Bathymetric Mapping for Shallow Water Using Landsat 8 via Artificial Neural Network Technique

2020 ◽  
pp. 717-733
Author(s):  
Arun Patel ◽  
S. K. Katiyar ◽  
Vishnu Prasad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shallow Water ◽  
Landsat 8 ◽  
Bathymetric Mapping ◽  
Artificial Neural

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 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 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.

scholarly journals Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1020 ◽  
Author(s):  
Yong Kown ◽  
Seung Baek ◽  
Young Lim ◽  
JongCheol Pyo ◽  
Mayzonee Ligaray ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Chlorophyll A ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Support Vector ◽  
Landsat 8 ◽  
Artificial Neural

Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms.

scholarly journals A SIMPLE ARTIFICIAL NEURAL NETWORK FOR FIRE DETECTION USING LANDSAT-8 DATA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 447-452
Author(s):  
Z. Liu ◽  
K. Wu ◽  
R. Jiang ◽  
H. Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Threshold Model ◽  
Optimal Solution ◽  
Fire Detection ◽  
Landsat 8 ◽  
Ann Model ◽  
Active Fire ◽  
Fixed Threshold ◽  
Artificial Neural

Abstract. Fixed threshold models have been widely used in active fire detection products. However, its accuracy is limited due to the complexity of setting up thresholds. Artificial neural network (ANN) is capable of learning from data and can decide weights automatically. Given enough data, an ANN model is able to optimize itself and quickly find an optimal solution. In this work, a simple ANN model is implemented to classify fire pixels from Landsat-8 data. Experimental results show that our ANN model effectively achieves fire detection and performs better than fixed threshold model in certain circumstances.

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.

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