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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Agromet Jurnal Pengabdian Kepada Masyarakat (Indonesian Journal of Community Engagement) Sociae Polites: Majalah Ilmiah Sosial-Politik Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal Pusat Inovasi Masyarakat International Journal of Science and Mathematics Education Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Idung Risdiyanto
Staff In Geophysics And Meteorology Department, Faculty Of Math And Natural Science, Bogor Agriculture University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Mathematics Social Sciences

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Journal : Agromet

 1 
WEATHER MONITORING MODEL BASED ON SATELLITE DATA(MODEL PEMANTAUAN CUACA BERDASAR PADA DATA SATELIT) Idung Risdiyanto
Agromet Vol. 22 No. 1 (2008): June 2008
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (589.665 KB) | DOI: 10.29244/j.agromet.22.1.%p

Abstract

Weather monitoring model is closely related to the problem of objective analysis of the field of meteorology. The amount of meteorological data is quite substantial and hence the processing of these data is one of primary problems is dynamic meteorology. Therefore, a weather system model must consider atmospheric process, which can be built by mechanistic model rather than statistical approach. Integration of numerical model and spatial model will produce spatial weather information. It should be managed in one computerized system called as an information system for weather monitoring. The approach of the research was divided into five tasks. First task was satellite data capturing and extracting, second was development of numerical modeling based on dynamic and thermodynamic of atmospheric process, third was integration of numerical modeling and geographic information system in the spatial model, fourth was to develop graphical user interface and the fifth task was application of system in the real-world. Temporal resolution of this model is one day, however, in reality weather is temporal state of atmosphere condition that change any time. Moreover, this model only describes weather condition when data satellite on the day could be captured. Therefore, to increase the temporal resolution of this model, the input data could be added or integrated with other satellite data such as GMS satellite that has one-hour temporal resolution. Spatial resolution in this model is 50x50 kilometers square for global and 8x8 kilometers for regional area. Actually, for the spatial resolution, this model has been prepared as NOAA’s spatial resolution. This model cannot simulate vertical distribution of atmosphere, so, it does not give information about relative humidity and precipitation. If air movement in vertical area could be simulated, the dew point temperature and lighting condensation level would be known therefore the relative humidity and precipitation could be predicted.
Spatial Distribution of Dryness on Oil Palm Plantations Using Landsat image Melda Hazrina; Idung Risdiyanto
Agromet Vol. 32 No. 2 (2018): DECEMBER 2018
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1279.765 KB) | DOI: 10.29244/j.agromet.32.2.51-61

Abstract

Peatland in Riau is commonly utilized for agricultural purposes including oil palm. This utilization has influenced on peat characteristics on the top soil leading to degraded peatland, associated drought-related fire. In this paper, we identified peat dryness from three different oil palm ages using drought indices proxy to derive information on spatial dryness. Two drought indices were used in this study including the Temperature Vegetation Dryness Index (TVDI) and the Crop Water Stress Index (CWSI). Our results showed that the TVDI value ranged from 0.46 to 0.92, while the CWSI value ranged from 0.18 to 0.80. The highest value of TVDI was found in 2-years old oil palm, and the lowest values was in the 11-years old oil palm. Our CWSI analysis confirmed this pattern that young oil palm has a high moisture stress, as many peat-soils were exposed to direct sunlight. Our findings also revealed that the TVDI and the CWSI were able to interpret soil moisture dynamics on the top layers (10 cm).
Correlation Analysis Between Urban Heat Island Intensity and Temperature Criticality Value in Denpasar City Putra, I Ketut Gede Arta; Risdiyanto, Idung; Hidayat, Rahmat
Agromet Vol. 37 No. 2 (2023): DECEMBER 2023
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j.agromet.37.2.66-76

Abstract

The compactness of buildings in Denpasar resulted in the formation of urban heat islands (UHI), which can be evaluated through the Urban Thermal Field Variance Index (UTFVI) and Environment Criticality Index (ECI). ECI is the ratio of land surface temperature to the Normalized Difference Vegetation Index (NDVI). It can be transformed into Temperature Criticality Value (TCV) using air temperature and Index-based Built-up Index (IBI). This study aims to identify the UHI intensity, the impact of land cover changes, and its association with the TCV. The study employs Landsat 8 imagery and field measurements data, and the findings demonstrate that the study area was mainly composed of built-up areas that had grown from 2015 to 2021. TFVI indicates the most intense UHI (>0.02) in the built-up areas, whereas the mean value of NDVI suggested a reduction in vegetation density. The density of built-up areas (IBI) had increased, while vegetation had decreased. TCV in 2015 ranged from -11.15°C.IBI to 6.42°C.IBI; 2018 between -9.96°C.IBI to 6.79°C.IBI; and 2021 between -10.84°C.IBI to 6.87°C.IBI showed that the environment had become increasingly critical from 2015 to 2021. A transect analysis revealed that more vigorous UHI intensity, denser buildings, and a more critical environment were present in urban centers compared to the suburbs. The correlation coefficient (r) between TCV and UTFVI was relatively robust (0.75–0.82), indicating that the growth of UHI intensity was associated with a more critical environment. TCV has the strongest (r=0.99) and strong correlation (r>0,80) with Built-up Index but inverse correlation with NDVI. Therefore, limiting the expansion of built-up areas and increasing vegetation could help to control the environment's criticality.
Analysis of Carbon Dioxide Emission from Forest Fires based on Fire Radiative Power in Riau Kusuma, Mochamad Afif Derma; Rohmawati, Fithriya Y; Risdiyanto, Idung
Agromet Vol. 37 No. 2 (2023): DECEMBER 2023
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j.agromet.37.2.108-116

Abstract

Riau is one of the susceptible regions in Indonesia, which faces frequent land and forest fires. Fires occur in various land covers and soil types, both peat and mineral soils, which emitted huge carbon to the atmosphere. Forest fires emit greenhouse gases, including carbon dioxide (CO2). The objective of the research was to quantify CO2 from land and forest fires. The quantification emission was for 2016 – 2018 based on the fire radiant power (FRP) dataset along with the buffer methodology for assessing fire-affected land extents across different land covers. The FRP dataset we used to be only at a confidence level of 70% or higher, which represents hotspots. The results revealed large numbers of FRP focal points (> 1000) that can be identified as fires for 2016 and 2018, whereas only small numbers (121) were identified for 2017. Then we quantified the area burned of 95,396 Ha in Riau for 2016, which was double to the 2018’s area burned. Further, this burning contributed to CO2 emission equal to 313,456 tCO2 for 2016. Emission in 2017 was a relatively low as not many observed fires detected.
Co-Authors A. E. Ramdani Agung Sandi Budiman Ahmad Bey AHMAD BEY Alan Nur Wahid Albarkah, Anita Maharani Ana Turyanti Bagaskara, Muhammad Fajar Farhan Kurniawan Fithriya Yulisiasih Rohmawati Indah Prasasti Indah Prasasti, Indah Ita Carolita Ita Carolita, Ita Kusuma, Mochamad Afif Derma LILIK BUDIPRASETYO M. Rokhis Khomarudin M.Rokhis Khomarudin Maulana Aziz Melda Hazrina Nada Radilla Putra, I Ketut Gede Arta Rahmat Hidayat Rajwa Hanan Ramdani, A. E. Rohmawati, Fithriya Y Sonni Setiawan Umar, Ali Wahid, Allan Nur Wim Ikbal