Synergistic use of hyperspectral imagery, Sentinel‐1 and LiDAR improves mapping of soil physical and geochemical properties at the farm‐scale

2021 ◽  
Author(s):  
Yakun Zhang ◽  
Alfred E. Hartemink ◽  
Jingyi Huang ◽  
Philip A. Townsend
Keyword(s):  
Hyperspectral Imagery ◽  
Geochemical Properties ◽  
Farm Scale

Regular estimates of paddock pasture mass can improve profitability on New Zealand dairy farms

2015 ◽  
Vol 77 ◽  
pp. 29-34 ◽  
Author(s):  
P.C. Beukes ◽  
S. Mccarthy ◽  
C.M. Wims ◽  
A.J. Romera
Keyword(s):  
Supply And Demand ◽  
Grazing Management ◽  
Average Error ◽  
Value Proposition ◽  
Perfect Knowledge ◽  
Operating Profit ◽  
A Value ◽  
Main Driver ◽  
Imperfect Knowledge ◽  
Farm Scale

Paddock selection is an important component of grazing management and is based on either some estimate of pasture mass (cover) or the interval since last grazing for each paddock. Obtaining estimates of cover to guide grazing management can be a time consuming task. A value proposition could assist farmers in deciding whether to invest resources in obtaining such information. A farm-scale simulation exercise was designed to estimate the effect of three levels of knowledge of individual paddock cover on profitability: 1) "perfect knowledge", where cover per paddock is known with perfect accuracy, 2) "imperfect knowledge", where cover per paddock is estimated with an average error of 15%, 3) "low knowledge", where cover is not known, and paddocks are selected based on longest time since last grazing. Grazing management based on imperfect knowledge increased farm operating profit by approximately $385/ha compared with low knowledge, while perfect knowledge added a further $140/ha. The main driver of these results is the level of accuracy in daily feed allocation, which increases with improving knowledge of pasture availability. This allows feed supply and demand to be better matched, resulting in less incidence of under- and over-feeding, higher milk production, and more optimal post-grazing residuals to maximise pasture regrowth. Keywords: modelling, paddock selection, pasture cover

Hydro-geochemical properties with respect to landuse in the southernmost river of Kerala

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Badusha M. ◽  
Santhosh S
Keyword(s):  
Organic Carbon ◽  
Drainage Basin ◽  
Anthropogenic Activities ◽  
Oxygen Demand ◽  
Ion Concentration ◽  
Total Hardness ◽  
Hydrogen Ion Concentration ◽  
Geochemical Properties ◽  
Quality Of Water

The hydro geochemical features of Neyyar River for a period of one year from May 2015 to April 2016 were analyzed. Six sampling sites were fixed considering physiography and present landuse pattern of the river basin. The residents in the drainage basin are primarily responsible for framing a better landuse and thereby maintain a good water and sediment regime. Geospatial pattern of the present landuse of the study area indicated that the sustainability of this river ecosystem is in danger due to unscientific landuse practices, which is reflected in the river quality as well. The parameters such as hydrogen ion concentration, electrical conductivity, chloride, Biological Oxygen Demand, total hardness and sulphate of river water and Organic Carbon of river bed sediments were analyzed in this study. The overall analysis shows that the highland areas are characterized by better quality of water together with low organic carbon, which is mainly due to better landuse and minimal reclamation. The midland and lowland areas are characterized by poor quality of water with high organic carbon, which is due to high anthropogenic activities and maximum pollutants associated with the region together with the alteration in landuse from a traditional eco-friendly pattern to a severely polluted current pattern.

Automated Cartographic Feature Attribution Using Panchromatic and Hyperspectral Imagery.

1999 ◽  
Author(s):  
David M. McKeown ◽  
McGlone Jr. ◽  
Ford J. C. ◽  
Cochran Stephen J. ◽  
Shufelt Steven D. ◽  
...  
Keyword(s):  
Hyperspectral Imagery

SPICEE: An Extension of SPICE for Sparse Endmember Estimation in Hyperspectral Imagery

2016 ◽  
Vol 13 (12) ◽  
pp. 1910-1914 ◽  
Author(s):  
Seniha Esen Yuksel ◽  
Sefa Kucuk ◽  
Paul D. Gader
Keyword(s):  
Hyperspectral Imagery

Detection of gaseous plumes in airborne hyperspectral imagery

2016 ◽  
Author(s):  
Eyal Agassi ◽  
Eitan Hirsch ◽  
Martin Chamberland ◽  
Marc-André Gagnon ◽  
Holger Eichstaedt
Keyword(s):  
Hyperspectral Imagery

scholarly journals Searching for optimum adsorption curve for metal sorption on soils: comparison of various isotherm models fitted by different error functions

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Péter Sipos
Keyword(s):  
Experimental Data ◽  
Error Function ◽  
Isotherm Models ◽  
Metal Sorption ◽  
Error Functions ◽  
Sorption System ◽  
Geochemical Properties ◽  
Function Combination ◽  
Soil Metal ◽  
Sorption Curve

AbstractStudies comparing numerous sorption curve models and different error functions are lacking completely for soil-metal adsorption systems. We aimed to fill this gap by studying several isotherm models and error functions on soil-metal systems with different sorption curve types. The combination of fifteen sorption curve models and seven error functions were studied for Cd, Cu, Pb, and Zn in competitive systems in four soils with different geochemical properties. Statistical calculations were carried out to compare the results of the minimizing procedures and the fit of the sorption curve models. Although different sorption models and error functions may provide some variation in fitting the models to the experimental data, these differences are mostly not significant statistically. Several sorption models showed very good performances (Brouers-Sotolongo, Sips, Hill, Langmuir-Freundlich) for varying sorption curve types in the studied soil-metal systems, and further models can be suggested for certain sorption curve types. The ERRSQ error function exhibited the lowest error distribution between the experimental data and predicted sorption curves for almost each studied cases. Consequently, their combined use could be suggested for the study of metal sorption in the studied soils. Besides testing more than one sorption isotherm model and error function combination, evaluating the shape of the sorption curve and excluding non-adsorption processes could be advised for reliable data evaluation in soil-metal sorption system.

scholarly journals Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight

2021 ◽  
Vol 13 (15) ◽  
pp. 3024
Author(s):  
Huiqin Ma ◽  
Wenjiang Huang ◽  
Yingying Dong ◽  
Linyi Liu ◽  
Anting Guo
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Hyperspectral Imagery ◽  
Spectral Feature ◽  
Support Vector ◽  
Detection Accuracy ◽  
Feature Combination ◽  
Field Scale ◽  
Head Blight ◽  
Detection Model

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R2 of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively.

scholarly journals Author Correction: Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kenneth Clarke ◽  
Andrew Hennessy ◽  
Andrew McGrath ◽  
Robert Daly ◽  
Sam Gaylard ◽  
...  
Keyword(s):  
Large Scale ◽  
Hyperspectral Imagery ◽  
Genus Distribution ◽  
Seagrass Cover

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