Hexbyte Glen Cove
The initial expectation of counting every scattered tree, in areas that previous models often represented by zero values, was erased by large overestimations in other areas of the earlier assessments. In previous attempts using satellites, cropland, and ground vegetation adversely affected optical images. If radar was used, topography, wetlands, and irrigated areas affected the radar backscatter, predicting higher carbon stocks than the current NASA estimations.
Hexbyte Glen Cove Deep-learning based mapping
Researchers applied deep-learning-based tree mapping, manually trained on about 90,000 trees, to a data set of nearly 300,000 satellite images to measure more than 9.9 billion woody plants that displayed a shadow and a crown area greater than 3 square meters. Only features that showed a distinct crown area and an associated shadow were selected, which allowed the team to exclude small bushes, clumps of grass, rocks, and other misleading features.
The imaged regions were correlated to reflect four rainfall zones; hyper-arid, arid, semi-arid, and dry sub-humid—as rainfall affects carbon uptake and storage. While the foliage only represents 3% of the total dry mass, it was used as an indirect measurement to quantify the total mass. The proportion of root mass is, on average, 15–20% of the total mass and was also derived based on foliage.
Want to visualize the large tree-mapping dataset in an interactive browser format? The researchers did too, so they created a nifty viewer to work with and have made it publicly available here.
The ability to track the effectiveness of carbon sequestration could take on global significance in the fight against climate change. Reforestation is a leading method by which
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