A high-resolution canopy height model of the Earth (2204.08322v1)

Abstract: The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity. Geospatially explicit and, ideally, highly resolved information is required to manage terrestrial ecosystems, mitigate climate change, and prevent biodiversity loss. Here, we present the first global, wall-to-wall canopy height map at 10 m ground sampling distance for the year 2020. No single data source meets these requirements: dedicated space missions like GEDI deliver sparse height data, with unprecedented coverage, whereas optical satellite images like Sentinel-2 offer dense observations globally, but cannot directly measure vertical structures. By fusing GEDI with Sentinel-2, we have developed a probabilistic deep learning model to retrieve canopy height from Sentinel-2 images anywhere on Earth, and to quantify the uncertainty in these estimates. The presented approach reduces the saturation effect commonly encountered when estimating canopy height from satellite images, allowing to resolve tall canopies with likely high carbon stocks. According to our map, only 5% of the global landmass is covered by trees taller than 30 m. Such data play an important role for conservation, e.g., we find that only 34% of these tall canopies are located within protected areas. Our model enables consistent, uncertainty-informed worldwide mapping and supports an ongoing monitoring to detect change and inform decision making. The approach can serve ongoing efforts in forest conservation, and has the potential to foster advances in climate, carbon, and biodiversity modelling.

• The paper presents a high-resolution global canopy height model by fusing NASA's GEDI LIDAR and ESA's Sentinel-2 imagery.
• The methodology uses a probabilistic deep learning framework with cost-sensitive reweighting to improve tall vegetation predictions, achieving an RMSE of 6.0 m.
• Results indicate that only 5% of Earth’s landmass has trees taller than 30 m, emphasizing critical areas for conservation and climate analysis.

Analysis of the High-Resolution Global Canopy Height Model

The paper "A high-resolution canopy height model of the Earth" introduces a novel approach to producing the first global canopy height map with a 10-meter ground sampling distance for the year 2020. This paper presents a remarkable fusion of data from NASA's GEDI LIDAR mission and ESA's Sentinel-2 optical satellite imagery, employing a probabilistic deep learning framework to map canopy height consistently across the global terrestrial landscape. This essay will provide a critical analysis of the paper's methodology, results, and broader implications within environmental monitoring and climate change mitigation contexts.

Methodology Overview

The researchers developed a deep learning framework employing fully convolutional neural networks (CNNs) to predict canopy height from Sentinel-2 images. These networks were trained using sparse supervision from GEDI LIDAR data, which provides vertical forest structure measurements across Earth's forest biomes. Acknowledging the limitations of each data source—GEDI's sparse coverage and Sentinel-2's inability to directly gauge vertical structures—the paper demonstrates a sensor fusion approach that reconciles these issues, producing a detailed global canopy height map.

Model training involved addressing the underestimation bias prevalent in tall canopy predictions by applying cost-sensitive learning strategies. By leveraging inverse frequency reweighting of canopy height intervals during training, the authors improved predictions for tall vegetation. The integration of geographical coordinates as input features served to enhance model predictions where local terrain characteristics might influence canopy height estimation.

Results and Performance Metrics

According to the global canopy height map produced, only 5% of Earth's landmass features trees taller than 30 meters, with nearly a third of these situated within protected areas. This high spatial resolution product advances the potential for evaluating forest carbon stocks and understanding vegetation's role in ecosystem services and biodiversity conservation.

Numerically, the model achieved a root mean square error (RMSE) of 6.0 meters and a mean error (ME) of 1.3 meters when validated against GEDI data, demonstrating commendable performance relative to existing methodologies. Further evaluation against LVIS airborne LIDAR data—which shares similar objectives to GEDI—confirms the model's robustness even beyond GEDI's geographic range.

Implications and Future Directions

The creation of a high-resolution canopy height map holds significant implications for global environmental monitoring strategies, conservation planning, and biodiversity studies. By estimating canopy heights with substantial accuracy and spatial coverage, the research paves the way for more informed decision-making regarding forest management and conservation priorities. Moreover, the generated uncertainty estimates enhance the map's utility by allowing users to filter out unreliable predictions, thereby improving decision-making processes.

Future research may focus on refining this high-resolution model further to encompass other ecological parameters, such as biomass and biodiversity indicators, using similar machine learning techniques. Additionally, continuing to update the model with future GEDI or similar mission data could enhance temporal analyses, enabling the monitoring of changes in global forests due to anthropogenic activities or natural dynamics over time.

Conclusion

The paper presents a significant advancement in the field of global vegetation monitoring by efficiently leveraging advanced computational techniques with Earth observation datasets to establish a high-resolution canopy height map. This development marks an essential step towards a comprehensive understanding of terrestrial ecosystem functions and offers a solid foundation for future research endeavors aiming to mitigate climate change and biodiversity loss through improved ecological data analysis. As such, this research will likely influence international efforts to achieve sustainable forest management and conservation goals in the coming decades.