Vegetation productivity

Assessment versions

Published (reviewed and quality assured)

• 03 Mar 2020, 12:47 PM
  - Vegetation productivity

Rationale

Justification for indicator selection

Intensive human use manifests in the over-exploitation of certain ecosystem services (such as food, fibre, etc.) and in intensive land-use and land-use change that can cause an irreversible loss of e.g. the supporting ecosystem services (Hill et al., 2008) leading to ecosystem degradation. Although ecosystem degradation results from a combination of natural and socio-economic drivers, it is generally perceptible from long lasting loss of vegetation cover and biomass productivity over time and in space (Hellden and Tottrup, 2008).

 The intense over-exploitation of certain ecosystem services (such as food, fibre, etc.), intensive land-use and land-use change and related ecosystem degradation are especially noticeable at the ecosystems level (Vitousek et al., 1997) therefore assessment methods should concentrate on this scale. Ecosystems condition and dynamics can be characterized based on the spatial distribution and change of the vegetation cover. The vegetation cover in turn can be basically described by three measures: physiognomy, dynamics and taxonomy. Taxonomy gains importance when the maintenance of biodiversity is the target. Physiognomy and dynamics deal with vegetation phenology and its change in space and time stressing on the importance of large scale, spatially continuous and repeatable assessment methods including information on trends. Monitoring vegetation productivity of lands is essential for understanding the interactions between the biosphere, the climate and biogeochemical cycles (Myneni et al., 1997; Nemani et al., 2003, Toth et al., 2013; Schut et al., 2015; Fensholt et al., 2015, Ivits et al., 2016, Horion et al, 2019). Furthermore, vegetation distribution is also associated with terrain characteristics and human activity (Azzali and Menenti, 2000) thus vegetation monitoring techniques may enable the assessment of these factors as well.

This monitoring has to be supported by quantitative, robust, reliable and comparable methods to map the condition and degradation of ecosystem and their services and thus supplying a standardized framework for ecosystem degradation studies  (Sommer et al., 2017; WAD, 2018). Remote sensing derived vegetation phenological and productivity parameters have strong potential in mapping the condition and degradation of ecosystem and their services because they capture the spatial patterns of vegetation dynamics repetitively over vast areas, they are directly related to key aspects of vegetation dynamism such as seasonality, productivity and inter-annual variation and they provide an integrated measure of ecosystem responses to climatic factors such as temperature and rainfall as well as fire and human induced disturbances.

Scientific references

• Eklundh L., Jönsson P., 2016. Timesat for processing time-series data from satellite sensors for land surface monitoring In: Ban Y. (eds) Multitemporal Remote Sensing. Remote Sensing and Digital Image Processing, vol 20. Springer, Cham, 177 – 194.
• Eklundh L., Jönsson P. , 2015. TIMESAT: A Software Package for Time-Series Processing and Assessment of Vegetation Dynamics. In: Kuenzer C., Dech S., Wagner W. (eds) Remote Sensing Time Series. Remote Sensing and Digital Image Processing, vol 22. Springer, Cham
• Fensholt, R., Horion, S., Tagesson, T., Ehammer, A., Ivits E., Rasmussen, K., 2015. Global-scale mapping of changes in ecosystem functioning from earth observation-based trends in total and recurrent vegetation. Global Ecology and Biogeography, Volume 24, Issue 9, pages 1003–1017.
• Ivits, E., Horion, S. Fensholt, R. Cherlet, M. 2014. Global Ecosystem Response Types Derived from the Standardized Precipitation Evapotranspiration Index and GIMMS3g FAPAR series. Remote Sensing, 6(5), 4266-4288; doi: 10.3390/rs6054266 .
• Schut, A.G.T., Ivits, E., Conijn, J.G., ten Brink, B., Fensholt, R., 2015. Trends in Global Vegetation Activity and Climatic Drivers Indicate a Decoupled Response to Climate Change PLOS ONE, 2015, DOI: 10.1371/journal.pone.0138013
• Fensholt, R., Horion, S., Tagesson, T., Ehammer, A., Ivits E., Rasmussen, K., 2015. Global-scale mapping of changes in ecosystem functioning from earth observation-based trends in total and recurrent vegetation Global Ecology and Biogeography, Volume 24, Issue 9, pages 1003–1017.
• Ivits, E., Horion, S., Erhard, M. and Fensholt, R., 2016. Assessing European ecosystem stability to drought in the vegetation growing season. Global Ecology and Biogeography, 25, 1131–1143.
• Jin, H., Eklundh, L., 2014. A physically based vegetation index for improved monitoring of plant phenology, Remote Sensing of Environment, 152, 512 – 525.
• Karkauskaite, P., Tagesson, T., Fensholt, R., 2017. Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone. Remote Sensing, 9 (485), 21 pp.
• Hill, J., Stellmes, M., Udelhoven, Th., Roder, A., Sommer, S., 2008. Mediterranean desertification and land degradation: mapping related land use change syndrimes based on satellite observations. Global Planetary Change 64, 146–157.
• Hellden, U., Tottrup, C., 2008. Regional desertification: a global synthesis. Global Planetary Change 64, 169–176.
• Vitousek, P.M., Mooney, H.A., Luchenco, J., Melillo, J.M., 1997. Human domination of earth’s ecosystems. Science 277, 494–49.
• Nemani, R., Keeling, C.D., Hashimoto, H., Jolly, W.M., Piper, S.C., Tucker, C.J., Myneni, R., Running, W., 2003. Climate driven increases in global terrestrial net primary production from 1982–1999. Science 300, 1562–1563.
• Myneni, R.B., Keeling, C.D., Tucker, C.J., Asrar, G., Nemani, R.R., 1997. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702.
• Azzali, S., Menenti, M., 2000. Mapping vegetation–soil–climate complexes in southernAfrica using temporal Fourier analysis of NOAA-AVHRR NDVI data. International Journal of Remote Sens. 21 (5), 973–996.
• Horion, S., Ivits, E., De Keerschmacker, W., Tagesson, T., Vogt, J., Fensholt, R., 2019. Mapping European ecosystem change types in response to land‐use change, extreme climate events and land degradation. Land Degradation and Development, DOI: 10.1002/ldr.3282.
• M. Cherlet, C. Hutchinson, J. Reynolds, J. Hill, S. Sommer & G. von Maltitz (Eds.), 2018. World Atlas of Desertification. Luxembour g: Publication Office of the  European Union.
• Sommer, s., Cherlet, M., Ivits E., 2017. Mapping Land Productivity Dynamics. detecting critical trajectories of global land transformations. In United Nations Convention to Combat Desertification. 2017. The Global Land Outlook, first edition. Bonn, Germany. ISBN: 978-92-95110-48-9

Indicator definition

The indicator addresses trends in land surface productivity derived from remote sensing observed time series of vegetation indices. The vegetation index used in the indicator is the Plant Phenology Index (PPI, Jin and Eklundh, 2014). PPI is based on the MODIS Nadir BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR. The product provides reflectance data for the MODIS “land” bands (1 - 7) adjusted using a bi-directional reflectance distribution function. This function models values as if they were collected from a nadir-view to remove so called cross-track illumination effects. The Plant Phenology Index (PPI) is a new vegetation index optimized for efficient monitoring of vegetation phenology. It is derived from radiative transfer solution using reflectance in visible-red (RED) and near-infrared (NIR) spectral domains. PPI is defined to have a linear relationship to the canopy green leaf area index (LAI) and its temporal pattern is strongly similar to the temporal pattern of gross primary productivity (GPP) estimated by flux towers at ground reference stations. PPI is less affected by presence of snow compared to commonly used vegetation indices such as Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI).

The product is distributed with 500 m pixel size (MODIS Sinusoidal Grid) with 8-days compositing period. 

References:

Jönsson P., Eklundh L., 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Computers & Geosciences 30 (2004) 833–845.

Eklundh L., Jönsson P., 2015. TIMESAT: A Software Package for Time-Series Processing and Assessment of Vegetation Dynamics. In: Kuenzer C., Dech S., Wagner W. (eds) Remote Sensing Time Series. Remote Sensing and Digital Image Processing, vol 22. Springer, Cham

Jin, H., Eklundh, L. 2014. A physically based vegetation index for improved monitoring of plant phenology, Remote Sensing of Environment, 152, 512 – 525.

Karkauskaite, P., Tagesson, T., Fensholt, R., 2017. Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone, Remote Sensing, 9 (485), 21 pp.

Jin, H.X.; Jönsson, A.M.; Bolmgren, K.; Langvall, O.; Eklundh, L., 2017. Disentangling remotely-sensed plant phenology and snow seasonality at northern Europe using MODIS and the plant phenology index. Remote Sensing of Environment 2017,198, 203-212.

Abdi, A. M., N. Boke-Olén, H. Jin, L. Eklundh, T. Tagesson, V. Lehsten and J. Ardö (2019). First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems. International Journal of Applied Earth Observation and Geoinformation 78: 249-260.

Jin, H., A. M. Jönsson, C. Olsson, J. Lindström, P. Jönsson and L. Eklundh (2019). New satellite-based estimates show significant trends in spring phenology and complex sensitivities to temperature and precipitation at northern European latitudes. International Journal of Biometeorology 63(6): 763-775.

Units

Measurement unit: The land productivity metrics is a dimensionless measure. It is calculated as the integral area under the yearly phenological curve, the function describing the growing season from the season start to the season end.

Spatial units: the proposed indicator is delivered as a set of raster data layers with cell size of 500 m by 500 m.

Policy context and targets

Context description

Addressing ecosystem services and their complex interactions call for a coherent approach to understand the coupled human-environment system.

In November 2013, the European Parliament and the European Council adopted the 7^th EU Environment Action Programme (7^th EAP) to 2020, ‘Living well, within the limits of our planet’. Degradation of ecosystems is recognized as one of the major threats to the provision of ecosystem services, biodiversity and Europe’s resilience to climate change and natural disasters within the 7^th Environmental Action Plan, priority objective 1, paragraph 23. Priority objective 5, paragraph 66 stats that environmental monitoring is one cornerstones of the Unions environmental policy and within Priority objective 5, paragraph 71 mapping and assessment of ecosystem services are recognized as a necessary basis for developing the most appropriate responses to environmental change.

The 7^th EAP is intended to help guide EU action on environment and climate change up to and beyond 2020. It highlights that ‘Action to mitigate and adapt to climate change will increase the resilience of the Union’s economy and society, while stimulating innovation and protecting the Union’s natural resources.’ Consequently, several priority objectives of the 7^th EAP refer to climate change adaptation.

In April 2013, the European Commission (EC) presented the EU Adaptation Strategy Package. This package consists of the EU Strategy on adaptation to climate change(COM/2013/216 final) and a number of supporting documents. The overall aim of the EU Adaptation Strategy is to contribute to a more climate-resilient Europe. One of the objectives of the EU Adaptation Strategy is better informed decision-making, which will be achieved by bridging the knowledge gap and further developing the European climate adaptation platform (Climate-ADAPT) as the ‘one-stop shop’ for adaptation information in Europe. Climate-ADAPT has been developed jointly by the EC and the EEA to share knowledge on (1) observed and projected climate change and its impacts on environmental and social systems and on human health, (2) relevant research, (3) EU, transnational, national and sub-national adaptation strategies and plans, and (4) adaptation case studies.

In September 2016, the EC presented an indicative roadmap for the evaluation of the EU Adaptation Strategy by 2018.

Biodiversity and ecosystem stability are tightly intertwined as “biodiversity loss reduces the efficiency by which ecological communities capture biologically essential resources, produce biomass, decompose and recycle biologically essential nutrients”. To halt the loss of Biodiversity and manage related ecosystem dynamics and degradation the EU states maintenance and restoration of ecosystems as target 2 of the Biodiversity Strategy to 2020.  

The Millennium Ecosystem Assessment and Action 5 of the EU Biodiversity Strategy to 2020 calls Member States to map and assess the state of ecosystems and their services in their national territory. 

Targets

No specific target.

Related policy documents

• Climate-ADAPT: Adaptation in EU policy sectors
  Overview of EU sector policies in which mainstreaming of adaptation to climate change is ongoing or explored
• Climate-ADAPT: Country profiles
  Overview of activities of EEA member countries in preparing, developing and implementing adaptation strategies
• Decision No 1386/2013/EU of the European Parliament and of the Council of 20 November 2013 on a General Union Environment Action Programme to 2020 ‘Living well, within the limits of our planet’
  Published: 2013-11-20 Corporate author(s): Council of the European Union , European Parliament Subject: biodiversity , economic growth , environmental impact , environmental protection , EU programme , investment , management of resources , pollution control
• DG CLIMA: Adaptation to climate change
  Adaptation means anticipating the adverse effects of climate change and taking appropriate action to prevent or minimise the damage they can cause, or taking advantage of opportunities that may arise. It has been shown that well planned, early adaptation action saves money and lives in the future. This web portal provides information on all adaptation activities of the European Commission.
• EU 2020 Biodiversity Strategy
  in the Communication: Our life insurance, our natural capital: an EU biodiversity strategy to 2020 (COM(2011) 244) the European Commission has adopted a new strategy to halt the loss of biodiversity and ecosystem services in the EU by 2020. There are six main targets, and 20 actions to help Europe reach its goal. The six targets cover: - Full implementation of EU nature legislation to protect biodiversity - Better protection for ecosystems, and more use of green infrastructure - More sustainable agriculture and forestry - Better management of fish stocks - Tighter controls on invasive alien species - A bigger EU contribution to averting global biodiversity loss
• EU Adaptation Strategy Package
  In April 2013, the European Commission adopted an EU strategy on adaptation to climate change, which has been welcomed by the EU Member States. The strategy aims to make Europe more climate-resilient. By taking a coherent approach and providing for improved coordination, it enhances the preparedness and capacity of all governance levels to respond to the impacts of climate change.
• General Union Environment Action Programme to 2020 - Living well, within the limits of the planet
  7th EAP. ISBN 978-92-79-34724-5 doi:10.2779/66315
• Green Infrastructure (GI) — Enhancing Europe’s Natural Capital
  Green infrastructure is a strategically planned network of natural and semi-natural areas with other environmental features designed and managed to deliver a wide range of ecosystem services such as water purification, air quality, space for recreation and climate mitigation and adaptation. This network of green (land) and blue (water) spaces can improve environmental conditions and therefore citizens' health and quality of life. It also supports a green economy, creates job opportunities and enhances biodiversity. The Natura 2000 network constitutes the backbone of the EU green infrastructure.           
• Science for Environment Policy. In Depth Report – Ecosystems Services and Biodiversity
  European Commission 2015

Methodology

Methodology for indicator calculation

The PPI time-series is affected by noise due to e.g. atmosphere and remaining cloud influence, resulting in some spikes and outlier values. Since large spikes and outliers might significantly affect the further function fitting, they first have to be removed from the data. This is done in an initial filtering process, further described in the TIMESAT software manual.

After the outlier removal the next step in the analysis is the determination of the number of growing seasons. This is based on a harmonic function fit (sine-cosine functions) to the data. The presence of a second season is established by evaluating the amplitudes of the first and second components of the harmonic fit. Presence of noise in the data complicates the decision on whether the given secondary maximum represents a true growing season or not. Therefore, an amplitude threshold is used to remove seasons that are smaller than the given threshold. A detailed description of the determination of the number of growing season is found in the TIMESAT software manual.

After the number of growing seasons have been determined double logistic functions are fitted to the data from each pixel. This is done to generate smooth continuous functions that well describe each individual growing season. It is assumed that most of the noise included in PPI (or any other vegetation index) results in negative bias of the values. Therefore, iterative adaptation of the logistic functions to the upper envelope of the data is applied in the following step. The function fit is performed on the PPI data. Values less than the first function fit are then considered as influenced by noise and thus less important, so their weights are decreased for the next iteration of the function fitting.

Phenological metrics (and other parameters describing character of the given growing season) are finally extracted from the fitted function data. The following parameters are extracted for each detected growing season to determine productivity:

• Start-Of-Season (SOS): date of the start of the season defined as the date when the PPI has increased to the 20% level of the average annual PPI amplitude (Jin et al. 2017). The average annual PPI amplitude is the difference between the average peak level and the average base level for each pixel.
• End-Of-Season (EOS): date of the end of the season defined as the date when the PPI drops under the 20 % level of the average annual PPI amplitude (Jin et al. 2017).
• Large integral: integral of the fitted function between the start and end of the season.
• Small integral: integral of the differences between the fitted function and the base level from start to end of the season.

Seasonal amplitude is calculated as a difference of the fitted curve maximum and the base level. The SOS and EOS points on the curve are then given as the fraction of the amplitude,  i.e. the date when the fitted curve reaches/drops below the defined percent fraction of the seasonal amplitude. For this indicator 20% of the seasonal PPI amplitude was used as the SOS and EOS detection threshold. 

The output of the process is a productivity metrics for each year of the time series 2000-2016 (17 years) covering the EEA39 territory. The spatial resolution of the productivity dataset is 500mx500m pixel size. In order to address change in productivity, a linear regression was fit to the productivity time series of each grid cell of the dataset. As PPI, and consequently also productivity, is a dimensionless measure, the change was expressed in the r value of the linear regression model instead of the slope of the fitted model. The r value, i.e. the coefficient of determination, is expressed between -1 and 1. It shows how close the data are to the fitted regression line. In general, the higher the value the better the model fits the data. The advantage of the R value is that unlike the slope of the linear regression model the r value can be compared across bioclimatic regions and between various ecosystems. The change was also expressed as the relative growth. The relative growth is expressed in percentage and is calculated as the change between the first and the last year of the time series in proportion of the first year`s productivity value.  

Detailed description of the methodology for calculating the productivity metric can be found in the TIMESAT software manual (publically available)

Eklundh, L., Jönsson, P., 2017. TIMESAT 3.3 Software manual, Lund and Malmö University, Sweden, available at: http://web.nateko.lu.se/timesat/docs/TIMESAT33_SoftwareManual.pdf

Scientific references:

Jönsson, P., Eklundh, L., 2002. TIMESAT – A program for analysing time-series of satellite sensor data, Computers and Geosciences, 30, 883 – 845. 

Jönsson, P., Eklundh, L., 2004. Seasonality extraction and noise removal by function fitting to time-series of satellite sensor data, IEEE Transactions of Geoscience and Remote Sensing, 40 (8), 1824 – 1832.

Jin, H.X., Jönsson, A.M., Bolmgren, K., Langvall, O., Eklundh, L., 2017. Disentangling remotely-sensed plant phenology and snow seasonality at northern Europe using MODIS and the plant phenology index. Remote Sens Environ 2017, 198, 203-212.

Methodology for gap filling

The PPI time-series is affected by noise due to e.g. atmosphere and remaining cloud influence, resulting in some spikes and outlier values. Since large spikes and outliers might significantly affect the further function fitting, they first have to be removed from the data. This is done in an initial filtering process, further described in the TIMESAT software manual.

The productivity metrics has no gaps. 

Methodology references

• Jönsson, P., Eklundh, L. ,2002. TIMESAT – A program for analysing time-series of satellite sensor data Computers & Geosciences 30 (2004) 833–845
• Jönsson, P., Eklundh, L., 2002. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions of Geoscience and Remote Sensing, 40 (8), 1824 – 1832. 
• Eklundh L., Jönsson P. , 2015. TIMESAT: A Software Package for Time-Series Processing and Assessment of Vegetation Dynamics. In: Kuenzer C., Dech S., Wagner W. (eds) Remote Sensing Time Series. Remote Sensing and Digital Image Processing, vol 22. Springer, Cham
• Eklundh, L., Jönsson, P. , 2017. TIMESAT 3.3 Software manual Lund and Malmö University, Sweden.

Data specifications

EEA data references

• Vegetation productivity 2000-2016 provided by European Environment Agency (EEA)

External data references

• Plant Phenology Index

Data sources in latest figures

Uncertainties

Methodology uncertainty

One question is the choice of fitting function in TIMESAT. In the scientific literature, several fitting methods have been used and proposed. In this analyses logistic functions were chosen since they are well founded in the scientific literature (e.g. Zhang et al. 2003, Fisher et al. 2006, Beck et al. 2006), and in a recent study have been found to be one of the most robust methods for regional phenology estimation (Cai et al. 2017).

Another source of uncertainty is the detection of the SOS and EOS points om the seasonal vegetation profile. In order to address appropriate levels for SOS and EOS we analysed estimates of GPP (gross primary productivity) from ground-measured data from carbon flux towers from the international FLUXNET network. This was done to evaluate if there was any significant difference in the SOS and EOS estimated from these measurements between different land cover classes. The analysis did not indicate any clear separability between the classes. This was due to high variability in the GPP data, and hence there was no basis for making individual choices for different land cover classes or climate zones. Therefore, a fixed threshold of 20 % of the annual PPI amplitude was used in the indicator assessment process. The chosen level was based on analyses in Jin et al. (2017). It cannot be ruled out that the use of a single threshold across Europe may have introduced some uncertainty. 

References:

Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C. and Huete, A., 2003. Monitoring vegetation phenology using MODIS, Remote Sensing of Environment, 84, 471-475.

Beck, P.S.A.; Atzberger, C.; HÞgda, K.A.; Johansen, B.; Skidmore, A.K., 2006. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sens Environ 2006, 100, 321-334.

Fisher, J.I., Mustard, J.F. and Vadeboncoeur, M.A., 2006. Green leaf phenology at Landsat resolution: Scaling from the field to the satellite, Remote Sensing of Environment, 100, 265-279

Cai, Z.Z.; Jönsson, P.; Jin, H.X.; Eklundh, L., 2017. Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data. Remote Sensing 2017 9.

Jin, H.X.; Jönsson, A.M.; Bolmgren, K.; Langvall, O.; Eklundh, L., 2017. Disentangling remotely-sensed plant phenology and snow seasonality at northern Europe using MODIS and the plant phenology index. Remote Sens of Environment 198, 203-212.

Data sets uncertainty

The dataset represents several plant functional types aggregated with 500 x 500 m pixels. Therefore, the dataset can only be used at the ecosystem level indicating productivity changes of main plant functional types. As opposed to filed measurement remote sensing products measure vegetation light absorption from a satellite at several hundred km height which might introduce bias due to atmospheric disturbances.

Rationale uncertainty

No uncertainty has been specified