干旱区生态环境知识资源中心(Arid): NDVI time series stochastic models for the forecast of vegetation dynamics over desertification hotspots

Arid

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, Vincent 2; 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.