PREDIKSI SUHU PERMUKAAN MENGGUNAKAN ARTIFICIAL NEURAL NETWORK-CELLULAR AUTOMATA DI WILAYAH CIREBON DAN SEKITARNYA

2021 ◽  
pp. 153
Author(s):  
Moh. Dede ◽  
Millary Agung Widiawaty ◽  
Yanuar Rizky Ramadhan ◽  
Arif Ismail ◽  
Wiko Nurdian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cellular Automata ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Radiative Transfer Equation ◽  
Landsat 8 ◽  
Line Density ◽  
Urban Heat ◽  
Artificial Neural

Ekspansi lahan terbangun melalui kehadiran pemukiman dan kawasan industri berimplikasi pada berkurang lahan bervegetasi di wilayah Cirebon dan sekitarnya. Kondisi menyebabkan peningkatan suhu yang berpotensi memunculkan urban heat island. Perubahan suhu dapat diprediksi menggunakan pemodelan spasial dinamis sebagai bagian dari proses perubahan lansekap. Penelitian ini bertujuan untuk memprediksi dinamika suhu permukaan di wilayah Cirebon dan sekitarnya menggunakan algoritma Artificial Neural Network-Cellular Automata (ANN-CA) dengan melibatkan variabel spasial seperti kepadatan bangunan, kerapatan vegetasi, dan kepadatan jaringan jalan. Sumber data yang digunakan dalam penelitian ini berasal dari citra Landsat-5 TM dan Landsat-8 OLI pada tahun 1999, 2009, serta 2019, sedangkan data jaringan jalan berasal dari OpenStreetMaps. Suhu permukaan diperoleh dari kanal termal yang diolah menggunakan Radiative Transfer Equation. Sementara variabel lainnya diperoleh dari Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) dan line density. Penelitian ini menunjukkan suhu permukaan hasil pemodelan ANN-CA pada tahun 2019 memiliki rerata sebesar 22,61 °C. Model ini memiliki overall accuracy 0,63 dan overall kappa sebesar 0,52. Bila dibandingkan dengan nilai aktual, model ini memiliki nilai r-square mencapai 0,80 dengan selisih sebesar 0,54 °C yang layak untuk prediksi suhu permukaan di masa mendatang. Model ANN-CA menunjukkan sebaran suhu permukaan yang lebih tinggi berpusat pada wilayah kota dan peri-urban. Kajian terkait prediksi suhu permukaan diharapkan dapat menjadi perhatian utama dalam mewujudan resiliensi perkotaan.

scholarly journals DINAMIKA SUHU PERMUKAAN DAN KERAPATAN VEGETASI DI KOTA CIREBON

2019 ◽  
Vol 6 (1) ◽  
pp. 23-31
Author(s):  
Moh Dede ◽  
Galuh Putri Pramulatsih ◽  
Millary Agung Widiawaty ◽  
Yanuar Rizky Rizky Ramadhan ◽  
Amniar Ati
Keyword(s):  
Transfer Equation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Radiative Transfer Equation ◽  
Landsat 8 ◽  
Heat Island ◽  
Landsat 8 Oli ◽  
Multi Temporal ◽  
Urban Heat ◽  
Landsat 5 Tm

Peningkatan suhu udara merupakan dampak dari pemanasan global serta berkurangnya vegetasi. Pada kawasan perkotaan, peningkatan suhu udara secara signifikan dapat memunculkan fenomena urban heat island yang dalam jangka panjang mampu mengubah iklim mikro. Estimasi suhu permukaan dan kerapatan vegetasi diperoleh dari data satelit penginderaan jauh secara multi-temporal. Penelitian ini bertujuan untuk menganalisis dinamika suhu permukaan dan kerapatan vegetasi di Kota Cirebon. Penelitian ini memanfaatkan data citra Landsat-5 TM dan Landsat-8 OLI yang divalidasi dengan data MODIS pada periode tahun 1998, 2008, serta 2018. Nilai suhu permukaan diekstraksi dengan radiative transfer equation, sedangkan informasi kerapatan vegetasi diperoleh dengan normalized difference vegetation index (NDVI). Interaksi antara suhu permukaan dan kerapatan vegetasi diketahui melalui analisis korelasi spasial. Sepanjang tahun 1998 hingga 2018 terjadi peningkatan suhu permukaan sebesar 1.18 oC yang disertai dengan menurunnya area bervegetasi rapat hingga 12.683 km2. Penelitian ini juga menunjukkan korelasi negatif yang signifikan antara suhu permukaan dan kerapatan vegetasi di Kota Cirebon. Suhu permukaan tertinggi terpusat pada CBD, pelabuhan, area rawan kemacetan, kawasan industri, dan terminal. Berdasarkan kajian ini, upaya menanggulangi suhu permukaan di Kota Cirebon perlu ditangani melalui penyediaan ruang terbuka hijau, green belt, maupun reforestrasi.

scholarly journals Mapping soil salinity using a combined spectral and topographical indices with artificial neural network

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0228494
Author(s):  
Vahid Habibi ◽  
Hasan Ahmadi ◽  
Mohammad Jafari ◽  
Abolfazl Moeini
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Geographical Location ◽  
Soil Formation ◽  
Landsat 8 ◽  
Model Soil ◽  
Ecological Resources ◽  
Artificial Neural

Monitoring the status of natural and ecological resources is necessary for conservation and protection. Soil is one of the most important environmental resources in agricultural lands and natural resources. In this research study, we used Landsat 8 and Artificial Neural Network (ANN) to monitor soil salinity in Qom plain. The geographical location of 72 surface soil samples from 7 land types was determined by the Latin hypercube method, and the samples were taken to determine the electrical conductivity (EC). Thirty percent of the data was considered as a validation set and 70% as a test set. In addition to the Landsat 8 bands, we used spectral indices of salinity, vegetation, topography, and drainage (DEM, TWI, and TCI) because of their impacts on soil formation and development. We used ANN with different algorithms to model soil salinity. We found that the GFF algorithm is the best for soil salinity modeling. Also, the TWI topography index and SI5 salinity index and NDVI vegetation index had the most effect on the outputs of the selected model. It was also found that flood plains and lowlands had the highest levels of salinity accumulation.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

scholarly journals Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi

2017 ◽  
Vol 11 (2) ◽  
pp. 141-150 ◽  
Author(s):  
Paul Macarof ◽  
Florian Statescu
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Heat Island ◽  
Sensing Technology ◽  
Urban Heat ◽  
Surface Urban Heat Island

Abstract This study compares the normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) as indicators of surface urban heat island effects in Landsat-8 OLI imagery by investigating the relationships between the land surface temperature (LST), NDBI and NDVI. The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural areas due to urbanization. With the development of remote sensing technology, it has become an important approach to urban heat island research. Landsat data were used to estimate the LST, NDBI and NDVI from four seasons for Iasi municipality area. This paper indicates than there is a strong linear relationship between LST and NDBI, whereas the relationship between LST and NDVI varies by season. This paper suggests, NDBI is an accurate indicator of surface UHI effects and can be used as a complementary metric to the traditionally applied NDVI.

scholarly journals Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qingyan Meng ◽  
Linlin Zhang ◽  
Qiuxia Xie ◽  
Shun Yao ◽  
Xu Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Landsat 8 ◽  
Combined Use ◽  
Artificial Neural ◽  
Agricultural Areas ◽  
Aperture Radar

Soil moisture is the basic condition required for crop growth and development. Gaofen-3 (GF-3) is the first C-band synthetic-aperture radar (SAR) satellite of China, offering broad land and ocean imaging applications, including soil moisture monitoring. This study developed an approach to estimate soil moisture in agricultural areas from GF-3 data. An inversion technique based on an artificial neural network (ANN) is introduced. The neural network was trained and tested on a training sample dataset generated from the Advanced Integral Equation Model. Incidence angle and HH or VV polarization data were used as input variables of the ANN, with soil moisture content (SMC) and surface roughness as the output variables. The backscattering contribution from the vegetation was eliminated using the water cloud model (WCM). The acquired soil backscattering coefficients of GF-3 and in situ measurement data were used to validate the SMC estimation algorithm, which achieved satisfactory results (R2 = 0.736; RMSE = 0.042). These results highlight the contribution of the combined use of the GF-3 synthetic-aperture radar and Landsat-8 images based on an ANN method for improving SMC estimates and supporting hydrological studies.

scholarly journals Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models

2018 ◽  
Vol 10 (8) ◽  
pp. 2878 ◽  
Author(s):  
Xiaoli Hu ◽  
Xin Li ◽  
Ling Lu
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Sustainable Development ◽  
Environmental Policy ◽  
Water Resources ◽  
Cellular Automata ◽  
Policy Change ◽  
Arid Regions ◽  
Artificial Neural

Land use and land cover change (LUCC) is an important issue in global environmental change and sustainable development, yet spatial simulation of LUCC remains challenging due to the land use system complexity. The cellular automata (CA) model plays a crucial role in simulating LUCC processes due to its powerful spatial computing power; however, the majority of current LUCC CA models are binary-state models that cannot provide more general information about the overall spatial pattern of LUCC. Moreover, the current LUCC CA models rarely consider background artificial irrigation in arid regions. Here, a multiple logistic-regression-based Markov cellular automata (MLRMCA) model and a multiple artificial-neural-network-based Markov cellular automata (MANNMCA) model were developed and applied to simulate complex land use evolutionary processes in an arid region oasis (Zhangye Oasis), constrained by water resources and environmental policy change, during the period 2000–2011. Results indicated that the MANNMCA model was superior to the MLRMCA model in simulated accuracy. Furthermore, combining the artificial neural network with CA more effectively captured the complex relationships between LUCC and a set of spatial driving variables. Although the MLRMCA model also showed some advantages, the MANNMCA model was more appropriate for simulating complex land use dynamics. The two integrated models were reliable, and could reflect the spatial evolution of regional LUCC. These models also have potential implications for land use planning and sustainable development in arid regions.

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