{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Elizabeth Foughty", "hasEmail": "mailto:elizabeth.a.foughty@nasa.gov"}, "description": "SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS\r\nMONITORING USING GAUSSIAN PROCESS\r\n\r\nVARUN CHANDOLA* AND RANGA RAJU VATSAVAI*\r\n\r\nAbstract. Biomass monitoring, specifically, detecting changes in the biomass or vegetation of\r\na geographical region, is vital for studying the carbon cycle of the system and has significant\r\nimplications in the context of understanding climate change and its impacts. Recently, several time\r\nseries change detection methods have been proposed to identify land cover changes in temporal\r\nprofiles (time series) of vegetation collected using remote sensing instruments. In this paper, we\r\nadapt Gaussian process regression to detect changes in such time series in an online fashion. While\r\nGaussian process (GP) has been widely used as a kernel based learning method for regression and\r\nclassification, their applicability to massive spatio-temporal data sets, such as remote sensing data,\r\nhas been limited owing to the high computational costs involved. In our previous work we proposed\r\nan efficient Toeplitz matrix based solution for scalable GP parameter estimation. In this paper we\r\napply these solutions to a GP based change detection algorithm. The proposed change detection\r\nalgorithm requires a memory footprint which is linear in the length of the input time series and\r\nruns in time which is quadratic to the length of the input time series. Experimental results show\r\nthat both serial and parallel implementations of our proposed method achieve significant speedups\r\nover the serial implementation. Finally, we demonstrate the effectiveness of the proposed change\r\ndetection method in identifying changes in Normalized Difference Vegetation Index (NDVI) data.", "distribution": [{"@type": "dcat:Distribution", "description": "SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/Paper_6_.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "Paper 6 .pdf"}], "identifier": "DASHLINK_228", "issued": "2010-10-13", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/228/", "modified": "2025-03-31", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS"}