Preseason heat requirement and days of precipitation jointly regulate plant phenological variations in Inner Mongolian grassland

The accurate estimation of grassland vegetation parameters at a high spatial resolution is important for the sustainable management of grassland areas. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) sensors with a single laser beam emission capability can rapidly detect grassland vegetation parameters, such as canopy height, fractional vegetation coverage (FVC) and aboveground biomass (AGB). However, there have been few reports on the ability to detect grassland vegetation parameters based on RIEGL VUX-1 UAV LiDAR (Riegl VUX-1) systems. In this paper, we investigated the ability of Riegl VUX-1 to model the AGB at a 0.1 m pixel resolution in the Hulun Buir grazing platform under different grazing intensities. The LiDAR-derived minimum, mean, and maximum canopy heights and FVC were used to estimate the AGB across the entire grazing platform. The flight height of the LiDAR-derived vegetation parameters was also analyzed. The following results were determined: (1) The Riegl VUX-1-derived AGB was predicted to range from 29 g/m2 to 563 g/m2 under different grazing conditions. (2) The LiDAR-derived maximum canopy height and FVC were the best predictors of grassland AGB (R2 = 0.54, root-mean-square error (RMSE) = 64.76 g/m2). (3) For different UAV flight altitudes from 40 m to 110 m, different flight heights showed no major effect on the derived canopy height. The LiDAR-derived canopy height decreased from 9.19 cm to 8.17 cm, and the standard deviation of the LiDAR-derived canopy height decreased from 3.31 cm to 2.35 cm with increasing UAV flight altitudes. These conclusions could be useful for estimating grasslands in smaller areas and serving as references for other remote sensing datasets for estimating grasslands in larger areas.

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Mapping quantitative trait loci associated with chilling requirement, heat requirement and bloom date in peach (Prunus persica)

New Phytologist ◽

10.1111/j.1469-8137.2009.03119.x ◽

2009 ◽

Vol 185 (4) ◽

pp. 917-930 ◽

Cited By ~ 148

Author(s):

Shenghua Fan ◽

Douglas G. Bielenberg ◽

Tetyana N. Zhebentyayeva ◽

Gregory L. Reighard ◽

William R. Okie ◽

...

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Prunus Persica ◽

Chilling Requirement ◽

Trait Loci ◽

Heat Requirement ◽

Bloom Date

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CHR: An Integrated Software Tool for chilling requirements

10.21203/rs.3.rs-34169/v1 ◽

2020 ◽

Author(s):

Chen Chen ◽

Wanyu Xu ◽

Ningning Gou ◽

Lasu Bai ◽

Lin Wang ◽

...

Keyword(s):

Statistical Analysis ◽

Software Tool ◽

Fruit Trees ◽

Cold Weather ◽

Chilling Requirements ◽

Deciduous Fruit ◽

Heat Requirement ◽

Integrated Software ◽

User Friendly ◽

Analyze Data

Abstract Background Bud dormancy in deciduous fruit trees enables plants to survive cold weather. The buds adopt dormant state and resume growth after satisfying the chilling requirements. Chilling requirements play a key role in flowering time. So far, several chilling models, including ≤ 7.2 °C model, the 0–7.2 °C model, Utah model, and Dynamic Model, have been developed; however, it is still time-consuming to determine the chilling requirements employing any model. This calls for efficient tools that can analyze data. Results In this study, we developed novel software Chilling and Heat Requirement (CHR), by flexibly integrating data conversions, model selection, calculations, statistical analysis, and plotting. Conclusion CHR is a tool for chilling requirements estimation, which will be very useful to researchers. It is very simple, easy, and user-friendly.

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