Impacts of Climate Change on European Grassland Phenology: A 20-Year Analysis of MODIS Satellite Data

Bellini, Edoardo; Moriondo, Marco; Dibari, Camilla; Leolini, Luisa; Staglianò, N.; Stendardi, Laura; Filippa, Gianluca; Galvagno, Marta; Argenti, Giovanni

doi:10.3390/rs15010218

Cited by 10 publications

(3 citation statements)

References 73 publications

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“…In this study, PaSim represented the effects of climate change and management options on the timing and extent of the growing season and C-N fluxes, together with biomass production and peaks. The longer growing season length was due to the extension of the potential growing season in both spring and autumn, as already observed in grasslands during the last decades [76,77]. The mean plant growth trend simulated with the model (30-year means) mirrors the observed pattern of vegetation growth during the growing season, indicating that the overall pattern of response to elevated atmospheric CO 2 concentration significantly stimulates leaf photosynthesis [78,79].…”

Section: Uncertainties In Climate Change Impacts and Adaptation Strat...supporting

confidence: 68%

Opportunities for Adaptation to Climate Change of Extensively Grazed Pastures in the Central Apennines (Italy)

Bellini

Martin

Argenti

et al. 2023

Land

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Future climate change is expected to significantly alter the growth of vegetation in grassland systems, in terms of length of the growing season, forage production, and climate-altering gas emissions. The main objective of this work was, therefore, to simulate the future impacts of foreseen climate change in the context of two pastoral systems in the central Italian Apennines and test different adaptation strategies to cope with these changes. The PaSim simulation model was, therefore, used for this purpose. After calibration by comparison with observed data of aboveground biomass (AGB) and leaf area index (LAI), simulations were able to produce various future outputs, such as length of growing season, AGB, and greenhouse gas (GHG) emissions, for two time windows (i.e., 2011–2040 and 2041–2070) using 14 global climate models (GCMs) for the generation of future climate data, according to RCP (Representative Concentration Pathways) 4.5 and 8.5 scenarios under business-as-usual management (BaU). As a result of increasing temperatures, the fertilizing effect of CO2, and a similar trend in water content between present and future, simulations showed a lengthening of the season (i.e., mean increase: +8.5 and 14 days under RCP4.5 and RCP8.5, respectively, for the period 2011–2040, +19 and 31.5 days under RCP4.5 and RCP8.5, respectively, for the period 2041–2070) and a rise in forage production (i.e., mean biomass peak increase of the two test sites under BaU: +53.7% and 62.75% for RCP4.5. and RCP8.5, respectively, in the 2011–2040 period, +115.3% and 176.9% in RCP4.5 and RCP8.5 in 2041–2070, respectively,). Subsequently, three different alternative management strategies were tested: a 20% rise in animal stocking rate (+20 GI), a 15% increase in grazing length (+15 GL), and a combination of these two management factors (+20 GI×15 GL). Simulation results on alternative management strategies suggest that the favorable conditions for forage production could support the increase in animal stocking rate and grazing length of alternative management strategies (i.e., +20 GI, +15 GL, +20 GI×15 GL). Under future projections, net ecosystem exchange (NEE) and nitrogen oxide (N2O) emissions decreased, whereas methane (CH4) rose. The simulated GHG future changes varied in magnitude according to the different adaptation strategies tested. The development and assessment of adaptation strategies for extensive pastures of the Central Apennines provide a basis for appropriate agricultural policy and optimal land management in response to the ongoing climate change.

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Section: Uncertainties In Climate Change Impacts and Adaptation Strat...supporting

confidence: 68%

Opportunities for Adaptation to Climate Change of Extensively Grazed Pastures in the Central Apennines (Italy)

Bellini

Martin

Argenti

et al. 2023

Land

Self Cite

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“…In contrast, Camps-Valls et al [36] proposed a kNDVI that is grounded in kernel approach principles, demonstrating its effectiveness in assessing vegetation dynamics through enhanced consistency with primary productivity, resistance to saturation, bias mitigation, and adaptation to the phenological cycle [36,37]. The kNDVI improves robustness and instability toward noise on both spatial and temporal scales and has proven effective for assessing vegetation dynamics [38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Detection and Attribution of Vegetation Dynamics in the Yellow River Basin Based on Long-Term Kernel NDVI Data

Yu,

Yang,

Jiang

et al. 2024

Remote Sensing

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Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address the possible threats posed by changing environments. On the basis of the kernel normalized difference vegetation index (kNDVI) and key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), and potential evapotranspiration (PET)) in the basin during the period from 1982 to 2022, we utilized the multivariate statistical approach to analyze the spatiotemporal patterns of vegetation dynamics, identified the key climate variables, and discerned the respective impacts of climate change (CC) and human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% of the YRB, with 83.2% exhibiting significant increases in kNDVI (p < 0.05). Conversely, 6.9% of vegetated areas displayed a browning trend, particularly concentrated in the alpine and urban areas. With the Hurst index of kNDVI exceeding 0.5 in 97.5% of vegetated areas, the YRB tends to be extensively greened in the future. Climate variability emerges as a pivotal determinant shaping diverse spatial and temporal vegetation patterns, with PRE exerting dominance in 41.9% of vegetated areas, followed by TEM (35.4%), SR (13%), and PET (9.7%). Spatially, increased PRE significantly enhanced vegetation growth in arid zones, while TEM and SR controlled vegetation variations in alpine areas and non-water-limited areas such as irrigation zones. Vegetation dynamics in the YRB were driven by a combination of CC and HA, with relative contributions of 55.8% and 44.2%, respectively, suggesting that long-term CC is the dominant force. Specifically, climate change contributed to the vegetation greening seen in the alpine region and southeastern part of the basin, and human-induced factors benefited vegetation growth on the Loess Plateau (LP) while inhibiting growth in urban and alpine pastoral areas. These findings provide critical insights that inform the formulation and adaptation of ecological conservation strategies in the basin, thereby enhancing resilience to changing environmental conditions.

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“…Besides, it is often necessary to use interpolation methods to fill the temporal and spatial gaps of the obtained local data (Mendiguren et al, 2015). On the other hand, remote sensing methods are a powerful alternative for acquiring data at a wide variety of spatial and temporal scales (Bellini et al, 2023;Yuan et al, 2023). The surface reflectance can be measured directly in the field using spectroradiometers (Karakoç & Karabulut, 2019) or estimated through satellite imagery (Lei et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Evaluating canopy water content in grasslands by using in situ hyperespectral data

Karakoç,

Karabulut

2023

Mediterr. Bot.

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The water content of vegetation is considered a key parameter for ecological analysis as well as for agricultural and forestry applications. Remote sensing methods offer significant advantages over traditional field methods for determining the water content of vegetation at the leaf, canopy, and landscape levels. In this study, the potential of hyperspectral vegetation indices in predicting canopy water content in grasslands was evaluated. For this purpose, data were collected from 3 several grasslands located at ~500 m, ~1200 m and ~1400 m altitudes. 71 samples were collected from each study area, and a total of 213 samples were analyzed. In this context, 59 ratio-based hyperspectral vegetation indices were tested. The relationship between canopy water content and hyperspectral vegetation indices was evaluated with linear, exponential, logarithmic and power regression models. The results showed that the NW-3 (920,970) index significantly represents the canopy water content variable. It was determined that the exponential regression model created with this index was able to explain the variations in canopy water content up to 85%. On the other hand, it has been detected that the high level of water content in the vegetation creates a significant saturation problem. Another finding of this study is that the predictive power reaches higher levels in low canopy water content characteristics. The results of this study show that in situ hyperspectral data has a very high potential in determining vegetation water content in grasslands.

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Impacts of Climate Change on European Grassland Phenology: A 20-Year Analysis of MODIS Satellite Data

Cited by 10 publications

References 73 publications

Opportunities for Adaptation to Climate Change of Extensively Grazed Pastures in the Central Apennines (Italy)

Opportunities for Adaptation to Climate Change of Extensively Grazed Pastures in the Central Apennines (Italy)

Detection and Attribution of Vegetation Dynamics in the Yellow River Basin Based on Long-Term Kernel NDVI Data

Evaluating canopy water content in grasslands by using in situ hyperespectral data

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