scholarly journals Frost Damage Assessment in Wheat Using Spectral Mixture Analysis

2019 ◽  
Vol 11 (21) ◽  
pp. 2476 ◽  
Author(s):  
Glenn J. Fitzgerald ◽  
Eileen M. Perry ◽  
Ken C. Flower ◽  
J. Nikolaus Callow ◽  
Bryan Boruff ◽  
...  

Frost damage to broadacre crops can cause up to an 85% loss in productivity. Although growers have few options for crop protection from frost, a rapid method for assessing frost-induced sterility would allow for timely management decisions (e.g., cutting for hay and altering marketing strategies). Spectral mixture analysis (SMA) has shown success in mapping landscape components and was used with hyperspectral data collected on the canopy, heads, and leaves of wheat at different sites to determine if this could quantify frost damage. Spectral libraries were assembled from canopy components collected from local field sites to generate spectral libraries for SMA from which a series of fraction sets was derived. The frost (Fr) fraction was then used to estimate final yield as a means of measuring frost damage. The best-fitting Fr fractions to yield were derived from the same data set as the source Fr spectra, and these ranged over R2 = 0.58–0.75 at the canopy scale. It was clear that spectral signatures need to be collected at scale to assess frost damage. While Fr fractions were able to estimate yield there was no “universal” endmember set from which a Fr fraction could be derived. The normalized difference vegetation index (NDVI) was not able to estimate frost damage consistently. Future work requires determining whether there is a “universal” set of endmembers and a minimum set of targeted wavebands that could lead to multispectral instruments for frost assessment for use in ground and aerial sensors.

2020 ◽  
Vol 3 (1) ◽  
pp. 63
Author(s):  
Lilik Norvi Purhartanto ◽  
Projo Danoedoro ◽  
Pramaditya Wicaksono

A forest plantation area of Melaleuca cajuputi at BDH Karangmojo, BKPH Yogyakarta are 2,325.20 ha. One of the efforts to keep its sustainability is to plan the target and realization of cajuputi leaf production considerwith forest condition. Advances in remote sensing technology can be an alternative in estimating the cajuputi leaf production on large areas with an efficient time and high accuracy and able to analyze the quality of cajuputi. This study aims to examine Sentinel-2A capabilities through a relationship model of some vegetation indices integrated with vegetative factors on the production to obtain estimates of leaf production, map and test the estimation model accuracy. The method used is to classify objects in pixels with Linear Spectral Mixture Analysis and build relationship between age, number of plants and vegetation index with cajuputi leaf production. The results showed that the unmixing method has 99,66% accuracy in classifying pixels into the fraction of cajuputi. MERIS Terrestrial Chlorophyll Index of unmixing cajuputi fraction simultaneously with age and number of plants has the highest correlation with value of r = 0,668 to the production and modeled in mapping the estimated cajuputi leaf production at the research location with Standard Error of Estimate is 0,183.


2016 ◽  
Vol 121 (10) ◽  
pp. 2004-2036 ◽  
Author(s):  
Yang Liu ◽  
Timothy D. Glotch ◽  
Noel A. Scudder ◽  
Meredith L. Kraner ◽  
Thomas Condus ◽  
...  

2011 ◽  
Vol 115 (5) ◽  
pp. 1115-1128 ◽  
Author(s):  
Kara N. Youngentob ◽  
Dar A. Roberts ◽  
Alex A. Held ◽  
Philip E. Dennison ◽  
Xiuping Jia ◽  
...  

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