International Journal of Remote Sensing and Earth Sciences (IJReSES)
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TEA PLANT HEALTH RESEARCH USING SPECTROMETER
Full text linkTea leaves are the most important part for consumption. Leaves that are healthy have a distinct color, while leaves that are not healthy have a color that is very different from the original. Chlorophyll in leaves effects the reflection of infrared light, allowing healthy plants to reflect more infrared light than unhealthy plants. Leaf color and chlorophyll have an important role in showing the growth and health of tea plants. Remote sensing consists of collecting information about objects and features without contacting the equipment. The Normalized Difference Vegetation Index (NDVI), one of the first remote sensing analysis products used to simplify the complexity of multispectral imaging, is now the most commonly used index for botanical assessment. inconsistencies in NDVI depending on sensor-specific spatial and spectral resolutions. Different parts of the leaf have discolored spots due to health conditions or nutritional stress, so there are different spectral values on different parts of the leaf. Unhealthy tea leaves have low NIR values due to disease, insects, and sunburn, which damage the chloroplast structure of the leaves, weaken the absorption of the appropriate band, and increase reflectance. There is a difference between the measurement results of the NDVI spectrometer and the sentinel image. This is due to the fact that the Sentinel-2 image can only retrieve image pixels with a resolution and not diseased leaf parts, as with the use of a spectrometer, which directly extracts the value of the infected area from the normal part of the plan
ANALYSIS OF TSUNAMI EVACUATION ROUTE PLANNING IN KULON PROGO REGENCY
Full text linkSituated on the southern coast of Java Island, Kulon Progo Regency is prone to tsunami hazards since it directly faces the subduction zone of the Eurasian Plate and the Indo-Australian Plate. The road condition on the coast of Kulon Progo Regency, which extends from east to west, can be an obstacle in the evacuation process if there is no proper evacuation route planning. Total population in the study area reached 149,574 people. Therefore, it is essential to plan an evacuation route in the coastal area of Kulon Progo Regency. This study proposes the tsunami evacuation route and evaluates it with field conditions on the coast of Kulon Progo Regency. The evacuation route was built using Multi-Criteria Based Least Cost Path Analysis, which uses road network, land use, and slope data as parameters. The least cost path analysis for determining the evacuation route was carried out in 2 scenarios, namely for vehicles and pedestrians. The results of the least cost path analysis of the vehicle scenario are considered less suitable because the results are more through land use and away from the road network. The pedestrian evacuation scenario is more in line with reality because it produces a path adjacent to the road network so that it can be passed either by vehicle or pedestrian
TSUNAMI DISASTER MODELING FOR NON-MILITARY DEFENSE IN PANGANDARAN REGENCY USING GEOGRAPHIC INFORMATION SYSTEMS
Full text linkThe tsunami disaster is one of the non-military threats to the State of Indonesia. Pangandaran Regency has a coastline of 91 km which is directly opposite the Megathrust of West-Central Java. The coastal area of Pangandaran Regency is an important center of tourism and economic activity and a high risk area for tsunamis due to earthquakes. This study was conducted to model the tsunami and analyze the magnitude of the inundation generated in settlements and tourist attractions in Pangandaran Regency as a form of defensive effort in disaster mitigation. The method used is tsunami modeling based on earthquake parameters using winITDB software. After modeling, it will be continued with H-Loss calculations based on tsunami run-up height data parameters, Digital Elevation Model (DEM) data, land use or cover data, and shoreline data using Geographic Information Systems. The results of the tsunami modeling are that the estimation waves height and estimation time arrival from three tide gauges are 15,34 m and 31,13 minutes. The total inundation area is 31.081 ha. The area of inundation according to the classification of land use is the most crucial and includes life, namely settlements and places of activity covering an area of 2.339,2 ha
SPATIAL ANALYSIS OF QUANTITATIVE PRECIPITATION FORECAST ACCURACY BASED ON STRUCTURE AMPLITUDE LOCATION (SAL) TECHNIQUE
Full text linkQuantitative Precipitation Forecast (QPF) is the final product of a short-term forecasting algorithm (nowcasting) based on weather radar data which is widely used in hydrometeorological aspects. The calculation of the accuracy value using point data on a rainfall gauge often causes a double penalty problem because the QPF prediction results are in the form of spatial objects. This study aims to apply object-based spatial verification in analyzing the accuracy of QPF based on the Short Term Ensemble Prediction System (STEPS) algorithm using the SAL technique. The verification process is carried out by calculating the index value of the structure component (S), amplitude (A), and location (L) in the QPF prediction results based on the results of weather radar observations. The index values for components S and A have a range of -2 to 2, and 0 to 1 for component L with a perfect value of 0. The case study used is the occurrence of heavy rains that caused flooding in Bogor Regency in 2020. SAL verification results from 26 case studies used shows the average value of the components S, A, and L, respectively 0.51, 0.38, and 0.21. As many as 75% of all case studies have S and L component values less than 0.5 which indicate the structure and location of the QPF prediction object is close to the structure and location of the object of observation. A positive value in component A indicates that the QPF prediction results based on the STEPS algorithm tend to be overestimated but on a low scale, namely 0.38 out of 2
SPATIAL ANALYSIS OF LAND USE AND LAND COVER VARIATIONS AFFECTING TEA PRODUCTION IN GUNUNGMAS PLANTATION THROUGH REMOTE SENSING TECHNIQUES
Full text linkTea is a manufactured beverage that is popular around the world. In value chain analysis to increase efficiency, remote sensing technology can be developed to monitor the phenomenon of land use land cover (LULC) change and vegetation health conditions. This study aims to identify LULC in tea plantations, identify the health condition of tea plantations, then analyze spatial trends of changes in tea productivity in Gunungmas Afdeling-1 due to changes in tea area or tea vegetation health condition. Identification of changes in LULC in tea plantations can be carried out using remote sensing technology and machine learning, in this study, Google Earth Engine (GEE) LULC identification was generated using a supervised classification with the random forest algorithm on the GEE. Tea productivity trends decreased from 2019 to 2020, but increased from 2020 to 2021. They show that the trend of changes in the area of tea plantation classification is decreasing. According to the NDVI result, most of the reduced area of tea plantations is in areas with healthy vegetation. The trends in tea productivity changes are not in line with changes in the LULC area of tea plantation classification class and tea vegetation health condition
THE RELATIONSHIP BETWEEN LAND USE AND LAND COVER TO RUN-OFF COEFFICIENT VALUE IN BRANTAS WATERSHED AREA, TULUNGAGUNG - EAST JAVA, INDONESIA
Full text linkThe Ngrowo-Ngasinan sub-watershed is a part of Brantas Watershed which has an important role for the aquatic ecosystems in the Brantas watershed. Land cover changes in this sub-watershed can be identified by utilizing remote sensing technology. The use of remote sensing technology by applying Landsat 8 image data can be done by classifying several classes of land cover in the study area. Land cover affected the flow rate of a watershed because of its association with several problems due to the conversion of land. Land cover which influences the watershed ecosystems is forest. In addition to land cover, regional rainfall also affects the flow rate (runoff) in the are
OBSTRUCTION ZONE MODELING AT HALIM PERDANAKUSUMA AIRPORT USING REMOTE SENSING DATA
Full text linkFlight safety plays a critical role in both the national economy and military defense. According to the National Transportation Safety Board (NTSB), the highest number of aircraft accidents between 2013 and 2018 occurred during takeoff (24%) and landing (40%). To model the obstruction zone based on building density and its impact on flight safety, this study utilizes remote sensing data from Sentinel 2A in 2022. The data is analyzed using the Normalized Difference Built-up Index (NDBI) algorithm, which serves as the basis for modeling potential aircraft accident zones. Specifically, the study focuses on the growth of buildings within a 15 km extended runway area during takeoff and landing. The findings reveal that the aircraft takeoff approach area in the Flight Operation Safety Zone (KKOP) at Halim Perdanakusuma Airport exhibits the highest building density. This area demonstrates a moderate level of building density, with a prevailing growth pattern and density that extend predominantly eastward, toward Bekasi city. Furthermore, the study highlights that nearly the entire region falls under the classification of "built-up areas." Consequently, establishing urban planning policies for development around landing and takeoff corridors becomes imperative while considering aviation safety factors. This research provides valuable insights to aviation authorities and decision-makers involved in infrastructure development and urban planning. By taking into account building density and the growth of surrounding areas along flight paths, appropriate measures can be implemented to ensure optimal flight safety and mitigate the risk of future aircraft accident
TEA PLANTATION MAPPING USING UAV MULTISPECTRAL IMAGERY
Full text linkTea is one of Indonesia’s most famous commodities, which is dominantly planted on the Java Island of Indonesia. Tea is one of the leading sources of exports, and the Indonesian government is very concerned about the stability of their export commodity sustainability. Therefore, monitoring and evaluating its sustainability and availability become necessary. One of the solutions to the tea plantation monitoring and management program is mapping through remote sensing and GIS. In this study, high-resolution multispectral imageries are captured from a UAV and used to map the tea plantation with three vegetation indexes (VIs). An Object-Based Image Analysis (OBIA) is used to classify the tea field’s condition based on spectral characteristics. The results of this study are: (i) high-resolution multispectral imageries can be used to map the tea plantation with different VIs, and (ii) SAVI is the best VI to map the tea plantation since it has the lowest RMSE value with observed data. Hopefully, this study can support the government program on their export commodity with valuable baseline information on the tea plantation