Lake and Stream Buffer Zone Widths' Effects on Nutrient Export to Lake Rawapening, Central Java, Indonesia: A Simple Simulation Study

Lake ecosystems in Indonesia face serious environmental problems. One of those problems is eutrophication caused by excessive plant nutrients, particularly nitrogen (N) and phosphorus (P). Water quality degradation and biodiversity loss are the effects of eutrophication. The government of Indonesia (GoI) has issued a regulation on determining lake and stream buffer zones, but it has not been fully implemented in the field. Additionally, the data related to the effects of each buffer zone width is not available. This study aims to begin to fill this gap. It simulates the effect of lake and stream buffer zone widths on nutrient export to Rawapening Lake. The Nutrient Retention sub-model, which is part of InVEST (Integrated Valuation of Environmental Services and Tradeoffs) software, has been used for this research to analyse information from several data sources, including a Digital Elevation Model (DEM) and measurements of soil depth, annual rainfall, land cover/use, watershed/sub-watershed boundaries, and biophysical conditions. Several studies of eutrophication in Rawapening Lake have measured the magnitude of eutrophication but have not discussed the effects of buffer zone widths. Therefore, this study accommodates the updated data on how much effect of buffer zone widths on the reduction of nutrient export. Five scenarios of buffer zone width are considered:  30 m., 90 m., and 150 m, where the lake buffer zone widths and the stream buffer zone width are 30 m. The results indicated that the maximum nutrient export reduction of lake buffer zones was only 2.63% (for N) and 3.56% (for P). On the other hand, the 30 m stream buffer zone width reduced the nutrient export to Rawapening Lake by up to 43.05% for N and by 44.90% for P. A 30 m combined lake and stream buffer zone width slightly increases the nutrient export reduction effectiveness, i.e., 0.41% and 0.56% for N and P, respectively

Leveraging Geospatial Technology for Enhanced Utility Management: A Case Study in Electrical Distribution Power Systems

Over the years, electricity has developed into a crucial commodity for any nation. The need to evaluate the rate of electricity consumption in regard to utility management and the spatial distribution of major devices to facilitate appropriate planning within the estate is the motivation for this research. This study used geospatial technology to evaluate the electricity distribution to support planning and management in Omole Estate (Phase One) and environs within the city of Lagos, Nigeria. The focus was on determining the land use in study area, geolocations of the transformers, along with the cost of energy consumed per household. Spatial data for the research area was collected through a Hand-held GPS. Google Earth images were downloaded to supplement the data, and a comprehensive analysis of administered and recovered questionnaires was conducted to enrich the dataset. ArcGIS 10.6.1 software was employed to create the database and depict the area, whilst modifying all of the details required within. The result confirms that 72% of the respondents use electricity for domestic use, 18% for commercial use while 10% utilise it for domestic and commercial use. A significant portion of homes (33%) still use outdated postpaid meters and 35% of respondents do not know how much power they use at home each month or the cost per unit of that electricity. Regarding the respondents, 67% have a prepaid card/electrical meter installed (per kilowatt). Concerning cost, 10% of the respondents spends between N1000-N5000 for their monthly electricity consumption, 27% of the respondents between N5,000 and N10,000, 38% between N10,000 and N17,000, 24% between N17,000 and N25,000 and 2% above N25,000 per month on electricity. These findings will assist effective power distribution within the estate and provide guidance on charge rates for commercial power users which is approximately 28% overall

Investigation of the Development of Tropical Storm Nicholas based on Global and Regional Climate Data

This paper studies the simulation of Cyclone Nicholas that occurred close to the coastal area of Western Australia and fell on the mainland of Southwestern Australia. The simulation was conducted via a dynamical downscaling model, Weather Research and Forecasting (WRF), to obtain a higher resolution with reference to the regional climate data. The model simulation is generated using a global reanalysis of climate data for the initial and lateral boundary conditions. We investigated the response of the tropical storm to the model regarding the track and intensity using a modified Kyklop method that appears more appropriate for a landfall cyclone. Our study suggests that the regional climate data computed by the model deviates from the storm track of the global climate data forcing field. In this study, the track of the simulated storm is parallel to the satellite data, but it is shifted slightly to the east, closer to the mainland. Nevertheless, the model simulation can implement the intensity of the storm as strongly as the observation, while the forcing data delivers substantial underestimation

Friends and Neighbours: Electoral Geography of 2020 Local Election in Metro City, Lampung, Indonesia

This article discusses local political dynamics in Indonesia, notably in the city of Metro. There are several factors why a particular candidate is more politically electable than others, including ethno-religious factors and money. Moreover, a traditional factor that needs to be considered in the study of electoral geography is the influence of the spatial effect upon voting behaviour. In the election, demographics and geography are two important factors in voting behaviour. The local election resulted in a competitive and dynamic political contest among the local elite in Metro. The result of the 2020 local election was particularly interesting because the independent candidate won and defeated the party-based candidate. This is a mixed methods approach combining the data from interviews and a qualitative survey. This research aims to analyse the spatial factor in Metro’s local election, looking at why a certain candidate won in a particular area and how the geographical factor influenced voting behaviour. Secondly, the result of the qualitative survey supported the finding that voters still consider ethno-religious factor. The finding obtained by this research reveals two significant narratives, specifically the crucial factor of ethno-religious sentiment on voting preference and the spatial factor related to residency in securing a victory for the candidate in the local election. Essentially, research concludes that the spatial factor is of importance in the context of Metro’s local election and supports Woolstencroft's (1980) classical concept of electoral geography comprising “friends and neighbours”

Monitoring Biochemical Oxygen Demand (BOD) Changes During a Massive Fish Kill Using Multitemporal Landsat-8 Satellite Images in Maninjau Lake, Indonesia

Maninjau Lake is one of Indonesia's lakes for hydroelectric power plants, tourism, and fish farming activities. Some activities around the lake cause pollution, leading to massive fish kill. Therefore, it is necessary to monitor water quality regularly. One of the critical water quality parameters is biochemical oxygen demand (BOD). This study aimed to analyze BOD changes using a remote sensing approach during massive fish kills in Maninjau Lake, Indonesia. Multi-temporal Landsat-8 satellite images are processed to estimate the BOD level based on Wang Algorithm. After that, the estimated BOD value is validated using in situ data measurement. The results of the average BOD concentration that occurred in Lake Maninjau was 1.85 mg/L and showed that R2 was 0.8334, and the standard error was 0.076 between the estimated BOD and in situ data. Furthermore, the average concentration of BOD obtained on 23rd August 2017, 13th December 2017, 30th January 2018, 19th March 2018, and 7th July 2018 are 4.96 mg/L, 4.82 mg/L, 5.31 mg/L, 6.94 mg/L, and 6.60 mg/L, respectively. Increased BOD concentration in January 2018 indicates moderate pollution in the waters. BOD concentration increases after the massive fish kill due to the decaying fish across the lake

Analysis of Urbanisation’s Relationship with Clean Water Supply Ecosystem Services in Sukoharjo Regency, Indonesia

The phenomenon of urban population growth is a global concern which will result in a decrease in the value of ecosystem services in an area. Sukoharjo Regency is an area affected by the development of Surakarta City; therefore, rapid growth is taking place. The objective of this study is to investigate the interplay between urbanization, ecosystem services, and the provision of clean water in Sukoharjo Regency in 2022. The methods used in the study were calculating the percentage of the urban population to determine the level of urbanisation, AHP and overlay to ascertain the ecosystem service score, together with cross-tabulation to establish the relationship between these two variables. The result of this study is that the level of urbanisation produces a pattern that districts in the north tend to comprise a higher level. The clean water supply ecosystem services in Sukoharjo Regency obtained results dominated by the low to medium level. The situation regarding the level of urbanisation and ecosystem services in Sukoharjo Regency reveals a relationship where an increase in the level of urbanisation will reduce the value of ecosystem services

Machine Learning-Based Rice Field Mapping in Kulon Progo using a Fusion of Multispectral and SAR Imageries

The land-conversion of rice fields can reduce rice production and negatively impact food security. Consequently, monitoring is essential to prevent the loss of productive agricultural land. This study uses a combination of Sentinel-2 MSI, Sentinel-1 SAR, along with SRTM (elevation and slope data) to monitor rice fields land-conversion. NDVI, NDBI and NDWI indices are transformed from the annual median composite Sentinel-2 MSI images used to identify different rice fields with another object. A monthly median composite of SAR images from Sentinel-1 data are used to identify cropping patterns of rice fields in the inundation phase. The classification is performed by using the Random Forest machine learning algorithm in the Google Earth Engine (GEE) platform. Random Forest classification is run using 1000 trees, with a 70:30 ratio of training and testing data from sample features extracted by visual interpretation of high resolution Google Earth imagery. In this study, Random Forest classification is effective in computing a high amount of multi-temporal and multi-sensory data to map rice-field land conversion with an accuracy rate of 96.16% (2021) and 95.95% (2017) for mapping paddy fields. From the multitemporal rice field maps in 2017—2021, a conversion of 826.66 hectares of rice-fields to non-rice fields was identified. Based on the spatial distribution, the conversion from rice-field to non-rice field is higher at the area near the roads, built area and Yogyakarta International Airport. Therefore, it is important to assess and ensure that National Strategic Projects are managed with due regard to environmental impacts and food security.

Validating the GIS-based Flood Susceptibility Model Using Synthetic Aperture Radar (SAR) Data in Sengah Temila Watershed, Landak Regency, Indonesia

In Indonesia, especially in regions where natural conditions and human activity coexist, flood disasters are a strong possibility. Flooding regularly has an impact on Sengah Temila, which is a component j/ of Indonesia's West Kalimantan Province. The issue in Sengah Temila is that there is little knowledge of the distribution of flood susceptibility in this region. The GIS-based flood susceptibility model has been widely used in Indonesia, but research dedicated to validating the model is limited. SAR-based analysis has been used for flood mapping in Indonesia, but its use for validating flood models has been limited.  The objective of this study is to identify the optimal weighting scenario for a GIS-based multi-criteria analysis flood model for use in the Sengah Temila Watershed. The GIS-based model is created by merging spatial parameters, including slope, elevation, flow accumulation, drainage density, land use and land cover (LULC), soil type, normalized difference vegetation index (NDVI), curvature, rainfall, distance to river, and topographic wetness index (TWI) with weighted multi-criteria analysis. In addition, Sentinel-1 GRD images from before and after the floods have been retrieved from Google Earth Engine using past floods of the watershed. In order to create a SAR-based flood model, the researchers then integrated and categorized the results. Eleven weighting scenarios were used to create eleven GIS-based flood models. To calculate the degree of spatial similarity, all of these models were contrasted with the SAR-based model using the Fuzzy Kappa approach. We found that in order to achieve ideal weighting, slope, topographic wetness index (TWI), rainfall, and flow accumulation should each be given a larger value

The Atmospheric Dynamics Related to Extreme Rainfall and Flood Events during September-October-November in South Sulawesi

This study was conducted to analyse the occurrence of extreme rainfall and the dynamics of the atmosphere prior to the occurrence of extreme rainfall and flood events in South Sulawesi during September-October-November (South Sulawesi’s dry season). The data used is daily data for the period 2001-2020. Using 50 mm/day and the 90th percentile rainfall threshold of 119 rain stations distributed over 24 regencies, extreme rainfall events in each region were identified. Furthermore, after screening for extreme rainfall events followed by flood events, a composite analysis was carried out to obtain patterns of atmospheric conditions before the extreme rainfall events. The results of the study confirm that spatially, the highest extreme rainfall indices values dominate in the western and northern regions of South Sulawesi, both frequency and intensity indicators. Flood events in South Sulawesi during September-October-November 2001-2020 were recorded as 23 days, of which 19 days were the flood events after extreme rainfall events. The dynamics of the atmosphere before the extreme rainfall event followed by the flood event showed anomalies in precipitable water, 850 mb winds, and 200 mb winds. An increase in the amount of precipitable water and a wind speed of 850 mb, as well as a decrease in wind speed of 250 mb compared to normal in the South Sulawesi region and its surroundings, has resulted in an increase in the formation of rain clouds that have the potential to increase the chance of extreme rainfall

Suitability of Mangrove Tourism Areas in Cilamaya Wetan District, Karawang Regency

The research described here was conducted at the Tangkolak Marine Center (TMC) tourist attraction in Cilamaya Wetan District, Karawang Regency, Indonesia in November and December 2019. This research aimed to analyze the suitability of the mangrove tourism area using PlanetScope sensor Dove-R satellite imagery. The research method consisted of literature review, observation, calculation of the NDVI (Normalized Difference Vegetation Index) formula using PlanetScope sensor Dove-R satellite imagery, and direct measurements of transects and sample plots. The variables used were thickness, density, mangrove types, biota objects, tides, area characteristics, and accessibility. The results showed that mangrove tourism in TMC could be classified into two categories: suitable (65%-80%) and conditionally-compliant. According to the classification, the area is characterized by a mangrove thickness of up to 175.0 meters, a mangrove density between 15-25 tree/100 m2, 3-5 types of mangrove species, and associated biota including mudskipper fish, shrimp, crab, and crane. Meanwhile, the other area classified as conditionally compliant is characterized by a thickness of up to 48.2 meters, a mangrove density of 5-10 tree/100 m2, 2 species of mangrove, and associated biota in the form of mudskipper fish, shrimp, and crab. The research highlights the successful application of remote sensing data, specifically PlanetScope satellite imagery, for studying mangrove tourism areas, indicating its potential as a valuable alternative data source for such investigations