Spatio-temporal assessment of beech growth in relation to climate extremes in Slovenia – An integrated approach using remote sensing and tree-ring data

Climate change is predicted to affect tree growth due to increased frequency and intensity of extreme events such as ice storms, droughts and heatwaves. Yet, there is still a lot of uncertainty on how trees respond to an increase in frequency of extreme events. Use of both ground-based wood increment (i.e. ring width) and remotely sensed data (i.e. vegetation indices) can be used to scale-up ground measurements, where there is a link between the two, but this has only been demonstrated in a few studies. We used tree-ring data together with crown features derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess the effect of extreme climate events on the growth of beech (Fagus sylvatica L.) in Slovenia. We found evidence that years with climate extremes during the growing season (drought, high temperatures) had a lower ring width index (RWI) but we could not find such evidence for the remotely sensed EVI (Enhanced Vegetation Index). However, when assessing specific events where leaf burning or wilting has been reported (e.g. August 2011) we did see large EVI anomalies. This implies that the impact of drought or heatwave events cannot be captured by EVI anomalies until physical damage on the canopy is caused. This also means that upscaling the effect of climate extremes on RWI by using EVI anomalies is not straightforward. An exception is the 2014 ice storm that caused a large decline in both RWI and EVI. Extreme climatic parameters explained just a small part of the variation in both RWI and EVI by, which could indicate an effect of other climate variables (e.g. late frost) or biotic stressors such as insect outbreaks. Furthermore, we found that RWI was lower in the year after a climate extreme occurred in the late summer. Most likely due to the gradual increase in temperature and more frequent drought we found negative trends in RWI and EVI. EVI maps could indicate where beech is sensitive to climate changes and could be used for planning mitigation interventions. Logical next steps should focus on a tree-based understanding of the short -and long-term effects of climate extremes on tree growth and survival, taking into account differential carbon allocation to the crown (EVI) and to wood-based variables. This research highlights the value of an integrated approach for upscaling tree-based knowledge to the forest level

Quantifying the effect of forest age in annual net forest carbon balance

Forests dominate carbon (C) exchanges between the terrestrial biosphere and the atmosphere on land. In the long term, the net carbon flux between forests and the atmosphere has been significantly impacted by changes in forest cover area and structure due to ecological disturbances and management activities. Current empirical approaches for estimating net ecosystem productivity (NEP) rarely consider forest age as a predictor, which represents variation in physiological processes that can respond differently to environmental drivers, and regrowth following disturbance. Here, we conduct an observational synthesis to empirically determine to what extent climate, soil properties, nitrogen deposition, forest age and management influence the spatial and interannual variability of forest NEP across 126 forest eddy-covariance flux sites worldwide. The empirical models explained up to 62% and 71% of spatio-temporal and across-site variability of annual NEP, respectively. An investigation of model structures revealed that forest age was a dominant factor of NEP spatio-temporal variability in both space and time at the global scale as compared to abiotic factors, such as nutrient availability, soil characteristics and climate. These findings emphasize the importance of forest age in quantifying spatio-temporal variation in NEP using empirical approaches

Estimating potato leaf chlorophyll content using ratio vegetation indices

Chlorophyll content at leaf level is an important variable because of its crucial role in photosynthesis and in understanding plant functioning. In this study, we tested the hypothesis that the ratio of a vegetation index (VI) for estimating canopy chlorophyll content (CCC) and one for estimating leaf area index (LAI) can be used to derive chlorophyll content at the leaf level. This hypothesis for estimating chlorophyll content at the leaf level was tested using simulations with the PROSAIL radiative transfer model and field spectroradiometry measurements in five consecutive years (2010–2014) for potato crops on experimental fields. During the growing season, in-situ field measurements of LAI and leaf chlorophyll content (LCC) were performed. Results showed that good estimates of LCC were feasible using ratio vegetation indices (VIs). This was tested at satellite level using RapidEye images. This letter presents a proof of concept for estimating LCC using Sentinel-2 data. Results confirm the importance of the red-edge bands for agricultural applications, but also showed that indices using the red-edge bands may be replaced by indices using green bands. It should now be tested with real Sentinel-2 data whether its spectral bands at 10 m spatial resolution are suitable for estimating LCC, avoiding the need for red-edge bands that only are available at 20 m

Relationship between MODIS EVI and LAI across time and space

The Leaf Area Index (LAI) is used as input in hydrological and bio-chemical models for the estimation of water-cycle characteristics, agricultural primary production and other processes. Vegetation Indices (VIs) are used to monitor vegetation state and health. Considering that easily computed VIs can be used for the estimation of LAI, this study applied a regression analysis between MODIS Enhanced Vegetation Index (EVI) and LAI data in five sites around the world. A linear model was found to provide a good description of the LAI–EVI relationship across all examined vegetation types and times. Medium accuracy models were improved when variability of time and vegetation type were considered, indicating that these parameters highly affect the LAI–EVI relationship. Sensitivity of EVI to LAI was higher in periods of high biomass production. Regression analysis between LAI–EVI showed a stronger relationship for the study sites characterized by dry and warm tropical climatic conditions

Towards a comprehensive and consistent global aquatic land cover characterization framework addressing multiple user needs

Aquatic land cover represents the land cover type that is significantly influenced by the presence of water over an extensive part of a year. Monitoring global aquatic land cover types plays an essential role in preserving aquatic ecosystems and maintaining the ecosystem service they provide for humans, while at the same time their accurate and consistent monitoring for multiple purposes (e.g. climate modelling, biodiversity conservation, water resource management) remains challenging. Although a number of global aquatic land cover (GALC) datasets are available for use to monitor aquatic ecosystems, there are prominent variabilities among these datasets, which is primarily caused by the inconsistency between different land versus water-related monitoring approaches and characterization schemes. As aquatic land cover exists in many different forms on Earth (e.g. wetland, open water) and can be mapped by different approaches, it is necessary to consider a much more consistent and comprehensive characterization framework that not only ensures the consistency in the monitoring of aquatic land cover but also serves the needs of multiple users (e.g. climate users, agricultural users) interested in different aspects of aquatic lands. In this study, we addressed this issue by 1) reviewing 33 GALC datasets and user needs identified from the citing papers of current datasets and international conventions, policies and agreements in relation to aquatic ecosystems, 2) proposing a global characterization framework for aquatic land cover based on the Land Cover Classification System (LCCS) classifier principles and the identified user needs, and 3) highlighting the opportunities and challenges provided by remote sensing techniques for the implementation of the proposed framework. Results show that users require or prefer various kinds of information on aquatic types including vegetation type, water persistence, the artificiality of cover (i.e. artificial vs natural), water salinity, and the accessibility to the sea (i.e. coastal vs inland). Datasets with medium to high spatial resolution, intra-annual dynamics and inter-annual changes are needed by many users. However, none of the existing datasets can meet all these requirements and a rigorous quantitative accuracy assessment is lacking to evaluate its quality for most of the GALC datasets. The proposed framework has three levels and users are allowed to derive their aquatic land cover types of interest by combining different levels and classifiers of information. This comprehensive mapping framework can help to bridge the gap between user needs and current GALC datasets as well as the gap between generic and aquatic land cover monitoring. The implementation of the framework can benefit from evolving satellite-data availability, improved computation capability and open-source machine learning algorithms, although at the same time it faces challenges mainly coming from the complexity of aquatic ecosystems. The framework proposed in this study provides insights for future operational aquatic land cover monitoring initiatives and will support better understanding and monitoring of complex aquatic ecosystems.</p

Downscaling of MODIS leaf area index using landsat vegetation index

Several organizations provide satellite Leaf Area Index (LAI) data regularly, at various scales, at high frequency, but at low spatial resolution. This study attempted to enhance the spatial resolution of the MODIS LAI product to the Landsat resolution level. Four climatically diverse sites in Europe and Africa were selected as study areas. Regression analysis was applied between MODIS Enhanced Vegetation Index (EVI) and LAI data. The regression equations were used as input in a downscaling model, along with Landsat EVI images and land-cover maps. The estimated LAI values showed high correlation with field-measured LAI during the dry period. The model validation gave statistically significant results, with correlation coefficient values ranging from relatively low (0.25–0.32), to moderate (0.48–0.64) and high (0.72–0.94). Limited samples per vegetation type, the diversity of species within the same vegetation type, land-use/land-cover changes and saturated EVI values affected the accuracy of the downscaling model

Potential of UAV-based sun-induced chlorophyll fluorescence to detect water stress in sugar beet

Sun-induced chlorophyll fluorescence (SIF) is a direct indicator of plant photosynthetic activities and can potentially indicate plant physiological changes caused by water stress. However, the direct effect of water stress on the physiological SIF responses in crops at the field level still needs further research to clearly understand the involved mechanisms. To study this relationship, we made use of Unmanned Aerial Vehicles (UAVs), which are flexible and cost-effective to acquire SIF data at a high temporal resolution. We acquired near-infrared SIF (760 nm) and red SIF (687 nm) measurements using a UAV platform over irrigated and non-irrigated sugar beet plots. To represent physiological changes in crops, we calculated the apparent SIF yield (SIF normalized by the absorbed photosynthetically active radiation) at 760 and 687 nm (SIF760yield and SIF687yield), the fluorescence emission yield at 760 nm (ΦF760), and the SIFratio (the ratio between SIF687 and SIF760). ΦF760 was estimated using the recently developed NIRvH approach. For an improved interpretation of the response of these SIF indicators, we also acquired additional UAV-based hyperspectral and thermal data. We found that on June 28, when sugar beets were experiencing water stress, SIF687yield, ΦF760, and SIFratio all showed a significant response to the recovery of the irrigated sugar beets (p-value < 0.05). On the other hand, on July 24 when both water stress and heat stress affected the crop, only ΦF760 and SIFratio weakly tracked the changes induced by the irrigation (p-value < 0.1). ΦF760 had similar changes to SIF760yield in both June and July, but ΦF760 was more sensitive to irrigation. This indicates the importance of correcting for the structural effect when interpreting the SIF response. The findings suggest that SIF indicators can indicate water stress at the field level, but its value to detect the changes of photosynthetic activities under severe stress needs more investigation

Decoupling physiological and non-physiological responses of sugar beet to water stress from sun-induced chlorophyll fluorescence

Due to the physiological connection with photosynthesis, sun-induced chlorophyll fluorescence (SIF) provides a promising indicator of vegetation physiological changes caused by environmental stress (e.g. water deficiency). SIF response to crop physiological alterations under water stress is complicated by concurrent non-physiological changes. The non-physiological variation stems from crop structure, leaf optical traits (i.e. pigments, leaf water content, and dry matter), and sun-target geometry. This study aims to disentangle the physiological effect from the non-physiological effect on SIF variations caused by water stress, providing more direct insights into the mechanism of SIF response to stress. We parameterized the radiative transfer model (RTM) SCOPE with top-of-canopy (TOC) reflectance and SIF measurements to decouple the joint effects on TOC SIF in sugar beet. SIF and reflectance data were acquired over irrigated and water-stressed plots using an Unmanned Aerial Vehicle (UAV) on two consecutive days. The non-physiological response was quantified with SCOPE by fitting the model parameters to the TOC reflectance measurements. Subsequently, fluorescence emission yield (ΦF) was estimated using SIF measurements to represent the actual physiological status. The results demonstrate that SIF variation caused by water stress both at 687 nm and 760 nm was affected by both the physiological alterations in ΦF and the non-physiological changes. At both 687 nm and 760 nm, the non-physiological contribution to SIF variations was lower than the contribution of ΦF variation induced by water stress. The lower non-physiological contribution was mainly due to the weak combined effect of the fraction of photosynthetically active radiation absorbed by leaf chlorophyll (fAPARchl) and the fluorescence escape fraction (fesc) on SIF responses. This study provided direct insights into the plant physiological status under water stress and further indicated the ability of the approach of combining RTMs, canopy reflectance, and SIF measurements to support the scalable quantitative use of SIF from the leaf to the ecosystem level

Downscaling of MODIS leaf area index using landsat vegetation index

Several organizations provide satellite Leaf Area Index (LAI) data regularly, at various scales, at high frequency, but at low spatial resolution. This study attempted to enhance the spatial resolution of the MODIS LAI product to the Landsat resolution level. Four climatically diverse sites in Europe and Africa were selected as study areas. Regression analysis was applied between MODIS Enhanced Vegetation Index (EVI) and LAI data. The regression equations were used as input in a downscaling model, along with Landsat EVI images and land-cover maps. The estimated LAI values showed high correlation with field-measured LAI during the dry period. The model validation gave statistically significant results, with correlation coefficient values ranging from relatively low (0.25–0.32), to moderate (0.48–0.64) and high (0.72–0.94). Limited samples per vegetation type, the diversity of species within the same vegetation type, land-use/land-cover changes and saturated EVI values affected the accuracy of the downscaling model

Improving the characterization of global aquatic land cover types using multi-source earth observation data

The sustainable management of aquatic resources requires spatially explicit information on the water and vegetation presence of aquatic ecosystems. Previous Global Aquatic Land Cover (GALC) mapping has been focused on water bodies while lacking information on vegetation, and aquatic types have always been characterized by low accuracies in global land cover products, calling for specific attention to improve GALC mapping. The availability of a wealth of open Earth Observation (EO) data on cloud-computing platforms provides opportunities to map aquatic land cover globally. This study aims to evaluate the potential of multi-source freely available EO data, including optical, Synthetic Aperture Radar (SAR), and various ancillary datasets, for improving the characterization of aquatic land cover comprising both water and vegetation types on a global scale. Using different combinations of features derived from these data, the classification performance of five land cover classes (i.e., trees, shrubs, herbaceous cover, bare/sparsely vegetated lands, and water bodies) in aquatic areas was cross-validated. Results showed that Sentinel-2 data alone achieved similarly good overall accuracy as those combining multi-source data. However, the single-sensor Sentinel-2 data cannot discriminate highly mixed and spectrally similar types, such as shrubs, trees, and herbaceous vegetation. Integrating SAR features from the ALOS/PALSAR mosaic and Sentinel-1 data with optical features provided by Sentinel-2 data could help address this limitation to some extent. Although with a lower spatial and temporal resolution, the ALOS/PALSAR mosaic had a stronger impact on GALC classification than Sentinel-1 data when they were synergistically used. Features provided by ancillary datasets did not lead to significant improvement in the overall GALC classification. At class-level, topographic and soil features helped to reduce the commission error of shrubs, and climate variables were useful to improve the characterization of bare aquatic lands. The Global Ecosystem Dynamics Investigation (GEDI) forest canopy height dataset helped to characterize trees but also resulted in a decrease in accuracies of shrubs. By assessing multi-source earth observation data, this research represents an important step forward in the global mapping of comprehensive aquatic land cover types at high spatial resolution (i.e., 10 m)