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

The ant and the grasshopper: Contrasting responses and behaviors to water stress of riparian trees along a hydroclimatic gradient

Riparian trees are particularly vulnerable to drought because they are highly dependent on water availability for their survival. However, the response of riparian tree species to water stress varies depending on regional hydroclimatic conditions, making them unevenly vulnerable to changing drought patterns. Understanding this spatial variability in stress responses requires a comprehensive assessment of water stress across broader spatial and temporal scales. Yet, the precise ecophysiological mechanisms underlying these responses remain poorly linked to remotely sensed indices. To address this gap, the implementation of remote sensing methods coupled with in situ validation is essential to obtain consistent results across diverse spatial and temporal contexts. We conducted a multi-tool analysis combining multispectral and thermal remote sensing indices with in situ ecophysiological measurements at different temporal scales to analyze the responses of white poplar (Populus alba) to seasonal changes in drought along a hydroclimatic gradient. Using this approach, we demonstrate that white poplars along the Rhône River (France) exhibit contrasting responses and behaviors during drought depending on the latitudinal context. White poplars in a Mediterranean climate show rapid stomatal closure to reduce water loss and maintain high minimum water potential levels, although this results in a decrease in remotely sensed greenness. Conversely, white poplars located upstream in a temperate climate show high transpiration and stable greenness but lower minimum water potential and water content. A site in the middle of the gradient has intermediate responses. These results demonstrate that white poplars along a climate gradient can have a range of responses to drought along the iso/anisohydricity continuum. These results are important for future climatic conditions because they show that the same species can have different mechanisms of drought resilience, even in the same river valley. This raises questions regarding how these riparian tree populations will respond to future climatic and hydrological conditions.

Mechanistic links between physiology and spectral reflectance enable previsual detection of oak wilt and drought stress

Tree mortality due to global change-including range expansion of invasive pests and pathogens-is a paramount threat to forest ecosystems. Oak forests are among the most prevalent and valuable ecosystems both ecologically and economically in the United States. There is increasing interest in monitoring oak decline and death due to both drought and the oak wilt pathogen (Bretziella fagacearum). We combined anatomical and ecophysiological measurements with spectroscopy at leaf, canopy, and airborne levels to enable differentiation of oak wilt and drought, and detection prior to visible symptom appearance. We performed an outdoor potted experiment with Quercus rubra saplings subjected to drought stress and/or artificially inoculated with the pathogen. Models developed from spectral reflectance accurately predicted ecophysiological indicators of oak wilt and drought decline in both potted and field experiments with naturally grown saplings. Both oak wilt and drought resulted in blocked water transport through xylem conduits. However, oak wilt impaired conduits in localized regions of the xylem due to formation of tyloses instead of emboli. The localized tylose formation resulted in more variable canopy photosynthesis and water content in diseased trees than drought-stressed ones. Reflectance signatures of plant photosynthesis, water content, and cellular damage detected oak wilt and drought 12 d before visual symptoms appeared. Our results show that leaf spectral reflectance models predict ecophysiological processes relevant to detection and differentiation of disease and drought. Coupling spectral models that detect physiological change with spatial information enhances capacity to differentiate plant stress types such as oak wilt and drought.

Assessment of dynamic drought-induced ecosystem risk: Integrating time-varying hazard frequency, exposure and vulnerability

Terrestrial ecosystems, occupying 28.26% of Earth's surface, are extensively at risk from droughts, which is likely to propagate into human communities owing to loss of vital services. Ecosystem risk also tends to fluctuate within anthropogenically-forced nonstationary environments, raising considerable concerns about effectiveness of mitigation strategies. This study aims to assess dynamic ecosystem risk induced by droughts and identify risk hotspots. Bivariate nonstationary drought frequency was initially derived as a hazard component of risk. By coupling vegetation coverage and biomass quantity, a two-dimensional exposure indicator was developed. Trivariate likelihood of vegetation decline was calculated under arbitrary droughts to intuitively determine ecosystem vulnerability. Ultimately, time-variant drought frequency, exposure and vulnerability were multiplied to derive dynamic ecosystem risk, followed by hotspot and attribution analyses. Risk assessment implemented in the drought-prevalent Pearl River basin (PRB) of China during 1982-2017 showed that meteorological droughts in eastern and western margins, although less frequent, were prolonged and aggravated in contrast to prevalence of less persistent and severe droughts in the middle. In 86.12% of the PRB, ecosystem exposure maintains high levels (0.62). Relatively high vulnerability (>0.5) occurs in water-demanding agroecosystems, exhibiting a northwest-southeast-directed extension. A 0.1-degree risk atlas unveils that high and medium risks occupy 18.96% and 37.99% of the PRB, while risks are magnified in the north. The most pressing hotspots with high risk continuing to escalate reside in the East River and Hongliu River basins. Our results provide knowledge of composition, spatio-temporal variability and driving mechanism of drought-induced ecosystem risk, which will assist in risk-based mitigation prioritization.

Critical water contents at leaf, stem and root level leading to irreversible drought-induced damage in two woody and one herbaceous species

Plant water content is a simple and promising parameter for monitoring drought-driven plant mortality risk. However, critical water content thresholds leading to cell damage and plant failure are still unknown. Moreover, it is unclear whether whole-plant or a specific organ water content is the most reliable indicator of mortality risk. We assessed differences in dehydration thresholds in leaf, stem and root samples, hampering the organ-specific rehydration capacity and increasing the mortality risk. We also tested eventual differences between a fast experimental dehydration of uprooted plants, compared to long-term water stress induced by withholding irrigation in potted plants. We investigated three species with different growth forms and leaf habits i.e., Helianthus annuus (herbaceous), Populus nigra (deciduous tree) and Quercus ilex (evergreen tree). Results obtained by the two dehydration treatments largely overlapped, thus validating bench dehydration as a fast but reliable method to assess species-specific critical water content thresholds. Regardless of the organ considered, a relative water content value of 60% induced significant cell membrane damage and loss of rehydration capacity, thus leading to irreversible plant failure and death.

The Interplay of the Tree and Stand-Level Processes Mediate Drought-Induced Forest Dieback: Evidence from Complementary Remote Sensing and Tree-Ring Approaches

Drought-induced forest dieback can lead to a tipping point in community dominance, but the coupled response at the tree and stand-level response has not been properly addressed. New spatially and temporally integrated monitoring approaches that target different biological organization levels are needed. Here, we compared the temporal responses of dendrochronological and spectral indices from 1984 to 2020 at both tree and stand levels, respectively, of a drought-prone Mediterranean Pinus pinea forest currently suffering strong dieback. We test the influence of climate on temporal patterns of tree radial growth, greenness and wetness spectral indices; and we address the influence of major drought episodes on resilience metrics. Tree-ring data and spectral indices followed different spatio-temporal patterns over the study period (1984–2020). Combined information from tree growth and spectral trajectories suggests that a reduction in tree density during the mid-1990s could have promoted tree growth and reduced dieback risk. Additionally, over the last decade, extreme and recurrent droughts have resulted in crown defoliation greater than 40% in most plots since 2019. We found that tree growth and the greenness spectral index were positively related to annual precipitation, while the wetness index was positively related to mean annual temperature. The response to drought, however, was stronger for tree growth than for spectral indices. Our study demonstrates the value of long-term retrospective multiscale analyses including tree and stand-level scales to disentangle mechanisms triggering and driving forest dieback.

High-resolution planet satellite imagery and multi-temporal surveys to predict risk of tree mortality in tropical eucalypt forestry

Global high-resolution imagery is a well-assimilated technology in forest mapping. The release of the Norway's International Climate & Forests Initiative (NICFI) Planet tropical basemaps time-series starting in 2015 at a 4.77-m resolution represents a unique opportunity to forecast climate change consequences such as drought episodes. Using multi-temporal ground surveys over 144 plots and publicly available high-resolution Planet dove time-series imagery we evaluate forest mortality patterns driven by imaging spectroscopy methods in Mato Grosso (Brazil) over an area planted with eucalypts severely affected by the 2019 drought. Changes in vegetation indexes before and after the 2019 drought were modelled using the effective logistic regression modelling to explain variation in tree mortality between the surveys, the dependent variable. We aimed to straightforwardly model tree mortality using change vectors in Planet's image mosaics co-registering in time with the observed tree mortality measurements in the field. The results showed differences in Normalized Difference Vegetation Index (NDVI) as the most significant predictor variable under the effective logistic regression modelling performed. The efficacy of 80.98% in concordance pairs correctly classified represented 0.81 of area under the Receiver Operating Curve (ROC). The release of the 2015-2020 Planet imagery in the tropics at 4.77-m resolution represents a valuable dataset to better understand previous natural disturbances and a powerful technology to detect in advance, and monthly after September 2020, eucalypt areas prone to harmful and increasingly frequent water-stress episodes.

Satellite data reveal differential responses of Swiss forests to unprecedented 2018 drought

Extreme events such as the summer drought of 2018 in Central Europe are projected to occur more frequently in the future and may cause major damages including increased tree mortality and negative impacts on forest ecosystem services. Here, we quantify the response of >1 million forest pixels of 10 × 10 m across Switzerland to the 2018 drought in terms of resistance, recovery, and resilience. We used the Normalized Difference Water Index (NDWI) derived from Sentinel-2 satellite data as a proxy for canopy water content and analyzed its relative change. We calculated NDWI change between the 2017 pre-drought and 2018 drought years (indicating resistance), 2018 and the 2019 post-drought (indicating recovery), and between 2017-2019 (indicating resilience). Analyzing the data from this large natural experiment, we found that for 4.3% of the Swiss forest the NDWI declined between 2017 and 2018, indicating areas with low resistance of the forest canopy to drought effects. While roughly 50% of this area recovered, in 2.7% of the forested area NDWI continued to decline from 2018 to 2019, suggesting prolonged negative effects or delayed damage. We found differential forest responses to drought associated with site topographic characteristics and forest stand characteristics, and to a lesser extent with climatic conditions and interactions between these drivers. Low drought resistance and high recovery were most prominent at forest edges, but also on south-facing slopes and lower elevations. Tree functional type was the most important driver of drought resilience, with most of the damage in stands with high conifer abundance. Our results demonstrate the suitability of satellite-based quantification of drought-induced forest damage at high spatial resolution across large areas. Such information is important to predict how local site characteristics may impact forest vulnerability to future extreme events and help in the search for appropriate adaptation strategies.

Hydraulic failure and tree mortality: from correlation to causation

Xylem hydraulic failure has been recognized as a pervasive factor in the triggering of drought-induced tree mortality. However, foundational evidence of the mechanistic link connecting hydraulic failure with living cell damage and tree death has not been identified yet, compromising our ability to predict mortality events. Meristematic cells are involved in the recovery of trees from drought, and focusing on their vitality and functionality after a drought event could provide novel information on the mechanistic link between hydraulic failure and drought-induced tree mortality. In this Opinion, we focus on the cell's critical hydration status for tree recovery from drought and how it links with the membrane integrity of the meristems.

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

Predicted increases in forest drought mortality highlight the need for predictors of incipient drought-induced mortality (DIM) risk that enable proactive large-scale management. Such predictors should be consistent across plants with varying morphology and physiology. Because of their integrative nature, indicators of water status are promising candidates for real-time monitoring of DIM, particularly if they standardize morphological differences among plants. We assessed the extent to which differences in morphology and physiology between Pinus ponderosa populations influence time to mortality and the predictive power of key indicators of DIM risk. Time to incipient mortality differed between populations but occurred at the same relative water content (RWC) and water potential (WP). RWC and WP were accurate predictors of drought mortality risk. These results highlight that variables related to water status capture critical thresholds during DIM and the associated dehydration processes. Both WP and RWC are promising candidates for large-scale assessments of DIM risk. RWC is of special interest because it allows comparisons across different morphologies and can be remotely sensed. Our results offer promise for real-time landscape-level monitoring of DIM and its global impacts in the near term.

Estimating the Leaf Water Status and Grain Yield of Wheat under Different Irrigation Regimes Using Optimized Two- and Three-Band Hyperspectral Indices and Multivariate Regression Models

Spectral reflectance indices (SRIs) often show inconsistency in estimating plant traits across different growth conditions; thus, it is still necessary to develop further optimized SRIs to guarantee the performance of SRIs as a simple and rapid approach to accurately estimate plant traits. The primary goal of this study was to develop optimized two- and three-band vegetation- and water-SRIs and to apply different multivariate regression models based on these SRIs for accurately estimating the relative water content (RWC), gravimetric water content (GWCF), and grain yield (GY) of two wheat cultivars evaluated under three irrigation regimes (100%, 75%, and 50% of crop evapotranspiration (ETc)) for two seasons. Results showed that the three plant traits and all SRIs showed significant differences (p < 0.05) between the three irrigation treatments for each wheat cultivar. The three-band water-SRIs (NWIs-3b) showed the best performance in estimating the three plant traits for both cultivars (R2 > 0.80), and RWC and GWCF under 75% ETc (R2 ≥ 0.65). Four out of six three-band vegetation-SRIs (NDVIs-3b) performed better than any other SRIs for estimating GY under 100% ETc and 50% ETC, and RWC under 100% ETc (R2 ≥ 0.60). All types of SRIs demonstrated excellent performance in estimating the three plant traits (R2 ≥ 0.70) when the data of all growth conditions were combined and analyzed together. The NWIs-3b coupled with Random Forest models predicted the three plant traits with satisfactory accuracy for the calibration (R2 ≥ 0.96) and validation (R2 ≥ 0.93) datasets. The overall results of this study elucidate that extracting an optimized NWIs-3b from the full spectrum data and combined with an appropriate regression technique could be a practical approach for managing deficit irrigation regimes of crops through accurately, timely, and non-destructively monitoring the water status and final potential yield.

Monitoring of Spatiotemporal Change of Green Spaces in Relation to the Land Surface Temperature: A Case Study of Belgrade, Serbia

Understanding the relationship between land use and land cover and thermal environment has recently become an emerging issue for urban planners and policy makers. We chose Belgrade, as a case study, to present a cost- and time-effective framework for monitoring spatiotemporal changes of green spaces in relation to the land surface temperature (LST). Time series analysis was performed using Landsat 5 TM and Landsat 8 OLI/TIRS imagery from 1991 to 2019 with an approximate 5-year interval (18 images in total). Spectral vegetation indices and supervised land cover classifications were used to examine changes of green spaces. The results showed a fluctuating trend of the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). The highest values were recorded in 2019, indicating vegetation recovery in the last decade. A significant positive correlation was determined between the spectral vegetation indices and the amount of precipitation during growing season. The land cover classification showed that the share of vegetated and bare land decreased by 11.74% during the study period. The most intensive conversion of green and bare land into built-up land cover occurred in the first decade (1991–2000). To assess spatiotemporal changes in the LST, Landsat Collection 2 Surface Temperature products were used. We found a negative correlation between change in the spectral vegetation indices and change in the LST. This indicates that the reduction in vegetation was associated with an increase in the LST. The municipalities that were the most affected in each decade were also identified with our framework. The findings of this study are of great relevance for actions targeting an improvement in urban thermal comfort and climate resilience.

Reviewing the Potential of Sentinel-2 in Assessing the Drought

This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth’s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.

The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis

Forests are increasingly subject to a number of disturbances that can adversely influence their health. Remote sensing offers an efficient alternative for assessing and monitoring forest health. A myriad of methods based upon remotely sensed data have been developed, tailored to the different definitions of forest health considered, and covering a broad range of spatial and temporal scales. The purpose of this review paper is to identify and analyse studies that addressed forest health issues applying remote sensing techniques, in addition to studying the methodological wealth present in these papers. For this matter, we applied the PRISMA protocol to seek and select studies of our interest and subsequently analyse the information contained within them. A final set of 107 journal papers published between 2015 and 2020 was selected for evaluation according to our filter criteria and 20 selected variables. Subsequently, we pair-wise exhaustively read the journal articles and extracted and analysed the information on the variables. We found that (1) the number of papers addressing this issue have consistently increased, (2) that most of the studies placed their study area in North America and Europe and (3) that satellite-borne multispectral sensors are the most commonly used technology, especially from Landsat mission. Finally, most of the studies focused on evaluating the impact of a specific stress or disturbance factor, whereas only a small number of studies approached forest health from an early warning perspective.

Use of Sentinel-2 Satellite Data for Windthrows Monitoring and Delimiting: The Case of “Vaia” Storm in Friuli Venezia Giulia Region (North-Eastern Italy)

On the 29th of October 2018, a storm named “Vaia” hit North-Eastern Italy, causing the loss of 8 million m3 of standing trees and creating serious damage to the forested areas, with many economic and ecological implications. This event brought up the necessity of a standard procedure for windthrow detection and monitoring based on satellite data as an alternative to foresters’ fieldwork. The proposed methodology was applied in Carnic Alps (Friuli Venezia Giulia, NE Italy) in natural stands dominated by Picea abies and Abies alba. We used images from the Sentinel-2 mission: 1) to test vegetation indices performance in monitoring the vegetation dynamics in the short period after the storm, and 2) to create a windthrow map for the whole Friuli Venezia Giulia region. Results showed that windthrows in forests have a significant influence on visible and short-wave infrared (SWIR) spectral bands of Sentinel-2, both in the short and the long-term timeframes. NDWI8A and NDWI were the best indices for windthrow detection (R2 = 0.80 and 0.77, respectively) and NDVI, PSRI, SAVI and GNDVI had an overall good performance in spotting wind-damaged areas (R2 = 0.60–0.76). Moreover, these indices allowed to monitor post-Vaia forest die-off and showed a dynamic recovery process in cleaned sites. The NDWI8A index, employed in the vegetation index differencing (VID) change detection technique, delimited damaged areas comparable to the estimations provided by Regional Forest System (2545 ha and 3183 ha, respectively). Damaged forests detected by NDWI8A VID ranged from 500 m to 1500 m a.s.l., mainly covering steep slopes in the south and east aspects (42% and 25%, respectively). Our results suggested that the NDWI8A VID method may be a cost-effective and accurate way to produce windthrow maps, which could limit the risks associated with fieldwork and may provide a valuable tool to plan tree removal interventions in a more efficient way.

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

Drought-induced tree mortality frequently occurs in patches with different spatial and temporal distributions, which is only partly explained by inter- and intraspecific variation in drought tolerance. We investigated whether bedrock properties, with special reference to rock water storage capacity, affects tree water status and drought response in a rock-dominated landscape. We measured primary porosity and available water content of breccia (B) and dolostone (D) rocks. Saplings of Fraxinus ornus were grown in pots filled with soil or soil mixed with B and D rocks, and subjected to an experimental drought. Finally, we measured seasonal changes in water status of trees in field sites overlying B or D bedrock. B rocks were more porous and stored more available water than D rocks. Potted saplings grown with D rocks had less biomass and suffered more severe water stress than those with B rocks. Trees in sites with B bedrock had more favourable water status than those on D bedrock which also suffered drought-induced canopy dieback. Bedrock represents an important water source for plants under drought. Different bedrock features translate into contrasting below-ground water availability, leading to landscape-level heterogeneity of the impact of drought on tree water status and dieback.

Multi-Source Remote Sensing Data Product Analysis: Investigating Anthropogenic and Naturogenic Impacts on Mangroves in Southeast Asia

This study investigated the drivers of degradation in Southeast Asian mangroves through multi-source remote sensing data products. The degradation drivers that affect approximately half of this area are unidentified; therefore, naturogenic and anthropogenic impacts on these mangroves were studied. Various global land cover (GLC) products were harmonized and examined to identify major anthropogenic changes affecting mangrove habitats. To investigate the naturogenic factors, the impact of the water balance was evaluated using the Normalized Difference Vegetation Index (NDVI), and evapotranspiration and precipitation data. Vegetation indices’ response in deforested mangrove regions depends significantly on the type of drivers. A trend analysis and break point detection of percentage of tree cover (PTC), percentage of non-tree vegetation (PNTV), and percentage of non-vegetation (PNV) datasets can aid in measuring, estimating, and tracing the drivers of change. The assimilation of GLC products suggests that agriculture and fisheries are the predominant drivers of mangrove degradation. The relationship between water balance and degradation shows that naturogenic drivers have a wider impact than anthropogenic drivers, and degradation in particular regions is likely to be a result of the accumulation of various drivers. In large-scale studies, remote sensing data products could be integrated as a remarkably powerful instrument in assisting evidence-based policy making.

The Application of Ground-Based and Satellite Remote Sensing for Estimation of Bio-Physiological Parameters of Wheat Grown Under Different Water Regimes

Remote sensing technologies have been widely studied for the estimation of crop biometric and physiological parameters. The number of sensors and data acquisition methods have been increasing, and their evaluation is becoming a necessity. The aim of this study was to assess the performance of two remote sensing data for describing the variations of biometric and physiological parameters of durum wheat grown under different water regimes (rainfed, 50% and 100% of irrigation requirements). The experimentation was carried out in Policoro (Southern Italy) for two growing seasons. The Landsat 8 and Sentinel-2 images and radiometric ground-based data were acquired regularly during the growing season with plant biometric (leaf area index and dry aboveground biomass) and physiological (stomatal conductance, net assimilation, and transpiration rate) parameters. Water deficit index was closely related to plant water status and crop physiological parameters. The enhanced vegetation index showed slightly better performance than the normalized difference vegetation index when plotted against the leaf area index with R2 = 0.73. The overall results indicated that the ground-based vegetation indices were in good agreement with the satellite-based indices. The main constraint for effective application of satellite-based indices remains the presence of clouds during the acquisition time, which is particularly relevant for winter–spring crops. Therefore, the integration of remote sensing and field data might be needed to optimize plant response under specific growing conditions and to enhance agricultural production.

Pre-Emptive Detection of Mature Pine Drought Stress Using Multispectral Aerial Imagery

Drought, ozone (O3), and nitrogen deposition (N) alter foliar pigments and tree crown structure that may be remotely detectable. Remote sensing tools are needed that pre-emptively identify trees susceptible to environmental stresses could inform forest managers in advance of tree mortality risk. Jeffrey pine, a component of the economically important and widespread western yellow pine in North America was investigated in the southern Sierra Nevada. Transpiration of mature trees differed by 20% between microsites with adequate (mesic (M)) vs. limited (xeric (X)) water availability as described in a previous study. In this study, in-the-crown morphological traits (needle chlorosis, branchlet diameter, and frequency of needle defoliators and dwarf mistletoe) were significantly correlated with aerially detected, sub-crown spectral traits (upper crown NDVI, high resolution (R), near-infrared (NIR) Scalar (inverse of NDVI) and THERM Δ, and the difference between upper and mid crown temperature). A classification tree model sorted trees into X and M microsites with THERM Δ alone (20% error), which was partially validated at a second site with only mesic trees (2% error). Random forest separated M and X site trees with additional spectra (17% error). Imagery taken once, from an aerial platform with sub-crown resolution, under the challenge of drought stress, was effective in identifying droughted trees within the context of other environmental stresses.