การพัฒนาแบบจำลองเชิงพื้นที่เพื่อประเมินคาร์บอนเหนือพื้นดิน ในป่าเต็งรังผสมป่าเบญจพรรณ ด้วยข้อมูลการรับรู้ระยะไกล:  กรณีศึกษาป่าชุมชนบ้านวังสำราญ จังหวัดลำปาง

Chutikan  Chalorm; Thanakorn  Lattirasuvan; Itsaree  Howpinjai; Ubonwan  Subhasaen; Sieneenard  Songsri; Wanna  Mangkita; Piyapit Khonkaen

Authors

Chutikan Chalorm Department of Forest Management, Maejo University Phrae Campus, Thailand
Thanakorn Lattirasuvan Department of Forest Management, Maejo University Phrae Campus, Thailand
Itsaree Howpinjai Department of Forest Industry Technology, Maejo University Phrae Campus, Thailand
Ubonwan Subhasaen Department of Political Science, Maejo University Phrae Campus, Thailand
Sieneenard Songsri Basic Science Group, Maejo University Phrae Campus, Thailand
Wanna Mangkita Department of Forest Management, Maejo University Phrae Campus, Thailand
Piyapit Khonkaen Department of Forest Management, Maejo University Phrae Campus, Thailand

Keywords:

aboveground carbon, Sentinel-2, vegetation indices, community forest, mixed deciduous and dry dipterocarp forest

Abstract

Background and Objectives: Forest ecosystems play an important role in Carbon stock and climate change mitigation because forests can accumulate aboveground biomass and store large amounts of carbon in living vegetation. Therefore, accurate estimation of aboveground biomass and carbon storage is essential for forest resource monitoring, sustainable forest management, and the development of community-level carbon credit mechanisms. However, assessment based solely on field data is time-consuming and labor-intensive. Consequently, Sentinel-2 satellite imagery, which provides multiple spectral bands and suitable spatial resolution for vegetation analysis, has been applied in this study. The objective of this research was to estimate aboveground biomass and aboveground carbon storage in the Mixed Deciduous and Dry Dipterocarp Forest of Ban Wang Samran Community Forest, Lampang Province, by integrating field data with Sentinel-2 imagery in order to analyze the relationships among spectral variables, develop an aboveground biomass estimation model, and compare satellite-derived results with field-based data.

Methodology: This study was conducted in Ban Wang Samran Community Forest, Lampang Province. Field data were collected from 17 sample plots and used to calculate aboveground biomass by applying allometric equations, while aboveground carbon was estimated using a constant factor of 0.47. These field-derived values were used as reference data for model development and validation. At the same time, Sentinel-2 satellite imagery covering the study area was processed to extract reflectance values from vegetation-related bands, including the blue, green, red, and near-infrared bands. In addition, 12 vegetation indices were calculated, namely EVI2, GNDVI, MSAVI, NDMI, NDRE, NDVI, SAVI, SR-RE, SR, VARI, MTVI2, and RTVIcore. Relationships between field-based carbon values and satellite-derived variables were then analyzed using linear regression, and the most suitable variables were selected based on the coefficient of determination (R²) for the development of a multiple linear regression model to estimate aboveground biomass. Furthermore, values derived from field measurements and satellite data were compared using inferential statistics, a paired t-test at the 95% confidence level, and Pearson’s correlation coefficient.

Main Results: Field survey results revealed that the Mixed Deciduous and Dry Dipterocarp Forest in the study area had a moderate potential for biomass accumulation and carbon storage. The mean aboveground biomass was 71.62 t/ha, with a minimum of 52.43 t/ha and a maximum of 140.52 t/ha. Mean aboveground carbon storage was 33.66 tC/ha, ranging from 24.64 to 66.04 tC/ha. Analysis of the relationship between aboveground carbon storage and Sentinel-2 variables showed that VARI was the best single predictor, with the regression equation y = 0.0009x + 0.0109 and an R² value of 0.28. The next best predictors were NDMI, MTVI2, and the red band, each with an R² of 0.12. Some variables, such as SR-RE, NDRE, and RTVIcore, could not explain the variation in the data. To improve estimation performance, a multiple linear regression model was developed using VARI and SR, yielding the equation: AGB = 409.313 + 996.771(VARI) - 165.375(SR), with an R² of 0.64. Comparison between field-based values and Sentinel-2-derived estimates showed that the two approaches produced very similar mean values. The paired t-test indicated no statistically significant difference at the 95% confidence level, and Pearson’s correlation coefficient was r = 0.80, indicating a strong positive relationship between the two datasets.

Conclusions: The findings of this study highlight the effectiveness of integrating Sentinel-2 satellite imagery with field-based inventory data for estimating aboveground carbon storage in the Mixed Deciduous and Dry Dipterocarp Forest of Ban Wang Samran Community Forest, Lampang Province. Although individual spectral variables had limitations in explaining data variation, combining VARI and SR significantly improved model performance. The statistical results, which showed no significant difference between field-based and Sentinel-2-derived values, together with the high correlation coefficient, support the potential of Sentinel-2 data for carbon estimation at the sample plot level. Therefore, the developed model can serve as a practical tool for sustainable monitoring and assessment of community forest resources and for supporting the development of local carbon credit initiatives.

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