Spatiotemporal Patterns of Pasture Quality Based on NDVI Time-Series in Mediterranean Montado Ecosystem

Abstract

The evolution of dryland pasture quality is closely related to the seasonal and inter-annual variability characteristic of the Mediterranean climate. This variability introduces great unpredictability in the dynamic management of animal grazing. The aim of this study is to evaluate the potential of two complementary tools (satellite images, Sentinel-2 and proximal optical sensor, OptRx) for the calculation of the normalized difference vegetation index (NDVI), to monitor in a timely manner indicators of pasture quality (moisture content, crude protein, and neutral detergent fiber). In two consecutive years (2018/2019 and 2019/2020) these tools were evaluated in six fields representative of dryland pastures in the Alentejo region, in Portugal. The results show a significant correlation between pasture quality degradation index (PQDI) and NDVI measured by remote sensing (R2 = 0.82) and measured by proximal optical sensor (R2 = 0.83). These technological tools can potentially make an important contribution to decision making and to the management of livestock production. The complementarity of these two approaches makes it possible to overcome the limitations of satellite images that result (i) from the interference of clouds (which occurs frequently throughout the pasture vegetative cycle) and (ii) from the interference of tree canopy, an important layer of the Montado ecosystem. This work opens perspectives to explore new solutions in the field of Precision Agriculture technologies based on spectral reflectance to respond to the challenges of economic and environmental sustainability of extensive livestock production systems.

Sensing and Mapping the Effects of Cow Trampling on the Soil Compaction of the Montado Mediterranean Ecosystem

“…The results of this study show ( Figure 17 ) an NDVI pattern that is typical of the vegetative cycle of dryland pastures in the Mediterranean region, already presented in other works [ 28 , 29 , 30 ]. This pattern is similar in “high” and “low” livestock trampling areas and reflects the effect of the evolution of air temperature and the distribution of precipitation [ 30 ].…”

“…The results of this study show ( Figure 17 ) an NDVI pattern that is typical of the vegetative cycle of dryland pastures in the Mediterranean region, already presented in other works [ 28 , 29 , 30 ]. This pattern is similar in “high” and “low” livestock trampling areas and reflects the effect of the evolution of air temperature and the distribution of precipitation [ 30 ]. In Figure 17 , two moments are identified where the NDVI patterns indicate significantly different behaviours in the two situations of livestock trampling (“high” and “low”): (i) “differential grazing management” in “Field A” and “Field B” (stocking rates); (ii) “pasture peak production”.…”

“…In this study, the impact of livestock trampling on pastures was evaluated by the NDVI time series obtained from Sentinel-2 imagery. Although this approach has some limitations, namely, the constraint resulting from poor-quality images on cloudy days, which can occur frequently during the pasture’s vegetative cycle [ 28 , 29 ], the NDVI can be used to monitor the pasture’s development status since it mainly reflects the chlorophyll content, an indicator of pastures’ vegetative vigour [ 29 , 30 ].…”

“…These preferential grazing areas are potentially at greater risk of compaction by animal trampling, especially in periods of higher precipitation, such as winter or spring in regions with a Mediterranean climate. The use of RS imagery based on the Sentinel-2 satellite to obtain vegetation indices, namely the Normalised Difference Vegetation Index (NDVI), also proved a promising tool to express the response pattern of pastures’ vegetative vigour [ 7 , 28 , 29 , 30 ]. Therefore, with the vegetation indices that can be obtained from digital images and are sensitive to changes in the vegetation cover of pastures, before and after grazing, it is possible to monitor and to identify degraded areas with overgrazing, as well as areas that are arid or without vegetation, for example, in large or small fields and at different time scales [ 7 ].…”

Use of Sentinel-2 Satellite for Spatially Variable Rate Fertiliser Management in a Sicilian Vineyard

“…The Sentinel-2 mission comprises a constellation of two polar-orbiting satellites placed in the same sun-synchronous orbit, phased at 180° to each other. It aims to monitor the spatial and temporal variability of land surface conditions [18,[25][26][27][28][29][30][31].…”