Periodic Reporting for period 1 - INDRO (Remote sensing INdicators for DROught monitoring)
Okres sprawozdawczy: 2017-05-01 do 2019-04-30
The INDRO project started on 1 May 2017 and ended on 20 August 2019, and was conducted by Dr. Manuela Balzarolo under supervision of the project's coordinator Prof. Josep Peñuelas at the Centre for Research on Ecology and Forestry Applications (CREAF).
Global patterns of air temperature and precipitation are changing. According to the 4th Assessment report of the IPCC (2012), a substantial part of the world will be affected by a decrease in precipitation. Under conservative scenarios, future climate changes are likely to include further increases in mean temperature (about 2–4 °C globally by 2100) with signicant drying in some regions, as well as increases in frequency and severity of extreme droughts, hot extremes, and heat waves.
Drought affects the terrestrial carbon balance by modifying both the rates of carbon uptake by photosynthesis (i.e. Gross Primary Productivity−GPP) and release by total ecosystem respiration, and the coupling between them.
The drought response of terrestrial ecosystems can be analyzed using remote sensing (RS) techniques. Remotely-sensed indicators can provide an effective way to obtain real-time conditions of ecosystems and offer a range of spatial and temporal observations on changes in ecosystem structure, function, and services. Different RS indicators differ in their sensitivity to changes in photosynthetic status, but most of them (including Normalized Different Vegetation Index−NDVI) are not sensitive to rapid changes in plant photosynthesis that are induced by common environmental stressors, such as drought. This is due to the fact that most RS indicators have no direct link to photosynthetic functioning and are rather indicators for green biomass and thus for canopy structure rather than functioning. Furthermore, the ecological modeling and the remote sensing communities are particularly interested in the Light Use Efficiency (LUE) concept. However, no consensus has been reached regarding the most suitable algorithm for LUE and scientists still need to fully understand if (and how) models should simulate LUE in function of environmental factors (i.e. temperature and water availability).
The general objective of the INDRO project was to understand the spatial and temporal variability of ecosystem gross primary productivity under several environmental conditions by investigating the relationship between remotely-based indicators and plant physiological status at global scale. The general objective has been translated into the following specific objectives:
Objective 1 - To review the definition of drought and to investigate the drought affect ecosystem productivity by analyzing in-situ observations.
Objective 2 - To determine the most appropriate remote sensing indicators for assessing ecosystem productivity in drought conditions.
Objective 3 - To demonstrate the applicability of the methodology and indicators defined by testing and validating them at specific test sites.
To achieve these objectives the INDRO project coupled in situ observations of ecosystem structure (i.e. biomass, leaf nitrogen content, leaf area index) and productivity (i.e. GPP derived from eddy covariance technique) with satellite data available from different platforms (i.e. MODIS, Proba-V and Sentinel-2).
Global patterns of air temperature and precipitation are changing. According to the 4th Assessment report of the IPCC (2012), a substantial part of the world will be affected by a decrease in precipitation. Under conservative scenarios, future climate changes are likely to include further increases in mean temperature (about 2–4 °C globally by 2100) with signicant drying in some regions, as well as increases in frequency and severity of extreme droughts, hot extremes, and heat waves.
Drought affects the terrestrial carbon balance by modifying both the rates of carbon uptake by photosynthesis (i.e. Gross Primary Productivity−GPP) and release by total ecosystem respiration, and the coupling between them.
The drought response of terrestrial ecosystems can be analyzed using remote sensing (RS) techniques. Remotely-sensed indicators can provide an effective way to obtain real-time conditions of ecosystems and offer a range of spatial and temporal observations on changes in ecosystem structure, function, and services. Different RS indicators differ in their sensitivity to changes in photosynthetic status, but most of them (including Normalized Different Vegetation Index−NDVI) are not sensitive to rapid changes in plant photosynthesis that are induced by common environmental stressors, such as drought. This is due to the fact that most RS indicators have no direct link to photosynthetic functioning and are rather indicators for green biomass and thus for canopy structure rather than functioning. Furthermore, the ecological modeling and the remote sensing communities are particularly interested in the Light Use Efficiency (LUE) concept. However, no consensus has been reached regarding the most suitable algorithm for LUE and scientists still need to fully understand if (and how) models should simulate LUE in function of environmental factors (i.e. temperature and water availability).
The general objective of the INDRO project was to understand the spatial and temporal variability of ecosystem gross primary productivity under several environmental conditions by investigating the relationship between remotely-based indicators and plant physiological status at global scale. The general objective has been translated into the following specific objectives:
Objective 1 - To review the definition of drought and to investigate the drought affect ecosystem productivity by analyzing in-situ observations.
Objective 2 - To determine the most appropriate remote sensing indicators for assessing ecosystem productivity in drought conditions.
Objective 3 - To demonstrate the applicability of the methodology and indicators defined by testing and validating them at specific test sites.
To achieve these objectives the INDRO project coupled in situ observations of ecosystem structure (i.e. biomass, leaf nitrogen content, leaf area index) and productivity (i.e. GPP derived from eddy covariance technique) with satellite data available from different platforms (i.e. MODIS, Proba-V and Sentinel-2).
Objective 1
We aimed to verify which vegetation and environmental factors are the most important in determining the spatial and temporal variability of GPP and LUE. The impact of different potential drivers (i.e. temperature and water-related variables, plant traits) of GPP and LUE were analyzed by using statistical methods. The main results of this analysis has been published by the researcher in Global Ecology and Biogeography (Balzarolo et al., 2019). This analysis revealed that drought-related variables had an impact on annual light use efficiency in temperate forests, while temperature-related variables affected annual of cold forest. Furthermore, the seasonal variability of LUE was strongly related to the meteorological variables and varied along the season and was stronger in summer than spring or autumn.
Objective 2
Satellite data available from different platforms (i.e. MODIS, Proba-V and Sentinel-2) were collected a different remote sensing indicators were calculated. We focused mainly on remotely-based indicators that reflect canopy functioning and physiology (i.e. chlorophyll content index−CCI, Photochemical Reflectance Index−PRI, green NDVI, Red-edge) rather than conventional indicators of canopy greenness (i.e. NDVI and fAPAR). The values of each RS indicator was regressed against GPP values, obtained by the eddy covariance technique, for the same days of image acquisitions in order to define the best fitting-functions in different environmental conditions (i.e. drought). We found limitation of MODIS and in situ NDVI in the capture of GPP seasonal dynamics for evergreen forests due to insufficient description of plant ecophysiology by the NDVIs (Balzarolo et al., 2019, published in Remote Sensing). MODIS NDVI and CCI identified the spatial variability of average annual GPP (Fernández-Martínez et al, 2019, published Remote Sensing).
Objective 3
The performance in predicting annual and seasonal GPP variability has been validated against in situ observations available from global databases (i.e. FLUXNET) and benchmarked against other remotely-based global models (e.g. MOD17). Furthermore, to improve the GPP upscaling and validation by in situ observations we focused on the analysis of the impact of landscape spatial heterogeneity on the relationship between GPP and RS indicators. The best performances were obtained for the highest spatial resolution (i.e. at 0.5 × 0.5 km for MODIS) (Balzarolo et al., 2019; Remote Sensing). Furthermore, we demonstrated that the landscape heterogeneity estimated using the spatial heterogeneity index is essential in upscaling in situ GPP data.
The results of the project were presented at scientific conferences and symposia and at meetings with potential stakeholders, as well on peer-reviewed scientific journals (see the section Publications).
We aimed to verify which vegetation and environmental factors are the most important in determining the spatial and temporal variability of GPP and LUE. The impact of different potential drivers (i.e. temperature and water-related variables, plant traits) of GPP and LUE were analyzed by using statistical methods. The main results of this analysis has been published by the researcher in Global Ecology and Biogeography (Balzarolo et al., 2019). This analysis revealed that drought-related variables had an impact on annual light use efficiency in temperate forests, while temperature-related variables affected annual of cold forest. Furthermore, the seasonal variability of LUE was strongly related to the meteorological variables and varied along the season and was stronger in summer than spring or autumn.
Objective 2
Satellite data available from different platforms (i.e. MODIS, Proba-V and Sentinel-2) were collected a different remote sensing indicators were calculated. We focused mainly on remotely-based indicators that reflect canopy functioning and physiology (i.e. chlorophyll content index−CCI, Photochemical Reflectance Index−PRI, green NDVI, Red-edge) rather than conventional indicators of canopy greenness (i.e. NDVI and fAPAR). The values of each RS indicator was regressed against GPP values, obtained by the eddy covariance technique, for the same days of image acquisitions in order to define the best fitting-functions in different environmental conditions (i.e. drought). We found limitation of MODIS and in situ NDVI in the capture of GPP seasonal dynamics for evergreen forests due to insufficient description of plant ecophysiology by the NDVIs (Balzarolo et al., 2019, published in Remote Sensing). MODIS NDVI and CCI identified the spatial variability of average annual GPP (Fernández-Martínez et al, 2019, published Remote Sensing).
Objective 3
The performance in predicting annual and seasonal GPP variability has been validated against in situ observations available from global databases (i.e. FLUXNET) and benchmarked against other remotely-based global models (e.g. MOD17). Furthermore, to improve the GPP upscaling and validation by in situ observations we focused on the analysis of the impact of landscape spatial heterogeneity on the relationship between GPP and RS indicators. The best performances were obtained for the highest spatial resolution (i.e. at 0.5 × 0.5 km for MODIS) (Balzarolo et al., 2019; Remote Sensing). Furthermore, we demonstrated that the landscape heterogeneity estimated using the spatial heterogeneity index is essential in upscaling in situ GPP data.
The results of the project were presented at scientific conferences and symposia and at meetings with potential stakeholders, as well on peer-reviewed scientific journals (see the section Publications).
The INDRO project gave a comprehensive and up-to-date picture of the use of remote sensing techniques for monitoring the effect of drought on ecosystems. Due to its multidisciplinary, the project has been received great attention from scientists working on different disciplines (ecology, agriculture, climate change, geography, geophysics). It advanced our understanding on the impact of drought climate change on ecosystems. It contributed to the ongoing discussion about the definition of the most suitable driver of LUE for estimating GPP at global scale.
After the severe drought and heat wave in 2003, EC established a European Drought Policy with the aim to mitigate and to adapt to droughts. The work conducted during the project represents an important tool for early detection of drought and mitigation of its impact on ecosystem services. It helps to build up new monitoring services and implementing new strategies new strategies to limit the damage on ecosystem services and preventing economic loss.
After the severe drought and heat wave in 2003, EC established a European Drought Policy with the aim to mitigate and to adapt to droughts. The work conducted during the project represents an important tool for early detection of drought and mitigation of its impact on ecosystem services. It helps to build up new monitoring services and implementing new strategies new strategies to limit the damage on ecosystem services and preventing economic loss.