Remotely sensed vegetation indices (VI) such as the Normalized Difference Vegetation Index (NDVI) are increasingly used as a proxy indicator of the state and condition of the land cover/vegetation, including forest. However, the Enhanced Vegetation Index (EVI) on the outcome of forest change detection has not been widely investigated. We compared the influence of using EVI and NDVI on the number and time of detected changes by applying Breaks for Additive Seasonal and Trend (BFAST), a change detection algorithm. We used MODIS 16-day NDVI and EVI composite images (April 2000-April 2012) of three pixels (pixels 352, 378, and 380) in the tropical peat swamp forest area around the flux tower of Palangka Raya, Central Kalimantan. The results of BFAST method were compared to the Normalized Difference Fraction Index (NDFI) maps and the maps were validated by the hotspot of the Infrastructure and Operational MODIS-Based Near Real-Time Fire(INDOFIRE). Overall, the number and time of changes detected in the three pixels differed with both time series data because of the data quality due to the cloud cover. Nonetheless, we found that EVI is more sensitive than NDVI for detecting abrupt changes such as the forest fires of August 2009-October 2009 that occurred in our study area and it was verified by the NDFI and the hotspot data. Our results demonstrated that the EVI for forest monitoring in the tropical peat swamp forest area which is covered by intense cloud cover is better than that NDVI. Nonetheless, further research with improving spatial resolution of satellite images for application of NDFI is highly recommended.
The hotspot of MODIS Aqua/Terra represents the high risk of land/forest fire due to an extreme temperature over the hotspot location. Spatial-temporal analysis of the hotspot can be used to map the regions with a high vulnerability of forest/land fires. This study has an objective to use hotspot data from MODIS Aqua/Terra to map the forest/land fires over the Humbang Hasudutan Regency from 2001 to 2019. In spatial, the hotspot mostly occurs in the eastern part of the study area (Doloksanggul and Pollung), which this area has assigned as the peatland area. Based on land cover, the hotspot often detected over the dryland agriculture, dryland with shrubs, and bare soil. In temporal, the hotspot mostly increases during the dry seasons such as February, June, and July. The hotspot decreases during the wet season, such as January, October, November, and December. Besides, there was an inversely proportional between the number of detected hotspots and rainfall over the study area.
This study aims to explore the contrasting characteristics of large-scale circulation that led to the precipitation anomalies over the northern parts of Sumatra Island. Further, the impact of varying the Asian–Australian Monsoon (AAM) was investigated for triggering the precipitation variability over the study area. The moisture budget analysis was applied to quantify the most dominant component that induces precipitation variability during the JJA (June, July, and August) period. Then, the composite analysis and statistical approach were applied to confirm the result of the moisture budget. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Anaysis Interim (ERA-Interim) from 1981 to 2016, we identified 9 (nine) dry and 6 (six) wet years based on precipitation anomalies, respectively. The dry years (wet years) anomalies over the study area were mostly supported by downward (upward) vertical velocity anomaly instead of other variables such as specific humidity, horizontal velocity, and evaporation. In the dry years (wet years), there is a strengthening (weakening) of the descent motion, which triggers a reduction (increase) of convection over the study area. The overall downward (upward) motion of westerly (easterly) winds appears to suppress (support) the convection and lead to negative (positive) precipitation anomaly in the whole region but with the largest anomaly over northern parts of Sumatra. The AAM variability proven has a significant role in the precipitation variability over the study area. A teleconnection between the AAM and other global circulations implies the precipitation variability over the northern part of Sumatra Island as a regional phenomenon. The large-scale tropical circulation is possibly related to the PWC modulation (Pacific Walker Circulation).
The scarcity of groundwater and precipitation stations has limited accurate assessments of basin-scale groundwater systems. This study proposes a workflow that integrates satellite and on-site observations to improve the spatial and temporal resolution of the groundwater level and enable recharge estimations for the Choushui River groundwater basin (CRGB) in Western Taiwan. The workflow involves multiple data processing steps, including analysis of correlation, evaluation of residuals, and geostatistical interpolation based on kriging methods. The observed groundwater levels and recharge are then the basis to assess spatial-temporal interactions between groundwater and recharge in the CRGB from 2006 to 2015. Results of correlation analyses show the high correlation between the groundwater level and the land surface elevation in the study area. However, the multicollinearity problem exists for the additional precipitation data added in the correlation analyses. The correlation coefficient, root mean square error, and normalized root mean square parameters indicate that the Regression Kriging (RK) performs better the groundwater variations than the Ordinary Kriging (OK) dose. The data-driven approach estimates an annual groundwater recharge of approximately 1.40 billion tons, representing 37% of the yearly precipitation. The correlation between groundwater levels and groundwater recharge exhibits low or negative correlation zones in the groundwater basin. These zones might have resulted from multipurpose pumping activities and the river and drainage networks in the area. The event-based precipitation and groundwater level have shown strong recharge behavior in the low-land area of the basin. Artificial weir operations at the high-land mountain pass might considerably influence the groundwater and surface water interactions.
Tornado wind often occurs in Humbang Hasudutan (Humbahas), North Sumatra. Even though the map of tornado vulnerability has constructed by The National Agency for Disaster Countermeasure (BNPB), but several regions did not yet have this map due to limited observation data. This study’s objective is to map the vulnerability of tornado in Humbahas using satellite data where the satellite data has more significant quality in area coverage and time-series data. The Composite Mapping Analysis (CMA) has been used to model the vulnerability map. There were three factors has used in this model, such as rainfall, land cover, and slope. Based on the tornado vulnerability map, there was an 802,406.24 ha area (74.63%) in Humbahas, classified as “high” risk to Tornado disaster. There was 271,945.46 ha (25.29%), which assigned as “moderate” risk. About 786.51 ha (0.07%) of the Humbahas area has a “low” risk of tornado disaster. As a result, the study area has a “moderate” to “high” risk of tornado wind disaster, then the adaption and mitigation plans need to be done in handling tornado hazard in this region.
Indonesia has diverse topographical conditions that result in Indonesia having a unique climate. One of the unique climate elements to be studied is rainfall, because rainfall has a different pattern in each region, this different rainfall pattern is caused by several climate phenomena factors that affect the rainfall pattern, including El-Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Madden Julian Oscillation (MJO). Medan City is the capital of North Sumatra province which is one of the areas in the flood-prone category in North Sumatra, where the factor of flooding is due to rainfall events in a long period of time, so the author wants to know which climatic phenomena factors can affect rainfall events in Medan city by using Machine Learning technology through the Matlab application, where in this study has a method by forming four combination models, namely the combination of the influence of IOD, SOI and MJO; second combination of IOD and SOI; third combination of SOI and MJO; and fourth combination of MJO and IOD, these four combinations will be the rainfall value of the four models. Furthermore, the rainfall value of the model is compared with the observed rainfall value and verification test using Mean Absolute Error (MAE) and correlation. Then the calculation of the comparison between the four rainfall models with the observed rainfall obtained the lowest MAE value during the SOI and MJO phenomenon of 15.0 mm and the highest correlation value during the IOD and SOI and SOI and MJO phenomena. So it is concluded that the combination of SOI and MJO has the best verification value. This shows that based on Machine Learning modeling, the model shown as the best predictor in Medan city is when the model combination consists of SOI and MJO.
The Regional Meteorology, Climatology, and Geophysics Agency (BMKG) plays a crucial role in providing accurate and reliable services related to meteorology, climatology, and geophysics. Temperature observation is one of the important tasks carried out by the BMKG as it is essential for weather and climate forecasting, as well as for predicting natural disasters. To ensure the accuracy of the data, the thermometers used for temperature observation must be in good working condition and calibrated regularly. According to the Republic of Indonesia Law No. 31, Article 48, Year 2009 on Meteorology, Climatology, and Geophysics (MKG), all observation equipment must be in good working condition and calibrated regularly. Calibration is a crucial step in ensuring the accuracy and operational fitness of the observation equipment. The International Organization for Standardization (ISO) / International Electrotechnical Commission (IEC) 17025:2017 also emphasizes the importance of ensuring the quality and accuracy of all measurement instruments. The Calibration Laboratory at the BMKG Regional Office I in Medan is accredited with ISO/IEC 17025:2017 by the National Accreditation Committee (KAN). However, the calibration process can be time-consuming and requires constant monitoring to achieve stable data. During temperature and humidity calibration, the calibration laboratory's environment must be conditioned to maintain the performance of sensitive instruments that are susceptible to environmental changes. This study aims to design an automated temperature calibration monitoring system using the Internet of Things (IoT) to improve the efficiency of the calibration process and achieve maximum calibration results at the BMKG Regional Office I in Medan. The system will enable the calibration personnel to monitor the calibration process remotely and receive real-time data, allowing for more effective analysis and decision-making.
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