Mapping forest types along ecological gradient in Pakistan
Environmental variables influence the spatial distribution, pattern and structure of vegetation in complex mountainous landscape along varied geographical conditions. This study explored the spatial distribution of four forest types across ecological gradient based on field data, climatic, topograph...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | Environmental Research Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2515-7620/adaf11 |
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| Summary: | Environmental variables influence the spatial distribution, pattern and structure of vegetation in complex mountainous landscape along varied geographical conditions. This study explored the spatial distribution of four forest types across ecological gradient based on field data, climatic, topographic, and soil variables using stepwise linear regression (SLR), decision trees (DT), random forests (RF), and Maxent modeling. Results showed that climatic variables particularly annual precipitation, precipitation of warmest and coldest quarter have achieved the highest correlation (R = −0.9, 0.8 respectively) for forest types mapping and outperformed other explanatory variables (topographic and edaphic). Among the rest of variables, elevation (R = 0.6), sand contents (R = 0.8) and soil carbon (R = 0.6) contained useful information in order explain forest type spatial distribution. Analysis of regression models revealed that RF has achieved the highest correlation (R ^2 = 0.923) and lowest RMSE 0.54, followed by the SLR model in which R ^2 value has been progressively increased from 0.41 (error 2.02) to 0.917 (0.77) with respect four different predictors models, each separate developed for topographic (n = 5), soil (n-11), climatic (n = 11) and combined of all datasets (n = 27). DT showed that annual precipitation was the most important predictor for forest type classification with risk estimate of 0.412 (std error 0.31) and 0.478 (std error 0.52) for training and validation respectively. Maxent modeling showed impressive predictive performance of all forest types (STPF, MTF and DTF) along ecological gradient with average AUC values of 0.968, 0.918, and 0.940 respectively and climatic variables have highest gain compared to topographic and soil predictors. This study suggests that mapping of forest types through machine learning algorithms may be improved by incorporating other explanatory variables such as microclimate, soil types, nutrients, anthropogenic, demographic factors and spectral indices. |
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| ISSN: | 2515-7620 |