Dynamical characterization of land use classification using multispectral remote sensing data for Guadalajara region

<p><strong>Abstract.</strong> Large amounts of data can be sensed and analyzed to discover patterns of human behavior in cities for the benefit of urban authorities and citizens, especially in the areas of traffic forecasting, urban planning, and social science. In New York, USA, social sensing, remote sensing, and urban land use information support the discovery of patterns of human behavior. This research uses two types of openly accessible data, namely, social sensing data and remote sensing data. Bike and taxi data are examples of social sensing data, whereas sentinel remote sensed imagery is an example of remote sensing data. This research aims to sense and analyze the patterns of human behavior and to classify land use from the combination of remote sensing data and social sensing data. A decision tree is used for land use classification. Bike and taxi density maps are generated to show the locations of people around the city during the two peak times. On the basis of a geographic information system, the maps also reflect the residential and office areas in the city. The overall accuracy of land use classification after the consideration of social sensing data is 85.3%. The accuracy assessment shows that the combination of remote sensing data and social sensing data facilitates accurate urban land use classification.</p>

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PEMETAAN WILAYAH LAHAN KERING MENGGUNAKAN DATA PENGINDERAAN JAUH

Akademika Jurnal Ilmiah Media Publikasi Ilmu Pengetahuan dan Teknologi ◽

10.31314/akademika.v6i2.48 ◽

2017 ◽

Vol 6 (2) ◽

Author(s):

Arthur Gani Koto

Keyword(s):

Remote Sensing ◽

Land Use ◽

Agricultural Development ◽

Agricultural Sector ◽

Landsat Imagery ◽

Remote Sensing Data ◽

Landsat 8 ◽

Land Use Classification ◽

Dry Land ◽

Sensing Data

Dry land occupies the largest area (90%) and has a strategic position in agricultural development activities in Indonesia. The biggest potential of natural resources in the agricultural sector in the district was reached 40.26%. One of the data provider of effective and efficient in terms of development activities and development of the region is remote sensing data. The purpose of this study is to map the area of dry land with the help of remote sensing data. Landsat imagery 8 extracted to obtain land cover information which is then further processed to produce a land use classification is based on the knowledge based classification. Analyzed land use to obtain the map of dry land. The results showed that the District of Wonosari has an area of dry land scattered in all districts and has an area of 185. 733 km2. Dry land area consists of mixed farms (162.811,8 km2) and bare land (22.921,2 km2). Tanah kering menempati area terbesar (90%) dan memiliki posisi strategis dalam kegiatan pembangunan pertanian di Indonesia. Potensi sumber daya alam terbesar di sektor pertanian di kabupaten ini mencapai 40,26%. Salah satu penyedia data yang efektif dan efisien dalam hal kegiatan pengembangan dan pengembangan kawasan adalah data penginderaan jauh. Tujuan dari penelitian ini adalah untuk memetakan daerah lahan kering dengan bantuan data penginderaan jarak jauh. Citra landsat 8 diekstraksi untuk mendapatkan informasi tutupan lahan yang kemudian diproses lebih lanjut untuk menghasilkan klasifikasi penggunaan lahan berdasarkan klasifikasi berbasis pengetahuan. Menganalisis penggunaan lahan untuk mendapatkan peta lahan kering. Hasil penelitian menunjukkan bahwa Kabupaten Wonosari memiliki lahan kering yang tersebar di semua kecamatan dan memiliki luas wilayah 185. 733 km2. Luas lahan kering terdiri dari lahan pertanian campuran (162,811,8 km2) dan lahan kosong (22.921,2 km2).

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