Improvement of the technology of monetary value of green areas by using remote sensing methods

Abstract

Trees act as a sink for carbon dioxide (CO2) by fixing carbon during photosynthesis and storing carbon as biomass. The net long-term CO2 source/sink dynamics of forests change through time as trees grow, die, and decay. Urban forests currently store carbon, which can be emitted back to the atmosphere after tree death, and sequester carbon as they grow. Thus, urban trees influence local climate, carbon cycles, energy use and climate change.

Article 6 of the Paris Agreement recognizes that Parties can voluntarily cooperate on the implementation of their Nationally Determined Contributions to facilitate higher ambition in mitigation and adaptation actions. The operationalization of the mechanisms under Article 6 is one of the key challenges which need to be overcome to enable carbon pricing to deliver on its potential for cost-effective decarbonization. Modalities to ensure environmental integrity and avoid double-counting, enable greater ambition and promote sustainable development are at the core of the discussions under the UNFCCC.

Estimate value of carbon dioxide reduced by the Kyoto Park in Kyiv was calculated in this article by using of two different methods: supervised classification of satellite images by ArcGIS (Maximum Likelihood Classification) and i-Tree Canopy. Tree canopy cover class area multiplying by carbon dioxide sequestration rates and monetary value of carbon dioxide removal.

The algorithm used by the Maximum Likelihood Classification tool is based on two principles:

• The cells in each class sample in the multidimensional space being normally distributed;
• Bayes' theorem of decision making.

The tool considers both the variances and covariances of the class signatures when assigning each cell to one of the classes represented in the signature file, in this case there are two classes: “tree” and “non-tree”. With the assumption that the distribution of a class sample is normal, a class can be characterized by the mean vector and the covariance matrix. Given these two characteristics for each cell value, the statistical probability is computed for each class to determine the membership of the cells to the class.

I-Tree Canopy tool is designed to allow users to easily and accurately estimate tree and other cover classes (e.g.,grass, building, roads, etc.) within their city or any area they like. This tool randomly lays points (number determined by the user) onto Google Earth imagery and the user then classifies what cover class each point falls upon. The user can define any cover classes that they like and the program will show estimation results throughout the interpretation process.

Keywords: carbon sequestration, net biome production, discount rate, emissions trading system, green zone, green space, city forest,  biomass density, carbon sequester monetary value, greenhouse cost reduction, greenhouse plan value, remote sensing, satellite image, supervised classification.