Karst of Gunung Sewu Land Use and Land Covers Dynamics: Spatio-Temporal Analysis

A 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

Implementing Support Vector Machine Algorithm for Early Slum Identification in Yogyakarta City, Indonesia Using Pleiades Images

Slums 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

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

Coastal Vulnerability Study on Potential Impact of Tsunami and Community Resilience in Pacitan Bay East Java

Collisions or harsh shifting of earth plates accompanied by an earthquake in the ocean would pose a potential tsunami. The coastal area in Pacitan Bay East Java faces directly to the Indian Ocean and is prone to tsunami disasters. This study aims to determine the vulnerability level of the area and the resilience of coastal communities against tsunamis. The geographic Information System (GIS) method was used in this study. This study applied weighted overlay calculation with four components: elevation, slope, and distance from the beach and the river to measure the vulnerability level. Moreover, Coastal Community Resilience (CCR) method was applied to measure the predictive response of the communities. The results indicated that most of the area in Pacitan Bay (79,70%) was categorised into high to very high vulnerable against tsunamis. The CCR results showed the low index structure design and post-disaster recovery elements

Simulated Mitigation of Tsunami Disasters in the Coastal Area of Purworejo Regency, Central Java, Indonesia

The coastal area of Purworejo Regency has the potential to be hit by a mega-tsunami disaster because it includes the southern coast of Java Island which is faced with seismic gaps that may produce large earthquakes in the future. This study aims to simulate tsunami disaster mitigation in the coastal area of Purworejo Regency in an effort to raise awareness and increase the community capacity for dealing with potential tsunamis so that the level of loss can be minimized. The tsunami risk analysis is based on the Disaster Crunch model, which is a combination of vulnerability analysis based on the weighted overlay quantitative method and tsunami hazard analysis based on tsunami inundation reduction modeling and cost distance analysis. The planning of the tsunami evacuation route is based on the network analysis method. The tsunami-risk area with a run-up scenario of three meters in the coastal area of Purworejo Regency 126,29 square kilometers or about 72,52% of the total coastal area. There are five tsunami evacuation plan points, with five main tsunami evacuation routes that lead directly to each of these points

Spatial Distribution of Potential Pollution Load Point Source of Bedadung River in the Urban Area Segment

The variety of community activities in urban areas and a poor domestic sewage system are thought to affect the water quality of the Bedadung River. The high level of river pollution is caused by the high amount of polluting waste that enters, thus increasing the pollution load. Point source pollutant sources are sources of pollutants originating from certain sources that can be identified directly, such as domestic waste disposal, industrial activities, and others. Organic pollutants from domestic waste that enter the river can reduce the dissolved oxygen concentration in the river which can affect the quality of river water. The quality of water from pollutants can be indicated by knowing the concentration of the oxygen content in the water. To find out, it is necessary to measure the amount of BOD (Biochemical Oxygen Demand). The BOD value can be used as an index number to measure the level of pollutants from waste in a water system. In addition, changes in land use are also the impact of population growth and increased human activity. Land-use changes that ignore the principles of ecosystem sustainability tend to harm the environment, including a decrease in water quality. This study was conducted to determine the potential point source pollution load of Bedadung River in the Urban Area using information about the Bedadung River both spatial data and pollutant water quality (BOD) data. All data is combined and processed using Geographical Information Systems (GIS) technology. The data were analyzed and plotted into a map depicting the distribution of potential point source pollutant loads in the Bedadung River Urban Area Segment. The results of the analysis of land cover pollutant source areas based on the boundary include rice fields of 18.97%, fields of 5.98%, gardens of 12.85%, rivers of 12.25%, and settlements having the largest land area of 49.96% of the total area of the study area. The results of the calculation of the potential point source pollution load showed that the highest potential pollution load value was in the Kaliwates village of 13.966 kg/day, the lowest was in the Antirogo village of 0.004 kg/day and the total point source pollution load was 36.31 kg/day

Spatial Dynamics of Land Cover Change in Ternate Tengah District, Ternate City, Indonesia

The phenomenon of urban growth has become an important issue that affects the land use system and land cover in a region for several reasons, such as population growth and the economy. This phenomenon has also become one of the main environmental issues lately because it has devastated urban ecosystems. Ternate Tengah District has the highest population growth rate in Ternate City and has experienced extensive urban development due to several reasons, such as the pace of urbanization, economic growth, and population. Urbanization accelerates the demand to land for living. As a result, there will be gaps or disparities between land needs and available land, a decline in environmental carrying capacity, and potential environmental harm in the future. Spatial modeling of future land covers is needed to provide data on policy-making. GIS and remote sensing methods have been widely introduced, but the most effective one is CA-Markov. This model has been used in various areas worldwide, but its application to predicting land use change in the populous city of a small island under threat of volcanic hazards like Ternate is limited. This study aims to evaluate and forecast the land-use changes brought on by urbanization in Ternate City's Central Ternate District. We used a cellular automata-Markov chain to examine and forecast land cover changes in 2002, 2012, 2022, and 2032. The findings indicate that residential area development will increase along with population expansion and land demand. The results of this study can support the policy-making related to the future arrangement and utilization of space in The Central Ternate District

Spatial Variability of Total Nitrogen, pH, and Organic Carbon in Organic and Inorganic Farming

Efforts have been made to transform traditional farming practices to organic method in order to ensure sustainable production and environmental conservation. Studying the differences between these two practices through mapping provide insight into the effectiveness of the transformation, as soil characteristics varies in space. Therefore, this study examined the spatial variability of Total Nitrogen, pH, and Organic Carbon in Lombok Kulon village, Wonosari Sub-district, Bondowoso district. The method used consists of: (a) data collection; (b) data input in GIS; (c) processing data, (d) analysis and (e) presentation of results. The results showed that soil organic content in the area was generally low. Furthermore, indicators such as pH, Organic Carbon and Total Nitrogen do not present significant differences between organic and Inorganic practices. The use of Kriging in GIS environment to analyze spatial variability showed variations and inform management decisions relevant to Total Nitrogen, pH and Organic Carbon

Spatial and Seasonal Patterns of Flood Inundation in Lokoja, Kogi State, Nigeria

The study examines spatial patterns of flood inundation in Lokoja, Kogi state, Nigeria. Maximum Likelihood Classifier algorithm of the supervised land use/cover classification technique was utilized. The results obtained from the analysis were used to estimate the magnitude and visualize the seasonal and spatial pattern of flood inundation event. Eight Landsat Images comprising of two sets for each year (dry and wet seasons) were acquired from the portal of United States Geological Survey (2018). The Landsat images were classified into land cover classes such as Built Up, Vegetation and Water Body. After completing the land cover classification, the area of each class was determined and converted to square kilometers and percentages for both wet and dry seasons. Based on the classification, the brown colour depicts the built-up areas, blue for water body, and green for vegetation. Finally, accuracy assessment was carried out using historical Google Earth images, informed knowledge of the area, and GPS coordinates. ArcMap 10.5 was used to produce land use/cover maps for the study period. The result overall, revealed the effect of flood inundation to be more intense on vegetation. 1.62%, 4.60%, 23.05% and 6.43% of vegetated land was lost in 1999, 2009, 2012 and 2018, respectively.  Therefore, efforts to improve resilience against variable weather, flood inundation and seasonal uncertainties should be encouraged

Factors Affecting Adoption of Climate Change Adaptation Strategies by Small Holder Farmers in Mountain and Lowland Agro-ecological Zones of Eastern Uganda

Farmers in tropical rural areas are confronted with several challenges but outstandingly climate change which can only be overcome by adopting to climate change resilience strategies. This study assessed the factors affecting adoption of climate change resilience strategies in Muyembe sub-county, Bulambuli district, Uganda. We used questionnaires, interviews, focused group discussions and field observations to collect the required data, which was analyzed using basic descriptive statistics and logistic regression model. Results indicate that, the dominant climate change resilience strategies adopted in the study were, soil/water conservation (65%), drought resistant crop varieties (59.4%), and irrigation (55.6). Results of the logistic regression indicated that, gender and family size were the most important factors that influenced adoption of climate change resilience strategies with coefficient -0.86 and P0.05, and0.18 and P0.05 respectively. On the other hand, the barriers to adoption of the same by majority farmers were dominated by financial constraints and adulteration of farm inputs at 93.4% and 74% respectively. We concluded that, many farmers are still locked in indigenous practices that have made them vulnerable to climate change effects characterized by low yields, crop failure hence low incomes, poverty and food insecurity. We recommended that, government should support the adaptation strategies to climate change by the smallholder farmers technically by providing both ground and surface water irrigation facilities and financially by providing agricultural loans as well as focusing on promoting awareness and advancing education on climate change to farmers through knowledge and skill sharing platforms such as training, conferences, and seminars