North American vegetation dynamics observed with multi-resolution satellite data

Abstract The North American monsoon (NAM) leads to a large increase in summer rainfall and a seasonal change in vegetation in the southwestern United States and northwestern Mexico. Understanding the interactions between NAM rainfall and vegetation dynamics is essential for improved climate and hydrologic prediction. In this work, the authors analyze long-term vegetation dynamics over the North American Monsoon Experiment (NAME) tier I domain (20°–35°N, 105°–115°W) using normalized difference vegetation index (NDVI) semimonthly composites at 8-km resolution from 1982 to 2006. The authors derive ecoregions with similar vegetation dynamics using principal component analysis and cluster identification. Based on ecoregion and pixel-scale analyses, this study quantifies the seasonal and interannual vegetation variations, their dependence on geographic position and terrain attributes, and the presence of long-term trends through a set of phenological vegetation metrics. Results reveal that seasonal biomass productivity, as captured by the time-integrated NDVI (TINDVI), is an excellent means to synthesize vegetation dynamics. High TINDVI occurs for ecosystems with a short period of intense greening tuned to the NAM or with a prolonged period of moderate greenness continuing after the NAM. These cases represent different plant strategies (deciduous versus evergreen) that can be adjusted along spatial gradients to cope with seasonal water availability. Long-term trends in TINDVI may also indicate changing conditions favoring ecosystems that intensively use NAM rainfall for rapid productivity, as opposed to delayed and moderate greening. A persistence of these trends could potentially result in the spatial reorganization of ecosystems in the NAM region.

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Estimation of vegetation dynamics affected by wildfire in Naoshima, Kagawa using satellite data

Journal of the Japanese Society of Revegetation Technology ◽

10.7211/jjsrt.32.3 ◽

2006 ◽

Vol 32 (1) ◽

pp. 3-8 ◽

Cited By ~ 1

Author(s):

Atsuko NONOMURA ◽

Takuro MASUDA ◽

Hitoshi MORIYA

Keyword(s):

Satellite Data ◽

Vegetation Dynamics

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Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

Hydrology and Earth System Sciences ◽

10.5194/hess-21-6235-2017 ◽

2017 ◽

Vol 21 (12) ◽

pp. 6235-6251 ◽

Cited By ~ 8

Author(s):

Guiomar Ruiz-Pérez ◽

Julian Koch ◽

Salvatore Manfreda ◽

Kelly Caylor ◽

Félix Francés

Keyword(s):

Remote Sensing ◽

Satellite Data ◽

Vegetation Dynamics ◽

Vegetation Index ◽

Remote Sensing Data ◽

Sub Saharan Africa ◽

Automatic Calibration ◽

Calibration And Validation ◽

Sensing Data ◽

Spatio Temporal

Abstract. Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment – the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.

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