A remote sensing approach for mapping teak (Tectona grandis) using vegetation phenology in Ghana's forest-savannah transition zone

This study explores the use of satellite-derived phenological traits to identify and map teak (Tectona grandis) plantations from other vegetation types in Ghana's forest-savanna transitional zone. Landsat 8 images were obtained for two dates (January 21st and May 13th) representing the leaf fall and full foliage phases of teak. Three hundred and sixteen field coordinates were collected for supervised classification (70 %) and accuracy assessment (30 %). Three distinct image band composites were created: two using image band difference and image band ratio vegetation phenology approaches and one using the dry season image bands (representing the conventional remote sensing classification approach). The Kruskal-Wallis test and Dunn's pairwise test with Bonferroni adjustment to analyse the separability of teak vegetation from other vegetation classes. The Random Forest algorithm was used to classify the three image composites. Overall accuracy, kappa, and McNemar's test were used to evaluate classification accuracies. The image difference technique yielded the highest accuracy at 71.28 %, outperforming both the image ratio method (61.7 %) and the conventional mapping approach (58.51 %) in distinguishing teak from other vegetation classes. Furthermore, comparing the land cover maps generated from image ratio and image difference methods revealed significant differences (McNemar's χ2 = 4.348, p-value = 0.04), indicating that the image difference approach is superior for mapping teak. However, the binary teak versus non-teak maps showed a similar representation of teak with equal class area (≈69 km2) and no statistical difference in spatial extent (McNemar's χ2 = 9.841, p-value = 0.06) for the two phenological approaches. The study demonstrates the effectiveness of vegetation phenology in distinguishing teak from other vegetation classes. This approach can support silvicultural efforts in managing teak plantations in Ghana's high forest zone.

Authors

Publication Details

Journal
Heliyon
Published
2025-11-20
DOI
https://doi.org/10.1016/j.heliyon.2025.e44180
Citations
1
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
1.72
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article

A remote sensing approach for mapping teak (Tectona grandis) using vegetation phenology in Ghana's forest-savannah transition zone

George Ashiagbor, Stephen Boahen Asabere, Isaac Stanisluv Essah, Ophelia Ehornam Ahorlu et al.
1 citations
Heliyon
Remote Sensing in Agriculture
1.72
article

A remote sensing approach for mapping teak (Tectona grandis) using vegetation phenology in Ghana's forest-savannah transition zone

George Ashiagbor, Stephen Boahen Asabere, Isaac Stanisluv Essah, Ophelia Ehornam Ahorlu, Michael Ofori Gyamfi
article en
1 citations

Abstract

This study explores the use of satellite-derived phenological traits to identify and map teak (Tectona grandis) plantations from other vegetation types in Ghana's forest-savanna transitional zone. Landsat 8 images were obtained for two dates (January 21st and May 13th) representing the leaf fall and full foliage phases of teak. Three hundred and sixteen field coordinates were collected for supervised classification (70 %) and accuracy assessment (30 %). Three distinct image band composites were created: two using image band difference and image band ratio vegetation phenology approaches and one using the dry season image bands (representing the conventional remote sensing classification approach). The Kruskal-Wallis test and Dunn's pairwise test with Bonferroni adjustment to analyse the separability of teak vegetation from other vegetation classes. The Random Forest algorithm was used to classify the three image composites. Overall accuracy, kappa, and McNemar's test were used to evaluate classification accuracies. The image difference technique yielded the highest accuracy at 71.28 %, outperforming both the image ratio method (61.7 %) and the conventional mapping approach (58.51 %) in distinguishing teak from other vegetation classes. Furthermore, comparing the land cover maps generated from image ratio and image difference methods revealed significant differences (McNemar's χ2 = 4.348, p-value = 0.04), indicating that the image difference approach is superior for mapping teak. However, the binary teak versus non-teak maps showed a similar representation of teak with equal class area (≈69 km2) and no statistical difference in spatial extent (McNemar's χ2 = 9.841, p-value = 0.06) for the two phenological approaches. The study demonstrates the effectiveness of vegetation phenology in distinguishing teak from other vegetation classes. This approach can support silvicultural efforts in managing teak plantations in Ghana's high forest zone.

HeliyonVol. 11(17)
Openalex Percentile: Top 12%
Remote Sensing in Agriculture
1.72
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