its Application：A Case Study in Shanghai

At present,the automatic extraction of urban built\|up area is still a problem.Taking Shanghai city as an example,this research uses the binary image,which was derived from an IBI(index\|based built\|up index)image,as the geographical cellular automaton initial input data.This was then imported to the extended cellular automata model for further processing.After a series of processing such as thresholding,corrosion and connectivity test,the urban built\|up area was simulated and can be extracted.Based on this method,the urban built\|up area of Shanghai in five different years between 1987 and 2015 was extracted using multi\|temporal Landsat images of Shanghai.In addition,the urban growth intensity index,the concentric buffers analysis and radar chart analysis were calculated and used to analysis spatiotemporal pattern of Shanghai urban expansion in the 28 study years.The result shows that the built\|up area of Shanghai was expanded from 195 km² in 1987 to 1759 km² in 2015,which is eight times as large as that in 1987.In the period between 1987 and 2002,the urban built\|up area expanded mainly along the North\|South axis of the Huangpu River.However,after 2002 the expansion rate was increased more quickly,and the direction of the expansion was shifted to an east to west direction.Generally speaking,Shanghai has experienced a rapid urban expansion in the recent three decades.     

Surverying Investigation by Remote Sensing Teehnique on the Bloeking一storing Potential of Ground Surface Water Resources in Sanyi Area

以三沂地区应用为例，从流域下垫面特征研究出发，阐述了利用遥感技术与信息未统结合常现方法计算地表水资源的途径，分析了区域地表水拦蓄潜力。     

Identification and Analysis of Archeological Remnants Using RemoteSensing Data in Juyan Oasis of Northwestern China

Archaeological ruins in ancient times can play an important role in studying and restoring the past human activities,as well as understanding regional environmental changes.There were many flourishingly agricultural activities in different historical periods that were developed in ancient Juyan Oasis in the downstream of the Heihe River Basin.A large number of historical ruins that reflect past human activities remained in numerous of the nebkhas and sand dunes.This study combined high-resolution remote sensing images and in situ truths investigated during the fieldwork,certain parts of unknown remnants were identified by the image features of ancient remnants that appear in the remotely sensed data,which were not found in the past field archaeological investigations and unreported in the past public literatures.Most of the unknown remnants that were identified using remote sensing images are distributed in the surroundings of the ancient city sites,including Lvcheng and BJ2008.New findings fill the gaps that the historical remnants were missed during the previous field surveys.     

A Hierarchical Classification and Iterative Model based Methodfor Remote Sensing Classification of Land Cover

Land cover classification based on remote sensing is an important means to analyze the change and spatial pattern of land use.In order to further improve the classification accuracy,this paper proposed a hierarchical classification and iterative CART model based method for remote sensing classification of landcover.Firstly,the extraction order of land cover classes was determined based on the class separability evaluation,which was water,vegetation,bare soil and built-up land.Secondly,we selected the optimal image segmentation parameters and a set of sensitive features for each class during the hierarchical classification process.Finally,object-based training samples were selected to be fed into the iterative CART algorithm for the successive extraction of the first three classes,with the remaining unclassified objects being directly assigned to the last class.Results demonstrated that the proposed method can significantly reduce the mixture between bare soil and built-up land,and is capable of achieving landcover classification with much higher accuracy.The proposed method achieved an overall accuracy of 85.76% and a Kappa efficient of 0.72,with the performance improvements ranging from 10.67% to 16.5% and 0.15 to 0.21 as compared SVM and CART single classification methods.The classification accuracy of a specific class can be flexibly adjusted using this method,giving different purposes of classification.This method can also be easily extended to other districts and disciplines involving remote sensing image classification.     

Correlation Analysis of Vegetation Physical Parameters and Polarization Decomposition Parameters based on Grey System Theory：#br# A Case Study in Poyang Lake Wetland

based on the Grey System Theory,combing the three periods(2013,2015,and 2016) of the Radarsat-2 Polarimetric SAR(PolSAR) data and the vegetation physical parameters data collected from Poyang Lake wetland,we established the relationship model for the vegetation physical parameters with vegetation biomass and the polarization decomposition components,respectively.We analyzed the contribution of different vegetation physical parameters to biomass accumulation and their influence on polarization decomposition components.The results show that from the vegetation growing faster to slower stage,the plant parameters and underlying surface parameters are the main factors that contribute to the vegetation biomass accumulation.The main effective factors for the polarization decomposition components are the land surface parameters and the stem parameters.The parameters of field sampling are analyzed and determined based on the larger correlation degree data at each stage.     

Comparative Study on Thermal Environment between Old Urban Areas and New Urban Areas based on Landsat 8 Remote Sensing Image：A Case Study of Chengdu City

A large number of new urban areas have emerged under the rapid urbanization background in recent years in China,and the characteristics of urban thermal environment have significant changes.To analyze the thermal environment diversities between old urban areas and new urban areas and explore the impacting factors,we chose Chengdu City in Sichuan Province as a typical study area.Key surface parameters,including Land Surface Temperature (LST),building index and vegetation index were derived based on Landsat 8 satellite image acquired on August 13,2014.The comparison study was conducted to analyze the differences of the parameters related to thermal environment changes,and the results indicated that：①Overall,the average surface temperature of the old urban area was higher than that of the new urban area.Regarding the spatial distribution,the central and northern region had higher temperature than the southern region for the old urban areas.In the new urban areas,although high temperature spots can be found in the central west or north,this region generally had a relatively low temperature.②The old urban area emphasized a higher Urban|Heat|Island|Ratio|Index (URI),which revealed the descending trend of surface temperature via analyzing the thermal field profile from the old to the new.This showed that the urban thermal environment effect of the old city was stronger than the new urban area.③“Heat Island Effect” was easy to emerge in those areas with high density urban construction and little urban vegetation coverage,whereas reasonable urban landscape planning and layout would help to perfect the urban thermal environment.The comparison of the thermal environment effects between the new urban areas and the old urban areas shows the new urban planning in Chengdu has positively contributed to improve the thermal environment in the new urban areas,which can provide important reference for the future urban planning.     

Analysis of Landscape Characteristics of Urban Heat /Sink Islandin Hangzhou based on ASTER Remote Sensing Imagery

Rapid urbanization has significant contributions to the Surface Urban Heat Island (SUHI).Analyzing the SUHI distribution and its impact factors using remote sensing data has received increasing attentions in the past decades,whereas few study has investigated that of the surface Urban Heat Sink Island (SUHI).The paper selects Hangzhou metropolis as a case study to explore SUHI/SUHS spatial patterns and its causes.We first retrieve the Land Surface Temperature (LST) using ASTER thermal infrared remote sensing imagery and extract the region of SUHI/SUHS using the Mean\|Standard deviation method.Landsat8 OLI data is used to classify land use and extract both impervious surface and vegetation information.After that,different landscape patterns within SUHI/SUHS area are analyzed and quantified by using several selected landscape index.The largest impact factors in SUHI/SUHS areas are identified.Finally,we analyze the spatial characteristics of LST using the spatial gradient analysis method,and reveal its relationship with vegetation and impervious surface.The results show that：(1) a large landscape pattern difference exists within SUHI/SUHS area;the impervious surface has the greatest impact on LST of the SUHI area,whereas the vegetation has more obviously cooling effect on LST of the SUHS area than the water body;(2) with the increasing distance from the city center,the same trend was found between the mean LST values and the impervious surface density (positive correlations),whereas the opposite trend between the mean LST values and the vegetation density (negative correlations).And the warming effect of impervious surface is greater than the cooling effect of vegetation in Hangzhou.     

Business Architecture： A Case Study of a Medium Enterprise in Mexico

Enterprise architecture is a subject that has increased its importance in the small and medium enterprises in the manufacturing sector of the industry in Mexico. The global competitiveness of the markets has influenced the adoption of methodologies that support the strategic alignment of the processes with the goals and strategic objectives of the firms. The components of the business architecture like mission, vision, strategic objectives, products, organizational structure, business prdcesses, clients and geographic region, were collected from the firm of the case study for the design of the architecture. As a result of the practical application, an implementation model has been created and four strategic objectives were established for to improve productivity and competitiveness. This paper is a result of the architecture in a medium size manufacturing company like partial research project of analysis, design and implementing business architecture of an enterprise using ontologies for representing the core elements of the business architecture; the study presents clearly the importance of the strategic planning for the analysis and the detection of the main faults for the success of the achievement of goals and objectives.     

Assessing Vertical Greenery Distribution and Spatial Allocation based on Multi-source Remote Sensing：A Case Study of Székesfehérvár City，Hungary#br#

Taking as Székesfehérvár,Hungary a case study,vertical greenery distribution and spatial allocation is studied.Based on the multi\|source remote sensing data,three-dimensional information of urban green space and buildings is extracted.A height sampling method is used to quantify the vertical distribution of green space and building space.According to the spatial allocation relationship,the vertical space is divided into “Green\|deficient layer” and “Green\|sufficient layer”,and the vertical spatial allocation characteristics of different function zonings are compared and analyzed.Case study findings indicate that：(1) greenery vertical structure of residential area and commercial area are similar,but residential area has greater allocation quantity; (2) the first to third building floors in commercial area are considered green-deficient due to the shortage of green space allocation and its monotonous vertical structure,and there is inadequate green space provision for high\|rise building floors in residential area; (3) the major cause for low\|rise green deprivation in commercial area is the high density of low\|rise building structure,and neglecting skyrise greenery results in the high\|rise green deprivation in residential area.      

Model Building and Application for Monitoring and Assessing the Aging Conditions of Asphalt Road Pavementsbased on Remote Sensing Data

To meet the demands in monitoring the health conditions of road pavements over a relatively large area,by means of derivative and continuum removal approaches this study analyzes the spectral features of asphalt road pavements aging degrees based on the field measurements of pavement spectra.Distinct spectral features of new and aged asphalt road pavements were observed in the wavelength regions of 400~680 nm and 860~970 nm.After that,a WorldView-2 image in Liangxiang area,Fangshan district,Beijing City were captured and the corresponding bands were used to create a Multiplication Aging Index (MAI) to reflect the aging conditions of asphalt road pavements.Comparison between the MAI and in-situ measurements of the spectra and aging conditions of the road pavements in the study area was performed,and statistical analysis was also conducted based on the Munsell brightness values collected in the field investigation.Through the contrast,the aging condition from MAI has good relevance to the in-situ measurements.Results indicate that the proposed MAI index can reflect the aging conditions well and is further used to monitor the pavement quality of the 14 road pavements in the study area.According to the evaluation,six roads in the study area need road maintenance.The research can offer a new technology for road management departments to make their road maintenance plans.     

Study on Glacier Classification Methods of Airborne Hyperspectral Data——A Case Study of the Zhongxi-1 Glacier

The glacier is an important natural and great potential of the fresh water resources,and plays a vital role in the regional ecological environment balance and stability.This study acquired the airborne hyperspectral data over Zhongxi-1 Glacier in August,2011.Firstly,the data preprocessing,including radiation calibration,atmospheric correction and geometric correction was performed on the hyperspectral data;secondly,using principal component analysis (PCA)and minimum noise transformation (MNF) for data dimensionality reduced respectively;thirdly,six classification methods,i.e.maximum likelihood method,minimum distance,Mahalanobis distance method,spectral angle method binary encoding,and spectral information divergence,were applied in the two datasets,and also the comparison results of the different classification methods were conducted to determine the optimal method of data dimensionality reduction and the optimal classification method;finally,the hyperspectral data for glacierclassification was compared with the HJ satellite multispectral data.The results show that: the classification accuracy of the PCA transform data from hyperspectral data is higher than that of MNF transform data;for the PCA transformed dataset of hyperspectraldata,the Mahalanobis distance method,maximum likelihood method,minimum distance method produced better classification results with the comparison to others,while for the MNF transformed dataset from hyperspectral data,the spectral angle method and spectral information divergence method is better than others.