Analisis Pola Perubahan Tutupan Lahan Berdasarkan Metode Spatial Cluster di Provinsi Jawa Barat

Secara empiris, tutupan lahan di Provinsi Jawa Barat menunjukkan adanya sebaran pola perubahan secara signifikan. Informasi pola sebaran ini menjadi penting untuk diketahui agar dapat diidentifikasi penyebabnya. Studi ini mempunyai tujuan untuk menemukenali pola perubahan lahan dari perspektif spasial, khususnya di Provinsi Jawa Barat. Pencapaian tujuan melibatkan analisis pola cluster global dan pola cluster local untuk mengetahui pola dan konsentrasi lokasi perubahan tutupan lahan. Analisis pola cluster global mengidentifikasi pola sebaran data di suatu wilayah, sementara analisis pola cluster lokal mengidentifikasi keberadaan cluster yang terjadi di setiap lokasi. Penentuan pola distribusi spasial menggunakan metode statistik Global Moran yang menghasilkan informasi umum tentang pola distribusi untuk seluruh area. Studi ini juga menganalisis pola setiap titik terhadap pola sekitarnya yang memanfaatkan metode Local Moran untuk penyelesaiannya. Pelibatan Sistem Informasi Geografis (SIG) melengkapi studi ini yang menunjukkan bahwa 8,2% dari luas yang ada di Jawa Barat terjadi perubahan tutupan lahan. Analisis Global Moran memberikan informasi bahwa perubahan lahan di Jawa Barat membentuk pola cluster. Analisis Local Moran menghasilkan 7,94% untuk tipe HH pada lokasi lahan yang mengalami perubahan. Tipe cluster HH ini menunjukkan bahwa lokasi-lokasi yang mengalami perubahan di kelilingi juga oleh lokasi yang berubah

Tropical Peatland Identification using L-Band Full Polarimetric Synthetic Aperture Radar (SAR) Imagery (Study Case: Siak Regency, Riau Province)

From previous research reported that tropical peatland is one of terrestrial carbon storage in Earth, and has contribution to climate change. Synthetic Aperture Radar (SAR) is one of remote sensing technology which is more efcient than optical remote sensing. Its ability to penetrate cloud makes it useful to monitor tropical environment. This research is conducted in a tropical peatland in Siak Regency, Riau Province. This research was conducted to identify tropical peatland in Siak Regency using polarimetric decomposition, unsupervised classifcation ISODATA, and Radar Vegetation Index (RVI) from SAR data that had been geometrically and radiometrically corrected. Polarimetric decomposition Freeman-Durden was performed to analyze radar backscattering mechanism in tropical peatland, which shows that volume and surface scattering was dominant because of the presence of vegetation and open area. Unsupervised classifcation ISODATA was then performed to extract “shrub class”. By assessing its accuracy, the class that represents shrub class in reference map was selected as the selected “shrub class”. RVI then was calculated using a certain formula. Spatial analysis was then conducted to acquire certain information that average value of RVI in tropical peatland tend to be higher than in non-tropical peatland. By integrating selected “shrub class” and RVI, peat classes were extracted. The best peat class was selected by comparing with peatland referenced map which is acquired from the Indonesian Agency for Agricultural Resources and Development (IAARD) using error matrix. In this research, the best peat class yielded 73.5 percent of Producer’s Accuracy (PA), 81.6 percent of User’s Accuracy (UA), 66.1 percent of Overall Accuracy (OA), and 0.1079 of Kappa coefcient (Ks)

Implementation of SExI–FS (Spatially Explicit Individual-based Forest Simulator) Model using UAV Aerial Photo Data Case Study: Jatinangor ITB Campus

Landscape architecture affected by interaction between built and natural environment such as vegetation. Nowadays, landscape architects are using 3D city models for simulations, which requires highly dynamic and time-varying attributes. 3D city modelling structure has been standardized by CityGML, although researches that are related to the storing of dynamic data had been conducted for the past years, it has not been supported by any standard until this very moment. In dynamizer, it is added as a data structure into a CityGML structure that is already existed, although the existing structure is a static one. Kolbe’s research on dynamic data using CityGML called dynamizer could use the spatial data in more dynamic way by changing its geometric, thematic, or appearance data, but its purpose is not specific for trees or vegetation. In this paper, a method of simulating the vegetation growth using SeXI-FS will be discussed to show the dynamic changes that happen in vegetation as part of the dynamic changes in landscape architecture. The result of this research will be used to address the importance of information on vegetation by studying its changes in Jatinangor ITB Campus and as initial research to build dynamizer in CityGML for landscape architecture

Pemodelan Tingkat Layanan Kesehatan Masyarakat Berbasiskan Sistem Informasi Geografis (Wilayah Studi: Kota Bandung)

Abstrak. Layanan kesehatan merupakan hak asasi manusia dan juga merupakan salah satu aspek penting yang menunjang pembangunan suatu bangsa. Fasilitas "“ fasilitas kesehatan memiliki peran penting dalam menjawab kebutuhan masyarakat akan pelayanan kesehatan. Semakin tinggi jumlah penduduk di suatu wilayah, maka kebutuhan akan ketersediaan fasilitas kesehatan pun akan semakin meningkat. Dalam memodelkan tingkat layanan kesehatan masyarakat, faktor aksesibilitas keruangan dan jumlah penduduk merupakan faktor pendukung dalam melakukan pertimbangan. Faktor "“ faktor pendukung tersebut memberikan pengaruh dalam melakukan proses pengolahan data dan analisis. Wilayah studi kasus dalam melakukan penelitian ini adalah Kota Bandung. Metode analisis spasial yang digunakan dalam penelitian ini adalah metode buffering dan pengukuran jarak lurus. Hasil akhir penelitian berupa peta cakupan pelayanan puskesmas dan peta cakupan pelayanan rumah sakit di Kota Bandung. Dari hasil pengolahan data yang dilakukan, banyak sekali fasilitas kesehatan di Kota Bandung yang belum memenuhi standardisasi yang telah ditetapkan oleh pemerintah, ditinjau dari segi pelayanan terhadap jumlah penduduk. Berdasarkan hasil akhir penelitian, terdapat wilayah "“ wilayah yang tercakup dalam lebih dari 1(satu) fasilitas kesehatan

Heritage Smart City Mapping, Planning and Land Administration (Hestya)

A smart city is a concept of urban development that requires different technologies to integrate all city elements into a sustainable city system. Land administration, including three-dimensional (3D) cadaster and planning, is a pre-condition for having a smart city. Land administration in the smart city will be more attractive when the city has a cultural heritage area that must be preserved for economic, social, and territory benefits. This paper describes the development of a multipurpose land administration system prototype of a city, especially in the cultural heritage area. The first activity of this development is to create a 3D city map for documentation and management of cities, especially for cultural heritage areas, and involve the role of the community in participatory mapping. The participatory mapping method is used to form a more detailed 3D building model using simple techniques for measuring the room distance on a building. Then, the 3D city model is stored in a spatial database and management system to visualize, analyze, and manage the data. This research uses the complex area of Kasepuhan Palace, Cirebon City, West Java, Indonesia, as a case study. That area is a cultural heritage area with complicated objects and unique information to document

Heritage Smart City Mapping, Planning and Land Administration (Hestya)

A smart city is a concept of urban development that requires different technologies to integrate all city elements into a sustainable city system. Land administration, including three-dimensional (3D) cadaster and planning, is a pre-condition for having a smart city. Land administration in the smart city will be more attractive when the city has a cultural heritage area that must be preserved for economic, social, and territory benefits. This paper describes the development of a multipurpose land administration system prototype of a city, especially in the cultural heritage area. The first activity of this development is to create a 3D city map for documentation and management of cities, especially for cultural heritage areas, and involve the role of the community in participatory mapping. The participatory mapping method is used to form a more detailed 3D building model using simple techniques for measuring the room distance on a building. Then, the 3D city model is stored in a spatial database and management system to visualize, analyze, and manage the data. This research uses the complex area of Kasepuhan Palace, Cirebon City, West Java, Indonesia, as a case study. That area is a cultural heritage area with complicated objects and unique information to document

3D Modeling of Individual Trees from LiDAR and Photogrammetric Point Clouds by Explicit Parametric Representations for Green Open Space (GOS) Management

The development and management of green open spaces are essential in overcoming environmental problems such as air pollution and urban warming. 3D modeling and biomass calculation are the example efforts in managing green open spaces. In this study, 3D modeling was carried out on point clouds data acquired by the UAV photogrammetry and UAV LiDAR methods. 3D modeling is done explicitly using the point clouds fitting method. This study uses three fitting meth-ods: the spherical fitting method, the ellipsoid fitting method, and the spherical harmonics fitting method. The spherical harmonics fitting method provides the best results and produces an R2 value between 0.324 to 0.945. In this study, Above-Ground Biomass (AGB) calculations were also carried out from the modeling results using three methods with UAV LiDAR and Photogrammetry data. AGB calculation using UAV LiDAR data gives better results than using photogrammetric data. AGB calculation using UAV LiDAR data gives an accuracy of 78% of the field validation results. How-ever, for visualization purposes with a not-too-wide area, a 3D model of photogrammetric data using the spherical harmonics method can be used.ISSN:2220-996

Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami

AbstractIn Indonesia, tsunamis are frequent events. In 2000–2016, there were 44 tsunami events in Indonesia, with financial losses reaching 43.38 trillion. In 2018, a tsunami occurred in the Sunda Strait due to the eruption of the Anak Krakatau Volcano, which caused many fatalities and much building damage. This study aimed to detect the building damage in the Labuan District, Banten Province. Machine learning methods were used to detect building damage using random forest with object-based techniques. No previous research has combined selected predictors into scenarios; hence, the novelty of this study is combining various random forest predictors to identify the extent of building damage using 14 predictor scenarios. In addition, field surveys were conducted two years and nine months after the tsunami to observe the changes and efforts made. The results of the random forest classification were validated and compared with three datasets, namely xBD, Copernicus, and field survey data. The results of this study can help classify the level of building damage using satellite imagery to improve mitigation in tsunami-prone areas

Development of Spatial Model for Food Security Prediction Using Remote Sensing Data in West Java, Indonesia

The food crisis is a problem that the world will face. The availability of growing areas that continues to decrease with the increase in food demand will result in a food crisis in the future. Good planning is needed to deal with future food crises. The absence of studies on the development of spatial models in estimating an area’s future food status has made planning for handling the food crisis suboptimal. This study aims to predict food security by integrating the availability of paddy fields with environmental factors to determine the food status in West Java Province. Food status modeling is done by integrating land cover, population, paddy fields productivity, and identifying the influence of environmental factors. The land cover prediction will be developed using the CA-Markov model. Meanwhile, to identify the influence of environmental factors, multivariable linear regression (MLR) was used with environmental factors from remote sensing observations. The data used are in the form of the NDDI (Normalized Difference Drought Index), NDVI (Normalized Difference Vegetation Index), land surface temperature (LST), soil moisture, precipitation, altitude, and slopes. The land cover prediction has an overall accuracy of up to 93%. From the food status in 2005, the flow of food energy in West Java was still able to cover the food needs and obtain an energy surplus of 6.103 Mcal. On the other hand, the prediction of the food energy flow from the food status in 2030 will not cover food needs and obtain an energy deficit of up to 13,996,292.42 Mcal. From the MLR results, seven environmental factors affect the productivity of paddy fields, with the determination coefficient reaching 50.6%. Thus, predicting the availability of paddy production will be more specific if it integrates environmental factors. With this study, it is hoped that it can be used as planning material for mitigating food crises in the future

Development of Spatial Model for Food Security Prediction Using Remote Sensing Data in West Java, Indonesia

The food crisis is a problem that the world will face. The availability of growing areas that continues to decrease with the increase in food demand will result in a food crisis in the future. Good planning is needed to deal with future food crises. The absence of studies on the development of spatial models in estimating an area’s future food status has made planning for handling the food crisis suboptimal. This study aims to predict food security by integrating the availability of paddy fields with environmental factors to determine the food status in West Java Province. Food status modeling is done by integrating land cover, population, paddy fields productivity, and identifying the influence of environmental factors. The land cover prediction will be developed using the CA-Markov model. Meanwhile, to identify the influence of environmental factors, multivariable linear regression (MLR) was used with environmental factors from remote sensing observations. The data used are in the form of the NDDI (Normalized Difference Drought Index), NDVI (Normalized Difference Vegetation Index), land surface temperature (LST), soil moisture, precipitation, altitude, and slopes. The land cover prediction has an overall accuracy of up to 93%. From the food status in 2005, the flow of food energy in West Java was still able to cover the food needs and obtain an energy surplus of 6.103 Mcal. On the other hand, the prediction of the food energy flow from the food status in 2030 will not cover food needs and obtain an energy deficit of up to 13,996,292.42 Mcal. From the MLR results, seven environmental factors affect the productivity of paddy fields, with the determination coefficient reaching 50.6%. Thus, predicting the availability of paddy production will be more specific if it integrates environmental factors. With this study, it is hoped that it can be used as planning material for mitigating food crises in the future