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Regional-Scale Analysis of Vegetation Dynamics Using Satellite Data and Machine Learning Algorithms: A Multi-Factorial Approach Cover

Regional-Scale Analysis of Vegetation Dynamics Using Satellite Data and Machine Learning Algorithms: A Multi-Factorial Approach

Open Access
|Oct 2023

Figures & Tables

Figure 1:

Overall flowchart of vegetation classification process in the GEE platform.
Overall flowchart of vegetation classification process in the GEE platform.

Figure 2:

Greater Sydney region, Australia.
Greater Sydney region, Australia.

Figure 3:

The schematic diagram of RF for season-based vegetation mapping.
The schematic diagram of RF for season-based vegetation mapping.

Figure 4:

Confusion matrix used in the proposed RF model's training process: (a) with only spectral indices; and (b) all input variables. (i), (ii), and (iii) present the normalized confusion matrix for summer, autumn, and winter seasons, respectively.
Confusion matrix used in the proposed RF model's training process: (a) with only spectral indices; and (b) all input variables. (i), (ii), and (iii) present the normalized confusion matrix for summer, autumn, and winter seasons, respectively.

Figure 5:

Visualization results of season-based vegetation mapping achieved by the proposed model for summer season: (a) original multi-temporal Sentinel-2 image, (b) results of RF+spectral indices, (c) results of RF+spectral indices+topographic factors, and (d) results of RF+spectral indices+topographic factors+texture information.
Visualization results of season-based vegetation mapping achieved by the proposed model for summer season: (a) original multi-temporal Sentinel-2 image, (b) results of RF+spectral indices, (c) results of RF+spectral indices+topographic factors, and (d) results of RF+spectral indices+topographic factors+texture information.

Figure 6:

Visualization results of season-based vegetation mapping achieved by the proposed model for autumn season: (a) original multi-temporal Sentinel-2 image, (b) results of RF+spectral indices, (c) results of RF+spectral indices+topographic factors, and (d) results of RF+spectral indices+topographic factors+texture information.
Visualization results of season-based vegetation mapping achieved by the proposed model for autumn season: (a) original multi-temporal Sentinel-2 image, (b) results of RF+spectral indices, (c) results of RF+spectral indices+topographic factors, and (d) results of RF+spectral indices+topographic factors+texture information.

Figure 7:

Visualization results of season-based vegetation mapping achieved by the proposed model for winter season: (a) original multi-temporal Sentinel-2 image, (b) results of RF+spectral indices, (c) results of RF+spectral indices+topographic factors, and (d) results of RF+spectral indices+topographic factors+texture information.
Visualization results of season-based vegetation mapping achieved by the proposed model for winter season: (a) original multi-temporal Sentinel-2 image, (b) results of RF+spectral indices, (c) results of RF+spectral indices+topographic factors, and (d) results of RF+spectral indices+topographic factors+texture information.

Figure 8:

Input variable importance in season-based vegetation mapping achieved by the RF model for the Greater Sydney region: (a) for summer, (b) for autumn, and (c) for winter.
Input variable importance in season-based vegetation mapping achieved by the RF model for the Greater Sydney region: (a) for summer, (b) for autumn, and (c) for winter.

Area of pixels in each class for each season in square km_

ClassArea (km2)
SummerNon-vegetation1,364.3155
Grass1,691.4873
Trees9,225.3798
Crops86.8179

AutumnNon-vegetation1,335.9587
Grass1,537.0343
Trees9,440.5423
Crops54.4652

WinterNon-vegetation1,370.4691
Grass1,485.4252
Trees9,440.9971
Crops71.1091

Spatial and spectral resolutions of Sentinel-2 satellite data_

BandCentral wavelength (nm)Spatial resolution (m)
Coastal aerosol44360
Blue49010
Green56010
Red66510
Vegetation red edge70520
Vegetation red edge74020
Vegetation red edge78320
NIR84210
Vegetation red edge86520
Water vapor94560
SWIR-Cirrus1,38060
SWIR1,61020
SWIR2,19020

Quantitative results were achieved by the suggested RF method for season-based vegetation mapping_

Precision (%)Recall (%)F1 score (%)OA (%)Kappa (%)
SummerRF+spectral indicesNon-vegetation82.4591.2286.6190.6586.11
Grass91.5483.9687.59
Trees98.9397.6487.59
Crops67.0175.0170.78
RF+ spectral indices+topographic factorsNon-vegetation82.9991.3986.9991.2987.06
Grass92.8084.4688.43
Trees99.2497.9498.58
Crops67.8778.1572.65
RF+ spectral indices+topographic factors+texture informationNon-vegetation84.2792.0888.0092.5688.96
Grass93.5087.3190.30
Trees99.3798.2898.82
Crops74.9880.8977.82

AutumnRF+spectral indicesNon-vegetation81.4588.7284.9390.0885.27
Grass90.1083.5086.68
Trees98.9397.8398.38
Crops66.6173.6469.95
RF+ spectral indices+topographic factorsNon-vegetation80.7092.3086.1190.6086.06
Grass90.3383.9887.04
Trees98.9697.8598.40
Crops71.6973.4672.57
RF+ spectral indices+topographic factors+texture informationNon-vegetation81.8792.4986.8691.6487.60
Grass91.9785.7088.72
Trees99.4498.2198.83
Crops73.5076.5675.00

WinterRF+spectral indicesNon-vegetation81.8891.6186.4791.3587.17
Grass90.9984.7387.75
Trees99.2598.2998.77
Crops73.8477.1075.44
RF+ spectral indices+topographic factorsNon-vegetation82.6291.9387.0392.0888.27
Grass91.0588.0489.52
Trees99.3798.3798.87
Crops78.4675.8777.14
RF+ spectral indices+topographic factors+texture informationNon-vegetation83.3692.5787.7292.8989.46
Grass92.7886.9689.78
Trees99.5098.6399.06
Crops80.1682.9781.54

The number of input variables for the RF method used to create season-based vegetation maps_

CategoryDescriptionInput variables number
TopographicElevation, slope, aspect3
Spectral bandsBlue, green, red, vegetation red edge, near-infrared, and SWIR10
Spectral indicesNDVI, NDBI, MNDWI, NDTI4
Textural informationVariance, contrast, dissimilarity, homogeneity, correlation5×10
Total variable 67
Language: English
Submitted on: Jul 19, 2023
Published on: Oct 21, 2023
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2023 Abolfazl Abdollahi, Biswajeet Pradhan, Abdullah Alamri, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.