imagery data
Recently Published Documents


TOTAL DOCUMENTS

284
(FIVE YEARS 78)

H-INDEX

21
(FIVE YEARS 5)

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2578
Author(s):  
Marcelo Rodrigues Barbosa Júnior ◽  
Danilo Tedesco ◽  
Rafael de Graaf Corrêa ◽  
Bruno Rafael de Almeida Moreira ◽  
Rouverson Pereira da Silva ◽  
...  

Imagery data prove useful for mapping gaps in sugarcane. However, if the quality of data is poor or the moment of flying an aerial platform is not compatible to phenology, prediction becomes rather inaccurate. Therefore, we analyzed how the combination of pixel size (3.5, 6.0 and 8.2 cm) and height of plant (0.5, 0.9, 1.0, 1.2 and 1.7 m) could impact the mapping of gaps on unmanned aerial vehicle (UAV) RGB imagery. Both factors significantly influenced mapping. The larger the pixel or plant, the less accurate the prediction. Error was more likely to occur for regions on the field where actively growing vegetation overlapped at gaps of 0.5 m. Hence, even 3.5 cm pixel did not capture them. Overall, pixels of 3.5 cm and plants of 0.5 m outstripped other combinations, making it the most accurate (absolute error ~0.015 m) solution for remote mapping on the field. Our insights are timely and provide forward knowledge that is particularly relevant to progress in the field’s prominence of flying a UAV to map gaps. They will enable producers to make decisions on replanting and fertilizing site-specific high-resolution imagery data.


2021 ◽  
Vol 17 (4) ◽  
Author(s):  
Norhafizi Mohamad ◽  
◽  
Anuar Ahmad ◽  
Ami Hassan Md Din ◽  
◽  
...  

2021 ◽  
Vol 17 (2) ◽  
pp. 59-68
Author(s):  
Arip Rahman ◽  
Lismining Pujiyani Astuti ◽  
Andri Warsa ◽  
Agus Arifin Sentosa

Turbidity is one of the remote sensing indicators on the  reservoir physical characteristics that can reduce its brightness level. Measuring reservoir physical characteristics traditionally are expensive and time consuming as well. Therefore, remote sensing is used as an alternative for turbidity measurement because it can provide data and products spatially, temporally as well as synoptically with low cost. This study aims to obtain an algorithm using a combination of in-situ turbidity data measurement and Sentinel-2A satellite imagery data. The resulting algorithm can be used to predict and map turbidity in Jatiluhur Reservoir. Based on the multiregression between B3 (green band) and B4 (red band) with in-situ turbidity data measurement, it is obtainted that the regression coefficients are a = 76.77, b = 63.22 and c = -34.31 respectively, with the equation of Y = 76, 77+63.22 X1-34.31X2 (Y=predicted turbidity, X1=lnB3, X2=lnB4). The correlation value between in situ and turbidity prediction is quite strong with a coefficient of determination (R2) of 0.60, and Root Mean Square Error (RMSE) of 1.95 NTU. Based on Mean Absolute Percentage Error (MAPE) analysis, the deviation is 31.1%. High levels of turbidity can reduce the main productivity of water and its organisms, especially in respiratory and visual problems. Sedimentation caused by high turbidity levels can make siltation which results in reservoir capacity loss.Keywords: Turbidity, remote sensing, Sentinel-2A satellite imagery data, Jatiluhur Reservoir, siltation


2021 ◽  
Vol 190 ◽  
pp. 106462
Author(s):  
Dimitrios Loukatos ◽  
Charalampos Templalexis ◽  
Diamanto Lentzou ◽  
Georgios Xanthopoulos ◽  
Konstantinos G. Arvanitis

Sign in / Sign up

Export Citation Format

Share Document