2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)最新文献_第4页

A robust approach for phenological change detection within satellite image time series 卫星影像时间序列物候变化检测的鲁棒方法

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.2011.6005042

J. Verbesselt, M. Herold, Rob J Hyndman, A. Zeileis, D. Culvenor

{"title":"A robust approach for phenological change detection within satellite image time series","authors":"J. Verbesselt, M. Herold, Rob J Hyndman, A. Zeileis, D. Culvenor","doi":"10.1109/MULTI-TEMP.2011.6005042","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005042","url":null,"abstract":"The majority of phenological studies have focussed on extracting critical points, i.e. phenological metrics such as start-of-season, in the seasonal growth cycle. These metrics do not exploit the full temporal detail of time series, depend on their definition or threshold, and are influenced by disturbances. Here, we evaluated a robust phenological change detection ability of a method for detecting abrupt, gradual, and phenological changes within time series. BFAST, Breaks For Additive Seasonal and Trend method, integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within trend and seasonal (i.e. phenology) component. We tested BFAST by analysing 16-day MODIS NDVI composites (MOD13C1 collection 5) between 2000–2009 covering Australia. This illustrated that the method is able to detect the timing of major phenological changes within time series while accounting for abrupt disturbances and gradual trends. It was also shown that the phenological change detection is influenced by the signal-to-noise ratio of the time series. The BFAST method is a generic change detection method which can be applied to any time series data. The methods are available in the BFAST package for R [1] from CRAN (http://CRAN.R-project. org/package=bfast).","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"31 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":"124703726","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

Phenology of the natural vegetation: A land cover specific approach for a reference dataset in Central Africa 自然植被物候学:中非参考数据集的土地覆盖具体方法

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.2011.6005097

A. Verhegghen, P. Defourny

引用次数: 0

Use of multi-annual MODIS Land Surface Temperature data for the characterization of the heat requirements for grapevine varieties 利用多年MODIS地表温度数据表征葡萄品种的热需求

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.2011.6005089

R. Zorer, D. Rocchini, L. Delucchi, F. Zottele, F. Meggio, M. Neteler

引用次数: 12

Time-series analysis of rainforest clearing in Sabah, Borneo using Landsat imagery 利用陆地卫星图像对婆罗洲沙巴雨林砍伐的时间序列分析

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.2011.6005102

K. Johansen, K. Johansen

引用次数: 0

Bathymetry from fusion of multi-temporal Landsat and radar altimetery 多时相陆地卫星和雷达测高仪融合的测深

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.2011.6005080

R. Abileah, S. Vignudelli

引用次数: 5

Spatiotemporal mining of ENVISAT SAR interferogram time series over the Haiyuan fault in China 海原断裂带ENVISAT SAR干涉图时间序列的时空挖掘

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.2011.6005067

N. Méger, Romain Jolivet, Cécile Lasserre, E. Trouvé, C. Rigotti, F. Lodge, M. Doin, Stephane Guillaso, Andreea Julea, P. Bolon

引用次数: 5

A hyperspectral reflectance data based model inversion methodology to detect reniform nematodes in cotton 基于高光谱反射数据的模型反演方法在棉花中检测肾形线虫

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.2011.6005095

P. K. Palacharla, S. Durbha, R. King, B. Gokaraju, G. Lawrence

{"title":"A hyperspectral reflectance data based model inversion methodology to detect reniform nematodes in cotton","authors":"P. K. Palacharla, S. Durbha, R. King, B. Gokaraju, G. Lawrence","doi":"10.1109/MULTI-TEMP.2011.6005095","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005095","url":null,"abstract":"Rotylenchulus reniformis is a newly emerging nematode species affecting the cotton crop and quickly spreading throughout the southeastern United States. Effective use of nematicides at a variable rate is the only economic counter measure. It requires the nematode population in the field to be known, which in turn depends on the collection of soil samples from the field and analyzing them in the laboratory. This process is economically prohibitive. Hence there is a need to develop alternative methods through which the actual numbers of reniform nematode present in the field can be determined. In this paper we propose a methodology in which a canopy reflectance model (PROSAIL) is inverted using machine learning approaches to retrieve the biophysical parameters, and relate the key variables to the nematode levels, so that it is possible to quantify at all multi-temporal intervals the nematode infestation at geographically distributed fields. A Support Vector Machine (SVM) Regression method is used for the inversion and retrieval of key biophysical parameters which help to understand and quantify the nature of the nematode infested vegetation. The performance of this approach is analyzed by the accuracy measures of RMSE and N-fold cross validation average on a considerable data set. Finally, a graphical web portal is being developed to facilitate the end users to use their field collected data to determine the extent of the nematode infestation in their crop and retrieve other spatio-temporal statistics.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"43 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":"121555539","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

Clustering analysis applied to NDVI/NOAA multitemporal images to improve the monitoring process of sugarcane crops 将聚类分析应用于NDVI/NOAA多时相图像，改进甘蔗作物的监测过程

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.2011.6005040

L. A. Romani, R. R. V. Gonçalves, B. Amaral, D. Y. T. Chino, J. Zullo, C. Traina, E. P. M. Sousa, A. J. Traina

引用次数: 17

Spatiotemporal dimensionality and time-space characterization of vegetation phenology from multitemporal MODIS EVI 基于多时相MODIS EVI的植被物候时空特征研究

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.2011.6005049

C. Small

{"title":"Spatiotemporal dimensionality and time-space characterization of vegetation phenology from multitemporal MODIS EVI","authors":"C. Small","doi":"10.1109/MULTI-TEMP.2011.6005049","DOIUrl":"https://doi.org/10.1109/MULTI-TEMP.2011.6005049","url":null,"abstract":"Spatiotemporal dimensionality refers to the structure of the continuum of spatial and temporal patterns in an image time series. Time-Space characterization refers to an approach for representing this continuum as combinations of spatial and temporal components with a minimum of assumptions about the forms of the patterns. Patterns can be related to processes through modeling — both deterministic and statistical. By combining characterization and modeling, two complementary analytical tools can be used together so that each resolves a key limitation of the other. Empirical Orthogonal Function analysis, used in conjunction with Temporal Mixture Models, provide a way to 1) Represent the spatiotemporal dimensionality of an image time series, 2) Identify distinct temporal modes and their spatial distributions, and 3) Map the relative contributions of these modes to the observed image time series as spatially continuous fields. Some strengths and limitations of Time-Space characterization are illustrated using multitemporal MODIS EVI time series of vegetation dynamics on the Ganges-Brahmaputra delta.","PeriodicalId":254778,"journal":{"name":"2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)","volume":"44 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":"127055687","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: 4

Land cover change detection thresholds for Landsat data samples 陆地卫星数据样本的土地覆盖变化检测阈值

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.2011.6005084

R. Rasi, O. Kissiyar, M. Vollmar

引用次数: 3