Correlation of Field-Measured and Remotely Sensed Plant Water Status as a Tool to Monitor the Risk of Drought-Induced Forest Decline

Abstract

Increased frequency of tree mortality and forest decline due to anomalous drought events calls for the adoption of effective monitoring of tree water status over large spatial and temporal scales. We correlated field-measured and remotely sensed plant water status parameters, to test the possibility of monitoring the risk of drought-induced dehydration and hydraulic failure using satellite images calibrated on reliable physiological indicators of tree hydraulics. The study was conducted during summer 2019 in the Karst plateau (NE Italy) in a woodland dominated by Fraxinus ornus L.; Sentinel-2 images were acquired on a seasonal scale on the same dates when absolute water content (AbWC), relative water content (RWC), and minimum water potential (Ψmin) were measured in the field. Plant water status parameters were correlated with normalized difference vegetation index (NDVI and NDVI 8A), normalized difference water index (NDWI), and soil-adjusted vegetation index (SAVI). Significant Pearson and Spearman linear correlations (α < 0.05) emerged between all tree-level measured variables and NDWI, while for NDVI, NDVI 8A, and SAVI no correlation was found. Our results suggest the possibility of using the NDWI as a proxy of tree water content and water potential.

Water Status of Polylepis Forests Related to Altitudinal Gradient, Microclimate Variables and Slope

“…This demonstrates the high effectiveness of the use of unmanned aerial vehicles that can capture multispectral images to represent NDVI values, and monitor the water status of plants, the water status of forests and crops, and for restoration actions (Tito et al, 2020). These results are consistent with the study conducted by Marusig et al (2020) in Northeast Italy, who monitored the water status of trees against the risk of dehydration during dry periods, as well as the results of Shimada et al (2012) who compared two methods to evaluate the relationship between water status and NDVI in crops, and monitored the response of vegetation to precipitation and temperature. Additionally, the mathematical models found are good predictors and useful, because of the significant correlation between variables such as: soil moisture, temperature and air moisture as input variables, which are those that influenced the results of the physiological status of the Polylepis.…”

Water ‘on the rocks’: a summer drink for thirsty trees?

“…We calculated normalized difference vegetation index (NDVI) as a proxy of green biomass, as follows:where ρ NIR is the near‐infrared reflectance, and ρ red is the red light reflectance. We also calculated the normalized difference water index (NDWI), which has been shown to correlate to the water status of F. ornus trees in our study area (Marusig et al ., 2020). We used this index as a proxy for large‐scale seasonal changes in water status of vegetation growing on bedrocks with contrasting porosities.…”

Relative water content consistently predicts drought mortality risk in seedling populations with different morphology, physiology, and times to death

“…Remote sensing estimates of plant water content -once standardized by the maximum values observed within a pixel- are likely to show similar properties than empirical RWC and to be a more consistent indicator across plants. While standardizing remote sensing estimates of absolute plant water content is not a task lacking in challenges, recent work has shown that this approach holds promise (Rao et al 2019; Marusig et al 2020). In summary, RWC stands out as a good candidate for large-scale, real-time assessments of DIM risk.…”

“…The common RWC values at turgor loss across species could indicate a common degree of maximum dehydration that most woody plants can withstand prior to risking death. Lastly -and unlike PLC and NSC-, a clear advantage of plant water content is that it can be estimated from organs to ecosystems via remote sensing (Ullah, Skidmore, Naeem & Schlerf 2012; Wang & Li 2012; Mirzaie et al 2014; Rao, Anderegg, Sala, Martínez-Vilalta & Konings 2019; Marusig et al 2020). Several remote sensing studies have observed declines in canopy water content followed by increased drought mortality (Saatchi et al 2013; Asner et al 2015).…”