‘The Best of Two Worlds’—Combining Classifier Fusion and Ecological Models to Map and Explain Landscape Invasion by an Alien Shrub
Abstract
:1. Introduction
2. Materials and Methods
2.1. General Workflow
2.2. Target Species
2.3. Study Area
2.4. Occurrence Data
2.5. Satellite Remote Sensing Data
2.6. Biomod2 Multi-Algorithm Supervised Classification Training and Evaluation
2.7. Variable Reduction and Importance Calculation
2.8. Classifier Fusion Ensemble
2.9. Assessing the Invasion Drivers of A. longifolia at the Landscape Level through Predictive Modelling
3. Results
3.1. Partial and Ensemble Fusion Classification Performance
3.2. Feature Importance in Image Classification
3.3. Acacia Longifolia Mapping
3.4. Predictive Modelling—Landscape-Level Drivers of A. longifolia Invasion
4. Discussion
4.1. Multispectral Remote Sensing Imagery and Data Fusion Techniques through biomod2 for A. longifolia Detection
4.2. Landscape Patterns and Drivers of Acacia longifolia Distribution
4.3. Applications in Invasion Management and Control
4.4. Future Improvements to the Methodology
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Month/Spectral Band/Index | Average Variable Importance Score | Importance Score Standard Deviation | % Relative Importance |
---|---|---|---|
December_b12 | 0.137 | 0.206 | 4.79 |
January_b2 | 0.124 | 0.109 | 4.32 |
January_lai | 0.120 | 0.183 | 4.18 |
April_b3 | 0.098 | 0.131 | 3.41 |
March_b3 | 0.084 | 0.162 | 2.94 |
December_b11 | 0.079 | 0.131 | 2.78 |
February_b9 | 0.070 | 0.133 | 2.44 |
December_b1 | 0.066 | 0.084 | 2.32 |
February_b12 | 0.065 | 0.132 | 2.29 |
March_b1 | 0.063 | 0.117 | 2.20 |
May_b3 | 0.061 | 0.127 | 2.14 |
December_b3 | 0.057 | 0.104 | 1.99 |
April_b2 | 0.054 | 0.065 | 1.89 |
February_b3 | 0.054 | 0.086 | 1.88 |
December_b4 | 0.048 | 0.075 | 1.69 |
March_lai | 0.046 | 0.090 | 1.60 |
December_lai | 0.045 | 0.097 | 1.60 |
Appendix B
Type | Acronym | Variable Description | Data Source |
---|---|---|---|
Climate | CL_BIO01 | Annual Mean Temperature | CHELSA Climate data v-1.2 (URL: https://chelsa-climate.org/, access date: 6 December 2020, spatial resolution: ~1 Km2, reference period: 1979–2013) |
CL_BIO02 | Mean Diurnal Range (Mean of monthly (maximum temperature–minimum temperature)) | ||
CL_BIO03 | Isothermality (CL_BIO2/CL_BIO7) (×100) | ||
CL_BIO04 | Temperature Seasonality (standard deviation × 100) | ||
CL_BIO05 | Maximum Temperature of Warmest Month | ||
CL_BIO06 | Minimum Temperature of Coldest Month | ||
CL_BIO07 | Temperature Annual Range (CL_BIO5–CL_BIO6) | ||
CL_BIO08 | Mean Temperature of Wettest Quarter | ||
CL_BIO09 | Mean Temperature of Driest Quarter | ||
CL_BIO10 | Mean Temperature of Warmest Quarter | ||
CL_BIO11 | Mean Temperature of Coldest Quarter | ||
CL_BIO12 | Annual Precipitation | ||
CL_BIO13 | Precipitation of Wettest Month | ||
CL_BIO14 | Precipitation of Driest Month | ||
CL_BIO15 | Precipitation Seasonality (Coefficient of Variation) | ||
CL_BIO16 | Precipitation of Wettest Quarter | ||
CL_BIO17 | Precipitation of Driest Quarter | ||
CL_BIO18 | Precipitation of Warmest Quarter | ||
CL_BIO19 | Precipitation of Coldest Quarter | ||
Disturbance | DT_BA10YR | Total Burnt Area (last 10 years) | Burnt Areas Dataset for Mainland Portugal (URL: https://geocatalogo.icnf.pt/, access date: 6 December 2020, spatial resolution: ~1 ha, reference period: 2000–2019) |
DT_BA20YR | Total Burnt Area (last 20 years) | ||
DT_BA5YR | Total Burnt Area (last 5 years) | ||
Land use | LU_PCPCO | Percent cover of permanent crops | Land Cover Map for Portugal (URL: http://mapas.dgterritorio.pt, access date: 11 December 2020: spatial resolution: ~100 m, reference year: 2018) |
LU_PCACO | Percent cover of annual crops | ||
LU_PCPAC | Percent cover of permanent and annual crops | ||
LU_PCNFO | Percent cover of native forests | ||
LU_PCEFO | Percent cover of eucalyptus (production) forest | ||
LU_PCPFO | Percent cover of maritime-pine (production) forest | ||
LU_PCSHL | Percent cover of shrublands | ||
LU_PCAFM | Percent cover of complex agroforestry mosaics | ||
LU_PCOPF | Percent cover of other production forests | ||
LU_PCPAS | Percent cover of pasturelands | ||
LU_PCWET | Percent cover of wetlands | ||
LU_PCBSD | Percent cover of beaches and sand dunes | ||
LU_PCRRL | Percent cover of roads and rails | ||
LU_PCBRS | Percent cover of bare rock surfaces | ||
LU_PCWTS | Percent cover of water surfaces | ||
LU_PCART | Percent cover of artificial/urban areas | ||
LU_PCSPV | Percent cover of sparsely vegetated areas | ||
Landscape pattern/configuration and heterogeneity | LP_MNPAR | Landscape Mean Patch Area | |
LP_PACOV | Landscape Patch Area Coefficient of variation | ||
LP_LAPAI | Landscape Largest Patch Index | ||
LP_SHDVI | Landscape Shannon Diversity | ||
LP_SPDVI | Landscape Simpson Diversity | ||
LP_PASTD | Landscape Patch Area Standard-deviation | ||
Linear elements | LE_EDGDN | Landscape edge density | |
LE_TLRIV | Total length of rivers | European River Catchment Database (URL: https://www.eea.europa.eu, access date: 6 December 2020: spatial resolution: 100 m, reference year: 2007) | |
LE_TLROD | Total length of all road types | Open Street Map (URL: https://download.geofabrik.de/europe/portugal.html, access date: 6 December 2020: reference year: 2020) | |
LE_TLMTW | Total length of motorways | ||
Soil properties | SO_AVWTC | Available water content | Topsoil physical properties for Europe (URL: https://esdac.jrc.ec.europa.eu, access date: 6 December 2020: spatial resolution: 500 m, reference year: 2009) |
SO_BULKD | Bulk Density | ||
SO_PCLAY | Percent of clay in soils | ||
SO_PCOAR | Percent of coarse elements in soils | ||
SO_PSAND | Percent of sand in soils | ||
SO_PSILT | Percent of silt in soils | ||
Topography/Geomorphology | TG_SLOPE | Slope (%) | SRTM v-4.1 (URL: https://srtm.csi.cgiar.org/, access date: 6 December 2020: spatial resolution: 90 m, reference year: 2008) |
TG_RADAV | Average Solar Radiation | ||
TG_TORGI | Topographic Ruggedness Index | ||
TG_TOWTI | Topographic Wetness Index |
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Index | Explanation | Specific Formula |
---|---|---|
NDVI | Enhances vegetation differences photosynthetically [46] | |
EVI | Distinguish differences in the canopy structure, architecture, and physiognomy [47,48] | |
MCARI | A measure of the leaf chlorophyll content enhancing vegetation differences [49] | |
MTCI | Enhances vegetation senescence and water/nutritional stress [50] | |
MSAVI 2 | Retrieves information about vegetation dynamics and reduced the soil background variations [51] | |
LAI | Measure for the total area of leaves per unit ground area [52] | S2 SNAP Toolbox biophysical processor. |
Environmental Factors | Driver Description | Acronym |
---|---|---|
(Bio)Climate | Annual Mean Temperature | CL_BIO01 |
Mean Diurnal Range | CL_BIO02 | |
Isothermality | CL_BIO03 | |
Temperature Seasonality | CL_BIO04 | |
Maximum Temperature of Warmest Month | CL_BIO05 | |
Minimum Temperature of Coldest Month | CL_BIO06 | |
Temperature Annual Range | CL_BIO07 | |
Mean Temperature of Wettest Quarter | CL_BIO08 | |
Mean Temperature of Driest Quarter | CL_BIO09 | |
Mean Temperature of Warmest Quarter | CL_BIO10 | |
Mean Temperature of Coldest Quarter | CL_BIO11 | |
Annual Precipitation | CL_BIO12 | |
Precipitation of Wettest Month | CL_BIO13 | |
Precipitation of Driest Month | CL_BIO14 | |
Precipitation Seasonality | CL_BIO15 | |
Precipitation of Wettest Quarter | CL_BIO16 | |
Precipitation of Driest Quarter | CL_BIO17 | |
Precipitation of Warmest Quarter | CL_BIO18 | |
Precipitation of Coldest Quarter | CL_BIO19 | |
Disturbance | Total Burnt Area (last 10 years) | DT_BA10YR |
Total Burnt Area (last 20 years) | DT_BA20YR | |
Total Burnt Area (last 5 years) | DT_BA5YR | |
Land use | Percent of permanent crops | LU_PCPCO |
Percent of annual crops | LU_PCACO | |
Percent of permanent and annual crops | LU_PCPAC | |
Percent of native forests | LU_PCNFO | |
Percent of eucalyptus (production) forest | LU_PCEFO | |
Percent of maritime-pine (production) forest | LU_PCPFO | |
Percent of shrublands | LU_PCSHL | |
Percent of complex agroforestry mosaics | LU_PCAFM | |
Percent of other production forests | LU_PCOPF | |
Percent of pasturelands | LU_PCPAS | |
Percent of wetlands | LU_PCWET | |
Percent of beaches and sand dunes | LU_PCBSD | |
Percent of roads and rails | LU_PCRRL | |
Percent of bare rock surfaces | LU_PCBRS | |
Percent of water surfaces | LU_PCWTS | |
Percent of artificial/urban areas | LU_PCART | |
Percent of sparsely vegetated areas | LU_PCSPV | |
Landscape pattern/configuration and heterogeneity | Landscape Mean Patch Area | LP_MNPAR |
Landscape Patch Area Coefficient of variation | LP_PACOV | |
Landscape Largest Patch Index | LP_LAPAI | |
Landscape Shannon Diversity | LP_SHDVI | |
Landscape Simpson Diversity | LP_SPDVI | |
Landscape Patch Area Standard-deviation | LP_PASTD | |
Linear elements | Landscape edge density | LE_EDGDN |
Total length of rivers | LE_TLRIV | |
Total length of all road types | LE_TLROD | |
Total length of motorways | LE_TLMTW | |
Soil properties | Available water content | SO_AVWTC |
Bulk Density | SO_BULKD | |
Percent of clay in soils | SO_PCLAY | |
Percent of coarse elements in soils | SO_PCOAR | |
Percent of sand in soils | SO_PSAND | |
Percent of silt in soils | SO_PSILT | |
Topography/Geomorphology | Slope (%) | TG_SLOPE |
Average Solar Radiation | TG_RADAV | |
Topographic Ruggedness Index | TG_TORGI | |
Topographic Wetness Index | TG_TOWTI |
TSS | ROC | KAPPA | ||||
---|---|---|---|---|---|---|
Classification Algorithm | Average | Standard Deviation | Average | Standard Deviation | Average | Standard Deviation |
GBM | 0.799 | 0.042 | 0.953 | 0.014 | 0.796 | 0.042 |
RF | 0.824 | 0.043 | 0.962 | 0.012 | 0.823 | 0.043 |
CTA | 0.715 | 0.045 | 0.886 | 0.027 | 0.715 | 0.045 |
GLM | 0.789 | 0.068 | 0.928 | 0.051 | 0.800 | 0.069 |
FDA | 0.827 | 0.030 | 0.964 | 0.009 | 0.831 | 0.030 |
ANN | 0.640 | 0.057 | 0.859 | 0.033 | 0.646 | 0.057 |
MAX | 0.726 | 0.045 | 0.890 | 0.025 | 0.731 | 0.045 |
GAM | 0.843 | 0.031 | 0.965 | 0.011 | 0.846 | 0.030 |
Evaluation Metric | Evaluation Metric Value (Test) | Cutoff Threshold | Sensitivity | Specificity |
---|---|---|---|---|
TSS | 0.895 | 539 | 96.0 | 93.4 |
ROC | 0.988 | 544 | 96.0 | 93.5 |
KAPPA | 0.857 | 724 | 88.7 | 96.7 |
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Mouta, N.; Silva, R.; Pais, S.; Alonso, J.M.; Gonçalves, J.F.; Honrado, J.; Vicente, J.R. ‘The Best of Two Worlds’—Combining Classifier Fusion and Ecological Models to Map and Explain Landscape Invasion by an Alien Shrub. Remote Sens. 2021, 13, 3287. https://doi.org/10.3390/rs13163287
Mouta N, Silva R, Pais S, Alonso JM, Gonçalves JF, Honrado J, Vicente JR. ‘The Best of Two Worlds’—Combining Classifier Fusion and Ecological Models to Map and Explain Landscape Invasion by an Alien Shrub. Remote Sensing. 2021; 13(16):3287. https://doi.org/10.3390/rs13163287
Chicago/Turabian StyleMouta, Nuno, Renato Silva, Silvana Pais, Joaquim M. Alonso, João F. Gonçalves, João Honrado, and Joana R. Vicente. 2021. "‘The Best of Two Worlds’—Combining Classifier Fusion and Ecological Models to Map and Explain Landscape Invasion by an Alien Shrub" Remote Sensing 13, no. 16: 3287. https://doi.org/10.3390/rs13163287
APA StyleMouta, N., Silva, R., Pais, S., Alonso, J. M., Gonçalves, J. F., Honrado, J., & Vicente, J. R. (2021). ‘The Best of Two Worlds’—Combining Classifier Fusion and Ecological Models to Map and Explain Landscape Invasion by an Alien Shrub. Remote Sensing, 13(16), 3287. https://doi.org/10.3390/rs13163287