Comparative Analysis of Supervised Machine Learning Algorithms for Forest Habitat Mapping in Cyprus
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Methodology
2.2.1. Training Samples Collection
2.2.2. Variable Selection
Satellite | Spectral Indices | Abbreviation | Equation | Ref. |
---|---|---|---|---|
S2 | Normalized Difference Vegetation Index | NDVI | [94] | |
Normalized Difference Red Edge Index | NDRE | [113] | ||
Enhanced Vegetation Index | EVI | [114] | ||
Green Leaf Index | GLI | [115] | ||
SAVI | SAVI | |||
Structure Insensitive Pigment Index | SIPI | [116] | ||
Atmospherically Resistant Vegetation Index | ARVI | [117] | ||
Bare Soil Index | BSI | [118] | ||
Normalized Difference Water Index | NDWI | [119] | ||
Advanced Vegetation Index | AVI | [120] | ||
Green Normalized Difference Vegetation Index | GNDVI | [113] | ||
Normalized Difference Moisture Index | NDMI | [95] | ||
Normalized Burn Ratio | NBR | [98] | ||
Burned Area Index | BAI | [101] | ||
Burned Area Index for Sentinel 2 | BAIS2 | [121] | ||
Char Soil Index | CSI | [102] | ||
Mid-Infrared Burn Index | MIRBI | [103] | ||
Normalized Burn Ratio SWIR | NBRSWIR | [99] | ||
Normalized Burn Ratio Plus | NBRplus | [100] | ||
S1 | Radar Vegetation Index | RVI | [122] | |
Normalized Difference Polarization Index | NDPI | [123] |
2.3. Machine Learning Algorithms
ML Algorithm | Hyperparameter | Value | Description | Source |
---|---|---|---|---|
RF | ‘numTrees’ | 50, 100, 500 | Corresponds to the number of decision trees to create. | [124] |
‘maxNodes’ | 10, 30, 100 | Specifies the maximum number of leaf nodes in the decision tree. If not specified, there is no limit on the maximum number of nodes by default. | ||
‘minLeafPopulation’ | 1, 10, 50 | Determines the minimum number of data points needed to generate new nodes while building the decision tree. By default, this value is set to one. | ||
CART | ‘maxNodes’ | 10, 30, 100 | ||
‘minLeafPopulation’ | 1, 10, 50 | |||
SVM | ‘kernelType’ | RBF: (exp(−γ × |u − v|2)) | RBF was selected for its effectiveness compared to the other kernels, better suitability to match the non-linear data characteristics, and widespread use [130]. The RBF is dependent on two important parameters, the cost (C) and gamma. | [128,133] |
‘cost’ | 1, 10, 50, 100 | The C parameter is useful for managing the misclassification of training samples. When C is set to a higher value, it leads to a decrease in the number of misclassified training examples. | ||
‘gamma’ | 0.01, 0.1, 0.5, 1 | The gamma parameter controls the range of influence for the kernel. A lower gamma value indicates that a single training sample has a broader impact, whereas a higher gamma value leads to a more localized area [134]. |
2.4. Accuracy Assessment
3. Results
3.1. Classification Performance
3.2. Selection of the Optimal Classifier
3.3. Feature Importance
3.4. Spatial Distribution of Forest Habitats in the Study Area
4. Discussion
Limitations of the Study and Future Work
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Hyperparameters | Hyperparameters Code |
---|---|
numTrees = 50 | RF_1 |
numTrees = 100 | RF_2 |
numTrees = 500 | RF_3 |
maxNodes = 10, minLeafPopulation = 1, numTrees = 50 | RF_4 |
maxNodes = 10, minLeafPopulation = 1, numTrees = 100 | RF_5 |
maxNodes = 10, minLeafPopulation = 1, numTrees = 500 | RF_6 |
maxNodes = 30, minLeafPopulation = 1, numTrees = 50 | RF_7 |
maxNodes = 30, minLeafPopulation = 1, numTrees = 100 | RF_8 |
maxNodes = 30, minLeafPopulation = 1, numTrees = 500 | RF_9 |
maxNodes = 100, minLeafPopulation = 1, numTrees = 50 | RF_10 |
maxNodes = 100, minLeafPopulation = 1, numTrees = 100 | RF_11 |
maxNodes = 100, minLeafPopulation = 1, numTrees = 500 | RF_12 |
maxNodes = 10, minLeafPopulation = 10, numTrees = 50 | RF_13 |
maxNodes = 10, minLeafPopulation = 10, numTrees = 100 | RF_14 |
maxNodes = 10, minLeafPopulation = 10, numTrees = 500 | RF_15 |
maxNodes = 30, minLeafPopulation = 10, numTrees = 50 | RF_16 |
maxNodes = 30, minLeafPopulation = 10, numTrees = 100 | RF_17 |
maxNodes = 30, minLeafPopulation = 10, numTrees = 500 | RF_18 |
maxNodes = 100, minLeafPopulation = 10, numTrees = 50 | RF_19 |
maxNodes = 100, minLeafPopulation = 10, numTrees = 100 | RF_20 |
maxNodes = 100, minLeafPopulation = 10, numTrees = 500 | RF_21 |
maxNodes = 10, minLeafPopulation = 50, numTrees = 50 | RF_22 |
maxNodes = 10, minLeafPopulation = 50, numTrees = 100 | RF_23 |
maxNodes = 10, minLeafPopulation = 50, numTrees = 500 | RF_24 |
maxNodes = 30, minLeafPopulation = 50, numTrees = 50 | RF_25 |
maxNodes = 30, minLeafPopulation = 50, numTrees = 100 | RF_26 |
maxNodes = 30, minLeafPopulation = 50, numTrees = 500 | RF_26 |
maxNodes = 100, minLeafPopulation = 50, numTrees = 50 | RF_27 |
maxNodes = 100, minLeafPopulation = 50, numTrees = 100 | RF_28 |
maxNodes = 100, minLeafPopulation = 50, numTrees = 500 | RF_29 |
cost = 1, kernel = RBF, gamma = 0.01 | SVM_1 |
cost = 10, kernel = RBF, gamma = 0.01 | SVM_2 |
cost = 50, kernel = RBF, gamma = 0.01 | SVM_3 |
cost = 100, kernel = RBF, gamma = 0.01 | SVM_4 |
cost = 1, kernel = RBF, gamma = 0.04 | SVM_5 |
cost = 10, kernel = RBF, gamma = 0.04 | SVM_6 |
cost = 50, kernel = RBF, gamma = 0.04 | SVM_7 |
cost = 100, kernel = RBF, gamma = 0.04 | SVM_8 |
cost = 1, kernel = RBF, gamma = 0.1 | SVM_9 |
cost = 10, kernel = RBF, gamma = 0.1 | SVM_10 |
cost = 50, kernel = RBF, gamma = 0.1 | SVM_11 |
cost = 100, kernel = RBF, gamma = 0.1 | SVM_12 |
cost = 1, kernel = RBF, gamma = 0.5 | SVM_13 |
cost = 10, kernel = RBF, gamma = 0.5 | SVM_14 |
cost = 50, kernel = RBF, gamma = 0.5 | SVM_15 |
cost = 100, kernel = RBF, gamma = 0.5 | SVM_16 |
cost = 1, kernel = RBF, gamma = 1 | SVM_17 |
cost = 10, kernel = RBF, gamma = 1 | SVM_18 |
cost = 50, kernel = RBF, gamma = 1 | SVM_19 |
cost = 100, kernel = RBF, gamma = 1 | SVM_20 |
maxNodes = 10, minLeafPopulation = 1 | CART_1 |
maxNodes = 10, minLeafPopulation = 10 | CART_2 |
maxNodes = 10, minLeafPopulation = 50 | CART_3 |
maxNodes = 30, minLeafPopulation = 1 | CART_4 |
maxNodes = 30, minLeafPopulation = 10 | CART_5 |
maxNodes = 30, minLeafPopulation = 50 | CART_6 |
maxNodes = 50, minLeafPopulation = 1 | CART_7 |
maxNodes = 50, minLeafPopulation = 10 | CART_8 |
maxNodes = 50, minLeafPopulation = 50 | CART_9 |
maxNodes = 100, minLeafPopulation = 1 | CART_10 |
maxNodes = 100, minLeafPopulation = 10 | CART_11 |
maxNodes = 100, minLeafPopulation = 50 | CART_12 |
maxNodes = 500, minLeafPopulation = 1 | CART_13 |
maxNodes = 500, minLeafPopulation = 10 | CART_14 |
maxNodes = 500, minLeafPopulation = 50 | CART_15 |
minLeafPopulation = 1 | CART_16 |
Appendix B
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Code | Habitat |
---|---|
H9540 | Mediterranean pine forests with endemic Mesogean pines |
H5420 | Sarcopoterium spinosum phrygana |
H5330 | Thermo-Mediterranean and pre-desert scrub |
H9320 | Olea and Ceratonia forests |
H9390 | Scrub and low forest vegetation with Quercus alnifolia |
H9560 | Endemic forests with Juniperus spp. |
H9590 | Cedrus brevifolia forests (Cedrosetum brevifoliae) |
H9530 | Mediterranean pine forests with endemic Mesogean pines |
Datasets | Band Combination |
---|---|
Dataset 1 | ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’, ‘B7’, ‘B8’, ‘B8A’, ’B11’, ’B12’, ’NDVI’, ’EVI’, ’SAVI’, ’NDMI’, ’NDRE1’, ’ELEVATION’, ’ASPECT’, ’SLOPE’ |
Dataset 2 | ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’, ‘B7’, ‘B8’, ‘B8A’, ‘B9’, ’B10’, ‘B11’, ‘B12’, ‘BSI’, ’ARVI’, ‘ASPECT’, ‘AVI’, ‘ELEVATION’, ‘EVI’, ‘GCI’, ‘GDVI’, ‘GLI’, ‘GNDVI’, ‘GOSAVI’, ‘GRVI’, ‘GSAVI’, ‘IPVI’, ‘NBRI’, ‘NDMI’, ‘NDRE1’, ‘NDVI’, ‘NDWI’, ‘RGR’, ‘SAVI’, ‘SIPI’, ‘SLOPE’, ‘TD’, ‘VH’, ‘VV’, ‘RVI’, ‘NRVI’ |
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Prodromou, M.; Gitas, I.; Mettas, C.; Tzouvaras, M.; Danezis, C.; Hadjimitsis, D. Comparative Analysis of Supervised Machine Learning Algorithms for Forest Habitat Mapping in Cyprus. Sustainability 2025, 17, 6021. https://doi.org/10.3390/su17136021
Prodromou M, Gitas I, Mettas C, Tzouvaras M, Danezis C, Hadjimitsis D. Comparative Analysis of Supervised Machine Learning Algorithms for Forest Habitat Mapping in Cyprus. Sustainability. 2025; 17(13):6021. https://doi.org/10.3390/su17136021
Chicago/Turabian StyleProdromou, Maria, Ioannis Gitas, Christodoulos Mettas, Marios Tzouvaras, Chris Danezis, and Diofantos Hadjimitsis. 2025. "Comparative Analysis of Supervised Machine Learning Algorithms for Forest Habitat Mapping in Cyprus" Sustainability 17, no. 13: 6021. https://doi.org/10.3390/su17136021
APA StyleProdromou, M., Gitas, I., Mettas, C., Tzouvaras, M., Danezis, C., & Hadjimitsis, D. (2025). Comparative Analysis of Supervised Machine Learning Algorithms for Forest Habitat Mapping in Cyprus. Sustainability, 17(13), 6021. https://doi.org/10.3390/su17136021