H2020 INDRO Project: Remote sensing indicators for drought monitoring
- Type Project
- Status Filled
- Execution 2017 -2019
- Assigned Budget 170.121,6 €
- Scope Europeo
- Main source of financing H2020
- Project website INDRO
Objective 1
We sought to verify which environmental and vegetation 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 was analyzed using statistical methods. The main results of this analysis have been published by the researcher in Global Ecology and Biogeography (Balzarolo et al., 2019). This analysis revealed that drought-related variables impacted annual light-use efficiency in temperate forests, while temperature-related variables affected annual efficiency in cool forests. Furthermore, seasonal variability in LUE was strongly correlated with meteorological variables and varied throughout the season, being stronger in summer than in spring or autumn.
Objective 2
Available satellite data from different platforms (i.e., MODIS, Proba-V, and Sentinel-2) were collected, and different remote sensing indicators were calculated. We primarily focused on remote 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 were regressed against GPP values, obtained using the eddy covariance technique, for the same image acquisition days to define the best fitting functions under different environmental conditions (i.e., drought). We found limitations of MODIS and in situ NDVI in capturing seasonal GPP dynamics for evergreen forests due to the insufficient description of plant ecophysiology by NDVI (Balzarolo et al., 2019, published in Remote Sensing). MODIS NDVI and CCI identified the spatial variability of mean annual GPP (Fernández-Martínez et al., 2019, published in Remote Sensing).
Objective 3
The performance of GPP in predicting annual and seasonal variability has been validated against in situ observations available from global databases (i.e., FLUXNET) and compared to other remotely based global models (e.g., MOD17). Furthermore, to improve the upscaling and validation of GPP using in situ observations, we focused on analyzing 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., 0.5 × 0.5 km for MODIS) (Balzarolo et al., 2019; Remote Sensing). Furthermore, we demonstrate that landscape heterogeneity estimated using the spatial heterogeneity index is essential for upscaling in situ GPP data.
The project results were presented at scientific conferences and symposia and in meetings with potential stakeholders, as well as in peer-reviewed scientific journals (see Publications section).
The INDRO project began on May 1, 2017, and ended on August 20, 2019. It was led by Dr. Manuela Balzarolo under the supervision of project coordinator Prof. Josep Peñuelas at the Center for Research in Forest Ecology and Applications (CREAF).
Global air temperature and precipitation patterns are changing. According to the IPCC Fourth Assessment Report (2012), a substantial portion of the world will be affected by decreased precipitation. Under conservative scenarios, future climate changes are likely to include further increases in mean temperature (about 2–4°C globally by 2100) with significant drying in some regions, as well as increases in the frequency and severity of extreme droughts, heat 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 response of terrestrial ecosystems to drought can be analyzed using remote sensing (RS) techniques. Remote sensing indicators can provide an effective way to obtain real-time ecosystem conditions 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 (including the Normalized Differential Vegetation Index (NDVI)) are not sensitive to rapid changes in plant photosynthesis induced by common environmental stressors, such as drought. This is because most RS indicators do not have a direct relationship with photosynthetic functioning and are rather indicators of green biomass and, therefore, canopy structure, rather than canopy function.
Furthermore, the ecological modeling and remote sensing communities are particularly interested in the concept of Light Use Efficiency (LUE). However, no consensus has been reached on the most appropriate algorithm for LUE, and scientists still need to fully understand whether (and how) models should simulate LUE based on environmental factors (i.e., temperature and water availability).
The overall objective of the INDRO project was to understand the spatial and temporal variability of ecosystem gross primary productivity under diverse environmental conditions by investigating the relationship between remote indicators and plant physiological status on a global scale. This overall objective has been translated into the following specific objectives:
Objective 1: Review the definition of drought and investigate its effect on ecosystem productivity by analyzing in situ observations.
Objective 2: Determine the most appropriate remote sensing indicators to assess ecosystem productivity under drought conditions.
Objective 3: Demonstrate the applicability of the defined methodology and indicators by testing and validating them at specific test sites.
To achieve these objectives, the INDRO project combined in situ observations of ecosystem structure (e.g., biomass, leaf nitrogen content, leaf area index) and productivity (e.g., GPP derived from the eddy covariance technique) with satellite data available from different platforms (e.g., MODIS, Proba-V, and Sentinel-2).
Droughts affect most of the world. According to recent projections by the Intergovernmental Panel on Climate Change (IPCC), more frequent and severe droughts are expected. This observation makes it a priority to improve existing methods for monitoring droughts and their impact on terrestrial ecosystems. The development of early warning systems is necessary. Remote sensing (RS) technologies are well positioned to provide such monitoring. Thanks to RS, droughts can be monitored on a large scale and with short temporal resolution (e.g., daily).
The proposed INDRO project will focus on defining new RS-based indicators capable of monitoring vegetation status and its response to drought. For example, the RS indicators currently implemented in the European Drought Observatory's (EDO) early warning system are not sensitive to rapid changes in plant photosynthesis, as these indicators are not directly related to plant photosynthetic function.
The project will analyze the relationships between ecophysiological variables, light use activity (LUE), and existing RS indicators calculated using data from various satellite sensors. This analysis will reveal which sensors and what spatial and temporal resolutions are most suitable for quantifying drought. A new generation of RS indicators will be developed to better describe plant photosynthetic functioning under drought conditions. The project will map these new indicators for Southern Europe to identify areas affected by drought.
Overall, the development of new RS drought indicators and the improved definition of the drought events they represent will contribute to the development of ecological models and early warning systems, and lay the groundwork for new avenues for improving national and international drought mitigation and adaptation strategies.
The INDRO project provided a comprehensive and up-to-date overview of the use of remote sensing techniques for monitoring the effects of drought on ecosystems. Thanks to its multidisciplinary approach, the project has received significant attention from scientists from different disciplines (ecology, agriculture, climate change, geography, geophysics). It has improved our understanding of the impact of drought and climate change on ecosystems. It contributed to the debate on defining the most appropriate LUE factor for estimating GPP on a global scale.
Following the severe drought and heat wave of 2003, the European Commission established a European Drought Policy with the aim of mitigating and adapting to droughts. The work carried out during the project represents an important tool for the early detection of drought and mitigating its impact on ecosystem services. It contributes to the development of new monitoring services and the implementation of new strategies to limit damage to ecosystem services and prevent economic losses.
- CENTRO DE INVESTIGACION ECOLOGICA Y APLICACIONES FORESTALES (CREAF)
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