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Willie Nelson Net Worth: A Journey Through Music, Movies, and Business Venturesgreendigital
Willie Nelson is a name that resonates within the world of music and entertainment. Known for his unique voice, and masterful guitar skills. and an extraordinary career spanning several decades. Nelson has become a legend in the country music scene. But, his influence extends far beyond the realm of music. with ventures in acting, writing, activism, and business. This comprehensive article delves into Willie Nelson net worth. exploring the various facets of his career that have contributed to his large fortune.
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Introduction
Willie Nelson net worth is a testament to his enduring influence and success in many fields. Born on April 29, 1933, in Abbott, Texas. Nelson's journey from a humble beginning to becoming one of the most iconic figures in American music is nothing short of inspirational. His net worth, which estimated to be around $25 million as of 2024. reflects a career that is as diverse as it is prolific.
Early Life and Musical Beginnings
Humble Origins
Willie Hugh Nelson was born during the Great Depression. a time of significant economic hardship in the United States. Raised by his grandparents. Nelson found solace and inspiration in music from an early age. His grandmother taught him to play the guitar. setting the stage for what would become an illustrious career.
First Steps in Music
Nelson's initial foray into the music industry was fraught with challenges. He moved to Nashville, Tennessee, to pursue his dreams, but success did not come . Working as a songwriter, Nelson penned hits for other artists. which helped him gain a foothold in the competitive music scene. His songwriting skills contributed to his early earnings. laying the foundation for his net worth.
Rise to Stardom
Breakthrough Albums
The 1970s marked a turning point in Willie Nelson's career. His albums "Shotgun Willie" (1973), "Red Headed Stranger" (1975). and "Stardust" (1978) received critical acclaim and commercial success. These albums not only solidified his position in the country music genre. but also introduced his music to a broader audience. The success of these albums played a crucial role in boosting Willie Nelson net worth.
Iconic Songs
Willie Nelson net worth is also attributed to his extensive catalog of hit songs. Tracks like "Blue Eyes Crying in the Rain," "On the Road Again," and "Always on My Mind" have become timeless classics. These songs have not only earned Nelson large royalties but have also ensured his continued relevance in the music industry.
Acting and Film Career
Hollywood Ventures
In addition to his music career, Willie Nelson has also made a mark in Hollywood. His distinctive personality and on-screen presence have landed him roles in several films and television shows. Notable appearances include roles in "The Electric Horseman" (1979), "Honeysuckle Rose" (1980), and "Barbarosa" (1982). These acting gigs have added a significant amount to Willie Nelson net worth.
Television Appearances
Nelson's char
3. 1. Introduction
3
Source: National Forest Inventory of Mongolia & Forest loss (Hansen et al., 2013 )
Boreal forest covers
about 9.2% of the total
area of Mongolia.
But those forests are
decreasing.
2020
2001
4. Forest area
(1000 ha)
Growing stock
(million m3
)
1990 14352.0 1382.2
2000 14263.9 1373.3
2010 14183.9 1365.4
2015 14178.3 1365.3
2020 14172.7 1364.6
Source: Global forest resources assessment 2020
4
Table 1. Boreal forest area change 1990 - 2020
Both area and stock of forests have been declining for the past 30 years!
Decreasing! Decreasing!
1.1 Introduction (cont’d)
5. 5
Forest loss is mostly due to
fire and insects!!
1.2 Introduction (cont’d) Forest loss
Forest fire and insect damages
6. 1.3 Introduction (cont’d)
6
Current situation of forest data in Mongolia
--- Have to be updated every 5 years in all forested area
--- Use field measurement and allometric equation
--- Spend more time, challenging to reach some area, and costly
7. 2. Research objectives
Main objective:
To map forest above-ground biomass and carbon stock using satellite data with
machine learning
Goals:
➢ To develop a suitable model
➢ To estimate forest above-ground biomass (AGB)
➢ To evaluate boreal forest carbon stock
7
8. 8
3. Materials and Methods
3.1 Study area
3.2 Field data
3.3 Satellite data
3.4 Methodology
9. 3.1 Study area
9
Location: Latitude: 47°N - 53°N,
Longitude: 96° E - 104°E
Climate: Average annual rainfall
270 mm, and annual mean
temperature around 1.3 °C
Climate classification: Dwc
(continental, dry winter, cold
summer)
Total area: 20466182 ha
Forested area: 9380532 ha (45.8%)
Source: Environmental information center (www.eic.mn )
10. 10
3.2 Field data
The distribution of field data
Plot arrangement
Total field plots in study area = 5720
AGB (Mg/ha) 1 - 50 50 - 100 100 - 150 150 - 200 200 - 250
Number of plot 1952 2570 1017 170 11
Average AGB (Mg/ha) 69.7
Number of plots
r = 6m DBH 6 cm - 14.9 cm
r = 12m DBH 15 cm - 29.9 cm
r = 20m DBH ≥ 30 cm
Note: Diameter breast height (DBH)
Source: Multi-purpose national forest inventory, Forest Resource Development Center
11. 3.3 Satellite data
11
Satellite name Part / Row Time
Landsat 8 OLI
p133r25, p133r27
p133r26
p134r25, p134r26, p134r27
p135r24, p135r25, p135r26, p135r27
p136r24, p136r25, p136r26, p136r27
p137r24
p137r25
p137r26
p138r25, p138r26
2014 September 10
2013 September 7
2014 September 1
2013 September 5
2019 August 28
2017 August 29
2020 September 6
2015 July 23
2018 September 8
ALOS-2 / PALSAR 2
N47E100, 101, 102
N48E098, 099, 100, 101, 102, 103, 104
N49E098, 099, 100, 101, 102, 103, 104
N50E096, 097, 098, 099,100, 101, 102, 103, 104
N51E097, 098, 099, 100, 101, 102, 103, 104
N52E097, 098, 099, 100, 101, 102
N53E098, 099
2015
Table 2. Collected satellites data
13. 13
4. Data analysis and results
4.1 Input features for machine learning (ML)
4.2 Accuracy of forest above-ground biomass (AGB) prediction
4.3 Hyperparameter values of ML algorithms
4.4 Feature importance for random forest (RF)
4.5 Predicted map derived from RF model
4.6 Reference data errors
14. 14
4.1 Input features for machine learning (ML)
Landsat 8 OLI
B2 - blue band
B3 - green band
B4 - red band
B5 - NIR
B6 - SWIR1
B7 - SWIR2
Normalised difference vegetation index (NDVI) NDVI = (NIR - red) / (NIR + red)
Normalised difference water index (NDWI) NDWI = (NIR - SWIR 1) / (NIR + SWIR 1)
Green leaf index (GLI) GLI = (2 * green - red - blue) / (2 * green + red + blue)
Enhanced vegetation index (EVI) EVI = 2.5 * (NIR - red) / (NIR + 6 * red - 7.5 * blue + 1)
Enhanced vegetation index 2 (EVI2) EVI2 = 2.5 * (NIR - red) / (NIR + 2.4 * red + 1)
Soil adjusted vegetation index (SAVI) SAVI = ((NIR - red) / (NIR + red + 0.5)) * 1.5
Ratio vegetation index (RVI) RVI = NIR / red
Difference vegetation index (DVI) DVI = NIR - red
Green normalised vegetation index (GNDVI) GNDVI = (NIR - green) / (NIR + green)
ALOS-2 / PALSAR 2
HH polarization
HV polarization
Ratio HH and HV (HH/HV)
Ratio HV and HH (HV/HH)
Difference HH and HV (HH - HV)
Radar forest degradation index (RFDI) RFDI = (HH - HV) / (HH + HV)
Topographic data
Digital Elevation Model (DEM), Slope, Aspect
Table 3. Input features
17. 17
4.3 Hyperparameter values of ML algorithms
Algorithm
learning
_rate
max_
depth
min_samples_leaf /
min_child_weight
n_estimators
/ C value
kernel gamma
R2
(training)
R2
(testing)
RMSE
Mg/ha
LR
(Linear Regression)
NA NA NA NA NA NA 0.210 0.151 35
XGB
(Extreme Gradient Boost )
0.1 5 2 100 NA 1 0.578 0.24 33
AdaBoost
0.01 NA NA 100 NA NA 0.211 0.187 34
DT
(Decision Tree )
NA 4 30 NA NA NA 0.211 0.162 35
RF
(Random Forest)
NA 20 10 1000 NA NA 0.570 0.24 33
KNN
(k-Nearest Neighbor)
n_neighbor = 25, metric = ‘minkowski’, p = 2 0.244 0.20 34
SVR
(Support Vector Regression)
NA NA NA 500 rbf NA 0.231 0.194 34
Table 4. Configured hyperparameters for each ML
18. 18
4.4 Feature importance for random forest (RF) model
High importance:
SWIR1 (Short-wave infrared band 1 of Landsat 8)
GLI (Green leaf index)
HV (Horizontal transmitting, vertical
receiving polarization of ALOS-2 )
DEM (Digital elevation model)
19. 19
4.5 Predicted map derived from RF model
Forest above-ground biomass Forest carbon stock
20. 20
4.6 Reference data errors
1. High AGB value on the sparse forest
2. Low AGB value on the dense forest
3. Plot covered by forest and non forest area
Notes: yellow number is AGB value, unit is Mg/ha, and yellow circle’s radius is 30 m.
1 2 3
Total 812 plots
Source: Bing aerial map & Google map
21. 21
5. Conclusion
* Hyperparameter values were effectively influenced by overfitting error.
* SVR, KNN, and LR models were high R2
in only using Landsat data.
* The best regression model was RF. The coefficient of determination (R2
) was 0.24 and
RMSE was 33 Mg/ha. Forest AGB was estimated 32.5 Mg/ha - 122.5 Mg/ha and forest carbon
stock was estimated 16.5 Mg C/ha - 62.5 Mg C/ha.
* The highest importance variables were SWIR1, GLI, HV and DEM for the RF model.
* After data screening, 812 plots of reference data were errors. From data screening analysis
and my research, forest AGB data of National Forest Inventory in Mongolia was bad quality.
* In the future, reference field data need validation and update.
22. 22
References
Dan, A. (2019). Multipurpose national forest inventory in Mongolia, 2014-2017-A tool to support sustainable forest
management. Geography, Environment, Sustainability, 12(3), 167-183.
Environmental information center. (2020). http://www.eic.mn
FAO. (2020). Global forest resources assessment 2020. FAO. http://www.fao.org/3/cb0031en/cb0031en.pdf
Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J.
Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013.
“High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data
available online from: http://earthenginepartners.appspot.com/science-2013-global-forest.
FRDC. (2017). Multi-purpose national forest inventory in Mongolia
Nachin, B., & Sukhbaatar, G. (2013). Some results of forest carbon stock calculation in northern Mongolia. UNREDD.
www.unredd.net