Riparian forest response to extreme drought is influenced by climatic context and canopy structure.

Droughts significantly impact forest ecosystems, reducing forest health and productivity, compromising ecosystem functioning, and nature-based solutions for climate change. The response and resilience of riparian forests to drought are poorly understood despite their key role in the functioning of aquatic and terrestrial ecosystems. Here we investigate riparian forest drought responses and resilience to an extreme drought event at a regional scale. We also examine how drought event characteristics, average climate conditions, topography, soil, vegetation structure, and functional diversity shape the resilience of riparian forests to drought. We used a time series of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) to calculate the resistance to and recovery after an extreme drought (2017-2018) in 49 sites across an Atlantic-Mediterranean climate gradient in North Portugal. We used generalized additive models and multi-model inference to understand which factors best explained drought responses. We found a trade-off between drought resistance and recovery (maximum r = -0.5) and contrasting strategies across the climatic gradient of the study area. Riparian forests in the Atlantic regions showed comparatively higher resistance, while Mediterranean forests recovered more. Canopy structure and climate context were the most relevant predictors of resistance and recovery. However, median NDVI and NDWI had not returned to pre-drought levels (RcNDWI mean = 1.21, RcNDVI mean = 1.01) three years after the event. Our study shows that riparian forests have contrasting drought response strategies and may be susceptible to extended legacy effects associated with extreme and/or recurring droughts, similarly to upland forests. This work highlights the drought vulnerability of riparian ecosystems and emphasises the need for further studies on long-term resilience to droughts.

Mapping the vulnerability of giant sequoias after extreme drought in California using remote sensing.

Between 2012 and 2016, California suffered one of the most severe droughts on record. During this period Sequoiadendron giganteum (giant sequoias) in the Sequoia and Kings Canyon National Parks (SEKI), California, USA experienced canopy water content (CWC) loss, unprecedented foliage senescence, and, in a few cases, death. We present an assessment of the vulnerability of giant sequoia populations to droughts that is currently lacking and needed for management. We used a temporal trend of remotely sensed CWC obtained between 2015 and 2017, and recently georeferenced giant sequoia crowns to quantify the vulnerability of 7,408 individuals in 10 groves in the northern portion of SEKI. CWC is sensitive to changes in liquid water in tree canopies; therefore, it is a useful metric for quantifying the response of sequoia trees to drought. Temporal trends indicated that 9% of giant sequoias had a significant decline or consistently low CWC, suggesting these trees were likely operating at low photosynthetic capacity and potentially at high risk to drought stress. We also found that 20% of the giant sequoias had an increase or consistently high level of CWC, indicating these trees were at low risk to drought stress. These vulnerability categories were used in a random forest model with a combination of topographic, fire-related, and climate variables to generate high-resolution vulnerability risk maps. These maps show that higher risk is associated with lower elevation and higher climate water deficit. We also found that sequoias at higher elevations but located near meadows had higher vulnerability risk. These results and the vulnerability maps can identify vulnerable sequoias that may be difficult to save or locations of refugia to be protected, and thus may aid forest managers in preparation for future droughts.

Old-growth forests show low canopy resilience to droughts at the southern edge of the taiga.

Forest mortality and resilience driven by drought disturbances have attracted tons of attention. However, the acquisition of continuous spatial-temporal data is generally enslaved to the conventional field investigations. In this study, the resilience of semiarid forest was characterized with canopy dynamics from remote sensing observations, combining the variations in canopy greenness and water content. We integrated dense normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) time series from Landsat datasets, intending to assess the canopy resilience in 24 conifer patches along a climatic aridity gradient at the southern edge of the taiga in northern Mongolia and southern Siberia of Russia. The results exhibited four patterns of coordinated NDVI-NDII variation trends, indicating that the canopy water content of coniferous forests may decrease at first during a drought period, and sustained water loss may, in turn, induce an accompanying reduction in canopy greenness. Meanwhile, the patches with canopy recovery growth after initial declines were considered to have resilience to climate change. We further observed the combined effects of aridity degree and tree age on canopy resilience, and all seven patches with no resilience corresponded to the old-tree group (the oldest trees reached or exceeded the age of 90). The observations indicated that the old-growth forests in semiarid regions were less likely to show canopy resilience, which corresponded to a higher risk of sustained decline.

Leaf surface water, not plant water stress, drives diurnal variation in tropical forest canopy water content.

Variation in canopy water content (CWC) that can be detected from microwave remote sensing of vegetation optical depth (VOD) has been proposed as an important measure of vegetation water stress. However, the contribution of leaf surface water (LWs ), arising from dew formation and rainfall interception, to CWC is largely unknown, particularly in tropical forests and other high-humidity ecosystems. We compared VOD data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and CWC predicted by a plant hydrodynamics model at four tropical sites in Brazil spanning a rainfall gradient. We assessed how LWs influenced the relationship between VOD and CWC. The analysis indicates that while CWC is strongly correlated with VOD (R2  = 0.62 across all sites), LWs accounts for 61-76% of the diurnal variation in CWC despite being < 10% of CWC. Ignoring LWs weakens the near-linear relationship between CWC and VOD and reduces the consistency in diurnal variation. The contribution of LWs to CWC variation, however, decreases at longer, seasonal to inter-annual, time scales. Our results demonstrate that diurnal patterns of dew formation and rainfall interception can be an important driver of diurnal variation in CWC and VOD over tropical ecosystems and therefore should be accounted for when inferring plant diurnal water stress from VOD measurements.

Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize.

BACKGROUND: Vegetation water content is one of the important biophysical features of vegetation health, and its remote estimation can be utilized to real-timely monitor vegetation water stress. Here, we compared the responses of canopy water content (CWC), leaf equivalent water thickness (EWT), and live fuel moisture content (LFMC) to different water treatments and their estimations using spectral vegetation indices (VIs) based on water stress experiments for summer maize during three consecutive growing seasons 2013-2015 in North Plain China. RESULTS: Results showed that CWC was sensitive to different water treatments and exhibited an obvious single-peak seasonal variation. EWT and LFMC were less sensitive to water variation and EWT stayed relatively stable while LFMC showed a decreasing trend. Among ten hyperspectral VIs, green chlorophyll index (CIgreen), red edge normalized ratio (NRred edge), and red-edge chlorophyll index (CIred edge) were the most sensitive VIs responding to water variation, and they were optimal VIs in the prediction of CWC and EWT. CONCLUSIONS: Compared to EWT and LFMC, CWC obtained the best predictive power of crop water status using VIs. This study demonstrated that CWC was an optimal indicator to monitor maize water stress using optical hyperspectral remote sensing techniques.

Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index.

Crop canopy water content (CWC) is an essential indicator of the crop's physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar beet and onion) and corresponding top-of-canopy (TOC) reflectance spectra acquired by the hyperspectral HyMap airborne sensor. First, commonly used water content index formulations were analysed and validated for the variety of crops, overall resulting in a R2 lower than 0.6. In an attempt to move towards more generically applicable indices, the two new CWC indices exploit the principal water absorption features in the near-infrared by using multiple bands sensitive to water content. We propose the Water Absorption Area Index (WAAI) as the difference between the area under the null water content of TOC reflectance (reference line) simulated with PROSAIL and the area under measured TOC reflectance between 911 and 1271 nm. We also propose the Depth Water Index (DWI), a simplified four-band index based on the spectral depths produced by the water absorption at 970 and 1200 nm and two reference bands. Both the WAAI and DWI outperform established indices in predicting CWC when applied to heterogeneous croplands, with a R2 of 0.8 and 0.7, respectively, using an exponential fit. However, these indices did not perform well for species with a low fractional vegetation cover (< 30%). HyMap CWC maps calculated with both indices are shown for the Barrax region. The results confirmed the potential of using generically applicable indices for calculating CWC over a great variety of crops.