High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model

Tree canopy height is one of the most important indicators of forest biomass, productivity, and ecosystem structure, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for regression to map the mean tree canopy height in the Amazon forest from Planet NICFI images at ~4.78 m spatial resolution for the period 2020-2024. The U-Net model was trained using canopy height models computed from aerial LiDAR data as a reference, along with their corresponding Planet NICFI images. Predictions of tree heights on the validation sample exhibited a mean error of 3.68 m and showed relatively low systematic bias across the entire range of tree heights present in the Amazon forest. Our model successfully estimated canopy heights up to 40-50 m without much saturation, outperforming existing canopy height products from global models in this region. We determined that the Amazon forest has an average canopy height of ~22 m. Events such as logging or deforestation could be detected from changes in tree height, and encouraging results were obtained to monitor the height of regenerating forests. These findings demonstrate the potential for large-scale mapping and monitoring of tree height for old and regenerating Amazon forests using Planet NICFI imagery.

Sentinel-1 SAR Based Weakly Supervised Learning For Tropical Forest Mapping

Tropical forests play an important role in regulating the global carbon cycle and are crucial for maintaining the tropical forest biodiversity. Therefore, there is an urgent need to map the extent of tropical forest ecosystems. Recently, deep learning has come out as a powerful tool to map these ecosystems with the caveat of curating high quality reference datasets. Since, manually annotating high quality reference datasets is time consuming and expensive, weakly supervised learning techniques offer the potential to train high quality models without the need for manually annotating large quantities of reference datasets. In this manuscript, we propose two weakly supervised approaches that are based on Sentinel-1 SAR images, sparsely distributed pixel-wise high quality reference labels and densely distributed noisy reference labels. The proposed approaches were tested in a tropical setting in the Brazilian amazon. The results demonstrate that high quality tropical forest maps can be derived from weakly supervised learning without the need for manually annotated labels.

Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction

The Amazon, the world's largest rainforest, faces a severe historic drought. The Rio Negro River, one of the major Amazon River tributaries, reaches its lowest level in a century in October 2023. Here, we used a U-net deep learning model to map water surfaces in the Rio Negro River basin every 12 days in 2022 and 2023 using 10 m spatial resolution Sentinel-1 satellite radar images. The accuracy of the water surface model was high with an F1-score of 0.93. The 12 days mosaic time series of water surface was generated from the Sentinel-1 prediction. The water surface mask demonstrated relatively consistent agreement with the Global Surface Water (GSW) product from Joint Research Centre (F1-score: 0.708) and with the Brazilian Mapbiomas Water initiative (F1-score: 0.686). The main errors of the map were omission errors in flooded woodland, in flooded shrub and because of clouds. Rio Negro water surfaces reached their lowest level around the 25th of November 2023 and were reduced to 68.1\% (9,559.9 km$^2$) of the maximum water surfaces observed in the period 2022-2023 (14,036.3 km$^2$). Synthetic Aperture Radar (SAR) data, in conjunction with deep learning techniques, can significantly improve near real-time mapping of water surface in tropical regions.