แบบจําลองความสัมพันธ์เชิงพื้นที่ระหว่างค่าผลผลิตปฐมภูมิสุทธิ และค่าดัชนีความแห้งแล้งที่ได้รับอิทธิพลจากปรากฏการณ์เอลนีโญ จากการสํารวจระยะไกล ในลุ่มน้ําน่าน

Abstract

The aims of the study entitled "Spatial Correlation Model Between Net Primary Productivity (NPP) and Drought Index Affected by the El Nino Phenomenon Using Remote Sensing in the Nan River Basin" are as follows: 1) to calculate the net primary productivity of the forests using remote sensing technology; 2) to calculate different types of drought index using remote sensing technology; and 3) to analyze the relationship between the net primary productivity of the forest and values from the drought index under normal conditions and during El Nino-affected periods. The data analysis was divided into two periods: November-December 2015 and January-April 2016, when severe El Nino events occurred, and November-December 2017 and January-April 2018 when conditions were normal. To calculate NPP in forest areas, the physiological model for predicting plant growth using satellite imaging (3PGs) was combined with satellite image data from Landsat-8 OLI with a pixel resolution of 30 meters. NPP levels could reflect the density of forests in the research area if they are high. In a year heavily influenced by the El Nino phenomena, the lowest value assessed from a satellite image was -6.13 in November 2015, as measured by the satellite. The highest was 6.28 in November 2017, which was a typical month. They are found predominantly in evergreen woods. This analysis considered the amount of water held in the Sirikit Dam reservoir as the physical component influencing the drop in NPP. Due to the drought value, the NPP will be diminished and dispersed when the water storage falls below 50 percent of its capacity. Due to the drought, their levels fall and distribute extensively, especially in locations below 400 meters. VHI and NDWI were calculated to determine the level of dryness in the Nan River Basin based on SPI data derived from statistical analysis of Landsat-8 OLI satellite image data. The correlation coefficient between SPI, VHI, and NDWI was unrelated. However, the positive correlation coefficient between VHI and NDWI was statistically significant. There is a relationship between the two variables in the same direction. The correlation coefficients for the years affected by the El Nino phenomena were 0.87, 0.78, 0.81, 0.81, 0.84, and 0.85 at a confidence level of 95%. The values for the typical year correlation coefficients were 0.69, 0.87, 0.73, 0.73, 0.66, and 0.72, respectively. The findings of spatial correlation analysis of NPP values with the regionally weighted regression model, VHI, and NDWI were calculated as the initial variable during El Nino's occurrence from November to December 2015 and January to April 2016. In this study, 10x10 km2 grids were utilized to investigate spatial modeling throughout the standard November-December 2017 and January-April 2018 rescarch periods. It was discovered that the correlation coefficients' directions were identical in calculating the correlation values. By cvaluating the geographical relationship (Local R-Squared (Local R')) in the Nan river basin, the grid level spatial correlation coefficient was 10x10 sq.km. during the El Nino event was found to be 0.86, 0.90, 0.87, 0.93, 0.95, and 0.95, respectively. NPP increases when VHI and NDWI index values rise. And at an altitude range of 800 to 1,400 meters. The area correlation was comparable when comparing the correlation level for cach time. Statistical analysis of image data from the Landsat-8 OLI satellite was used to construct VHI and NDWI to estimate drought severity in the Nan River Basin. The correlation coefficient between the variable SPI and the variables VHI and NDWI was determined to be unrelated. Nevertheless, the connection between VHI and NDWI was statistically significant in the positive direction. Both variables exhibit a link in the same direction. At a confidence level of 95%, the correlation coefficients for the El Nino-influenced years were 0.87, 0.78,0.81, 0.81, 0.84, and 0.85, respectively. The normal year correlation coefficients have the following values: 0.69, 0.87, 0.73, 0.73, 0.66, and 0.72. E1 Nino occurred from November to December 2015 and January to April 2016, and spatial correlation analysis of NPP values with the regionally weighted regression model, VHI, and NDWI established the starting variable. Throughout this study, typical November- December 2017 and January-April 2018 examinations, 10x10 sq.km. grid-level spatial modeling analysis was conducted. In order to calculate the correlation values, it was determined that the correlation coefficients have to point in the same direction. It was discovered that the grid level spatial correlation coefficient of 10x10 sq. km. during the E1 Nino event was 0.86,0.90, 0.87,0.93, 0.95, and 0.95, respectively, by studying the spatial relationship (Local R-Squared (Local R3)) in the Nan river basin. The values of normal situations were 0.90, 0.86, 0.77, 0.93, 0.93, and 0.88, respectively. The spatial model results in the correlation coefficient closing 1, which is highly correlated and in the same direction. In other words, the NPP tends to increase when the VHI and NDWI indices increase. In addition, the spatial correlation between 800 and 1,400 meters in altitude was comparable when comparing correlation levels for each time.