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Validating the GIS-based Flood Susceptibility Model Using Synthetic Aperture Radar (SAR) Data in Sengah Temila Watershed, Landak Regency, Indonesia
Full text linkIn 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 Peri-Urban Gentrification Process in Cisauk Subdistrict, Tangerang Regency, Indonesia
Full text linkGentrification is a phenomenon that occurs in many regions. Gentrification is characterized by the influx of more affluent residents into initially low-value areas. This influx increases the value of these areas, potentially leading to the displacement of people who cannot afford to remain in the neighborhood. This study aims to examine the cause and impacts of peri-urban gentrification in Cisauk District, Tangerang Regency. This study uses qualitative and remote sensing-based analysis to investigate gentrification's impact on six Cisauk sub-district villages. We used primary and secondary data for the analysis. The primary data was collected from observations, interviews, questionnaires, and Google Earth Engine data catalog to get Landsat 7 and 8 image-ries. The secondary was collected from the Central Bureau of Statistics. The Landsat data was used to identi-fy the change on the physical aspect, while the others were used to analyze the non-physical aspect. After-ward, positive and negative effects were explored, emphasizing residents' responses and adaptations. The re-sults showed that Cisauk Subdistrict faced a gentrification process driven by locational factors (i.e., proximity to toll road) and local government policies (i.e., spatial planning policy designating it as a high-density residential area). The positive impact of the gentrified area is that the region is developing economically much better, and many areas function better economically and so benefit the region and its people. On the other hand, the negative impacts of gentrified areas are that indigenous people are threatened with being forced to leave their villages, there are significant increases in land prices, and changes in people's social lives occur. To minimize the negative impact of gentrification, the government should promote the capacity of low-income households to access its benefits
Friends and Neighbours: Electoral Geography of 2020 Local Election in Metro City, Lampung, Indonesia
Full text linkThis 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”
Karst of Gunung Sewu Land Use and Land Covers Dynamics: Spatio-Temporal Analysis
Full text linkA study of karst land use and land cover dynamics is critical for managing karst areas, which provide many pivotal services for people. This study aims to study such dynamics, especially in relation to the karst of Gunung Sewu, due to its development as a new emerging sector. Using a mixed methods approach, the study combines spatial data analysis with qualitative analysis. Spatial analysis was performed to examine the dynamic of the land cover derived from 2013 and 2021 Landsat 8 imagery, analyzed with the Google Earth Engine tool, together with analysis of spatial patterns using Global Moran’s I and LISA. The spatial analysis results were complemented by a qualitative analysis of the environmental history and development trends, as an explanatory method. The land cover analysis reveals a conversion from vegetation to agriculture, while the spatial pattern analysis shows that such conversion has mostly taken place in the northern part of the study area of Wonosari Basin. The environmental history of teak forest exploitation and agriculture is key to understanding current land use related to the emerging tourism sector, which is fundamental to the region. To manage the negative impacts, sustainable land use with a firm policy framework urgently needs to be implemented
Investigation of the Development of Tropical Storm Nicholas based on Global and Regional Climate Data
Full text linkThis 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
Monitoring Biochemical Oxygen Demand (BOD) Changes During a Massive Fish Kill Using Multitemporal Landsat-8 Satellite Images in Maninjau Lake, Indonesia
Full text linkManinjau 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
The Atmospheric Dynamics Related to Extreme Rainfall and Flood Events during September-October-November in South Sulawesi
Full text linkThis 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
Implementing Support Vector Machine Algorithm for Early Slum Identification in Yogyakarta City, Indonesia Using Pleiades Images
Full text linkSlums are one of the urban problems that continue to get the attention of the government and the city of Yogyakarta. Over time, cities continue to experience changes in land use due to population growth and migration. Therefore, it is necessary to monitor the existence of slums continuously. The objectives of this study are to conduct early identification of the slum using the Support Vector Machine (SVM) Algorithm, which is applied to the Pleiades Image in parts of Yogyakarta City, to test the accuracy of the slum mapping results generated from the SVM compared to the Slum Map of the KOTAKU Program. The data used are Pleiades Image, administrative maps, and existing slum maps of the KOTAKU Program, which are used to test the accuracy. The method used is Machine Learning with a Support Vector Machine Algorithm. The parameters used for early identification of the slums are the characteristics of the object (characteristics of buildings), settlement (density and shape), and the environment (location and its proximity to rivers and industries). We separate slum and non-slum based on texture, morphology, and spectral approaches. Based on the accuracy test results between the SVM classification results map of the slum and the map from the KOTAKU Program, the accuracy is 86.25% with a kappa coefficient of 0.796
Spatial Matrices of Urban Expansion in Lafia, North-Central Nigeria
Full text linkRapid urbanisation in African cities has caused considerable problems by hindering their ability to meet infrastructure and service needs, resulting in rising land-use consumption. This study examines how land use/land cover change in Lafia, a city in North-central Nigeria, has impacted the city's boundaries between 1999 and 2019 and includes a projection using GIS simulation of land use/ land cover to 2029. The methodology includes remote sensing techniques, spatiotemporal analysis of geographical measurements, and statistical models. This study involved spatial analysis and projection of city growth from 1999 to 2029 in Lafia using GIS. This analysis focuses on the changes in built-up areas, vegetal cover, bare land, and water bodies using land-use/landcover data. The results indicated significant urban expansion and its impact on the city's spatial patterns. The Urban Expansion Differentiation Index (UEDI) and Urban Expansion Intensity Index (UEII)were used to assess urban sprawl and socioeconomic patterns such as population density and density gradient. High residential and employment densities, varied land uses, continuous development, and multi-modal transportation are all important for sustainable urban growth. The study indicates a direct relationship between population growth and urban expansion, as seen in Lafia. Furthermore, the findings suggest that cities grow beyond their typical boundaries, resulting in peri-urban expansion, as shown in the Alakio districts of the Lafia Metropolis. The study findings have important implications for urban growth policy and land use/land cover change. They will contribute to a better understanding of the effects of urban growth on the spatial matrix and morphology of cities, assisting city planners in recognizing these effects. Furthermore, the study adds evidence to the continuing debate about urban expansion, liveability, and spatial sustainability in African cities. The thorough examination of land use/land cover change in Lafia sheds light on the spatial dynamics of urbanisation and its implications for sustainable urban development
Land Use Change Modelling Using Logistic Regression, Random Forest and Additive Logistic Regression in Kubu Raya Regency, West Kalimantan
Full text linkKubu Raya Regency is a regency in the province of West Kalimantan which has a wetland ecosystem including a high-density swamp or peatland ecosystem along with an extensive area of mangroves. The function of wetland ecosystems is essential for fauna, as a source of livelihood for the surrounding community and as storage reservoir for carbon stocks. Most of the land in Kubu Raya Regency is peatland. As a consequence, peat has long been used for agriculture and as a source of livelihood for the community. Along with the vast area of peat, the regency also has a potential high risk of peat fires. This study aims to predict land use changes in Kubu Raya Regency using three statistical machine learning models, specifically Logistic Regression (LR), Random Forest (RF) and Additive Logistic Regression (ALR). Land cover map data were acquired from the Ministry of Environment and Forestry and subsequently reclassified into six types of land cover at a resolution of 100 m. The land cover data were employed to classify land use or land cover class for the Kubu Raya regency, for the years 2009, 2015 and 2020. Based on model performance, RF provides greater accuracy and F1 score as opposed to LR and ALR. The outcome of this study is expected to provide knowledge and recommendations that may aid in developing future sustainable development planning and management for Kubu Raya Regency