{"title":"泰国清迈省Pa Sak Ngam森林比例、土壤水分指数和净初级生产力的关系评估","authors":"","doi":"10.52939/ijg.v19i2.2563","DOIUrl":null,"url":null,"abstract":"The objective of this study was to determine the relationship between the proportion of forest area, soil moisture index, and net primary productivity in the Pa Sak Ngam, Luang Nuea Subdistrict, Doi Saket District Chiang Mai, Thailand. The investigation was conducted during dry season in 2009 and 2019 utilizing systematic sampling inside a 500 m × 500 m image grid to measure these factors. Landsat 5 TM and Landsat 8 OLI/TIRS satellite images were classified using the Random Forest to obtain the proportion of forest area. Soil moisture was calculated using the soil moisture index obtained from land surface temperature and the normalized difference vegetation index. The Physiological Processes Predicting Growth (3-PGs) model was used to compute net primary productivity. In 2009, the analysis revealed a moderately strong positive correlation between the proportion of forest area and both soil moisture and net primary productivity. In contrast, in 2019, a weak positive association was found between low forest cover percentage and both soil moisture and net primary productivity. A comparison of the results from the two time periods indicated that the association between the three variables was stronger in 2009 than in 2019. This may be attributed to the increase in average forest cover from 85.583% to 92.349% over the two time periods. Effective management of forest restoration and expansion can enhance the water cycle and increase the flow of energy and productivity.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing the Relationship between Forest Proportion, Soil Moisture Index and Net Primary Productivity in Pa Sak Ngam, Chiang Mai Province, Thailand\",\"authors\":\"\",\"doi\":\"10.52939/ijg.v19i2.2563\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The objective of this study was to determine the relationship between the proportion of forest area, soil moisture index, and net primary productivity in the Pa Sak Ngam, Luang Nuea Subdistrict, Doi Saket District Chiang Mai, Thailand. The investigation was conducted during dry season in 2009 and 2019 utilizing systematic sampling inside a 500 m × 500 m image grid to measure these factors. Landsat 5 TM and Landsat 8 OLI/TIRS satellite images were classified using the Random Forest to obtain the proportion of forest area. Soil moisture was calculated using the soil moisture index obtained from land surface temperature and the normalized difference vegetation index. The Physiological Processes Predicting Growth (3-PGs) model was used to compute net primary productivity. In 2009, the analysis revealed a moderately strong positive correlation between the proportion of forest area and both soil moisture and net primary productivity. In contrast, in 2019, a weak positive association was found between low forest cover percentage and both soil moisture and net primary productivity. A comparison of the results from the two time periods indicated that the association between the three variables was stronger in 2009 than in 2019. This may be attributed to the increase in average forest cover from 85.583% to 92.349% over the two time periods. Effective management of forest restoration and expansion can enhance the water cycle and increase the flow of energy and productivity.\",\"PeriodicalId\":38707,\"journal\":{\"name\":\"International Journal of Geoinformatics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Geoinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.52939/ijg.v19i2.2563\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Geoinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52939/ijg.v19i2.2563","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Social Sciences","Score":null,"Total":0}
Assessing the Relationship between Forest Proportion, Soil Moisture Index and Net Primary Productivity in Pa Sak Ngam, Chiang Mai Province, Thailand
The objective of this study was to determine the relationship between the proportion of forest area, soil moisture index, and net primary productivity in the Pa Sak Ngam, Luang Nuea Subdistrict, Doi Saket District Chiang Mai, Thailand. The investigation was conducted during dry season in 2009 and 2019 utilizing systematic sampling inside a 500 m × 500 m image grid to measure these factors. Landsat 5 TM and Landsat 8 OLI/TIRS satellite images were classified using the Random Forest to obtain the proportion of forest area. Soil moisture was calculated using the soil moisture index obtained from land surface temperature and the normalized difference vegetation index. The Physiological Processes Predicting Growth (3-PGs) model was used to compute net primary productivity. In 2009, the analysis revealed a moderately strong positive correlation between the proportion of forest area and both soil moisture and net primary productivity. In contrast, in 2019, a weak positive association was found between low forest cover percentage and both soil moisture and net primary productivity. A comparison of the results from the two time periods indicated that the association between the three variables was stronger in 2009 than in 2019. This may be attributed to the increase in average forest cover from 85.583% to 92.349% over the two time periods. Effective management of forest restoration and expansion can enhance the water cycle and increase the flow of energy and productivity.