• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1117-1132
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• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.251-267
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• 2021

Remotely sensed vegetation indices (VIs) are empirically related with gross primary productivity (GPP) in various spatio-temporal scales. The uncertainties in GPP-VI relationship increase with temporal resolution. Uncertainty also exists in the eddy covariance (EC)-based estimation of GPP, arising from the partitioning of the measured net ecosystem CO2 exchange (NEE) into GPP and ecosystem respiration (RE). For two forests and two agricultural sites, we correlated the EC-derived GPP in various time scales with three different near-surface remotely sensed VIs: (1) normalized difference vegetation index (NDVI), (2) enhanced vegetation index (EVI), and (3) near infrared reflectance from vegetation (NIRv) along with NIRvP (i.e., NIRv multiplied by photosynthetically active radiation, PAR). Among the compared VIs, NIRvP showed highest correlation with half-hourly and monthly GPP at all sites. The NIRvP was used to test the reliability of GPP derived by two different NEE partitioning methods: (1) original KoFlux methods (GPP_Ori) and (2) machine-learning based method (GPP_ANN). GPP_ANN showed higher correlation with NIRvP at half-hourly time scale, but there was no difference at daily time scale. The NIRvP-GPP correlation was lower under clear sky conditions due to co-limitation of GPP by other environmental conditions such as air temperature, vapor pressure deficit and soil moisture. However, under cloudy conditions when photosynthesis is mainly limited by radiation, the use of NIRvP was more promising to test the credibility of NEE partitioning methods. Despite the necessity of further analyses, the results suggest that NIRvP can be used as the proxy of GPP at high temporal-scale. However, for the VIs-based GPP estimation with high temporal resolution to be meaningful, complex systems-based analysis methods (related to systems thinking and self-organization that goes beyond the empirical VIs-GPP relationship) should be developed.

• Journal of Korean Society of Forest Science
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• v.82 no.2
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• pp.152-165
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• 1993

This is the basic study in order to know the amount of transpirational water loss in a Larix leptorepis stand by a heat pulse method. Especially this study has been measured and discussed the diurnal and seasonal trends of heat pulse velocity by changes of radiation, temperature and humidity, differences of heat pulse velocity by direction and depth in stem, differences of heat pulse velocity by dominant, codominant and suppressed trees, diurnal change of heat pulse velocity by change of leaf water potential, sap flow path way in sapwood by dye penetration and amount of daily and annual transpiration in a tree and stand. The results obtained as follows : 1. Relation between heat pulse velocity(V) and sap flow rate(SFR) was established as a equation of SFR=1.37V($r=0.96^{**}$). 2. The sap flow rate presented in the order of dominant, codominant and suppressed tree, respectively. The daily heat pulse velocity was changed by radiation, temperature and vapor pressure deficit. 3. The heat pulse velocity in individual trees did not differ in early morning and in late night, but had some differed from 12 to 16 hours when radiation was relatively high. 4. The heat pulse velocity and leaf water potential showed similar diurnal variation. 5. The seasonal variation of heat pulse velocity was highest in August, but lowest in October and similar value of heat pulse velocity in the other months. 6. The heat pulse velocity in stem by direction was highest in eastern, but lowest in southern and similar velocity in western and northern. 7. The difference of heat pulse velocity in according to depths was highest in 2.0cm depth, medium in 1.0cm depth, and lowest in 3.0cm depth from surface of stem. 8. The sap flow path way in stem showed spiral ascent turning right pattern in five sample trees, especially showed little spiral ascent turning right in lower part than 3m hight above ground, but very speedy in higher than 3m hight. 9. The amount of sap flow(SF) was presented as a equation of SF=1.37AV and especially SF in dominant tree was larger than in codominant or suppressed tree. 10. The amount of daily transpiration was 30.8ton/ha/day and its composition ratio was 83% at day and 17% at night. 11. The amount of stand transpiration per month was largest in August(1,194ton/ha/month), lowest in May (386ton/ha/month). The amount of stand transpiration per year was 3,983ton/ha/year.