Quantifying Categorical Information Loss in Forest Compositional Mapping: Implications for the Accuracy of Forest Assessment in Lualaba Province (DR Congo).
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| Title: | Quantifying Categorical Information Loss in Forest Compositional Mapping: Implications for the Accuracy of Forest Assessment in Lualaba Province (DR Congo). |
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| Authors: | Mpanda Mukenza, Médard1,2,3 (AUTHOR), Kikuni Tchowa, John2,3 (AUTHOR), Nantenaina Ramalason, Felana2,3,4 (AUTHOR), Khoji Muteya, Heritier1,4 (AUTHOR), Bogaert, Jan1,3 (AUTHOR) j.bogaert@uliege.be, Useni Sikuzani, Yannick1,2 (AUTHOR), Bastin, Jean-François3 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1979. 28p. |
| Subjects: | Forest mapping, Artificial neural networks, Provinces, Forest measurement, Biomass estimation, Tropical dry forests, Vegetation patterns |
| Geographic Terms: | Congo (Democratic Republic) |
| Abstract: | Highlights: What are the main findings? Categorical forest maps discard on average 46.9% of compositional information per pixel in Lualaba Province. A Multi-Output Neural Network (MONN) enables continuous, spatially explicit mapping of forest compositional gradients across three forest types. What are the implications of the main findings? Quantifying categorical information loss provides a diagnostic framework for assessing the reliability of forest inventory and carbon stock estimates. Accounting for subpixel compositional mixing improves the accuracy of forest assessment in heterogeneous tropical landscapes. Forests of Lualaba Province (DR Congo) form a compositionally complex mosaic of dry dense forest, gallery forest, and Miombo woodland. Yet, categorical land-cover maps impose discrete boundaries on these inherently continuous vegetation gradients, systematically discarding subpixel compositional information critical for forest monitoring and carbon accounting. The magnitude of this information loss at the landscape scale, however, remains largely unquantified. In this study, we train a Multi-Output Neural Network (MONN) using Sentinel-2 spectral and textural predictors (2025) to estimate the proportional cover of three forest types across the province. Model performance is benchmarked against a normalised Random Forest (RF) using spatial block cross-validation. Categorical information loss is quantified pixel-wise using two complementary metrics, dominant class proportion and Shannon compositional entropy, alongside a derived interpretive quantity, categorical information loss. The MONN slightly outperformed RF (R2 = 0.648 vs. 0.630; RMSE = 0.224 vs. 0.229), yet the results reveal a fundamentally heterogeneous landscape structure. The mean dominant-class proportion was only 56.2%, indicating that categorical maps discard, on average, 43.8% of compositional information per pixel. Only 7.9% of forested pixels exceeded the 75% dominance threshold, while Shannon entropy reached 74.1% of its theoretical maximum, indicating that forest types coexist in near-equal proportions across most pixels. This renders categorical attribution structurally inadequate for most of the forested landscape. Across 92.1% of forested pixels, no single forest type achieved clear dominance. These results show that compositional mixing is the dominant structural condition of the landscape, and that compositional mapping is essential for representing tropical forest structure in heterogeneous drylands. By formally quantifying categorical information loss at the landscape scale, this study shows that continuous compositional mapping converts this structural ambiguity into a spatially explicit ecological signal, with direct implications for monitoring vegetation dynamics and biodiversity, suggesting a structural source of error in carbon stock estimation in tropical dry forests that warrants empirical validation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Categorical forest maps discard on average 46.9% of compositional information per pixel in Lualaba Province. A Multi-Output Neural Network (MONN) enables continuous, spatially explicit mapping of forest compositional gradients across three forest types. What are the implications of the main findings? Quantifying categorical information loss provides a diagnostic framework for assessing the reliability of forest inventory and carbon stock estimates. Accounting for subpixel compositional mixing improves the accuracy of forest assessment in heterogeneous tropical landscapes. Forests of Lualaba Province (DR Congo) form a compositionally complex mosaic of dry dense forest, gallery forest, and Miombo woodland. Yet, categorical land-cover maps impose discrete boundaries on these inherently continuous vegetation gradients, systematically discarding subpixel compositional information critical for forest monitoring and carbon accounting. The magnitude of this information loss at the landscape scale, however, remains largely unquantified. In this study, we train a Multi-Output Neural Network (MONN) using Sentinel-2 spectral and textural predictors (2025) to estimate the proportional cover of three forest types across the province. Model performance is benchmarked against a normalised Random Forest (RF) using spatial block cross-validation. Categorical information loss is quantified pixel-wise using two complementary metrics, dominant class proportion and Shannon compositional entropy, alongside a derived interpretive quantity, categorical information loss. The MONN slightly outperformed RF (R2 = 0.648 vs. 0.630; RMSE = 0.224 vs. 0.229), yet the results reveal a fundamentally heterogeneous landscape structure. The mean dominant-class proportion was only 56.2%, indicating that categorical maps discard, on average, 43.8% of compositional information per pixel. Only 7.9% of forested pixels exceeded the 75% dominance threshold, while Shannon entropy reached 74.1% of its theoretical maximum, indicating that forest types coexist in near-equal proportions across most pixels. This renders categorical attribution structurally inadequate for most of the forested landscape. Across 92.1% of forested pixels, no single forest type achieved clear dominance. These results show that compositional mixing is the dominant structural condition of the landscape, and that compositional mapping is essential for representing tropical forest structure in heterogeneous drylands. By formally quantifying categorical information loss at the landscape scale, this study shows that continuous compositional mapping converts this structural ambiguity into a spatially explicit ecological signal, with direct implications for monitoring vegetation dynamics and biodiversity, suggesting a structural source of error in carbon stock estimation in tropical dry forests that warrants empirical validation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121979 |
