TATSSI: A Free and Open-Source Platform for Analyzing Earth Observation Products with Quality Data Assessment
| Autor: | María Isabel Cruz-López, Inder Tecuapetla-Gómez, Gerardo López-Saldaña, Rainer Ressl |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Earth observation
VIIRS 010504 meteorology & atmospheric sciences Computer science Group method of data handling Geography Planning and Development 0211 other engineering and technologies Context (language use) 02 engineering and technology satellite imagery computer.software_genre 01 natural sciences quality data assessment Earth and Planetary Sciences (miscellaneous) Computers in Earth Sciences Time series 021101 geological & geomatics engineering 0105 earth and related environmental sciences computer.programming_language Graphical user interface Geography (General) Database Application programming interface business.industry Python software Python (programming language) MODIS Data quality G1-922 time series business Landsat computer |
| Zdroj: | ISPRS International Journal of Geo-Information, Vol 10, Iss 267, p 267 (2021) ISPRS International Journal of Geo-Information Volume 10 Issue 4 |
| ISSN: | 2220-9964 |
| DOI: | 10.3390/ijgi10040267 |
| Popis: | Earth observation (EO) data play a crucial role in monitoring ecosystems and environmental processes. Time series of satellite data are essential for long-term studies in this context. Working with large volumes of satellite data, however, can still be a challenge, as the computational environment with respect to storage, processing and data handling can be demanding, which sometimes can be perceived as a barrier when using EO data for scientific purposes. In particular, open-source developments which comprise all components of EO data handling and analysis are still scarce. To overcome this difficulty, we present Tools for Analyzing Time Series of Satellite Imagery (TATSSI), an open-source platform written in Python that provides routines for downloading, generating, gap-filling, smoothing, analyzing and exporting EO time series. Since TATSSI integrates quality assessment and quality control flags when generating time series, data quality analysis is the backbone of any analysis made with the platform. We discuss TATSSI’s 3-layered architecture (data handling, engine and three application programming interfaces (API)) by allowing three APIs (a native graphical user interface, some Jupyter Notebooks and the Python command line) this development is exceptionally user-friendly. Furthermore, to demonstrate the application potential of TATSSI, we evaluated MODIS time series data for three case studies (irrigation area changes, evaluation of moisture dynamics in a wetland ecosystem and vegetation monitoring in a burned area) in different geographical regions of Mexico. Our analyses were based on methods such as the spatio-temporal distribution of maxima over time, statistical trend analysis and change-point decomposition, all of which were implemented in TATSSI. Our results are consistent with other scientific studies and results in these areas and with related in-situ data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|