EXTRACTION METHODS AND EXPERIMENT ON ESSENTIAL FOREST VARIABLES FOR GEOSPATIALLY-ENABLED SDGs MONITORING
- 1China University of Mining and Technology (Beijing), Beijing, 100083, China
- 2National Geomatics Center of China, Beijing 100830, China
Keywords: Sustainable Development Goals, Geospatially-enabled monitoring, Essential forest variables, SDG Indicators, Global scale experiment, 0.2° grids
Abstract. Since the United Nations passed the 17 Sustainable Development Goals (SDGs) and 169 sub-goals in 2015, many researchers have tried their best to put them into practise. However, mass data and different understanding of SDGs indicators make this work much more difficult. In this article we reference the concept of essential variables which may have some contribution to established a standard system for SDGs monitoring. Here we take SDG15 as an example. Firstly, we deeply analyse the main contents of SDG15 and select some geospatial-related indicators involved with forest land. From those forest-related and geospatial-enabled indicators we concise 5 essential forest variables (EFV4SDG15) which could be classified into 3 different types: land cover distribution and transformation, aggregative pattern and intensity and spatial-temporal evolution process. Secondly, formalized expression of EFV4SDG15 and extraction of them from multiple data source are necessary, including data processing and mathematical expression of the variables. Finally, we take global region as research target to carry out preliminary experiments. In this part, we only select 2 EFV4SDG15s: the forest coverage rate and forest land transfer matrix to show detailed operation process and then present the results in forms of figure and table. The paper has preliminary attempts to the application of essential variables in SDGs monitoring and provides an example for other fields.