Ecosystem functional units characterized by satellite observed phenology and productivity gradients: A case study for Europe

Ivits, Éva; Michael, Cherlet; Mehl, W.; Sommer, Stefan

doi:10.1016/j.ecolind.2012.11.010

Cited by 44 publications

(41 citation statements)

References 32 publications

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“…On one hand, the climate and phenological data both have different time and spatial scales and associated noise/error that might play a role in the limited amount of explained variance. On the other hand, these results are consistent with Ivits et al [19] showing a limited temperature and precipitation dependency of the remote sensing derived phenological and productivity parameters for Europe. That it is not only climate controlling the distribution of EFTs is further supported by the correspondence analysis between the Köppen climate zones and the EFTs, which was 47%.…”

Section: Discussionsupporting

confidence: 82%

“…That it is not only climate controlling the distribution of EFTs is further supported by the correspondence analysis between the Köppen climate zones and the EFTs, which was 47%. Especially the cold and arid zones scored high, showing that over these regions vegetation phenology and productivity agrees with bio-climatic classification systems, which are mostly dependent on climatic constraints [19]. In other biomes, however, the correspondence was very low (low DCA fit) indicating that the Earth Observation derived variables comprise ecosystem functional information that is not inherent in bio-climatic classifications.…”

Section: Discussionmentioning

confidence: 90%

“…The methodology is based on computing a delayed and a forward shifted time-series using a moving average filter and extracting the intersection points of these with the reference time-series. While Reed et al [47] determined a general lag based on a priori knowledge about the average phenology of the study area, Phenolo is strictly data driven using for each individual pixel its time series dynamics to determine the lag in order to render it applicable to a wide geographical extent (see [19] for a complete description of the method). Once the intersection points are defined a series of phenological and productivity metrics were computed for each year in the time-series (see Figure 1).…”

Section: Derivation Of Phenological and Productivity Parametersmentioning

confidence: 99%

“…Phenological and productivity parameters were derived using the Phenolo software developed in the EC Joint Research Centre [19]. In order to generate results comparable between data sources with different time aggregation windows, daily values were calculated by an iterative linear interpolation of the NDVI time-series decades.…”

Section: Derivation Of Phenological and Productivity Parametersmentioning

confidence: 99%

“…Satellite derived vegetation indices, such as the NDVI, have proven their value in the field of large scale monitoring of vegetation cover and dynamics for land degradation and desertification studies (for an overview see [4,7,17]). Especially the de-convolution of the original time series into phenological and productivity metrics yield additional information on various aspects of vegetation dynamics and ecosystem functioning [18] that can be related to land use [19]. Monitoring vegetation phenology using satellite remote sensing [20][21][22][23][24][25] offers an optimal framework for ecosystem studies because in situ phenological data are comparatively scarce in many parts of the world [26] and because long time and large spatial scales can be addressed simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Global Biogeographical Pattern of Ecosystem Functional Types Derived From Earth Observation Data

et al. 2013

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Abstract:The present study classified global Ecosystem Functional Types (EFTs) derived from seasonal vegetation dynamics of the GIMMS3g NDVI time-series. Rotated Principal Component Analysis (PCA) was run on the derived phenological and productivity variables, which selected the Standing Biomass (approximation of Net Primary Productivity), the Cyclic Fraction (seasonal vegetation productivity), the Permanent Fraction (permanent surface vegetation), the Maximum Day (day of maximum vegetation development) and the Season Length (length of vegetation growing season) variables, describing 98% of the variation in global ecosystems. EFTs were created based on Isodata classification of the spatial patterns of the Principal Components and were interpreted via gradient analysis using the selected remote sensing variables and climatic constraints (radiation, temperature, and water) of vegetation growth. The association of the EFTs with existing climate and land cover classifications was demonstrated via Detrended Correspondence Analysis (DCA). The ordination indicated good description of the global environmental gradient by the EFTs, supporting the understanding of phenological and productivity dynamics of global ecosystems. Climatic constraints of vegetation growth explained 50% of variation in the phenological data along the EFTs showing that part of the variation in the global phenological gradient is not climate related but is unique to the Earth Observation derived variables. DCA demonstrated good correspondence of the EFTs to global climate and also to land use classification. The results show the great potential of Earth Observation derived OPEN ACCESS Remote Sens. 2013, 5 3306 parameters for the quantification of ecosystem functional dynamics and for providing reference status information for future assessments of ecosystem changes.

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Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 90%

Section: Derivation Of Phenological and Productivity Parametersmentioning

confidence: 99%

Section: Derivation Of Phenological and Productivity Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Global Biogeographical Pattern of Ecosystem Functional Types Derived From Earth Observation Data

et al. 2013

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Mapping European ecosystem change types in response to land‐use change, extreme climate events, and land degradation

et al. 2019

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Extreme climate events and nonsustainable land use are important drivers altering the functioning of European ecosystems, resulting in loss of the services provided. Yet a consensus method for regular continental scale assessment of ecosystem condition in relation to land degradation (LD) is still lacking. Here, we propose a new remote sensing‐based approach allowing for improved, repeated assessment of changing pressure on terrestrial ecosystems. On the basis of segmented trend analysis of water‐use efficiency (WUE), a map of ecosystem change type (ECT) was produced over Europe for the period 1999 to 2013. Results were related to drought and change in land use and land cover and to known cases of soil degradation (LD case‐studies). More than 30% of the European ecosystems experienced significant changes in WUE, of which more than 20% were categorized as abrupt. Large‐scale positive reversals in WUE were observed over regions with increasing crop yield and intensification of wood production, whereas decreased WUE was observed over grassland areas coinciding with high farmland abandonment. Evidence of drought pressure on ecosystem functioning (EF) was observed, with abrupt changes in functioning observed during major European drought events. The ECTs also provided relevant information on the location and type of change in EF over the LD case studies. We conclude that mapping of gradual and abrupt changes in EF is expected to be valuable tool for ecosystem condition assessment that is essential for assessing the success of reaching the LD neutrality objectives set by the United Nations Convention to Combat Desertification.

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Monitoring Land Productivity Trends in Souss-Massa Region Using Landsat Time Series Data to Support SDG Target 15.3

Moussa

Brirchi

Alami

2021

Geospatial Intelligence

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Ecosystem functional units characterized by satellite observed phenology and productivity gradients: A case study for Europe

Cited by 44 publications

References 32 publications

Global Biogeographical Pattern of Ecosystem Functional Types Derived From Earth Observation Data

Global Biogeographical Pattern of Ecosystem Functional Types Derived From Earth Observation Data

Mapping European ecosystem change types in response to land‐use change, extreme climate events, and land degradation

Monitoring Land Productivity Trends in Souss-Massa Region Using Landsat Time Series Data to Support SDG Target 15.3

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