oalib
Search Results: 1 - 10 of 100 matches for " "
All listed articles are free for downloading (OA Articles)
Page 1 /100
Display every page Item
High performance computing software package for multitemporal Remote-Sensing computations
Nizar Ben Achhab,,Naoufal Raissouni,,Abdelilah Azyat,Asaad Chahboun
International Journal of Engineering and Technology , 2010,
Abstract: With the huge satellite data actually stored, remote sensing multitemporal study is nowadays one of the most challenging fields of computer science. The multicore hardware support and Multithreading can play an important role in speeding up algorithm computations. In the present paper, a software package (called Multitemporal Software Package for Satellite Remote sensing data (MSPSRS)) has been developed for the multitemporal treatment of satellite remote sensing images in a standard format. Due to portability intend, the interface was developed using the QT application framework and the core wasdeveloped integrating C++ classes. MSP.SRS can run under different operating systems (i.e., Linux, Mac OS X, Windows, Embedded Linux, Windows CE, etc.). Final benchmark results, using multiple remote sensing biophysical indices, show a gain up to 6X on a quad core i7 personal computer.
Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data  [PDF]
Soe W. Myint,May Yuan,Randall S. Cerveny,Chandra P. Giri
Sensors , 2008, DOI: 10.3390/s8021128
Abstract: Remote sensing techniques have been shown effective for large-scale damagesurveys after a hazardous event in both near real-time or post-event analyses. The paperaims to compare accuracy of common imaging processing techniques to detect tornadodamage tracks from Landsat TM data. We employed the direct change detection approachusing two sets of images acquired before and after the tornado event to produce a principalcomponent composite images and a set of image difference bands. Techniques in thecomparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment isbased on Kappa coefficient calculated from error matrices which cross tabulate correctlyidentified cells on the TM image and commission and omission errors in the result. Overall,the Object-oriented Approach exhibits the highest degree of accuracy in tornado damagedetection. PCA and Image Differencing methods show comparable outcomes. Whileselected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approachperforms significantly better with 15-20% higher accuracy than the other two techniques.
Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data
Soe W. Myint,May Yuan,Randall S. Cerveny,Chandra P. Giri
Sensors , 2008,
Abstract: Remote sensing techniques have been shown effective for large-scale damagesurveys after a hazardous event in both near real-time or post-event analyses. The paperaims to compare accuracy of common imaging processing techniques to detect tornadodamage tracks from Landsat TM data. We employed the direct change detection approachusing two sets of images acquired before and after the tornado event to produce a principalcomponent composite images and a set of image difference bands. Techniques in thecomparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment isbased on Kappa coefficient calculated from error matrices which cross tabulate correctlyidentified cells on the TM image and commission and omission errors in the result. Overall,the Object-oriented Approach exhibits the highest degree of accuracy in tornado damagedetection. PCA and Image Differencing methods show comparable outcomes. Whileselected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approachperforms significantly better with 15-20% higher accuracy than the other two techniques.
Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN) and Landsat Remote Sensing Imagery  [PDF]
Luis Samaniego,Karsten Schulz
Remote Sensing , 2009, DOI: 10.3390/rs1040875
Abstract: Nearest neighbor techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to find an adaptive metric that combines a local variance reducing technique and a linear embedding of the observation space into an appropriate Euclidean space. To illustrate the application of this technique, two agricultural land cover classifications using mono-temporal and multi-temporal Landsat scenes are presented. The results of the study, compared with standard approaches used in remote sensing such as maximum likelihood (ML) or k-Nearest Neighbor (k-NN) indicate substantial improvement with regard to the overall accuracy and the cardinality of the calibration data set. Also, using MNN in a soft/fuzzy classification framework demonstrated to be a very useful tool in order to derive critical areas that need some further attention and investment concerning additional calibration data.
Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey  [PDF]
Sel?uk Reis
Sensors , 2008, DOI: 10.3390/s8106188
Abstract: Mapping land use/land cover (LULC) changes at regional scales is essential for a wide range of applications, including landslide, erosion, land planning, global warming etc. LULC alterations (based especially on human activities), negatively effect the patterns of climate, the patterns of natural hazard and socio-economic dynamics in global and local scale. In this study, LULC changes are investigated by using of Remote Sensing and Geographic Information Systems (GIS) in Rize, North-East Turkey. For this purpose, firstly supervised classification technique is applied to Landsat images acquired in 1976 and 2000. Image Classification of six reflective bands of two Landsat images is carried out by using maximum likelihood method with the aid of ground truth data obtained from aerial images dated 1973 and 2002. The second part focused on land use land cover changes by using change detection comparison (pixel by pixel). In third part of the study, the land cover changes are analyzed according to the topographic structure (slope and altitude) by using GIS functions. The results indicate that severe land cover changes have occurred in agricultural (36.2%) (especially in tea gardens), urban (117%), pasture (-72.8%) and forestry (-12.8%) areas has been experienced in the region between 1976 and 2000. It was seen that the LULC changes were mostly occurred in coastal areas and in areas having low slope values.
Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery  [PDF]
Luis C. Alatorre,Raquel Sánchez-Andrés,Santos Cirujano,Santiago Beguería,Salvador Sánchez-Carrillo
Remote Sensing , 2011, DOI: 10.3390/rs3081568
Abstract: In remote sensing, traditional methodologies for image classification consider the spectral values of a pixel in different image bands. More recently, classification methods have used neighboring pixels to provide more information. In the present study, we used these more advanced techniques to discriminate between mangrove and non?mangrove regions in the Gulf of California of northwestern Mexico. A maximum likelihood algorithm was used to obtain a spectral distance map of the vegetation signature characteristic of mangrove areas. Receiver operating characteristic (ROC) curve analysis was applied to this map to improve classification. Two classification thresholds were set to determine mangrove and non-mangrove areas, and two performance statistics (sensitivity and specificity) were calculated to express the uncertainty (errors of omission and commission) associated with the two maps. The surface area of the mangrove category obtained by maximum likelihood classification was slightly higher than that obtained from the land cover map generated by the ROC curve, but with the difference of these areas to have a high level of accuracy in the prediction of the model. This suggests a considerable degree of uncertainty in the spectral signatures of pixels that distinguish mangrove forest from other land cover categories.
Estimating CO2 Sequestration by Forests in Oita Prefecture, Japan, by Combining LANDSAT ETM+ and ALOS Satellite Remote Sensing Data  [PDF]
Kazadi Sanga-Ngoie,Kotaro Iizuka,Shoko Kobayashi
Remote Sensing , 2012, DOI: 10.3390/rs4113544
Abstract: CO2 sequestration of the forests in Oita Prefecture, Japan, was estimated using satellite remote sensing data. First, hybrid classification of the optical LANDSAT ETM+ data was performed using GIS to produce a detailed land cover map. CO2 sequestration for each forest type was calculated using the sequestration rates per unit area multiplied by the forest areas obtained from the land cover map This results in 3.57 MtCO2/yr for coniferous, 0.77 MtCO2/yr for deciduous broadleaf, and 2.25 MtCO2/yr for evergreen broadleaf, equivalent to a total of 6.60 MtCO2/yr for all the forest covers in Oita. Then, two different methodologies were used to improve these estimates by considering tree ages: the Normalized Difference Vegetation Index (NDVI) and the stem volume methods. Calculation using the NDVI method shows the limitation of this method in providing detailed estimations for trees older than 15 years, because of NDVI saturation beyond this age. In the stem volume method, tree ages were deduced from stem volume values obtained by using PALSAR backscattering data. Sequestration based on tree age forest subclasses yields 2.96 MtCO2/yr (coniferous) and 0.31 MtCO2/yr (deciduous broadleaf forests). These results show the importance of using not only detailed forest types, but also detailed tree age information for more realistic CO2 sequestration estimates. In so doing, overestimation of the sequestration capacity of forests could be avoided, and the information on the status and location of forest resources could be improved, thereby leading to sounder decision making in sustainable management of forest resources.
REMOTE SENSING ANALYSIS OF THE RELATIONSHIPS BETWEEN DAYTIME GROUND BRIGHT TEMPERATURE AND LAND-USE TYPES OF CITY——WITH SHANGHAI AS AN EXAMPLE
城市白天地面亮温与下垫面类型关系的遥感分析——以上海为例

YIN Qiu,ZHU Shan-You,GONG Cai-Lan,
尹球
,祝善友,巩彩兰

红外与毫米波学报 , 2009,
Abstract: Weakening the effect of heat island by environment construction is one of the key aspects for city development.To assure the reasonable planning of city construction,it is helpful analyzing the effect of land use types on ground heat distribution to get scientific data.In this study,by using Landsat ETM+ daytime remote sensing data of Shanghai,the inversion of ground bright temperature(GBT),the compose of color map and the classification of land use were made.On this basis,the relationships between daytime ...
基于Landsat 8的深圳市森林碳储量遥感反演研究
Remote Sensing Retrieval of Forest Carbon Storage in Shenzhen Based on Landsat 8 Images
 [PDF]

邹琪,孙华,王广兴,林辉,谭一凡,马中刚
- , 2017, DOI: 10.3969/j.issn.1001-7461.2017.04.29
Abstract: 以2014年Landsat 8遥感影像为数据源,研究了深圳市森林碳储量遥感反演模型的构建及其空间分布情况,对城市生态系统碳循环研究具有重要意义。采用分层随机抽样的方式布设168个样地,结合外业样地数据,从遥感影像中提取31个植被指数作为自变量,分别构建了多元线性回归模型、Logistic回归模型和Radical Basis Function(RBF)径向基函数神经网络模型,进而估算该地区的森林碳储量并比较分析。结果表明,RBF神经网络模型的估算精度最高,决定系数最大且均方根误差最小,分别为0.829 t?hm-2和9.131 t?hm-2;Logistic回归模型估算精度次之,决定系数和均方根误差分别为0.523 t?hm-2和11.821 t?hm-2;多元线性回归模型估算精度最低,决定系数最小,均方根误差最大,分别为0.438 t?hm-2和12.870 t?hm-2。可见,RBF神经网络模型能更好地模拟森林碳储量与各个因子之间的关系。研究区森林碳储量的空间分布特点表现为东南沿海部分碳储量大,中西部城市经济开发区碳储量小,与实际森林分布基本一致。
With Landsat 8 remote sensing images acquired in 2014 as datum source,remote sensing retrieval of forest carbon storage in Shenzhen was conducted and the spatial distribution was analyzed.A total of 168 sample plots were selected by a stratified random sampling procedure.A total of 31 vegetation indices were extracted from the images and used as independent variables,and urban forest carbon storage from field sampling plots was a dependent variable.Multivariate-stepwise regression model,Logistic regression model and radical basis function (RBF) neutral network model were developed to estimate forest carbon storage of the study area.The results showed that:the estimation accuracy of RBF neutral network model was the highest with the greatest determination coefficient and smallest root mean square error (RMSE) of 0.829 and 9.131 t?hm-2,respectively; the determination coefficient and RMSE of Logistic regression model were 0.723 and 11.821 t?hm-2,taking the second place.Multi-stepwise regression model had the lowest estimation accuracy with the determination coefficient and RMSE of 0.662 and 12.870 t?hm-2.Therefore,the relationship between urban forest carbon and image derived spectral variables could be modeled and described better by RBF neutral network model.The spatial distribution of forest carbon storage of the study area was characterized by larger estimates in the southeast coast and smaller estimates in the development zones of the mid-west city,being consistent with the actual spatial patterns of forests
Monitoring of Net Primary Production in California Rangelands Using Landsat and MODIS Satellite Remote Sensing  [PDF]
Shuang Li, Christopher Potter, Cyrus Hiatt
Natural Resources (NR) , 2012, DOI: 10.4236/nr.2012.32009
Abstract: In this study, we present results from the CASA (Carnegie-Ames-Stanford Approach) model to estimate net primary production (NPP) in grasslands under different management (ranching versus unmanaged) on the Central Coast of California. The latest model version called CASA Express has been designed to estimate monthly patterns in carbon fixation and plant biomass production using moderate spatial resolution (30 m to 250 m) satellite image data of surface vegetation characteristics. Landsat imagery with 30 m resolution was adjusted by contemporaneous Moderate Resolution Imaging Spectroradiometer (MODIS) data to calibrate the model based on previous CASA research. Results showed annual NPP predictions of between 300 - 450 grams C per square meter for coastal rangeland sites. Irrigation increased the predicted NPP carbon flux of grazed lands by 59 grams C per square meter annually compared to unmanaged grasslands. Low intensity grazing activity appeared to promote higher grass regrowth until June, compared to the ungrazed grassland sites. These modeling methods were shown to be successful in capturing the differing seasonal growing cycles of rangeland forage production across the area of individual ranch properties.
Page 1 /100
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.