Drought is a significant challenge to semiarid Mediterranean grasslands, Increasing the accuracy of monitoring allows improving the conservation and management of these vital ecosystems. Meteorological drought is commonly monitored by the Standard Precipitation Index (SPI) or the Standard Precipitation Evapotranspiration Index (SPEI). On the other hand, agriculture drought is estimated by the Vegetation Health Index (VHI). This work aims to optimise the correlation between both drought types using the best transformation of VHI and the most appropriate time scale. Two drought-vulnerable Mediterranean grasslands were selected to evaluate the performance of the drought indexes. The SPI and the SPEI were calculated using data obtained from nearby weather stations. MODIS data were used to calculate the VHI. This index was standardised, naming it as SVHI. Our results revealed that SPEI was better correlated with VHI compared to SPI. In addition, SVHI obtained better results in the critical vegetation phases than VHI. Overall, SPEI and SVHI were the best correlated indexes. The quarterly scale showed stronger relationships than the monthly scale and the most correlated time frame were Mediterranean spring and autumn. This fact suggests that SPEI and SVHI could provide a plus point for increasing the precision of vegetation monitoring during these periods.
<p>Grassland ecosystems are extremely complex and set up intricate structures, whose characteristics and dynamic properties are greatly influenced by climate and meteorological patterns. Climate change and global warming are factors that could impact negatively in the quality and productivity of these ecosystems.</p><p>Remote sensing techniques have been demonstrated as a powerful tool for monitoring extensive areas. In this study, two semi-arid grassland plots were selected in the centre of Spain. This region is characterized by low precipitation and moderate productivity per unit. Through scientific research, spectral vegetation indices (VIs) have been developed to characterize vegetation cover. The most common VI is the Normalized Difference Vegetation Index (NDVI). However, in vegetation scarcity conditions, bare soil reflectance is increased, and the feasibility of NDVI is reduced. This study aims to perform a method to compare soil and agro-climatic variables effect on vegetation time-series indices.</p><p>The construction of the time series was based on multispectral images of MODIS TERRA (MOD09A1.006) product acquired from 2002 till 2018. Three pixels with a temporal resolution of 8 days and a spatial resolution of 500 x 500 m were chosen in each area. To estimate and analyse VIs series, Red (620-670 nm) and Near Infrared (841-876 nm) channels were extracted and filtered by the quality of pixel. All spectral bands showed statistically significant differences confirming that both areas presented different soil properties. Moreover, average annual precipitation was different in each area of study.</p><p>NDVI calculation is only based on NIR and RED bands. To improve the estimation of vegetation in semi-arid areas, several indices have been developed to minimize the soil effect. Each one of them incorporates soil influence in a different way, i.e., Soil Adjusted Vegetation Index (SAVI) adds a constant soil adjustment factor (L), whereas, MSAVI, incorporate an L variable and dependant on soil characteristics.</p><p>Recurrence plots (RP) and recurrence quantification analysis (RQA) were computed to characterize the influence of agro-climatic variables in vegetation index dynamics. Characterization was based on various RQA measures, such as Determinism (DET), average diagonal length (LT) or entropy (ENT).</p><p>Our results showed different RPs depending on the area, VI utilized and precipitation. MSAVI patterns were further distinct, meanwhile, NDVI showed a noisy pattern. LT values in MSAVI were higher than in SAVI implying that MSAVI recurrent events are much longer than SAVI. Simultaneously, LT and DET values in ZSO, with a higher rain, were above ZEA values in MSAVI.</p><p>This indicates that incorporating more detailed information of soil and precipitation reinforce vegetation index estimation and allow to obtain a more distinct pattern of the time series. Therefore, in arid-semiarid grasslands, they should be considered.</p><p><strong>ACKNOWLEDGEMENTS</strong></p><p>The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish <em>Ministerio de Ciencia Innovaci&#243;n y Universidades</em> of Spain and the funding from the Comunidad de Madrid (Spain) and Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330, are highly appreciated.</p>
Multiple studies revealed that pasture grasslands are a time-varying complex ecological system. Climate variables regulate vegetation growing, being precipitation and temperature the most critical driver factors. This work aims to assess the response of two different Vegetation Indices (VIs) to the temporal dynamics of temperature and precipitation in a semiarid area. Two Mediterranean grasslands zones situated in the center of Spain were selected to accomplish this goal. Correlations and cross-correlations between VI and each climatic variable were computed. Different lagged responses of each VIs series were detected, varying in zones, the year’s season, and the climatic variable. Recurrence Plots (RPs) and Cross Recurrence Plots (CRPs) analyses were applied to characterise and quantify the system’s complexity showed in the cross-correlation analysis. RPs pointed out that short-term predictability and high dimensionality of VIs series, as well as precipitation, characterised this dynamic. Meanwhile, temperature showed a more regular pattern and lower dimensionality. CRPs revealed that precipitation was a critical variable to distinguish between zones due to their complex pattern and influence on the soil’s water balance that the VI reflects. Overall, we prove RP and CRP’s potential as adequate tools for analysing vegetation dynamics characterised by complexity.
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