scholarly journals The 2018 European heatwave led to stem dehydration but not to consistent growth reductions in forests

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Roberto L. Salomón ◽  
Richard L. Peters ◽  
Roman Zweifel ◽  
Ute G. W. Sass-Klaassen ◽  
Annemiek I. Stegehuis ◽  
...  
Keyword(s):  
Tree Growth ◽  
Forest Ecosystems ◽  
Spatial Scales ◽  
Water Status ◽  
Climate Extremes ◽  
Stem Growth ◽  
High Temporal Resolution ◽  
Conifer Species ◽  
Transient Stress

AbstractHeatwaves exert disproportionately strong and sometimes irreversible impacts on forest ecosystems. These impacts remain poorly understood at the tree and species level and across large spatial scales. Here, we investigate the effects of the record-breaking 2018 European heatwave on tree growth and tree water status using a collection of high-temporal resolution dendrometer data from 21 species across 53 sites. Relative to the two preceding years, annual stem growth was not consistently reduced by the 2018 heatwave but stems experienced twice the temporary shrinkage due to depletion of water reserves. Conifer species were less capable of rehydrating overnight than broadleaves across gradients of soil and atmospheric drought, suggesting less resilience toward transient stress. In particular, Norway spruce and Scots pine experienced extensive stem dehydration. Our high-resolution dendrometer network was suitable to disentangle the effects of a severe heatwave on tree growth and desiccation at large-spatial scales in situ, and provided insights on which species may be more vulnerable to climate extremes.

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

scholarly journals Multimodal in vivo recording using transparent graphene microelectrodes illuminates spatiotemporal seizure dynamics at the microscale

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Nicolette Driscoll ◽  
Richard E. Rosch ◽  
Brendan B. Murphy ◽  
Arian Ashourvan ◽  
Ramya Vishnubhotla ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Spatial Scales ◽  
Epileptic Seizures ◽  
Therapeutic Interventions ◽  
State Transitions ◽  
Integrated Analysis ◽  
High Temporal Resolution ◽  
Seizure Onset ◽  
Acute Seizures

AbstractNeurological disorders such as epilepsy arise from disrupted brain networks. Our capacity to treat these disorders is limited by our inability to map these networks at sufficient temporal and spatial scales to target interventions. Current best techniques either sample broad areas at low temporal resolution (e.g. calcium imaging) or record from discrete regions at high temporal resolution (e.g. electrophysiology). This limitation hampers our ability to understand and intervene in aberrations of network dynamics. Here we present a technique to map the onset and spatiotemporal spread of acute epileptic seizures in vivo by simultaneously recording high bandwidth microelectrocorticography and calcium fluorescence using transparent graphene microelectrode arrays. We integrate dynamic data features from both modalities using non-negative matrix factorization to identify sequential spatiotemporal patterns of seizure onset and evolution, revealing how the temporal progression of ictal electrophysiology is linked to the spatial evolution of the recruited seizure core. This integrated analysis of multimodal data reveals otherwise hidden state transitions in the spatial and temporal progression of acute seizures. The techniques demonstrated here may enable future targeted therapeutic interventions and novel spatially embedded models of local circuit dynamics during seizure onset and evolution.

scholarly journals Open-path, quantum cascade-laser-based sensor for high-resolution atmospheric ammonia measurements

2014 ◽  
Vol 7 (1) ◽  
pp. 81-93 ◽  
Author(s):  
D. J. Miller ◽  
K. Sun ◽  
L. Tao ◽  
M. A. Khan ◽  
M. A. Zondlo
Keyword(s):  
Gas Phase ◽  
Quantum Cascade Laser ◽  
High Temporal Resolution ◽  
Reduced Pressure ◽  
Total Uncertainty ◽  
Atmospheric Ammonia ◽  
Quantum Cascade ◽  
Open Path ◽  
Time Calibration

Abstract. We demonstrate a compact, open-path, quantum cascade-laser-based atmospheric ammonia sensor operating at 9.06 μm for high-sensitivity, high temporal resolution, ground-based measurements. Atmospheric ammonia (NH3) is a gas-phase precursor to fine particulate matter, with implications for air quality and climate change. Currently, NH3 sensing challenges have led to a lack of widespread in situ measurements. Our open-path sensor configuration minimizes sampling artifacts associated with NH3 surface adsorption onto inlet tubing and reduced pressure sampling cells, as well as condensed-phase partitioning ambiguities. Multi-harmonic wavelength modulation spectroscopy allows for selective and sensitive detection of atmospheric pressure-broadened absorption features. An in-line ethylene reference cell provides real-time calibration (±20% accuracy) and normalization for instrument drift under rapidly changing field conditions. The sensor has a sensitivity and noise-equivalent limit (1σ) of 0.15 ppbv NH3 at 10 Hz, a mass of ~ 5 kg and consumes ~ 50 W of electrical power. The total uncertainty in NH3 measurements is 0.20 ppbv NH3 ± 10%, based on a spectroscopic calibration method. Field performance of this open-path NH3 sensor is demonstrated, with 10 Hz time resolution and a large dynamic response for in situ NH3 measurements. This sensor provides the capabilities for improved in situ gas-phase NH3 sensing relevant for emission source characterization and flux measurements.

The Role of Ectomycorrhizal Symbiosis in the Resistance of Forests to Water Stress

2000 ◽  
Vol 29 (1) ◽  
pp. 63-69 ◽  
Author(s):  
Jean Garbaye
Keyword(s):  
Water Stress ◽  
Sustainable Management ◽  
Fine Roots ◽  
Forest Ecosystems ◽  
Water Status ◽  
Forest Trees ◽  
Direct Effects ◽  
Ectomycorrhizal Symbiosis ◽  
Protection Mechanisms

Forest trees live in enforced symbiosis with specialized fungi that form composite organs (ectomycorrhizas) with fine roots. This paper examines how this association contributes to the water status of trees and how it plays a major role in the protection mechanisms by which trees and forest stands resist drought-induced water stress. It shows how ectomycorrhizal symbiosis has both direct effects (at the uptake level) and indirect effects (at the regulation level) on the water status of trees. The facts presented are discussed in terms of forest adaptation to changing environmental conditions and the practical consequences for the sustainable management of forest ecosystems.

scholarly journals Global downscaling of remotely sensed soil moisture using neural networks

2018 ◽  
Vol 22 (10) ◽  
pp. 5341-5356 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Jana Kolassa ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Pierre Gentine
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Ancillary Data ◽  
Repeat Time ◽  
Spatiotemporal Scales ◽  
Better Than

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

scholarly journals Spatial scales of temperature and salinity variability estimated from Argo observations

2015 ◽  
Vol 12 (4) ◽  
pp. 1793-1814
Author(s):  
F. Ninove ◽  
P. Y. Le Traon ◽  
E. Remy ◽  
S. Guinehut
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Deep Ocean ◽  
General Tendency ◽  
Zero Crossing ◽  
Tropical Regions ◽  
Covariance Functions ◽  
Salinity Variability ◽  
First Time

Abstract. Argo observations from 2005 to 2013 are used to characterize spatial scales temperature and salinity variations from the surface down to 1500 m. Simulations are first performed to analyze the sensitivity of results to Argo sampling; they show that several years of Argo observations are required to estimate the spatial scales of ocean variability over 20° × 20° boxes. Spatial scales are then computed over several large scale areas. Zonal and meridional spatial scales (Lx and Ly which are also zero crossing of covariance functions) vary as expected with latitudes. Scales are of about 100 km at high latitudes and more of 700 km in the Indian and Pacific equatorial/tropical regions. Zonal and meridional scales are similar: except in these tropical/equatorial regions where zonal scales are much larger (by a factor of 2 to 3) than meridional scales. Spatial scales are the largest close to the surface and have a general tendency for temperature to increase in deeper layers. There are significant differences between temperature and salinity scales, in particular, in the deep ocean. Results are consistent with previous studies based on sparse in-situ observations or satellite altimetry. They provide, however, for the first time a global description of temperature and salinity scales of variability and a characterization of their variations according to depths.

scholarly journals Population survey of Milicia excelsa, Pouteria adolfi-fridercii, Antiaris toxicaria and Prunus africana in south and south-western Ethiopia: implications for domesticating and establishing Seed Production Areas

2020 ◽  
Vol 5 (4) ◽  
pp. 153-162
Author(s):  
Mohammed Adefa Seid ◽  
Yigardu Mulatu ◽  
Agena Anjulo ◽  
Semaigzer Ayalew ◽  
Hailu Belay ◽  
...  
Keyword(s):  
Seed Production ◽  
Forest Ecosystems ◽  
Natural Forest ◽  
Target Species ◽  
Stem Height ◽  
Prunus Africana ◽  
Milicia Excelsa ◽  
Western Ethiopia ◽  
Tree Stem

The population dynamics and genetic qualities of stem height and dbh of the populations of Milicia excelsa, Pouteria adolfi-fridercii, Antiaris toxicaria and Prunus africana were assessed and studied in 14 forest ecosystems in south and south-western Ethiopia. A systematic random quadrat sampling technique was employed to identify potential habitat area for selected timber species in south and south-western Ethiopia. So, a total of 10 parallel transect lines were set out across each forest habitat. Milicia excelsa was assessed in four forest ecosystems; Bebeka -Duduka natural forest (45 tree stem ha-1) and Yayu coffee mixed forest (40 tree stem ha-1) appeared to have relatively denser population of M. excelsa compared to the other pilot forests. Similarly, statistically larger stem height (m) is observed in Bebeka-Duduka natural forest (x̅=29.5, SD=4.2) and larger dbh size (cm) in Bebeka 1 coffee mixed forest (x̅=48.5, SD=25.2) at p<0.05. Hence, Bebeka-Duduka natural forest and Bebeka 1 coffee mixed forests are identified for in-situ SPA establishment project of the target species. Pouteria adolfi-fridercii was assessed in eight forest ecosystems; Masha-Gora shewi forest appeared to have dense population of P. adolfi-fridercii (150 tree stem ha-1) followed by Bebeka-Kebereta (80 tree stem ha-1) as compared to the remaining pilot forests. However, statistically larger stem height (m) is scored in Bebeka 2 natural forest (x̅=30.6, SD=2.98) and Bebeka-kebereta forest (x̅=30.0, SD=4.4) and larger dbh size (cm) in Bebeka 2 natural forest (x̅=96.5, SD=19.9) at p<0.05. So, Bebeka 2 natural forest and Bebeka-kebereta forest are identified for SPA establishment project of the target species. Antiaris toxicaria was assessed in two forest ecosystems where both forest habitats appeared to have the same density of Antiaris toxicaria in hectare. Moreover, analyses of mean stem height (cm) of Bebeka 1 forest (x̅=21.4, SD=5.2) and Bebeka-kebereta forest (x̅=22.4, SD=2.7), and mean dbh size (cm) of Bebeka 1 forest (x̅=48, SD=16.4) and Bebeka-kebereta forest (x̅=48, SD=8.2) appeared to have not significantly different at p<0.05. Prunus africana was assessed in five forest ecosystems; Masha-Gora shewi forest appeared to have dense population of Prunus africana (150 tree stem ha-1) followed by Kaho-shemeta forest (130 tree stem ha-1) as compared to the remaining pilot forests. However, statistically larger stem height (m) is scored in Kaho-shemeta natural forest (x̅=31, SD=7.6) and larger dbh (cm) in Masha-Gora shewi forest (x̅=64.7, SD=28.2) at p<0.05. As the result, Kaho-shemeta natural forest and Bonga-Teja-adela forest are identified for establishment of seed production area of the target species in-situ. Hence, while implementing the project of domestication and seed production area establishment in south and south-western Ethiopia, in-situ and ex-situ areas must be selected and delineated in accordance with this preliminary information of the population dynamics and genetic qualities of stem height and diameter at breast height. The survey data generated in this study would bridge the research gap in relation to the population status of the target tree species in the designated area.

scholarly journals Extinction coefficients retrieved in deep tropical ice clouds from lidar observations using a CALIPSO-like algorithm compared to in-situ measurements from the Cloud Integrated Nephelometer during CRYSTAL-FACE

2006 ◽  
Vol 6 (5) ◽  
pp. 10649-10672 ◽  
Author(s):  
V. Noel ◽  
D. M. Winker ◽  
T. J. Garrett ◽  
M. McGill
Keyword(s):  
Spatial Scales ◽  
Deep Convection ◽  
Retrieval Algorithm ◽  
Crystal Face ◽  
Ice Clouds ◽  
Extinction Coefficients ◽  
Convective Systems ◽  
Lidar Ratio ◽  
Deep Convective Systems

Abstract. This paper presents a comparison of lidar ratios and volume extinction coefficients in tropical ice clouds, retrieved using observations from two instruments: the 532-nm Cloud Physics Lidar (CPL), and the in-situ Cloud Integrating Nephelometer (CIN) probe. Both instruments were mounted on airborne platforms during the CRYSTAL-FACE campaign and took measurements up to 17 km. Coincident observations from two cases of ice clouds located on top of deep convective systems are compared. First, lidar ratios are retrieved from CPL observations of attenuated backscatter, using a retrieval algorithm for opaque cloud similar to one used in the soon-to-be launched CALIPSO mission, and compared to results from the regular CPL algorithm. These lidar ratios are used to retrieve extinction coefficient profiles, which are compared to actual observations from the CIN in-situ probe, putting the emphasis on their vertical variability. When observations coincide, retrievals from both instruments are very similar. Differences are generally variations around the average profiles, and general trends on larger spatial scales are usually well reproduced. The two instruments agree well, with an average difference of less than 11% on optical depth retrievals. Results suggest the CALIPSO Deep Convection algorithm can be trusted to deliver realistic estimates of the lidar ratio, leading to good retrievals of extinction coefficients.

scholarly journals UAV-BASED AUTOMATIC TREE GROWTH MEASUREMENT FOR BIOMASS ESTIMATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 685-688
Author(s):  
M. Karpina ◽  
M. Jarząbek-Rychard ◽  
P. Tymków ◽  
A. Borkowski
Keyword(s):  
Tree Growth ◽  
Point Clouds ◽  
In Situ Measurements ◽  
Unmanned Aircraft ◽  
Volume Estimation ◽  
Biomass Estimation ◽  
Test Field ◽  
3D Point Clouds ◽  
Growth Estimation

Manual in-situ measurements of geometric tree parameters for the biomass volume estimation are time-consuming and economically non-effective. Photogrammetric techniques can be deployed in order to automate the measurement procedure. The purpose of the presented work is an automatic tree growth estimation based on Unmanned Aircraft Vehicle (UAV) imagery. The experiment was conducted in an agriculture test field with scots pine canopies. The data was collected using a Leica Aibotix X6V2 platform equipped with a Nikon D800 camera. Reference geometric parameters of selected sample plants were measured manually each week. In situ measurements were correlated with the UAV data acquisition. The correlation aimed at the investigation of optimal conditions for a flight and parameter settings for image acquisition. The collected images are processed in a state of the art tool resulting in a generation of dense 3D point clouds. The algorithm is developed in order to estimate geometric tree parameters from 3D points. Stem positions and tree tops are identified automatically in a cross section, followed by the calculation of tree heights. The automatically derived height values are compared to the reference measurements performed manually. The comparison allows for the evaluation of automatic growth estimation process. The accuracy achieved using UAV photogrammetry for tree heights estimation is about 5cm.

scholarly journals The electrical self-potential method is a non-intrusive snow-hydrological sensor

2015 ◽  
Vol 9 (4) ◽  
pp. 4437-4457 ◽  
Author(s):  
S. S. Thompson ◽  
B. Kulessa ◽  
R. L. H. Essery ◽  
M. P. Lüthi
Keyword(s):  
Spatial Scales ◽  
Chemical Properties ◽  
Potential Method ◽  
Measurement Procedure ◽  
Theoretical Modelling ◽  
Avalanche Forecasting ◽  
Physical And Chemical ◽  
Self Potential ◽  
Water Contents

Abstract. Our ability to measure, quantify and assimilate hydrological properties and processes of snow in operational models is disproportionally poor compared to the significance of seasonal snowmelt as a global water resource and major risk factor in flood and avalanche forecasting. Encouraged by recent theoretical, modelling and laboratory work, we show here that the diurnal evolution of aerially-distributed self-potential magnitudes closely track those of bulk meltwater fluxes in melting in-situ snowpacks at Rhone and Jungfraujoch glaciers, Switzerland. Numerical modelling infers temporally-evolving liquid water contents in the snowpacks on successive days in close agreement with snow-pit measurements. Muting previous concerns, the governing physical and chemical properties of snow and meltwater became temporally invariant for modelling purposes. Because measurement procedure is straightforward and readily automated for continuous monitoring over significant spatial scales, we conclude that the self-potential geophysical method is a highly-promising non-intrusive snow-hydrological sensor for measurement practice, modelling and operational snow forecasting.

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