The alarming rate of global mangrove forest degradation corroborates the need for providing fast, up-to-date and accurate mangrove maps. Conventional scene by scene image classification approach is inefficient and time consuming. The development of Google Earth Engine (GEE) provides a cloud platform to access and seamlessly process large amount of freely available satellite imagery. The GEE also provides a set of the state-of-the-art classifiers for pixel-based classification that can be used for mangrove mapping. This study is an initial effort which is aimed to combine machine learning and GEE for mapping mangrove extent. We used two Landsat 8 scenes over Agats and Timika Papua area as pilot images for this study; path 102 row 64 (2014/10/19) and path 103 row 63 (2013/05/16). The first image was used to develop local training areas for the machine learning classification, while the second one was used as a test image for GEE on the cloud. A total of 838 points samples were collected representing mangroves (244), non-mangroves (161), water bodies (311), and cloud (122) class. These training areas were used by support vector machine classifier in GEE to classify the first image. The classification result show mangrove objects could be efficiently delineated by this algorithm as confirmed by visual checking. This algorithm was then applied to the second image in GEE to check the consistency of the result. A simultaneous view of both classified images shows a corresponding pattern of mangrove forest, which mean the mangrove object has been consistently delineated by the algorithm.
Branchless banking is a banking service based on ICT, one of which is an agent. This service can be found in both urban and rural areas. This research aims at identifying the existence and advantages of agents-based branchless banking. The locations of branchless banking agents was also analyzed to explore the spatial pattern. This research applies a quantitative method. Agents and customers are selected by undergoing a survey. The data are obtained by structurally interviewing agents on the kinds of services they provide and the benefits of services for customers. Meanwhile, an accidental sampling technique is used to select customers. Besides, spatial analysis is applied using kernel density analysis and accessibility analysis. The result of the research shows that the advantages of branchless banking through agent alter the perception of spatial benefits to access banking services. The space for banking services is in the same place for other purposes such as business units and home. In urban areas, the making use of agents is mainly based on the factors of being closely located and easy to access. Meanwhile, in suburban and rural areas, conveniences are of primary consideration to make use of agents. Concerning the geographical field, location plays an essential role in the substantial changes related to the spatial functions of economic services, mainly banking services, which are not always located in downtown areas. On the contrary, it is possible to locate economic services in rural areas, with the help of ICT-based services. The result of the analysis shows that banks do not optimally consider location, range and service area for branchless banking represented by agents.
The existence of high-spatial resolution imagery that are now available free by Planet Labs opens up opportunities in detailed scale mapping research, both as basic data and as reference data for geometry accuracy assessment. However, the use of several satellite sensors types with different recording times is the biggest obstacle in the use of high spatial resolution imagery as reference data because the shoreline instantaneous imaging at the data acquisition time does not consider the spatial and temporal variability of the shoreline boundaries. The purpose of this study was to analyze the effect of tidal correction on shoreline mapping in Jepara Regency using Landsat 8 OLI imagery in 2018.The effect of tidal correction analysis is done by comparing the position of the shoreline corrected by tides with the shoreline that is not corrected for tides. The influence of tidal correction is marked by differences in the position of the two shorelines. Shoreline shift calculation when there is a difference in tidal conditions between the test shoreline and the reference shoreline is carried out using the theory of right triangle (also called as one-line shift method).Based on the analysis of tidal correction effects, it is known that the shift in shoreline position after tidal correction varies from 0.21 m to 1.8 m, the value does not exceed one pixel of the PlanetScope image (3 m) so that tidal correction does not needs to be done because the effect is insignificant and undetectable on PlanetScope imagery.
Keywords: tidal correction, shoreline, Planetscope, Landsat 8 OLI, Jepara
Photos shared by social media users act as an approach in identifying tourist activity. Popular tourist attractions are judged based on the large number of photos or high photo density. In Bantul Regency, Indonesia, beaches have diverse attractions which tourists can enjoy and immortalize through photos. Analyzing the contents of photos on Flickr provides information on the type(s) of beaches or coastal attractions preferred by tourists. This study examined the availability of geotagged Flickr photos to assist in making relevant beach tourism management policies. It employed pattern analysis with the average nearest neighbor, density analysis with kernel density estimation, image content analysis with tourist attraction as the variable, and overlay analysis to formulate recommendations for beach tourism management based on the popularity level of the attractions. The results indicate that each of the local beaches offers different attractions with varying popularity levels and that natural beauty is the main feature attracting tourists to visit all beaches, except Baros. Based on the pattern analysis, the Flickr photos are clustered on several beaches of high popularity, such as Parangtritis, Baros, Depok, and Cemara Sewu. By using geotagged Flickr photo data and refers to the concept of tourism supply and demand, recommendations for developing the attractive features on these beaches have been compiled according to their respective themes and popularity levels to target specific tourist market segments and design integrated tour or travel packages.
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