对比高分辨率与中等分辨率影像，发现复杂地块需亚米级影像才能精准识别可可树。
Is sub-metre resolution necessary for cocoa mapping? A landscape-stratified evaluation of very high resolution imagery, decametric Earth Observation inputs, and operational products in Cote d'Ivoire
- 用0.5米高清影像、10米哨兵2号及基础模型嵌入进行可可地图绘制
- 0.5米影像F1达0.92，复杂地块下仍保持0.90以上准确率
- 基础模型嵌入可替代部分高分辨率数据，适合大范围制图
精准可可地图对森林砍伐监测、供应链透明度和监管至关重要。传统中分辨率遥感影像在小农户混合景观中易因空间聚合而漏检可可树。本文在科特迪瓦评估了不同景观条件下可可制图表现，验证了亚米级遥感影像是否具显著优势，并测试了基础模型嵌入在十米级影像上提升制图能力的效果。研究采用0.5米Pleiades超高分辨率影像、10米哨兵2号年度合成影像，以及TESSERA和AlphaEarth Foundation（AEF）的基础模型嵌入，还评估了四个公开可得的可可地图产品。通过2,821个独立标注参考点进行分层精度评估，覆盖树冠密度和景观破碎度梯度。结果表明，0.5米影像模型性能最高（F1=0.92），在所有分层中均维持0.90以上得分；十米级输入中，TESSERA表现最佳（F1=0.86），AEF为0.82，哨兵2号为0.76。现有产品中，Kalischek产品最优（F1=0.83），接近内部训练的AEF模型。在景观破碎度高或树冠密度极端情况下，高分辨率与十米级方法的性能差距扩大。因此，在复杂可可种植区，定向获取超高分辨率影像尤为重要；而基础模型嵌入则为大范围制图提供了可扩展替代方案。
原文摘要 · Abstract (English)
Accurate cocoa mapping is increasingly important for deforestation monitoring, supply-chain transparency, and regulatory applications. Spatial aggregation in conventional medium-resolution Earth observation (EO) imagery may limit cocoa detection in heterogeneous smallholder landscapes. In Cote d'Ivoire, we therefore evaluated how mapping performance varies across landscape conditions, whether very high resolution (VHR) imagery provides a meaningful advantage, and whether foundation-model embeddings improve decametric cocoa mapping. We developed models using 0.5 m Pleiades VHR imagery, a 10 m Sentinel-2 annual composite, and embeddings from TESSERA and AlphaEarth Foundations (AEF), and additionally assessed four publicly available cocoa mapping products. Performance was evaluated through a landscape-stratified accuracy assessment using 2,821 independently interpreted reference points distributed across gradients of tree cover density and landscape fragmentation. The VHR model achieved the highest performance (F1 = 0.92) and maintained F1-scores above 0.90 across all strata. Among the decametric inputs, TESSERA performed best (F1 = 0.86), followed by AEF (F1 = 0.82) and Sentinel-2 (F1 = 0.76). Of the existing cocoa products, the Kalischek product performed best (F1 = 0.83), comparable to the internally trained AEF model. Performance differences between VHR and decametric approaches increased with fragmentation and under low and high tree cover density conditions. Targeted VHR acquisition may therefore be particularly beneficial in complex cocoa landscapes, while foundation-model embeddings offer a scalable alternative for large-area mapping.
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