Effects of root diameter and root nitrogen concentration on in situ root respiration among different seasons and tree species

2010 ◽  
Vol 25 (5) ◽  
pp. 983-993 ◽  
Author(s):  
Dima Chen ◽  
Lixia Zhou ◽  
Xingquan Rao ◽  
Yongbiao Lin ◽  
Shenglei Fu
Keyword(s):  
Tree Species ◽  
Root Respiration ◽  
Nitrogen Concentration ◽  
Root Diameter ◽  
Different Seasons ◽  
Root Nitrogen

scholarly journals Large Scale Operational Soil Moisture Mapping from Passive MW Radiometry: SMOS product evaluation in Europe & USA

2019 ◽  
Author(s):  
Khidir Abdalla Kwal Deng ◽  
Salim Lamine ◽  
Andrew Pavlides ◽  
Yansong Bao ◽  
George Petropoulos ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Water Security ◽  
Product Evaluation ◽  
Practical Applications ◽  
Study Results ◽  
Different Seasons ◽  
Lower Correlation ◽  
Land Types

Earth Observation (EO) allows deriving from a range of sensors, often globally, operational estimates of surface soil moisture (SSM) at range of spatiotemporal resolutions. Yet, an evaluation of the accuracy of those products in a variety of environmental conditions has been often limited. In this study the accuracy of the SMOS SSM global operational product across 2 continents (USA, and Europe) is investigated. SMOS predictions were compared against near concurrent in-situ SSM measurements from the FLUXNET observational network. In total, 7 experimental sites were used to assess the accuracy of SMOS derived soil moisture for 2 complete years of observations (2010 to 2011). The accuracy of the SMOS SSM product is investigated in different seasons for the seasonal cycle as well as different continents and land types. Results showed a generally reasonable agreement between the SMOS product and the in-situ soil moisture measurements in the 0-5 cm soil moisture layer. Root Mean Square Error (RMSE) in most cases was close to 0.1 m3 m-3 (minimum 0.067 m3 m-3). With a few exceptions, Pearson’s correlation coefficient was found up to approx. 55%. Grassland, shrublands and woody savanna land cover types attained a satisfactory agreement between satellite derived and in-situ measurements but needleleaf forests had lower correlation. Better agreement was found for the grassland sites in both continents. Seasonally, summer and autumn underperformed spring and winter. Our study results provide supportive evidence of the potential value of this operational product for meso-scale studies in a range of practical applications, helping to address key challenges present nowadays linked to food and water security.

In Situ Study of Barrier Layers Using Spectroscopic Ellipsometry and Mass Spectroscopy of Recoiled Ions

2000 ◽  
Vol 619 ◽  
Author(s):  
Y. Gao ◽  
A.H. Mueller ◽  
E.A. Irene ◽  
O. Auciello ◽  
A.R. Krauss ◽  
...  
Keyword(s):  
Real Time ◽  
Mass Spectroscopy ◽  
Spectroscopic Ellipsometry ◽  
Nitrogen Concentration ◽  
Random Access ◽  
Copper Interconnects ◽  
Access Memory ◽  
Barrier Layers ◽  
The Stability

ABSTRACTAn in situ study of barrier layers using spectroscopic ellipsometry (SE) and Time-of-Flight (ToF) mass spectroscopy of recoiled ions (MSRI) is presented. First the formation of copper silicides has been observed by real-time SE and in situ MSRI in annealed Cu/Si samples. Second TaSiN films as barrier layers for copper interconnects were investigated. Failure of the TaSiN layers in Cu/TaSiN/Si samples was detected by real-time SE during annealing and confirmed by in situ MSRI. The effect of nitrogen concentration on TaSiN film performance as a barrier was also examined. The stability of both TiN and TaSiN films as barriers for electrodes for dynamic random access memory (DRAM) devices has been studied. It is shown that a combination of in situ SE and MSRI can be used to monitor the evolution of barrier layers and detect the failure of barriers in real-time.

In situ ruminal degradation and effective degradation of foliage from six tree species during dry and rainy seasons in Veracruz, Mexico

2018 ◽  
Vol 93 (1) ◽  
pp. 123-133 ◽  
Author(s):  
Lidia Ascencio-Rojas ◽  
Braulio Valles-de la Mora ◽  
Epigmenio Castillo-Gallegos ◽  
Muhammad Ibrahim
Keyword(s):  
Tree Species ◽  
Ruminal Degradation ◽  
Dry And Rainy Seasons

Population Assessment of An Endemic Tree, Croton Scabiosus Bedd. (Euphorbiaceae)

2017 ◽  
Vol 24 (4) ◽  
pp. 209-216
Author(s):  
S. Salamma ◽  
A. Narayanaswamy ◽  
M. Naik ◽  
D. Veeranjaneyulu ◽  
M.V. Babu ◽  
...  
Keyword(s):  
Tree Species ◽  
Random Sampling ◽  
Andhra Pradesh ◽  
Ex Situ Conservation ◽  
Adult Population ◽  
Ex Situ ◽  
Eastern Ghats ◽  
Population Assessment ◽  
Conservation Measures

The population of Croton scabiosus, an endemic tree species of southern Eastern Ghats of Andhra Pradesh, India was assessed through random sampling in 15 localities spread over 8 locations. In sampled 37.5 h area, a total of 8737 mature individuals of Croton scabiosus was recorded. Of the 8 locations, Sanipaya sub population was found stable and considered elite owing to the maximum number of mature individuals, girth and height. In spite of good adult population, low number of seedlings and saplings of the species across the native terrain indicates its poor germination and recruitment warranting both in situ and ex situ conservation measures.

Carbon allocation in early successional tree species at elevated air humidity and different soil nitrogen sources

2021 ◽  
Author(s):  
Marili Sell ◽  
Ivika Ostonen ◽  
Gristin Rohula-Okunev ◽  
Azadeh Rezapour ◽  
Priit Kupper
Keyword(s):  
Biomass Allocation ◽  
Tree Species ◽  
Root Respiration ◽  
Fine Root ◽  
Silver Birch ◽  
Organic Carbon Content ◽  
Climate Change Scenarios ◽  
Air Humidity ◽  
Species Specific ◽  
Exudation Rate

<p>Global climate change scenarios predict increasing air temperature, enhanced precipitation and air humidity for Northern latitudes. We investigated the effects of elevated air relative humidity (RH) and different inorganic nitrogen sources (NO<sub>3</sub><sup>-</sup>, NH<sub>4</sub><sup>+</sup>) on above- and belowground traits in different tree species, with particular emphasis on rhizodeposition rates. Silver birch, hybrid aspen and Scots pine saplings were grown in PERCIVAL growth chambers with stabile temperature, light intensity and two different air humidity conditions: moderate (mRH, 65% at day and 80% at night) and elevated (eRH, 80% at day and night). The collection of fine root exudates was conducted by a culture-based cuvette method and total organic carbon content was determined by Vario TOC analyser. Fine root respiration was measured with an infra-red gas analyser CIRAS 2.  </p><p>We analysed species-specific biomass allocation, water and rhizodeposition fluxes, foliar and fine root traits in response to changing environmental conditions. The eRH significantly decreased the transpiration flux in all species. In birch the transpiration flux was also affected by the nitrogen source. The average carbon exudation rate for aspen, birch and pine varied from 2 to 3  μg C g<sup>-1</sup> day <sup>-1</sup>. The exudation rates for deciduous tree species tended to increase at eRH, while conversely decreased for coniferous trees (p=0.045), coinciding with the changes in biomass allocation. C flux released by fine root respiration varied more than the fine root exudation, whereas the highest root respiration was found in silver birch and lowest in aspen. At eRH the above and belowground biomass ratio in aspen increased, at the expense of decreased root biomass and root respiration.  </p><p>Moreover, eRH significantly affected fine root morphology, whereas the response of specific root area was reverse for deciduous and coniferous tree species. However, fine roots with lower root tissue density had higher C exudation rate. Our findings underline the importance of considering species-specific differences by elucidating tree’s acclimation to environmental factors and their interactions.   </p>

scholarly journals Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification

2020 ◽  
Vol 12 (9) ◽  
pp. 1414
Author(s):  
Victoria M. Scholl ◽  
Megan E. Cattau ◽  
Maxwell B. Joseph ◽  
Jennifer K. Balch
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Training Data ◽  
Data Sets ◽  
Airborne Remote Sensing ◽  
Species Classification ◽  
Crown Diameter ◽  
External Data ◽  
Data Products

Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements of individual trees, including stem locations, species, and crown diameter, along with the NEON Airborne Observation Platform (AOP) airborne remote sensing imagery, including hyperspectral, multispectral, and light detection and ranging (LiDAR) data products. An important aspect of predicting species using remote sensing data is creating high-quality training sets for optimal classification purposes. Ultimately, manually creating training data is an expensive and time-consuming task that relies on human analyst decisions and may require external data sets or information. We combine in-situ and airborne remote sensing NEON data to evaluate the impact of automated training set preparation and a novel data preprocessing workflow on classifying the four dominant subalpine coniferous tree species at the Niwot Ridge Mountain Research Station forested NEON site in Colorado, USA. We trained pixel-based Random Forest (RF) machine learning models using a series of training data sets along with remote sensing raster data as descriptive features. The highest classification accuracies, 69% and 60% based on internal RF error assessment and an independent validation set, respectively, were obtained using circular tree crown polygons created with half the maximum crown diameter per tree. LiDAR-derived data products were the most important features for species classification, followed by vegetation indices. This work contributes to the open development of well-labeled training data sets for forest composition mapping using openly available NEON data without requiring external data collection, manual delineation steps, or site-specific parameters.

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