A systematic review of remote sensing and machine learning approaches for accurate carbon storage estimation in natural forests

Abstract

The assessment of carbon storage in natural forests is paramount in the ongoing efforts against climate change. While traditional field-based methods for quantifying carbon storage pose challenges, recent advancements in remote sensing and machine learning offer efficient and innovative alternatives. This systematic literature review investigates the latest developments in utilising optical, radar, and light detection and ranging (LiDAR) remote sensing data, coupled with cutting-edge machine learning algorithms, to estimate carbon storage in natural forests. Non-parametric machine-learning algorithms commonly applied to multispectral datasets have emerged as prominent tools for predicting aboveground carbon storage. Nonetheless, accurately assessing forest carbon storage using remote sensing data can be arduous in regions characterised by complex terrain and diverse species where dataset noise may be pronounced. Alternatively, the adoption of freely available optical sensors with moderate resolution has showcased reliability in estimating forest carbon storage. Hence, leveraging the integration of multi-sensor data with machine learning techniques has yielded substantial improvements in the accuracy of carbon storage estimation. This study identifies the most sensitive remote sensing variables that correlate with measurable biophysical parameters, thus highlighting the pivotal role of geospatial technologies in estimating terrestrial aboveground carbon storage. The study also delineates gaps and limitations inherent in current practices, underscoring the need for further investigations in this rapidly evolving field. Through the unification of conventional methods with state-of-the-art technologies, this study contributes to the advancement of accurate and efficient carbon storage assessments. By assuming such a transformative role, this research holds substantial promise in bolstering global climate change mitigation efforts. Ultimately, the purpose of this study was to demonstrate to researchers, policy makers and practitioners the importance of embracing the combined power of remote sensing and machine learning as a tool for safeguarding our natural forests and fight against climate change.