scholarly journals Can Leaf Water Content Be Estimated Using Multispectral Terrestrial Laser Scanning? A Case Study With Norway Spruce Seedlings

2018 ◽  
Vol 9 ◽  
Author(s):  
Samuli Junttila ◽  
Junko Sugano ◽  
Mikko Vastaranta ◽  
Riikka Linnakoski ◽  
Harri Kaartinen ◽  
...  
Keyword(s):  
Water Content ◽  
Norway Spruce ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Leaf Water ◽  
Leaf Water Content

scholarly journals MEASURING LEAF WATER CONTENT USING MULTISPECTRAL TERRESTRIAL LASER SCANNING

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 81-85
Author(s):  
S. Junttila ◽  
M. Vastaranta ◽  
R. Linnakoski ◽  
J. Sugano ◽  
H. Kaartinen ◽  
...  
Keyword(s):  
Water Content ◽  
Fungal Pathogens ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Stable Conditions ◽  
Tree Stress ◽  
Simple Ratio ◽  
Shortwave Infrared

Climate change is increasing the amount and intensity of disturbance events, i.e. drought, pest insect outbreaks and fungal pathogens, in forests worldwide. Leaf water content (LWC) is an early indicator of tree stress that can be measured remotely using multispectral terrestrial laser scanning (MS-TLS). LWC affects leaf reflectance in the shortwave infrared spectrum which can be used to predict LWC from spatially explicit MS-TLS intensity data. Here, we investigated the relationship between LWC and MS-TLS intensity features at 690&amp;thinsp;nm, 905&amp;thinsp;nm and 1550&amp;thinsp;nm wavelengths with Norway spruce seedlings in greenhouse conditions. We found that a simple ratio of 905&amp;thinsp;nm and 1550&amp;thinsp;nm wavelengths was able to explain 84&amp;thinsp;% of the variation (R2) in LWC with a respective prediction accuracy of 0.0041&amp;thinsp;g/cm<sup>2</sup>. Our results showed that MS-TLS can be used to estimate LWC with a reasonable accuracy in environmentally stable conditions.

scholarly journals Leaf water content estimation using top-of-canopy airborne hyperspectral data

2021 ◽  
Vol 102 ◽  
pp. 102393
Author(s):  
Rahul Raj ◽  
Jeffrey P. Walker ◽  
Vishal Vinod ◽  
Rohit Pingale ◽  
Balaji Naik ◽  
...  
Keyword(s):  
Water Content ◽  
Hyperspectral Data ◽  
Leaf Water ◽  
Leaf Water Content

scholarly journals Terrestrial laser scanning intensity captures diurnal variation in leaf water potential

2021 ◽  
Vol 255 ◽  
pp. 112274
Author(s):  
S. Junttila ◽  
T. Hölttä ◽  
E. Puttonen ◽  
M. Katoh ◽  
M. Vastaranta ◽  
...  
Keyword(s):  
Diurnal Variation ◽  
Water Potential ◽  
Leaf Water Potential ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Leaf Water

scholarly journals Simulating Heat Stress of Coal Gangue Spontaneous Combustion on Vegetation Using Alfalfa Leaf Water Content Spectral Features as Indicators

2021 ◽  
Vol 13 (13) ◽  
pp. 2634
Author(s):  
Qiyuan Wang ◽  
Yanling Zhao ◽  
Feifei Yang ◽  
Tao Liu ◽  
Wu Xiao ◽  
...  
Keyword(s):  
Heat Stress ◽  
Water Content ◽  
Spontaneous Combustion ◽  
Leaf Water ◽  
Coal Gangue ◽  
Leaf Water Content ◽  
Spectral Features ◽  
Lasso Regression ◽  
Stress Assessment ◽  
First Derivative

Vegetation heat-stress assessment in the reclamation areas of coal gangue dumps is of great significance in controlling spontaneous combustion; through a temperature gradient experiment, we collected leaf spectra and water content data on alfalfa. We then obtained the optimal spectral features of appropriate leaf water content indicators through time series analysis, correlation analysis, and Lasso regression analysis. A spectral feature-based long short-term memory (SF-LSTM) model is proposed to estimate alfalfa’s heat stress level; the live fuel moisture content (LFMC) varies significantly with time and has high regularity. Correlation analysis of the raw spectrum, first-derivative spectrum, spectral reflectance indices, and leaf water content data shows that LFMC and spectral data were the most strongly correlated. Combined with Lasso regression analysis, the optimal spectral features were the first-derivative spectral value at 1661 nm (abbreviated as FDS (1661)), RVI (1525,1771), DVI (1412,740), and NDVI (1447,1803). When the classification strategies were divided into three categories and the time sequence length of the spectral features was set to five consecutive monitoring dates, the SF-LSTM model had the highest accuracy in estimating the heat stress level in alfalfa; the results provide an important theoretical basis and technical support for vegetation heat-stress assessment in coal gangue dump reclamation areas.

Spectral assessments of wheat plants grown in pots and containers under saline conditions

2013 ◽  
Vol 40 (4) ◽  
pp. 409 ◽  
Author(s):  
Harald Hackl ◽  
Bodo Mistele ◽  
Yuncai Hu ◽  
Urs Schmidhalter
Keyword(s):  
Water Potential ◽  
Water Content ◽  
Aboveground Biomass ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Spectral Indices ◽  
Fresh Weight ◽  
Normalised Difference Vegetation Index ◽  
Spectral Sensing ◽  
Reflectance Ratio

Spectral measurements allow fast nondestructive assessment of plant traits under controlled greenhouse and close-to-field conditions. Field crop stands differ from pot-grown plants, which may affect the ability to assess stress-related traits by nondestructive high-throughput measurements. This study analysed the potential to detect salt stress-related traits of spring wheat (Triticum aestivum L.) cultivars grown in pots or in a close-to-field container platform. In two experiments, selected spectral indices assessed by active and passive spectral sensing were related to the fresh weight of the aboveground biomass, the water content of the aboveground biomass, the leaf water potential and the relative leaf water content of two cultivars with different salt tolerance. The traits were better ascertained by spectral sensing of container-grown plants compared with pot-grown plants. This may be due to a decreased match between the sensors’ footprint and the plant area of the pot-grown plants, which was further characterised by enhanced senescence of lower leaves. The reflectance ratio R760 : R670, the normalised difference vegetation index and the reflectance ratio R780 : R550 spectral indices were the best indices and were significantly related to the fresh weight, the water content of the aboveground biomass and the water potential of the youngest fully developed leaf. Passive sensors delivered similar relationships to active sensors. Across all treatments, both cultivars were successfully differentiated using either destructively or nondestructively assessed parameters. Although spectral sensors provide fast and qualitatively good assessments of the traits of salt-stressed plants, further research is required to describe the potential and limitations of spectral sensing.

Effect of dust deposition on spectrum-based estimation of leaf water content in urban plant

2019 ◽  
Vol 104 ◽  
pp. 41-47 ◽  
Author(s):  
Wenpeng Lin ◽  
Yuan Li ◽  
Shiqiang Du ◽  
Yuanfan Zheng ◽  
Jun Gao ◽  
...  
Keyword(s):  
Water Content ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Dust Deposition

scholarly journals Spatial variation and mechanisms of leaf water content in grassland plants at the biome scale: evidence from three comparative transects

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruomeng Wang ◽  
Nianpeng He ◽  
Shenggong Li ◽  
Li Xu ◽  
Mingxu Li
Keyword(s):  
Spatial Variation ◽  
Water Content ◽  
Large Scale ◽  
Life Forms ◽  
Important Variable ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Desert Grassland ◽  
Large Spatial Scale ◽  
Grassland Plants

AbstractLeaf water content (LWC) has important physiological and ecological significance for plant growth. However, it is still unclear how LWC varies over large spatial scale and with plant adaptation strategies. Here, we measured the LWC of 1365 grassland plants, along three comparative precipitation transects from meadow to desert on the Mongolia Plateau (MP), Loess Plateau, and Tibetan Plateau, respectively, to explore its spatial variation and the underlying mechanisms that determine this variation. The LWC data were normally distributed with an average value of 0.66 g g−1. LWC was not significantly different among the three plateaus, but it differed significantly among different plant life forms. Spatially, LWC in the three plateaus all decreased and then increased from meadow to desert grassland along a precipitation gradient. Unexpectedly, climate and genetic evolution only explained a small proportion of the spatial variation of LWC in all plateaus, and LWC was only weakly correlated with precipitation in the water-limited MP. Overall, the lasso variation in LWC with precipitation in all plateaus represented an underlying trade-off between structural investment and water income in plants, for better survival in various environments. In brief, plants should invest less to thrive in a humid environment (meadow), increase more investment to keep a relatively stable LWC in a drying environment, and have high investment to hold higher LWC in a dry environment (desert). Combined, these results indicate that LWC should be an important variable in future studies of large-scale trait variations.

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