Köhler, Jonas und Kuenzer, Claudia (2020) Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review. Remote Sensing, 12 (21), Seite 3513. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs12213513. ISSN 2072-4292.
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Offizielle URL: https://www.mdpi.com/2072-4292/12/21/3513
Kurzfassung
Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.
elib-URL des Eintrags: | https://elib.dlr.de/137319/ | ||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||
Titel: | Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review | ||||||||||||
Autoren: |
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Datum: | 26 Oktober 2020 | ||||||||||||
Erschienen in: | Remote Sensing | ||||||||||||
Referierte Publikation: | Ja | ||||||||||||
Open Access: | Ja | ||||||||||||
Gold Open Access: | Ja | ||||||||||||
In SCOPUS: | Ja | ||||||||||||
In ISI Web of Science: | Ja | ||||||||||||
Band: | 12 | ||||||||||||
DOI: | 10.3390/rs12213513 | ||||||||||||
Seitenbereich: | Seite 3513 | ||||||||||||
Verlag: | Multidisciplinary Digital Publishing Institute (MDPI) | ||||||||||||
ISSN: | 2072-4292 | ||||||||||||
Status: | veröffentlicht | ||||||||||||
Stichwörter: | forecast; Earth Observation; land surface; land use; land cover; time series; machine learning; Markov chains; modeling | ||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Vorhaben Beobachtung und Datenauswertung (alt) | ||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||
Institute & Einrichtungen: | Deutsches Fernerkundungsdatenzentrum Deutsches Fernerkundungsdatenzentrum > Dynamik der Landoberfläche | ||||||||||||
Hinterlegt von: | Köhler, Jonas | ||||||||||||
Hinterlegt am: | 09 Nov 2020 10:16 | ||||||||||||
Letzte Änderung: | 25 Okt 2023 08:44 |
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