Breaking the temporal barrier: exploring the potential of identifying mangrove growth hotspots through single-temporal high-resolution satellite imagery

Mangroves are crucial to coastal ecosystem stability and blue-carbon sequestration. Timely identification of current growth hotspots is essential for effective management and protection, which typically requires a large amount of multi-temporal high-resolution image series. In this study, we are aiming to explore the potential of identifying growth hotspots of mangrove using single-temporal high-resolution remote sensing imagery. A straightforward yet effective machine learning method is employed, with the core idea of modeling the indirect relationship between mangrove growth rates and single-temporal image-derived features. We first derive a reference indicator for hotspot intensity by quantifying mangrove changes over multiple years, namely the Mangrove Growth Hotspots Index (MGHI). Second, we compute features from a single high-resolution satellite image, including band-wise spectral features, spectral indices, and texture descriptors, and further incorporate geographical constraints to account for the characteristic expansion of mangroves from edge areas toward tidal flats. Three machine learning algorithms were employed to quantify the relations between image-derived features and reference MGHI, including random forest, eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). The best-performing XGBoost regressor achieves R2 = 0.780, indicating that single image-derived features explain a substantial proportion of the variance in MGHI. The corresponding Root Mean Square Error (RMSE) =1.513, Mean Absolute Error (MAE) = 0.605, and Mean Bias Error (MBE)=−0.007 quantify the typical magnitude of prediction errors, supporting accurate and reliable hotspot estimation. The visual comparison and analysis also indicate that mangrove edges with blurred boundaries and rough textures may have relatively high growth rates, and a greater probability of being growth hotspots. Thus, we advocate for prioritizing attention to these areas to underpin the restoration and sustainable conservation of mangroves.