Université de Lorraine, AgroParisTech, Inra, Silva, F-54000 Nancy, AgroParisTech, 14 rue Girardet, F-54042 Nancy Cedex, France.
Dynafor, Université de Toulouse, INRA, INPT, INPT - EI PURPAN, Castanet-Tolosan, France, École d'Ingénieurs de PURPAN, 75 voie du TOEC, BP57611, 31076 Toulouse Cedex 3, France.
Sci Total Environ. 2019 Feb 15;651(Pt 2):2874-2885. doi: 10.1016/j.scitotenv.2018.10.052. Epub 2018 Oct 11.
Several studies use satellite-based normalized difference vegetation index (NDVI) to monitor the impact of climate change on vegetation covers. Good understanding of the drivers of NDVI patterns is hindered by the difficulties in disentangling the effects of environmental factors from anthropogenic changes, by the limited number of environmental predictors studied, and by the diversity of responses according to periods and land covers. This study aims to improve our understanding of the different environmental drivers of NDVI spatial variations for different stand type characteristics of mountain and Mediterranean biomes. Using NDVI values extracted from MODIS Terra time series, we calculated Spring Greenness (SG) and annual Relative Greenness (RGRE) to depict spring and summer vegetation activity, respectively, in a contrasted area of 10,255 km located in the south of France. We modeled SG and RGRE at different scales, using 20 environmental predictors characterizing available energy, water supply, and nutrient supply calculated for different periods of the year. In spring, high minimum temperatures, good nitrogen availability, and acidic or neutral pH turned out to be determining for greenness, particularly for stand types located in altitude. In summer, an important soil water reserve and low temperatures promoted vegetation dynamics, particularly for stands located in areas with a Mediterranean climate. Our results show that NDVI dynamics was not only driven by climatic variability, and should not be studied using only mean temperature and rainfall. They highlight that different environmental factors act complementarily, and that soil parameters characterizing water stress and soil nutrition should be taken into account. While the factors limiting NDVI values varied according to the season and the position of the stands along the ecological gradients, we identified a global temperature and water-stress threshold when considering the whole vegetation.
几项研究使用基于卫星的归一化差异植被指数 (NDVI) 来监测气候变化对植被覆盖的影响。对 NDVI 模式驱动因素的深入了解受到以下因素的阻碍：难以将环境因素的影响与人为变化区分开来，研究的环境预测因子数量有限，以及根据时期和土地覆盖的多样性产生不同的响应。本研究旨在更好地理解山区和地中海生物群落不同林分类型特征的 NDVI 空间变化的不同环境驱动因素。我们使用从 MODIS Terra 时间序列中提取的 NDVI 值，计算了 Spring Greenness (SG) 和 annual Relative Greenness (RGRE)，分别描绘了位于法国南部一个对比区域的春季和夏季植被活动，该区域面积为 10,255 平方公里。我们使用 20 个环境预测因子在不同尺度上对 SG 和 RGRE 进行建模，这些预测因子用于描述全年不同时期的可用能量、水供应和养分供应。在春季，高的最低温度、良好的氮供应以及酸性或中性 pH 值对绿色度起决定性作用，尤其是对位于高海拔地区的林分类型而言。在夏季，大量的土壤水分储备和低温促进了植被动态，尤其是对位于地中海气候地区的林分类型而言。研究结果表明，NDVI 动态不仅受到气候变异性的驱动，并且不应该仅使用平均温度和降雨量来进行研究。结果还突出表明，不同的环境因素具有互补性，应该考虑表征土壤水分胁迫和土壤营养的土壤参数。虽然限制 NDVI 值的因素根据季节和林分在生态梯度上的位置而有所不同，但当考虑整个植被时，我们确定了一个全球温度和水分胁迫阈值。
