2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)最新文献_第2页

Tools for multitemporal analysis and classification of multisource satellite imagery 多源卫星图像的多时相分析和分类工具

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005085

A. Masse, D. Ducrot, P. Marthon

引用次数: 9

Exploring the capacity to grasp multi-annual seasonal variability of winter wheat in Continental Climates with MODIS 探索利用MODIS技术掌握大陆气候条件下冬小麦多年季节变化的能力

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005088

R. d’Andrimont, G. Duveiller, P. Defourny

{"title":"Exploring the capacity to grasp multi-annual seasonal variability of winter wheat in Continental Climates with MODIS","authors":"R. d’Andrimont, G. Duveiller, P. Defourny","doi":"10.1109/MULTI-TEMP.2011.6005088","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005088","url":null,"abstract":"This paper presents some exploratory results of the FP-7 MOCCCASIN project that aims to MOnitor Crops in Continental Climates through ASsimilation of Satellite Information. MOCCCASIN is a collaborative project which focuses on improving the monitoring of winter-wheat and forecasting of winter-wheat yield in Russia by combining modelling techniques with satellite data assimilation [1]. In continental climate, winter wheat is particularly affected by low temperatures during the winter which determine whether rapid regrowth is possible in spring. A pre-requisite to use satellite earth observation to characterize the effect of winter kill on wheat is to determine if the multi-annual seasonal variability over the entire growing season can be grasped by remote sensing indicators. The results over an exploratory study site in Tula region for 5 years (2005–2009) demonstrate that it was possible to retrieve crop status indicators using an approach combining radiative transfer modeling and neural networks which could inform on where winter kill has stricken.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114715937","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: 0

Detection of small changes in airborne hyperspectral imagery: Experimental results over urban areas 机载高光谱图像微小变化的检测:城市地区的实验结果

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005033

Salvatore Resta, N. Acito, M. Diani, G. Corsini, T. Opsahl, T. Haavardsholm

引用次数: 13

Braided river dynamics determined using satellite imagery — Upper Rakaia River, Canterbury, New Zealand 利用卫星图像确定辫状河动态-新西兰坎特伯雷拉凯亚河上游

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005092

M. Tuohy

引用次数: 0

Using NASA'S Long Term Data Record version 3 for the monitoring of land surface vegetation 使用NASA的长期数据记录版本3来监测陆地表面植被

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005096

J. Sobrino, Y. Julien, C. Mattar, R. Oltra-Carrió, J. Jiménez-Muñoz, G. Sòria, B. Franch, V. Hidalgo

引用次数: 0

NDVI time series and Markov chains to model the change of fuzzy vegetative drought classes NDVI时间序列和马尔可夫链对模糊植被干旱类型变化的模拟

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005083

S. Ding, C. M. Rulinda, A. Stein, W. Bijker

{"title":"NDVI time series and Markov chains to model the change of fuzzy vegetative drought classes","authors":"S. Ding, C. M. Rulinda, A. Stein, W. Bijker","doi":"10.1109/MULTI-TEMP.2011.6005083","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005083","url":null,"abstract":"The objective of this study is to explore the potential of using Markov chains to model the changes of vegetative drought classes. NOAA-AVHRR dekadal NDVI images and fuzzy functions are used to characterize the drought classes while capturing the gradual transition between them. The transition probabilities are estimated using the maximum class membership values at a location. The Markov transition probability matrix is then used to model the changes of vegetative drought classes at selected locations. Future vegetative drought classes are predicted using the estimated transition matrix, then compared with actual data. Twenty pixel locations clustered in four regions of the two main agricultural type in Kenya are selected to implement this approach. Half of the pixels are predicted correctly. 5 of them are predicted either one class higher or lower and 2 of them, two classes higher. We can conclude that Markov chains applied to fuzzy numbers have the potential to model the changes of of vegetative drought classes at a pixel, hence provide a benefit for early warning systems.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121766868","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: 3

Unravelling long-term vegetation change patterns in a binational watershed using multitemporal land cover data and historical photography 利用多时相土地覆盖数据和历史摄影揭示两国流域的长期植被变化模式

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005058

M. Villarreal, L. Norman, R. Webb, Diane E. Boyer, R. Turner

{"title":"Unravelling long-term vegetation change patterns in a binational watershed using multitemporal land cover data and historical photography","authors":"M. Villarreal, L. Norman, R. Webb, Diane E. Boyer, R. Turner","doi":"10.1109/MULTI-TEMP.2011.6005058","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005058","url":null,"abstract":"A significant amount of research conducted in the Sonoran Desert of North America has documented, both anecdotally and empirically, major vegetation changes over the past century due to human land use activities. However, many studies lack coincidental landscape-scale data characterizing the spatial and temporal manifestation of these changes. Vegetation changes in a binational (USA and Mexico) watershed were documented using a series of four land cover maps (1979–2009) derived from multispectral satellite imagery. Cover changes are compared to georeferenced, repeat oblique photographs dating from the late 19th century to present. Results indicate the expansion of grassland over the past 20 years following nearly a century of decline. Historical repeat photography documents early-mid 20th century mesquite invasions, but recent land cover data and rephotography demonstrate declines in xeroriparian/riparian mesquite communities in recent decades. These vegetation changes are variable over the landscape and influenced by topography and land management.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132783909","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: 0

Multi-temporal damage assessment of linear infrastructural objects using Dynamic Bayesian Networks 基于动态贝叶斯网络的线性基础设施物体多时态损伤评估

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005048

D. Frey, M. Butenuth

引用次数: 1

Deriving plant phenology from remote sensing 基于遥感的植物物候学研究

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005098

G. Roerink, M. Danes, O. G. Prieto, A. de Wit, A. V. van Vliet

{"title":"Deriving plant phenology from remote sensing","authors":"G. Roerink, M. Danes, O. G. Prieto, A. de Wit, A. V. van Vliet","doi":"10.1109/MULTI-TEMP.2011.6005098","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005098","url":null,"abstract":"Plant phenology is the study of the timing of periodic vegetation cycles and their connection to climate. Examples are the date of emergence of leaves and flowers or the date of leaf colouring and fall in deciduous trees. It is an independent measure on how ecosystems are responding to climate change and therefore experiencing renewed interest from the scientific research community. This paper describes a method to derive plant phenology indicators from time series of satellite images. The satellite images are Normalized Difference Vegetation Index (NDVI) images from the MODIS sensor, which encompass the European continent from 2000 onwards. The Harmonic Analysis of NDVI Time Series (HANTS) algorithm is used to process and analyse the time series of satellite images for each individual year. The resulting amplitude and phase values are translated into commonly understandable phenology indicators like start of growing season, which can be linked again to the biological definitions of plant phenology. The indicators are validated with field observations, recorded by a volunteer's network in the Netherlands and Germany. Conclusions are that the method produces consistant maps, which correlate well with the crop type. However, on average the remote sensing derived start of season is 14 days earlier than the observed values.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134315980","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: 7

Dynamic mapping of cropland areas in Sub-Saharan Africa using MODIS time series 使用MODIS时间序列的撒哈拉以南非洲农田面积动态制图

2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp) Pub Date : 2011-07-12 DOI: 10.1109/MULTI-TEMP.2011.6005038

C. Vancutsem, Jean-François Pekel, F. Kayitakire

引用次数: 4