Integration of Google Earth and a Spreadsheet (LESS-FOR-01) to Create an Online Tree-Level Carbon Storage Map at Khao Nam Sub, Kasetsart University Sriracha Campus, Chonburi Province

Abstract

Background and Objectives: Climate change has become a critical global issue affecting ecosystems worldwide, especially forests, which play a key role in carbon sequestration. While assessments often focus on large forests, small green spaces such as green university, nature trails, and urban green areas—also contribute to carbon storage and serve as platforms for environmental education. This study aimed to assess carbon storage of individual trees and generate spatially interactive carbon map the Khao Nam Sub Forest area at Kasetsart University, Sriracha Campus, Chonburi Province. This knowledge can apply the sustainable management of urban small green areas, including serve as a database for supporting environmental learning, as well as to supports the Sustainable Development Goals (SDGs 13 and 15) and aligns with Thailand’s Bio-Circular-Green (BCG) Economy Model.

Methodology: The study was conducted within Khao Nam Sub area comprising dry evergreen forest (DEF) and mixed deciduous forest (MDF) types. Field data were collected along two designated nature trails: the summit trail (1.5 km) and the foothill loop trail (2.5 km), during August–October 2024. All trees and lianas with a diameter at breast height (DBH) ≥ 4.5 cm were identified, measured for DBH and total height, and georeferenced using the WGS84 coordinate system via the Google Earth mobile application. Some morphological characteristics were also photographed. Aboveground biomass (AGB) was estimated using forest-type-specific allometric equations, and belowground biomass (BGB) was derived as 0.27 × AGB. Carbon stock was calculated as 47% of total biomass (AGB + BGB) and expressed in tones of CO₂ equivalent (tCO₂e). The integrated application between Google Earth and Spreadsheet LESS-FOR-01Version 6 which developed by Thailand Greenhouse Gas Management Organization (TGO) was used to create the interactive map that included information of scientific names, GBH, carbon values, and morphological images of each tree in both 2D and 3D formats. The research was done during August to October 2024.

Main Results: A total of 467 individual trees and climbing lianas were recorded along two designated nature trails, representing 70 species, 59 genera, and 33 families, indicating relatively high species richness within the study area. The most abundant climbing species was Lasiobema scandens. Across all individuals, the average diameter at breast height (DBH) was 15.40 ± 8.00 cm, and the average height was 8.76 ± 4.21 m, reflecting a structurally mixed stand composed of both small and maturing tree stages. The estimated total tree biomass for the entire surveyed area was 61.56 tons, comprising 48.47 tons of aboveground biomass (AGB) and 13.09 tons of belowground biomass. This corresponded to a total carbon stock of 28.93 tons carbon (tC), or 106.07 tons of CO_₂-equivalent (tCO_₂e). Upon analysis, clear structural differences were evident. The Summit trail contained the highest number of individuals (321 stems representing 48 species), which were mostly small in size, with a mean DBH of 14.37 ± 7.47 cm and a mean height of 7.89 ± 2.45 m. The total biomass was 31.51 tons (24.81 tons AGB and 6.70 tons BGB), storing 14.81 tC or 54.29 tCO₂e. In contrast, the Foothill trail contained fewer trees (146 stems from 52 species), but trees were generally larger and taller, with a mean DBH of 17.87 ± 8.16 cm and a mean height of 10.65 ± 5.04 m. Its total biomass was 30.05 tons (23.66 tons AGB and 6.39 tons BGB), sequestering 14.12 tC or 51.78 tCO₂e. Despite the smaller number of individuals, the foothill trail showed comparable carbon stocks to the summit trail, highlighting the greater influence of large-sized trees on carbon storage compared with high densities of small-sized trees. The forest composition and structure along both routes reflect a transitional mosaic of DEF and MDF tree species, with stratified canopy layers and varied stem sizes indicating ongoing regeneration and partial recovery from past disturbance. However, the accumulated carbon stock was lower compared with estimates from full-plot assessments of the entire Khao Nam Sub Forest. This discrepancy may be attributed to several limitations, including measurement uncertainties of tree height measured, the use of a fixed root-to-shoot ratio (0.27), and the linear-based sampling design along nature trails, which access forest edges and cannot fully represent the entire landscape. Although the positional accuracy of Google Earth Mobile depends on mobile signal quality, it proved suitable for generating interactive mapping and spatial visualization in the Khao Nam Sub Forest. The resulting map enables users to easily and clearly access tree-specific information along the nature trails, including scientific name, GBH, total height, carbon stock, and morphological images, with georeferenced positions displayed in both 2D and 3D formats. Such tools facilitate participatory learning, future research applications, and effective management of small-scale green spaces.

Conclusion: Integrating Google Earth with the LESS-FOR-01 spreadsheet proved to be an effective method for assessing and visualizing tree-level carbon storage in small green areas. The resulting interactive Tree Carbon Storage Map enhances the assessment of carbon sequestration potential at a micro-scale level and spatial-based learning while raising awareness of each tree’s contribution to carbon storage in an ecosystem. This low-cost and replicable approach provides practical support for green space management, environmental education, and Thailand’s pursuit of Carbon Neutrality and the Sustainable Development Goals (SDGs 13 and 15) within academic institutions and local communities.