Environmental Engineering
A. Suharyanto; A. Maulana; D. Suprayogo; Y.P. Devia; S. Kurniawan
Abstract
BACKGROUND AND OBJECTIVES: This study aims to determine the relationships between land cover presented by vegetation index and land surface temperature, between vegetation index and the built-up index, between built-up index and land surface temperature, and between land surface temperature and rainfall ...
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BACKGROUND AND OBJECTIVES: This study aims to determine the relationships between land cover presented by vegetation index and land surface temperature, between vegetation index and the built-up index, between built-up index and land surface temperature, and between land surface temperature and rainfall characteristics in East Java Province, Indonesia.METHODS: Three cities and four regencies were used as examples. Landsat imagery scanned in 1995, 2001, 2015, and 2020 were used. Daily rainfall data recorded in the same years with Landsat data are used. The pixel values along the urban heat island line were used to analyze the interrelationships between vegetation index, built-up index, and land surface temperature. The land surface temperature and daily rainfall data from each Thiessen polygon were used to analyze the relationship between land surface temperature and rainfall characteristics. Image processing analysis was used to analyze the vegetation index, built-up index, and land surface temperature. The mathematical interrelationship between vegetation index, built-up index, land surface temperature, and rainfall intensity was analyzed using linear regression.FINDINGS: The results of the analysis show that the relationship between vegetation index and built-up index is inversely proportional and with land surface temperature is nearly inversely proportional to a coefficient of determination greater than 0.5. For the relationship between the built-up index and land surface temperature, the results of the analysis show that both have a directly proportional relationship, with a significant coefficient of determination (R2>0.5). For the relationship between land surface temperature and rainfall characteristics, the results of the analysis show that land surface temperature has a directly proportional but weak relationship with rainfall intensity and an inversely proportional but weak relationship with the number of rainfall days. Decreasing environmental conditions indicated by decreasing vegetation index will influence increasing land surface temperature and its effect on increasing rainfall intensity and decreasing rainfall days.CONCLUSION: Changes in land use/land cover are characterized by a change in vegetation cover to built-up land. These changes affect the land surface temperature. Changes in land surface temperature affect the occurrences of rainfall intensity. When the vegetation index decreases, the built-up index increases, and the land surface temperature increases as well. The increase in land surface temperature will increase the rainfall intensity. Satellite remote sensing imagery is effective and efficient for analyzing vegetation index, built-up index, and land surface temperature.
Environmental Engineering
G.R. Puno; R.A. Marin; R.C.C. Puno; A.G. Toledo-Bruno
Abstract
BACKGROUND AND OBJECTIVES: The study explored the capability of the geographic information system interface for the water erosion prediction project, a process-based model, to predict and visualize the specific location of soil erosion and sediment yield from the agricultural watershed of Taganibong.METHODS: ...
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BACKGROUND AND OBJECTIVES: The study explored the capability of the geographic information system interface for the water erosion prediction project, a process-based model, to predict and visualize the specific location of soil erosion and sediment yield from the agricultural watershed of Taganibong.METHODS: The method involved the preparation of the four input files corresponding to climate, slope, land management, and soil properties. Climate file processing was through the use of a breakpoint climate data generator. The team had calibrated and validated the model using the observed data from the three monitoring sites.FINDINGS: Model evaluation showed a statistically acceptable performance with coefficient of determination values of 0.64 (probability value = 0.042), 0.85 (probability value = 0.000), and 0.69 (probability value = 0.001) at 95% level, for monitoring sites 1, 2, and 3, respectively. A further test revealed a statistically satisfactory model performance with root mean square error-observations standard deviation ratio, Nash-Sutcliffe efficiency, and percent bias of 0.62, 0.61, and 44.30, respectively, for monitoring site 1; 0.65, 0.56, and 25.60, respectively, for monitoring site 2; and 0.60, 0.65, and 27.90, respectively, for monitoring site 3. At a watershed scale, the model predicted the erosion and sediment yield at 89 tons per hectare per year and 22 tons per hectare per year, respectively, which are far beyond the erosion tolerance of 10 tons per hectare per year. The sediment delivery ratio of 0.20 accounts for a total of 126,390 tons of sediments that accumulated downstream in a year.CONCLUSION: The model generated maps that visualize a site-specific hillslope, which is the source of erosion and sedimentation. The study enables the researchers to provide information helpful in the formulation of a sound policy statement for sustainable soil management in the agricultural watershed of Taganibong.
