Detection of trend and seasonal changes in non-stationary remote sensing data: Case study of Tunisia vegetation dynamics
文献类型：期刊论文
| 作者 | Rhif, Manel4; Ben Abbes, Ali2,4; Martinez, Beatriz3; de Jong, Rogier1; Sang, Yanfang5; Farah, Imed Riadh4 |
| 刊名 | ECOLOGICAL INFORMATICS
 |
| 出版日期 | 2022-07-01 |
| 卷号 | 69页码:14 |
| ISSN号 | 1574-9541 |
| 关键词 | Spatio-temporal analysis Time-series Trend detection Non-stationary Vegetation Remote sensing images |
| DOI | 10.1016/j.ecoinf.2022.101596 |
| 通讯作者 | Rhif, Manel(manel.rhif@ensi-uma.tn) |
| 英文摘要 | The availability of long-term time series (TS) derived from remote sensing (RS) images is favorable for the analysis of vegetation variation and dynamics. However, the choice of appropriate methods is a challenging task. This article presented an experimental comparison of four methods widely used for the detection of long-term trend and seasonal changes of TS, with a case study in north-western Tunisia. The four methods are the Ensemble Empirical Mode Decomposition (EEMD), Multi-Resolution Analysis-Wavelet transform (MRA-WT), Breaks for Additive Season and Trend (BFAST), and Detecting Breakpoints and Estimating Segments in Trend (DBEST). Their efficiencies were compared by analysing Normalized Difference Vegetation Index (NDVI) TS from 2001 to 2017 in the study area, obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The variations of long-term NDVI trends were analysed using non-parametric statistical tests. Results indicated that MRA-WT gave efficient results for both trend and seasonal changes, especially in forest area. Moreover, it exhibited the fastest efficiency in terms of time of execution and thus recommended for detecting detailed features (such as forest fire detection). DBEST also showed a good performance for trend detection in forest area as MRA-WT, however, it was more constrained to a longer computational time of execution. BFAST and EEMD exhibited a better performance in bare soil and cropland areas, and the latter can be taken as an appropriate and fast alternative for a general long-term trend overview with long TS. |
| WOS关键词 | EMPIRICAL MODE DECOMPOSITION ; TIME-SERIES ANALYSIS ; LAND-COVER ; MAIN DRIVERS ; TERM TRENDS ; NDVI ; TEMPERATURE ; DISTURBANCE ; PHENOLOGY |
| 资助项目 | LSA-SAF CDOP-3 (EUMETSAT) , ESCENARIOS[CGL2016 75239-R] ; ECCE EO[PID2020-118036RB-I00] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000792911200006 |
| 资助机构 | LSA-SAF CDOP-3 (EUMETSAT) , ESCENARIOS ; ECCE EO |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/177445]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Rhif, Manel |
| 作者单位 | 1.Univ Zurich, Dept Geog, Remote Sensing Labs, CH-8057 Zurich, Switzerland 2.FRB CESAB, F-34000 Montpellier, France 3.Univ Valncia, Fac Fsica, Dept Fsica Terra & Termodinm, Environm Remote Sensing Grp, Burjassot 46100, Spain 4.Natl Sch Comp Sci, Riady Labs, Mannouba, Tunisia 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Rhif, Manel,Ben Abbes, Ali,Martinez, Beatriz,et al. Detection of trend and seasonal changes in non-stationary remote sensing data: Case study of Tunisia vegetation dynamics[J]. ECOLOGICAL INFORMATICS,2022,69:14. |
| APA | Rhif, Manel,Ben Abbes, Ali,Martinez, Beatriz,de Jong, Rogier,Sang, Yanfang,&Farah, Imed Riadh.(2022).Detection of trend and seasonal changes in non-stationary remote sensing data: Case study of Tunisia vegetation dynamics.ECOLOGICAL INFORMATICS,69,14. |
| MLA | Rhif, Manel,et al."Detection of trend and seasonal changes in non-stationary remote sensing data: Case study of Tunisia vegetation dynamics".ECOLOGICAL INFORMATICS 69(2022):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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