This paper proposes a method to integrate the advantages of machine learning and spatial statistics, different datasets, and multiple environmental covariates to improve the accuracy of aboveground biomass estimation models, which provides a useful reference for climate change mitigation. This combined method can make full use of data from different sources, and realize the complementary advantages of machine learning and spatial statistics, which has important implications for other fields.
We propose a low-cost approach that combines machine learning with spatial statistics to construct a regional forest C sequestration map from non-representative sample units. The experimental results demonstrate that the combined methods can improve the accuracy of the C sequestration map. This work provides a useful reference for climate change mitigation and other cases that used non-representative sample units.
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization
devoted to the development of international cooperation for the advancement of photogrammetry and
remote sensing and their applications. The Society operates without any discrimination on grounds
of race, religion, nationality, or political philosophy.
Useful External Links
Leibniz University Hannover
Institute of Photogrammetry and GeoInformation
Nienburger Str. 1