The development of big cities in Indonesia especially Jakarta City which is developing very rapidly is marked by the rapid development of physical development, thus affecting the increasing population and land use resulting in a decrease in the amount of vegetation cover. The main problem of the existence of Open Green Space (RTH) in Jakarta is the increasingly reduced / limited land and inconsistencies in implementing spatial planning. The reduced green space is caused by changes in land use that is relatively significant so that green space in Jakarta has not met the target of 30% of the total area, especially in the District of Kramatjati. The purpose of this study is to calculate the need for green space within a district. The method used is the initial data processing (radiometric correction, pancarrage, mosaic, cropping) and calculation of vegetation density values based on Normalized Defference Vegetation Index (NDVI). Based on the results of NDVI calculations using Pleiades Image Data in 2015, that in Kramat Jati Subdistrict there were 225.17 ha as vegetation areas, while 918.93 ha were non-vegetation areas. The results of the calculation are then divided into density levels, ie, a rare density of 48,595 ha, medium density of 34,446 ha, and high density of 160,609 ha. The conclusion obtained is that green open space in Kramat Jati Sub-district is planned to cover 12.38% of the entire Kramat Jati area. However, based on NDVI results, green open space in Kramatjati has reached 19.68% of the entire district area. And  terms of quantity, then the amount of green space has been fulfilled. Key Word : open green space (RTH), Normalized Defference Vegetation Index (NDVI), Pleiades Image ABSTRAKPerkembangan kota-kota besar di Indonesia khususnya Kota Jakarta yang berkembang dengan sangat pesat ditandai perkembangan pembangunan fisik yang cepat, Sehingga mempengaruhi semakin meningkatnya jumlah penduduk dan pemanfaatan lahan yang mengakibatkan berkurangnya jumlah tutupan vegetasi. Permasalahan utama keberadaan Ruang Terbuka Hijau (RTH) di Kota Jakarta adalah semakin berkurangnya/keterbatasan lahan dan ketidak konsisten dalam menerapkan tata ruang. Berkurangnya RTH disebabkan oleh perubahan penggunaan lahan yang relatif signifikan sehingga RTH Jakarta belum memenuhi target 30% dari total luas wilayahnya terutama di Kecamatan Kramatjati. Tujuan dari penelitian ini adalah untuk menghitung kebutuhan RTH dalam satu lingkup kecamatan. Metode yang digunakan adalah pengolahan data awal (koreksi radiometrik, pansharpen, mozaik, cropping) dan perhitungan nilai kerapatan vegetasi berdasarkan Normalized Defference Vegetation Indeks (NDVI). Berdasarkan hasil perhitungan NDVI dengan menggunakan data Citra Pleiades Tahun 2015, bahwa di Kecamatan Kramat Jati terdapat 225,17 ha merupakan daerah vegetasi, sedangkan 918,93 ha adalah daerah non vegetasi. Hasil perhitungan tersebut kemudian di bagi dalam tingkat kerapatan yaitu kerapatan jarang sebesar 48.595 ha, kerapatan menengah sebesar 34.446 ha, dan kerapatan tinggi sebesar 160.609 ha. Kesimpulan yang diperoleh adalah RTH di Kecamatan Kramat Jati direncanakan seluas 12,38 % dari seluruh wilayah Kramat Jati. Namun, berdasarkan hasil NDVI, RTH di Kramatjati sudah mencapai 19,68% dari seluruh luas kecamatan dan dari segi kuantitas, maka jumlah RTH telah terpenuhi.    Kata Kunci: Ruang Terbuka Hijau (RTH), Normalized Defference Vegetation Indeks (NDVI), Citra Pleiades

The development of computer vision technology as a type of artificial intelligence is increasing rapidly in various fields. This method uses deep learning methods based on artificial neural networks, a well-performed algorithm in multi-parameter analysis. One of the development of computer vision models and algorithms is for a thematic digital image classification, such as environmental analysis. Remote sensing based digital image classification is one of the reliable tools for environmental quality analysis. This study aims to perform neural network optimization for the analysis of the urban environment comfort based on satellite data. The input data used are 4 types of geobiophysical indexes as urban environmental comfort parameters derived from cloud-free annual mosaics Landsat-8 remote sensing satellite data. The results obtained in this study indicate that the 1 hidden layer neural network architecture with 16 neurons for the classification of urban environmental comfort and 10 other land cover classes is quite good. The result of the classification using this optimized artificial neural network shows that the distribution of classes is very uncomfortable which dominates the Greater Jakarta area and its surroundings. For other classes in the study area, some are uncomfortable and rather comfortable.  By using this method, we obtained a fast classification training time of 18 seconds for 145 iterations to achieve an RMS Error of 0.01, and has a fairly high classification accuracy overall 89% with a Kappa coefficient of 0.88, while the 2 hidden layer neural network architecture does not succeed in achieving convergence

The role of agriculture is directly related to SDG No.2, which is running a programme until 2030 to reduce national poverty, eradicate hunger by increasing food security and improving nutrition and support sustainable agriculture. Problems faced include the reduction in agricultural land, which results in lower rice production, and the limited information on the monitoring of paddy fields using spatial data. The purpose of this study is to identify paddy fields using LAPAN A3 satellite imagery based on OBIA classification. The data used were from LAPAN A3 multispectral imagery dated 19 June 2017, Landsat 8 imagery dated 17 June 2017, DEM SRTM (BIG), and the Administrative Boundary Map (BIG). The analysis method was segmentation by grouping image pixels, and supervised classification by taking several sample areas based on Random Stratified Sampling. The results will be carried using a confusion matrix. The classification results produced four classes; watery paddy fields, vegetation paddy fields, fallow paddy fields, and non-paddy fields, using of the green, red, and NIR bands for the LAPAN A3 data. From the results of the segmentation process, there remain some oversegmented features in the appearance of the same object. Oversegmentation is due to an inaccurate value assignment to each algorithm parameter when the segmentation process is performed. For example, watery paddy fields appear almost the same as open land (fallow paddy fields), the water object is darker purple. The visual classification results (Landsat 8 data) are considered as the reference for the digital classification results (LAPAN A3). Forty-eight samples were taken and divided into four classes, with each class consisting of 12 samples. The results of the accuracy test show that the total accuracy of the object-based digital classification for visual classification is 62.5% with a Kappa accuracy value of 0.5. The conclusion is that LAPAN A3 data can be used to identify paddy fields based on spectral resolution and to complement Landsat 8 data. To improve the accuracy of the classification results, more samples and the correct RGB composition are needed.