Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1080/01431161.2019.1697008 |
NDVI time series stochastic models for the forecast of vegetation dynamics over desertification hotspots | |
Mutti, Pedro R.1,2; Lucio, Paulo S.1,3; Dubreuil, Vincent2; Bezerra, Bergson G.1,3 | |
通讯作者 | Mutti, Pedro R. |
来源期刊 | INTERNATIONAL JOURNAL OF REMOTE SENSING |
ISSN | 0143-1161 |
EISSN | 1366-5901 |
出版年 | 2020 |
卷号 | 41期号:7页码:2759-2788 |
英文摘要 | Land degradation in semi-arid natural environments is usually associated with climate vulnerability and anthropic pressure, leading to devastating social, economic and environmental impacts. In this sense, remotely sensed vegetation parameters, such as the Normalized Difference Vegetation Index (NDVI), are widely used in the monitoring and forecasting of vegetation patterns in regions at risk of desertification. Therefore, the objective of this study was to model NDVI time series at six desertification hotspots in the Brazilian semi-arid region and to verify the applicability of such models in forecasting vegetation dynamics. We used NDVI data obtained from the MOD13A2 product of the Moderate Resolution Imaging Spectroradiometer sensor, comprising 16-day composites time series of mean NDVI and NDVI variance for each hotspot during the 2000-2018 period. We also used rainfall measured by weather stations as an explanatory variable in some of the tested models. Firstly, we compared Holt-Winters with Box-Jenkins and Box-Jenkins-Tiao (BJT) models. In all hotspots the Box-Jenkins and BJT models performed slightly better than Holt-Winters models. Overall, model performance did not improve with the inclusion of rainfall as an exogenous explanatory variable. Mean NDVI series were modelled with a correlation of up to 0.94 and a minimum mean absolute percentage error of 5.1%. NDVI variance models performed slightly worse, with a correlation of up to 0.82 and a minimum mean absolute percentage error of 22.0%. After the selection of the best models, we combined mean NDVI and NDVI variance models in order to forecast mean-variance plots that represent vegetation state dynamics. The combined models performed better in representing dry and degraded vegetation states if compared to robust and heterogeneous vegetation during wet periods. The forecasts for one seasonal period ahead were satisfactory, indicating that such models could be used as tools for the monitoring of short-term vegetation states. |
类型 | Article |
语种 | 英语 |
国家 | Brazil ; France |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000510133600017 |
WOS关键词 | CLIMATE-CHANGE ; SPATIAL HETEROGENEITY ; BRAZILIAN CERRADO ; LAND DEGRADATION ; TREND ANALYSIS ; NORTHEAST ; DROUGHT ; AREAS ; VARIABILITY ; PATTERNS |
WOS类目 | Remote Sensing ; Imaging Science & Photographic Technology |
WOS研究方向 | Remote Sensing ; Imaging Science & Photographic Technology |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/314784 |
作者单位 | 1.Univ Fed Rio Grande do Norte UFRN, Programa Posgrad Ciencias Climat, Natal, RN, Brazil; 2.Univ Rennes 2, Dept Geog, UMR 6554, CNRS,COSTEL LETG, Rennes, France; 3.Univ Fed Rio Grande do Norte UFRN, Dept Ciencias Atmosfer & Climat, Natal, RN, Brazil |
推荐引用方式 GB/T 7714 | Mutti, Pedro R.,Lucio, Paulo S.,Dubreuil, Vincent,et al. NDVI time series stochastic models for the forecast of vegetation dynamics over desertification hotspots[J],2020,41(7):2759-2788. |
APA | Mutti, Pedro R.,Lucio, Paulo S.,Dubreuil, Vincent,&Bezerra, Bergson G..(2020).NDVI time series stochastic models for the forecast of vegetation dynamics over desertification hotspots.INTERNATIONAL JOURNAL OF REMOTE SENSING,41(7),2759-2788. |
MLA | Mutti, Pedro R.,et al."NDVI time series stochastic models for the forecast of vegetation dynamics over desertification hotspots".INTERNATIONAL JOURNAL OF REMOTE SENSING 41.7(2020):2759-2788. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。