MODIS time series contribution for the estimation of nutritional properties of alpine grassland

Abstract

Despite the Normalised Difference Vegetation Index (NDVI) has been used to make predictions on forage quality, its relationship with bromatological field data has not been widely tested. This relationship was investigated in alpine grasslands of the Gran Paradiso National Park (Italian Alps). Predictive models were built using remotely sensed derived variables (NDVI and phenological information computed from MODIS) in combination with geo-morphometric data as predictors of measured biomass, crude protein, fibre and fibre digestibility, obtained from 142 grass samples collected within 19 experimental plots every two weeks during the whole 2012 growing season. The models were both crossvalidated and validated on an independent dataset (112 samples collected during 2013). A good predictability ability was found for the estimation of most of the bromatological measures, with a considerable relative importance of remotely sensed derived predictors; instead, a direct use of NDVI values as a proxy of bromatological variables appeared not to be supported.

Extrinsic and intrinsic factors affecting the activity budget of alpine marmots (Marmota marmota)

“…Foraging activity was high in the first period (June) and decreased in the second (July-August), the period characterized by higher vegetation productivity (Ranghetti et al 2016). This suggests that the availability of high-quality food resources during July reduces the time necessary to fulfil the energetic demands of the alpine marmots, similar to findings on mouflon (Ovis gmelini musimon × Ovis sp.…”

“…; Bourgoin et al 2008). Previously, we showed that vegetation starts to grow around the end of May-early June at our study site (Ranghetti et al 2016). We therefore suggest that, in the first period, marmots maximize their foraging effort to regain energy after hibernation, and during the second period, marmots can devote more time to other activities, such as social interactions.…”

“…We are also aware that food resources are still scarce in May at the elevations we studied marmots, while in June, vegetation starts to grow. We suggest that in June, marmots spend most of their time foraging to recover body mass and energy, while, during July, marmots devote more time to agonistic behaviours because there is greater food availability (Ranghetti et al 2016) and the young of the year need to be protected.…”

Linking alternative reproductive tactics and habitat selection in Northern chamois

“…The lack of difference between territorial and nonterritorial males in the selection of vegetation productivity seemingly contradicts the hypothesized differences in terms of foraging opportunities between ARTs, especially in spring and summer. The use of NDVI as an index of vegetation productivity associated with ARTs, however, needs to be treated cautiously, as the direct use of NDVI values as a proxy of bromatological variables (e.g., crude protein, neutral and acid detergent fiber, and aboveground biomass, indicators of plant quality and nutrient contents) is weakly supported in our study area (Ranghetti et al, 2016). Similar values of "greenness" may thus entail different quality of pastures, and data on the forage energetic value associated with the areas used by the two male tactics are needed to further investigate ART-specific foraging opportunities.…”

The Dynamic of Vegetation Growth with Regular Climate and Climatic Fluctuations in a Subtropical Mountainous Island, Taiwan

“…The successful detections of phenological patterns and their variation from landscape to global levels have demonstrated that the satellite data can serve as a useful and reliable means to disentangle how vegetation phenology responds to climate change on a broader scale [19,20]. The advanced very high-resolution radiometer (AVHRR; 1.1 km in spatial resolution) and the successive monitoring sensor Moderate Resolution Imaging Spectroradiometer (MODIS; 250 m-1000 m in spatial resolution) have provided datasets for more than three decades with a high capability for regional vegetation-climate monitoring and ecosystem process modeling [21][22][23][24]. Chang et al [25] indicated that MODIS NDVI and EVI positively related to both temperature and precipitation on a monthly timescale, but were not significant at the annual timescale in subtropical Taiwan, and suggested that the finer spatial and temporal scales could be better to reveal the climatic controls over vegetation growth.…”