Irene Garonna's research while affiliated with University of Zurich and other places

Publications (15)

Article
Full-text available
Timing and accumulation of snow are among the most important phenomena influencing land surface phenology in mountainous ecosystems. However, our knowledge on their influence on alpine land surface phenology is still limited, and much remains unclear as to which snow metrics are most relevant for studying this interaction. In this study, we analyze...
Article
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Land surface phenology (LSP), the study of seasonal dynamics of vegetated land surfaces from remote sensing, is a key indicator of global change, that both responds to and influences weather and climate. The effects of climatic changes on LSP depend on the relative importance of climatic constraints in specific regions—which are not well understood...
Presentation
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Meteorological conditions impact the autumn phenology of alpine grasslands in various ways. However, in comparison to spring phenology our knowledge about the characteristic and magnitude of these effects is still limited and needs further investigation. We examined the relationships between a number of meteorological factors and phenology in the a...
Article
Full-text available
Snow cover impacts alpine land surface phenology in various ways but our knowledge about the effect of snow cover on alpine land surface phenology is still limited. We studied this relationship in the European Alps using satellite-derived metrics of Snow Cover Phenology (SCP), namely First Snow Fall, Last Snow Day and Snow Cover Duration (FSF, LSD...
Presentation
Full-text available
Snow cover duration plays a vital role in alpine ecosystems and has a large impact on the alpine spring phenology. However, our knowledge about the correlation of snow cover duration with alpine spring phenology is still limited, as is the dependence of this correlation on altitude. In this study, we used satellite derived parameters such as Snow C...
Article
Monitoring land surface phenology (LSP) is important for understanding both the responses and feedbacks of ecosystems to the climate system, and for representing these accurately in terrestrial biosphere models. Moreover, by shedding light on phenological trends at a variety of scales, LSP provides the potential to fill the gap between traditional...
Article
Integrated Coastal Zone Management (ICZM) aims to promote sustainable management of coastal zones based on ecosystem and holistic management approaches. In this context, policies have to consider the complex interactions that influence the fragile equilibrium of coastal ecosystems. Beaches represent both valuable and vulnerable natural resources be...
Article
Full-text available
Land Surface Phenology (LSP) is the most direct representation of intra-annual dynamics of vegetated land surfaces as observed from satellite imagery. LSP plays a key role in characterizing land-surface fluxes, and is central to accurately parameterizing terrestrial biosphere-atmosphere interactions, as well as climate models. In this paper we pres...
Article
Full-text available
The growth of human populations has many direct and indirect impacts on tropical forest ecosystems both locally and globally. This is particularly true in the Solomon Islands, where coastal rainforest cover still remains, but where climate change and a growing human population is putting increasing pressure on ecosystems. This study assessed recent...

Citations

... The high spatial variability characterizing mountain terrain causes strong variations in snow accumulation patterns over short distances 9,10 which translate into snow depth distribution and its persistence 11-14 . This spatial variability can strongly influence the distribution and phenology of plant communities 15-17 , as well as the occurrence of endemic or very rare species with special conservation interest and thus must be included along with other meaningful variables (elevation, soil thickness, slope) 5,18 when analyzing communities distribution and its temporal evolution 19,20 . ...
... Crop phenology is usually scored by using Zadoks, Feekes-Large, or the Haun systems. The remote sensing community has been using VIs to estimate the stage of development of various crops using variations in the VIs during growth (Duncan et al., 2015, Piao et al., 2019, Yamasaki et al., 2017. Recent advances in the phenotyping community allowing collection of images at a closer range to the crops have enabled an unprecedented spatial resolution. ...
... A similar result was also demonstrated in the low-elevation regions of the Ili Valley based on the MODIS snow and vegetation products [42]. Like the Alps [16], the SOS of grassland was influenced primarily by a decline in snow cover, secondarily by snowmelt amount, while the NDVI max was equally affected by SCF FMA and SWE max . Its SOS had a significant positive relationship with SCF FMA , but a negative relationship with Snowmelt FMA , indicating that snow plays a vital role in grassland growth onset. ...
... This is in line with findings by Fu et al. (2015) and Menzel et al. (2020) who noted that the slopes of phenological trends are already slowing down in recent years. Furthermore, there is also evidence that the influence of minimum temperature was reduced at the expense of photoperiod and soil moisture over the last decades (Garonna et al. 2018). ...
... The further the temporal distance of ESS to the snow-free SOS was, the larger the absolute value of ΔSOS. These findings were consistent with previous studies [37,38,63,64]. An earlier ESS results in an earlier estimate of SOS, while a later ESS results in a later estimate of SOS. ...
... for local validation [16]. Thus, remote sensing can help to close gaps in biodiversity observation data collected on the ground [17,18], and provide global spatial assessments of select traits [19]. Recent work provides the precursors for a coherent set of Essential Biodiversity Variables (EBVs) derived from satellite remote sensing [20], which can be matched with field observations of key variables at sites worldwide. ...
... These algorithms are mainly divided into three types: 'prediction-fifilter approach' [5], 'deep learning' [6,7] and 'matrix completion (MC)' [8,9]. Most prediction-fifilter approachs exploit the temporal correlation of the data to predict missing data [10,11], while a few utilise the spatio-temporal properties to predict missing data [12]. But the prediction method cannot be fully utilise the features of samples. ...
... Wave effects are very limited in the Azov Sea due to water depth is shallow (mean 7 m) and thus anthropogenic activities are the primary threat to beaches and spits here (Sinigireva et al. 2015;Alpenidze et al. 2018). It should be emphasized that the shallow coasts in the north are susceptible to variation, so they will be primarily affected by sea-level rise due to climate change (Allenbach et al. 2015;Poulos and Collins 2002). The best examples of spit changes are detected in the Azov Sea (Fig. 10). ...
... Autumn phenology of vegetation is one of the most important indicators of vegetation growth and plays a critical role in the terrestrial ecosystem's water and carbon cycles (Keenan et al., 2014;Richardson et al., 2013;Wu et al., 2013). The variation in autumn phenology is closely related to climate factors and has gradually become an important variable in studies regarding the impact of global change on terrestrial ecosystems and feedback mechanisms (Garonna et al., 2014;Hou et al., 2014;Shen et al., 2022a). However, studies have shown that variations of autumn phenology are more complicated than of spring phenology because it has complex dependencies on both environmental variables (Chmielewski and Rtzer, 2001) and the growth status of vegetation prior to the autumn phenology (Liu et al., 2016a;Wu et al., 2016), which are coupled with less-understood forcing mechanisms. ...
... Pixels overlapping with humanmade structures such as roads and buildings were identified and removed before analysis. To correct for environmental noise, the NDVI values were smoothed, following the method established by Garonna and colleagues (Garonna et al., 2009). Specifically, the data for each pixel checked for rapid decreases or increases (a difference of 0.3 or more from one date to the next) that were immediately followed by a rapid return to previous values. ...