The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture
The availability of high-resolution satellite imagery has boosted the modelling of tropical forest attributes based on texture metrics derived from grey-level co-occurrence matrices (GLCMs). This procedure has shown that GLCM metrics are good predictors of vegetation attributes. Nonetheless, the pro...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | International Journal of Forestry Research |
| Online Access: | http://dx.doi.org/10.1155/2024/7178211 |
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| author | J. Alberto Gallardo-Cruz Jonathan V. Solórzano Edgar J. González Jorge A. Meave |
| author_facet | J. Alberto Gallardo-Cruz Jonathan V. Solórzano Edgar J. González Jorge A. Meave |
| author_sort | J. Alberto Gallardo-Cruz |
| collection | DOAJ |
| description | The availability of high-resolution satellite imagery has boosted the modelling of tropical forest attributes based on texture metrics derived from grey-level co-occurrence matrices (GLCMs). This procedure has shown that GLCM metrics are good predictors of vegetation attributes. Nonetheless, the procedure is also sensitive to the scale of analysis (image resolution and plot size). This study aimed to analyse the effect of spatial scale on the modelling of forest attributes, and to provide some ecological insight into such effect. Nineteen 32 × 32 m sampling plots were used to quantify forest structure (basal area: BA; mean height: H; standard deviation of height, HSD; density, D; and aboveground biomass, AGB). The 19 plots were subdivided into four 16 × 16 m, one of which was subdivided into four 8 × 8 m plots. To match this design, 12 GLCM metrics were calculated from a GeoEye-1 image (pixel size ≤ 2 m) using a 5-, 9-, and 21-pixel window from the R, NIR, NDVI, and EVI bands. For each of the windows, we modelled the five structural variables as linear combinations of the 12 metrics through linear models. The modelling potential ranged from high (R2 = 0.70) to low (0.11). H was the best-predicted attribute; this occurred at the smallest scale, with increasing scales producing lower R2 values. The second best-predicted attribute was HSD, which peaked at the intermediate scale. D and AGB displayed a similar pattern. BA was the only attribute best predicted at the largest scale. Thus, in predicting tropical forest attributes from GLCM-derived texture metrics, the spatial scale to be used should reflect the spatial scale at which ecological processes occur. Therefore, understanding how ecological processes express themselves in a remotely sensed image becomes a critical task. |
| format | Article |
| id | doaj-art-47df8a850c5149e990e1b5eb8d00a900 |
| institution | Kabale University |
| issn | 1687-9376 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Forestry Research |
| spelling | doaj-art-47df8a850c5149e990e1b5eb8d00a9002025-02-03T05:55:21ZengWileyInternational Journal of Forestry Research1687-93762024-01-01202410.1155/2024/7178211The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image TextureJ. Alberto Gallardo-Cruz0Jonathan V. Solórzano1Edgar J. González2Jorge A. Meave3Centro Transdisciplinar Universitario para la Sustentabilidad (CENTRUS)Centro de Investigaciones en Geografía Ambiental (CIGA)Departamento de Ecología y Recursos NaturalesDepartamento de Ecología y Recursos NaturalesThe availability of high-resolution satellite imagery has boosted the modelling of tropical forest attributes based on texture metrics derived from grey-level co-occurrence matrices (GLCMs). This procedure has shown that GLCM metrics are good predictors of vegetation attributes. Nonetheless, the procedure is also sensitive to the scale of analysis (image resolution and plot size). This study aimed to analyse the effect of spatial scale on the modelling of forest attributes, and to provide some ecological insight into such effect. Nineteen 32 × 32 m sampling plots were used to quantify forest structure (basal area: BA; mean height: H; standard deviation of height, HSD; density, D; and aboveground biomass, AGB). The 19 plots were subdivided into four 16 × 16 m, one of which was subdivided into four 8 × 8 m plots. To match this design, 12 GLCM metrics were calculated from a GeoEye-1 image (pixel size ≤ 2 m) using a 5-, 9-, and 21-pixel window from the R, NIR, NDVI, and EVI bands. For each of the windows, we modelled the five structural variables as linear combinations of the 12 metrics through linear models. The modelling potential ranged from high (R2 = 0.70) to low (0.11). H was the best-predicted attribute; this occurred at the smallest scale, with increasing scales producing lower R2 values. The second best-predicted attribute was HSD, which peaked at the intermediate scale. D and AGB displayed a similar pattern. BA was the only attribute best predicted at the largest scale. Thus, in predicting tropical forest attributes from GLCM-derived texture metrics, the spatial scale to be used should reflect the spatial scale at which ecological processes occur. Therefore, understanding how ecological processes express themselves in a remotely sensed image becomes a critical task.http://dx.doi.org/10.1155/2024/7178211 |
| spellingShingle | J. Alberto Gallardo-Cruz Jonathan V. Solórzano Edgar J. González Jorge A. Meave The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture International Journal of Forestry Research |
| title | The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture |
| title_full | The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture |
| title_fullStr | The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture |
| title_full_unstemmed | The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture |
| title_short | The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture |
| title_sort | effect of spatial scale on the prediction of tropical forest attributes from image texture |
| url | http://dx.doi.org/10.1155/2024/7178211 |
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