Modeling the Underlying Drivers of Natural Vegetation Occurrence in West Africa with Binary Logistic Regression Method
Abstract
:Highlights
- Underlying drivers of natural vegetation were identified by Binary Logistic Regression.
- Multiple underlying drivers were significant at p < 0.05 with varying impacts.
- Human activities indicators were the dominant underlying drivers.
- The response of natural vegetation to climate was altered by intensification of human activities.
1. Introduction
2. Study Area and Datasets
2.1. Study Area: Selected Hotspots in West Africa
2.1.1. Site 1: Diourbel-Louga (Senegal)
2.1.2. Site 2: Hodh el Gharbi (Mauritania)
2.1.3. Site 3: Zinder-Maradi (Niger)
2.1.4. Site 4: Centre and Centre Sud (Burkina Faso)
2.1.5. Site 5: Ghana (Ashanti Region)
2.1.6. Site 6: Nigeria (Niger State)
2.2. Datasets
3. Methodology
3.1. Binary Logistic Regression (BLR) Model
3.2. Data Preparation
3.3. Multi-Collinearity Analysis
3.4. Model Development
3.5. Model Validation
4. Results
4.1. Multi-Collinearity
4.2. Significant Underlying Drivers of Natural Vegetation Identified from the Binary Logistic Regresion (BLR) Model
4.3. Model Validation
4.4. The Relationship between the Significant Underlying Drivers and Natural Vegetation
5. Discussion
5.1. Multi-Collinearity
5.2. Significant Underlying Drivers Identified by the Binary Logistic Regression (BLR) Model
5.3. Model Validation
5.4. The Relationship between the Significant Underlying Drivers and Occurrence of Natural Vegetation
5.4.1. Human Activities (HANPP) and Demography (Population Density)
5.4.2. Livestock Density
5.4.3. Accessibility (Travel Time)
5.4.4. Climate (Wetness Index)
5.4.5. Soil Type, Elevation, and Slope
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Sources | Spatial Resolution | Temporal Coverage |
---|---|---|---|
LULC raster map | USGS(2000) | 2000 m | 2000 |
Elevation | NASA-SRTM | 30 m | Static |
Slope | USGSS | 1 km | Static |
Soil Type | FAO GEONETWORK | 10 km | Static |
Wetness Index | CGIAR-CSI (1950–2000) | 1 km | 1950–2000 |
Travel Time | FAO GEONETWORK-Harvest Choice | 10 km | Benchmarked 2000 |
Live Stock Density | FAO GEONETWORK-Harvest Choice | 5 km | Benchmarked 2000 |
HANPP | SEDEC | 28 km | 1975–2000 |
Population Density | SEDEC | 1 km | 2000 |
Study Sites | Variables | Mean | SD | Median | Minimum | Maximum | Range | Skew | Kurtosis | SE |
---|---|---|---|---|---|---|---|---|---|---|
1. Diourbel-Louga, Senegal | HANPP (g) | 8.94 × 1010 | 1.03 × 1011 | 4.15 × 1010 | 1.05 × 1010 | 4.89 × 1011 | 4.78 × 1011 | 1.53 | 1.25 | 1.27 × 109 |
Number of Samples = 6557 | Livestock Density (TLU/km2) | 26.19 | 8.64 | 25.40 | 0.20 | 96.25 | 96.05 | 0.47 | 3.08 | 0.11 |
Population Density (people km−2) | 57.73 | 79.92 | 11.58 | 2.55 | 308.24 | 305.69 | 2.14 | 3.59 | 0.99 | |
Slope (m) | 251.71 | 37.02 | 237.91 | 203.81 | 583.57 | 379.76 | 2.47 | 9.55 | 0.46 | |
Travel Time (hr) | 2.60 | 1.17 | 2.38 | 0.17 | 10.27 | 10.10 | 1.63 | 4.60 | 0.01 | |
Wetness Index | 0.20 | 0.03 | 0.20 | 0.13 | 0.28 | 0.15 | 0.20 | −0.37 | 0.00 | |
Elevation (m) | 40.96 | 11.28 | 43.86 | 2.52 | 60.64 | 58.13 | −0.85 | −0.05 | 0.14 | |
2. Hodh el Gharbi | HANPP (g) | 6.39 × 109 | 5.82 × 109 | 5.15 × 109 | 8.58 × 108 | 6.37 × 1010 | 6.29 × 1010 | 4.82 | 2.85 × 101 | 5.42 × 107 |
Number of Samples = 11,498 | Livestock Density (TLU/km2) | 10.43 | 13.64 | 2.49 | 0.00 | 57.30 | 57.30 | 1.29 | 0.47 | 0.13 |
Population Density (people km−2) | 4.55 | 6.72 | 3.97 | 0.11 | 114.77 | 114.65 | 9.49 | 127.97 | 0.06 | |
Slope (m) | 327.46 | 119.27 | 290.00 | 205.20 | 899.99 | 694.79 | 2.17 | 5.11 | 1.11 | |
Travel Time (hr) | 7.86 | 4.22 | 6.47 | 1.62 | 23.66 | 22.04 | 1.41 | 1.49 | 0.04 | |
Wetness Index | 0.11 | 0.03 | 0.10 | 0.06 | 0.21 | 0.15 | 0.84 | 0.30 | 0.00 | |
Elevation (m) | 213.22 | 44.44 | 201.50 | 140.13 | 444.44 | 304.31 | 1.68 | 3.68 | 0.41 | |
3. Zinder-Maradi, Niger | HANPP (g) | 4.52 × 1010 | 5.59 × 1010 | 1.41 × 1010 | 3.00 × 109 | 3.49 × 1011 | 3.46 × 1011 | 1.55 | 2.05 | 2.82 × 108 |
Number of Samples = 39,277 | Livestock Density (TLU/km2) | 14.23 | 23.06 | 10.00 | 0.00 | 526.20 | 526.20 | 14.93 | 315.94 | 0.12 |
Population Density (people km−2) | 25.22 | 64.95 | 9.66 | 0.22 | 2593.00 | 2592.78 | 20.87 | 593.26 | 0.33 | |
Slope (m) | 299.47 | 80.40 | 280.15 | 200.36 | 896.25 | 695.88 | 2.70 | 10.71 | 0.41 | |
Travel Time (hr) | 6.70 | 8.11 | 3.48 | 0.29 | 48.13 | 47.84 | 2.44 | 5.74 | 0.04 | |
Wetness Index | 0.11 | 0.06 | 0.12 | 0.02 | 0.29 | 0.27 | 0.17 | −0.85 | 0.00 | |
Elevation (m) | 428.74 | 43.17 | 430.33 | 304.08 | 603.33 | 299.24 | 0.02 | −0.45 | 0.22 | |
4. Centre-Centre Sud, Burkina Faso | HANPP (g) | 1.72 × 1011 | 1.20 × 1011 | 1.44 × 1011 | 2.38 × 1010 | 8.18 × 1011 | 7.94 × 1011 | 1.77 | 4.67 | 2.14 × 1019 |
Number of Samples = 3155 | Livestock Density (TLU/km2) | 51.13 | 24.15 | 49.40 | 0.00 | 108.90 | 108.90 | 0.09 | −0.80 | 0.43 |
Population Density (people km−2) | 119.08 | 312.64 | 55.93 | 6.93 | 1886.85 | 1879.91 | 4.98 | 23.99 | 5.57 | |
Slope (m) | 343.12 | 43.10 | 335.39 | 254.52 | 734.40 | 479.89 | 2.76 | 14.67 | 0.77 | |
Travel Time (hr) | 2.75 | 1.56 | 2.29 | 0.18 | 8.78 | 8.61 | 1.38 | 1.87 | 0.03 | |
Wetness Index | 0.42 | 0.03 | 0.42 | 0.36 | 0.50 | 0.15 | 0.44 | −0.82 | 0.00 | |
Elevation (m) | 293.25 | 26.19 | 295.44 | 205.57 | 356.24 | 150.67 | −0.41 | −0.08 | 0.47 | |
5. Ashanti Region, Ghana | HANPP (g) | 2.14 × 1011 | 1.19 × 1011 | 2.01 × 1011 | 5.26 × 1010 | 8.13 × 1011 | 7.60 × 1011 | 1.26 | 2.54 | 1.62 × 109 |
Number of Samples = 5456 | Livestock density (TLU/km2) | 5.39 | 1.88 | 5.27 | 0.00 | 12.90 | 12.90 | 0.32 | 0.08 | 0.03 |
Population density (people km−2) | 153.47 | 456.63 | 83.25 | 17.07 | 4849.50 | 4832.42 | 8.61 | 78.17 | 6.18 | |
Slope (m) | 493.16 | 157.32 | 436.65 | 261.99 | 900.00 | 638.01 | 1.01 | −0.08 | 2.13 | |
Travel time (hr) | 3.52 | 2.49 | 2.74 | 0.12 | 14.36 | 14.24 | 1.52 | 1.78 | 0.03 | |
Wetness index | 0.84 | 0.09 | 0.84 | 0.69 | 1.06 | 0.37 | 0.18 | −1.11 | 0.00 | |
Elevation (m) | 221.80 | 83.85 | 211.26 | 77.43 | 625.99 | 548.56 | 0.87 | 0.78 | 1.14 | |
6. Niger State, Nigeria | HANPP (g) | 6.25 × 1010 | 2.85 × 1010 | 6.03 × 1010 | 1.72 × 1010 | 1.70 × 1011 | 1.53 × 1011 | 7.05 × 10−1 | 4.16 × 10−1 | 2.27 × 108 |
Number of Samples = 15,731 | Livestock density (TLU/km2) | 19.85 | 14.43 | 17.10 | 0.00 | 94.00 | 94.00 | 1.45 | 3.14 | 0.12 |
Population density (people km−2) | 47.35 | 103.52 | 38.77 | 14.16 | 3117.93 | 3103.77 | 17.70 | 373.64 | 0.83 | |
Slope (m) | 465.36 | 111.76 | 445.55 | 200.10 | 900.00 | 699.90 | 0.91 | 1.24 | 0.89 | |
Travel time (hr) | 3.75 | 2.65 | 2.97 | 0.11 | 20.61 | 20.49 | 2.74 | 10.14 | 0.02 | |
Wetness index | 0.61 | 0.05 | 0.62 | 0.49 | 0.76 | 0.27 | 0.05 | 0.41 | 0.00 | |
Elevation (m) | 250.28 | 107.31 | 244.83 | 44.56 | 620.58 | 576.02 | 0.48 | −0.10 | 0.86 |
Arid Region | ||||||
---|---|---|---|---|---|---|
Study Sites | Variables | Parameter Estimate β | Standard Error | Wald (Z Value) | Pr(>│Z│) | Significance Codes |
1. Diourbel-Louga, Senegal | Intercept | −1.150 | 6.200 × 10−1 | −1.845 | 6.500 × 10−2 | • |
Number of Samples = 6557 | HANPP | −9.162 × 10−12 | 7.512 × 10−13 | −1.220 × 101 | 2.000 × 10−16 | *** |
Training = 5099 | Travel Time | 6.626 × 10−1 | 5.126 × 10−2 | 1.293 × 10 | 2.000 × 10−16 | *** |
Testing = 1458 | Slope | 1.057 × 10−2 | 1.545 × 10−3 | 6.840 | 7.940 × 10−12 | *** |
Area Under the ROC/AUC | Livestock Density | −2.878 × 10−2 | 5.103 × 10−3 | −5.639 | 1.710 × 10−8 | *** |
Climate Drivers = 0.83 | Elevation | 3.659 × 10−2 | 5.087 × 10−3 | 7.193 | 6.320× 10−13 | *** |
Human Drivers = 0.87 | Soil Type | 1.949 × 10−2 | 5.878 × 10−3 | 3.316 | 9.130 × 10−4 | *** |
Climate and Human = 0.88 | Wetness Index | −1.187 × 101 | 2.256 | −5.263 | 1.420 × 10−7 | *** |
Population Density | 2.046 × 10−3 | 8.291 × 10−4 | 2.468 | 1.358 × 10−2 | * | |
2. Hodh el Gharbi, Mauritania | Intercept | 2.977 | 3.423 × 10−1 | 8.698 | 2.000 × 10−16 | *** |
Number of Samples 11,408 | Livestock Density | 2.965 × 10−2 | 4.116 ×10−3 | 7.205 | 5.820 × 10−13 | *** |
Training Samples = 8942 | Elevation | −9.042 × 10−3 | 7.009 × 10−4 | −1.290 × 101 | 2.000 × 10−16 | *** |
Testing Samples = 2556 | Wetness Index | 2.008 × 101 | 2.216 | 9.063 | 2.000 × 10−16 | *** |
Area Under the ROC/AUC | HANPP | −5.577 × 10−11 | 6.688 × 10−12 | −8.338 | 2.000 × 10−16 | *** |
Climate Drivers = 0.74 | Slope | −2.113 × 10−3 | 2.530 × 10−4 | −8.353 | 2.000 × 10−16 | *** |
Human Drivers = 0.71 | Soil Type | −5.705 × 10−2 | 8.708 × 10−3 | −6.551 | 5.700 × 10−11 | *** |
Climate and Human = 0.75 | Travel Time | −4.374 × 10−2 | 7.004 ×10−3 | −6.245 | 4.240 × 10−10 | *** |
Population Density | −2.104 × 10−2 | 4.141 × 10−3 | −5.081 | 3.750 × 10−7 | *** | |
3. Zinder-Maradi, Niger | Intercept | 6.035 | 2.276 × 10−1 | 2.651 × 10 | 2.00 × 10−16 | *** |
Number of Samples = 39,277 | Wetness Index | −2.250 × 101 | 7.572 × 10−1 | −2.972 × 101 | 2.000 × 10−16 | *** |
Training Samples = 30,549 | Population Density | −1.382 × 10−2 | 1.042 × 10−3 | −1.326 × 101 | 2.000 × 10−16 | *** |
Testing Samples = 8728 | Slope | −3.063 × 10−3 | 1.923 × 10−4 | −1.593 × 101 | 2.000 × 10−16 | *** |
Area Under the ROC/AUC | Travel Time | −3.527 × 10−2 | 3.264 × 10−3 | −1.081 × 101 | 2.000 × 10−16 | *** |
Climate Drivers = 0.86 | HANPP | −4.768 × 10−12 | 6.011 × 10−13 | −7.932 | 2.150 × 10−15 | *** |
Human Drivers = 0.87 | Soil Type | −2.089 × 10−2 | 4.910 × 10−3 | −4.255 | 2.090 × 10−5 | *** |
Climate and Human = 0.88 | Livestock Density | 5.008 × 10−3 | 1.585 × 10−3 | 3.159 | 1.583 × 10−3 | ** |
DEM | −1.498 × 10−3 | 4.078 × 10−4 | −3.674 | 2.390 × 10−4 | *** | |
Humid Region | ||||||
4. Centre-Centre Sud, Burkina Faso | Intercept | −1.761 | 1.255 | −1.403 | 1.606 × 10−1 | |
Number of Samples = 3155 | Wetness Index | 1.277 × 10 | 2.433 | 5.247 | 1.550 × 10−7 | *** |
Training Samples = 2453 | Livestock Density | −1.290 × 10−2 | 2.555 × 10−3 | −5.050 | 4.430 × 10−7 | *** |
Testing Samples = 702 | Population Density | −5.907 × 10−4 | 1.701 × 10−4 | −3.473 | 5.160 × 10−4 | *** |
Area Under the ROC/AUC | HANPP | −1.546 × 10−12 | 4.601 × 10−13 | −3.359 | 7.810 × 10−4 | *** |
Climate Drivers = 0.70 | Elevation | −4.879 × 10−3 | 1.888 × 10−3 | −2.585 | 9.751 × 10−3 | ** |
Human Drivers = 0.71 | Slope | −2.376 × 10−3 | 1.212 × 10−3 | −1.960 | 4.995 × 10−2 | * |
Climate and Human = 0.72 | Travel Time | 4.854 × 10−2 | 3.027 × 10−2 | 1.603 | 1.089 × 10−1 | |
5.Ashanti Region, Ghana | Intercept | −8.415 × 10−2 | 2.295 × 10−1 | −3.670×10−1 | 7.139 × 10−1 | |
Number of Samples = 5456 | HANPP | −3.001 × 10−12 | 4.520 × 10−13 | −6.639 | 3.170 × 10−11 | *** |
Training Samples = 4243 | Slope | 1.947 × 10−3 | 2.850 × 10−4 | 6.832 | 8.380 × 10−12 | *** |
Testing Samples = 1213 | Travel Time | 7.631 × 10−2 | 1.997 × 10−2 | 3.821 | 1.330 × 10−4 | *** |
Area Under the ROC/AUC | Elevation | −1.388 × 10−3 | 5.701 × 10−4 | −2.435 | 1.489 × 10−2 | * |
Climate Drivers = 0.64 | Population Density | −3.783 × 10−4 | 1.574 × 10−4 | −2.403 | 1.626 × 10−2 | * |
Human Drivers = 0.66 | Soil Type | −1.329 × 10−3 | 5.089 × 10−4 | −2.611 | 9.038 × 10−3 | ** |
Climate and Human = 0.70 | Livestock Density | 4.529 × 10−2 | 1.893 × 10−2 | 2.393 | 1.671 × 10−2 | * |
6. Niger State, Nigeria | Intercept | 7.181 × 10−1 | 1.164 × 10−1 | 6.168 | 6.910 × 10−10 | *** |
Number of Samples 15,731 | Livestock Density | −2.247 × 10−2 | 1.471 × 10−3 | −1.528 × 101 | 2.000 × 10−16 | *** |
Training Samples = 12,235 | HANPP | −8.350 × 10−12 | 7.862 × 10−13 | −1.062 × 101 | 2.000 × 10−16 | *** |
Testing Samples = 3496 | Slope | 1.660 × 10−3 | 1.791 × 10−4 | 9.267 | 2.000 × 10−16 | *** |
Area Under the ROC/AUC | Travel Time | 6.759 ×1 0−2 | 9.492 × 10−3 | 7.121 | 1.070 × 10−12 | *** |
Climate Drivers = 0.57 | Elevation | −1.173 × 10−3 | 1.819 × 10−4 | −6.449 | 1.130 × 10−10 | *** |
Human Drivers = 0.63 | Soil Type | 8.765 × 10−4 | 2.307 × 10−4 | 3.799 | 1.450 × 10−4 | *** |
Climate and Human = 0.65 | Population Density | −4.071 × 10−4 | 2.129 × 10−4 | −1.912 | 5.585 × 10−2 | • |
Significance. Codes | p < 0.001 *** | p < 0.01 ** | p < 0.05 * | p < 0.1 • | 1 |
Study Sites | Continuous Variables | VIF (Full Model) | Eigen Values |
---|---|---|---|
1. Diourbel-Louga-Senegal | Elevation | 16.8 | 0.1 |
AIC = 4255.4 | Slope | 16.1 | 0.6 |
BIC = 4314.2 | Wetness Index | 20.6 | 0.3 |
Log Likelihood = −2118.7 | Travel Time | 18.6 | 0.4 |
Deviance = 4247.4 | Live Stock Density | 9.9 | 1.7 |
Training samples = 5099.0 | HANPP | 30.4 | 3.1 |
Kappa and Conditional Number = 23.5 | Population Density | 22.4 | 0.8 |
2. Hodh el Gharbi, Mauritania | Elevation | 8.7 | 0.2 |
AIC = 8935.7 | Slope | 8.1 | 0.7 |
BIC = 8999.6 | Wetness Index | 39.1 | 0.4 |
Log Likelihood = −4458.9 | Travel Time | 7.8 | 0.5 |
Deviance = 8917.7 | Live Stock Density | 28 | 1.5 |
Training Samples = 8942.0 | HANPP | 13.5 | 2.8 |
kappa and Conditional Number = 17.9 | Population Density | 6.6 | 0.9 |
3. Zinder-Maradi, Niger | Elevation | 9.4 | 0.1 |
AIC = 27,333.9 | Slope | 7.2 | 0.7 |
BIC = 27,408.9 | Wetness Index | 58.6 | 0.5 |
Log Likelihood = −13,658.0 | Travel Time | 21.4 | 0.6 |
Deviance = 27,315.9 | Live Stock Density | 40.5 | 1.3 |
Training Samples = 30,549.0 | HANPP | 34.5 | 2.9 |
Kappa and conditional Number = 24.4 | Population Density | 145.8 | 0.8 |
4.Centre-Cente-Sud, Burkina Faso | Elevation | 6.1 | 0.3 |
AIC = 2988.6 | Slope | 7.2 | 0.8 |
BIC = 2980.1 | Wetness Index | 15.1 | 0.4 |
Log Likelihood = −1458.8 | Travel Time | 5.5 | 0.7 |
Deviance = 2917.6 | Live Stock Density | 9.4 | 1.2 |
Training Samples = 2453 | HANPP | 7.9 | 2.5 |
Kappa and conditional Number = 10.1 | Population Density | 6.9 | 1.1 |
5. Ashanti Region, Ghana | Elevation | 10.6 | 0.2 |
AIC = 5366.5 | Slope | 9.5 | 0.7 |
BIC = 5417.3 | Wetness Index | 7.5 | 0.5 |
Log Likelihood = −2675.2 | Travel Time | 10.9 | 0.3 |
Deviance = 5350.5 | Live Stock Density | 5.4 | 1.4 |
Training Samples = 4243.0 | HANPP | 14.4 | 2.8 |
Kappa and conditional Number = 13.9 | Population Density | 21.9 | 0.9 |
6.Niger State, Nigeria | Elevation | 4.9 | 0.5 |
AIC = 15,426.0 | Slope | 5.1 | 0.9 |
BIC = 15,485.3 | Wetness Index | 6.0 | 0.6 |
Log Likelihood = −7705.0 | Travel Time | 7.9 | 0.8 |
Deviance = 15,409.9 | Live Stock Density | 5.5 | 1.3 |
Training Samples = 12,235.0 | HANPP | 6.7 | 2.1 |
Kappa and conditional Number = 4.6 | Population Density | 6.5 | 0.9 |
Study Sites | Underlying Driving Factors | |||||||
---|---|---|---|---|---|---|---|---|
West Africa | HANPP | Pop. Density | L. Density | Travel Time | Slope | Elevation | Soil Type | W. Index |
1. Diourbel-Louga, Senegal | Negative *** | Positive * | Negative *** | Positive *** | Positive *** | Positive *** | Positive *** | Negative *** |
2. Hodh el Gharbi, Mauritania | Negative *** | Negative **** | Positive *** | Negative *** | Negative *** | Negative *** | Negative *** | Positive *** |
3. Zinder-Maradi, Niger | Negative *** | Negative**** | Positive ** | Negative *** | Negative *** | Negative *** | Negative *** | Negative *** |
4. Centre-Centre-Sud, Burkina Faso | Negative *** | Negative**** | Negative *** | Insignificant | Negative * | Negative ** | Insignificant | Positive *** |
5. Ashanti Region, Ghana | Negative *** | Negative * | Positive * | Positive *** | Positive *** | Negative * | Negative ** | Insignificant |
6. Niger State, Nigeria | Negative *** | Negative • | Negative *** | Positive *** | Positive *** | Negative *** | Positive *** | Insignificant |
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Asenso Barnieh, B.; Jia, L.; Menenti, M.; Jiang, M.; Zhou, J.; Zeng, Y.; Bennour, A. Modeling the Underlying Drivers of Natural Vegetation Occurrence in West Africa with Binary Logistic Regression Method. Sustainability 2021, 13, 4673. https://doi.org/10.3390/su13094673
Asenso Barnieh B, Jia L, Menenti M, Jiang M, Zhou J, Zeng Y, Bennour A. Modeling the Underlying Drivers of Natural Vegetation Occurrence in West Africa with Binary Logistic Regression Method. Sustainability. 2021; 13(9):4673. https://doi.org/10.3390/su13094673
Chicago/Turabian StyleAsenso Barnieh, Beatrice, Li Jia, Massimo Menenti, Min Jiang, Jie Zhou, Yelong Zeng, and Ali Bennour. 2021. "Modeling the Underlying Drivers of Natural Vegetation Occurrence in West Africa with Binary Logistic Regression Method" Sustainability 13, no. 9: 4673. https://doi.org/10.3390/su13094673