A Survey of Computer Vision Techniques for Forest Characterization and Carbon Monitoring Tasks

Abstract

Estimation of terrestrial carbon balance is one of the key tasks in the understanding and prognosis of climate change impacts and the development of tools and policies according to carbon mitigation and adaptation strategies. Forest ecosystems are one of the major pools of carbon stocks affected by controversial processes influencing carbon stability. Therefore, monitoring forest ecosystems is a key to proper inventory management of resources and planning their sustainable use. In this survey, we discuss which computer vision techniques are applicable to the most important aspects of forest management actions, considering the wide availability of remote sensing (RS) data of different resolutions based both on satellite and unmanned aerial vehicle (UAV) observations. Our analysis applies to the most occurring tasks such as estimation of forest areas, tree species classification, and estimation of forest resources. Through the survey, we also provide a necessary technical background with a description of suitable data sources, algorithms’ descriptions, and corresponding metrics for their evaluation. The implementation of the provided techniques into routine workflows is a significant step toward the development of systems of continuous actualization of forest data, including real-time monitoring. It is crucial for diverse purposes on both local and global scales. Among the most important are the implementation of improved forest management strategies and actions, carbon offset projects, and enhancement of the prediction accuracy of system changes under different land-use and climate scenarios.

Combining Gaussian Process Regression with Poisson Blending for Seamless Cloud Removal from Optical Remote Sensing Imagery for Cropland Monitoring

“…For example, the characterization and classification of forest types often requires a series of multi-spectral remote sensing images to capture phenological changes in the forest, like crop monitoring and classification. However, the occurrence of cloud-contaminated images may degrade predictive performance [55]. Thus, optical images reconstructed by cloud removal can be effectively employed for the multi-temporal analysis of forests.…”

Practical AI Cases for Solving ESG Challenges

“…For example, in forest management, ML algorithms are widely useful for the evaluation of the leaf area index, vegetation structure, moister level, and number of trees per acre [35][36][37][38]. These parameters provide a good health indication for a particular forest and may be used to evaluate the overall situation in the considered region [39].…”

Wildfire spreading prediction using multimodal data and deep neural network approach

“…Soil erosion and slow vegetation recovery have been thoroughly studied to evaluate the total damage to the environment caused by wildfires 3 . Forest losses affect carbon balance on a global scale and these changes are often caused by human activities 4 . Due to all these reasons, significant efforts are being made to predict wildfire spreading, prevent it and mitigate the damage.…”