Mariculture Zones Extraction Using NDWI and NDVI

2013 ◽  
Vol 659 ◽  
pp. 153-155 ◽  
Author(s):  
Hong Jun Pan ◽  
Xue Xian Li ◽  
Guang Wei Wang ◽  
Chong Song Qi
Keyword(s):  
Remote Sensing ◽  
Spectral Characteristics ◽  
Remote Sensing Images ◽  
Distribution Planning ◽  
Surface Features ◽  
Water Index ◽  
Normalized Difference Water Index

On the analysis of spectral characteristics of Aoshan remote sensing images, we find the spectral differences between mariculture zones and other surface features. This paper combines normalized difference water index with mariculture zones distribution planning to complete the extraction and the statistics of the mariculture zones, in order to effectively achieve the regulation of mariculture zones.

scholarly journals Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation

2020 ◽  
Vol 12 (24) ◽  
pp. 4115
Author(s):  
Xiaoli Li ◽  
Jinsong Chen ◽  
Longlong Zhao ◽  
Shanxin Guo ◽  
Luyi Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Noise Immunity ◽  
Voronoi Tessellation ◽  
Spectral Characteristics ◽  
Remote Sensing Image ◽  
Effective Characteristics ◽  
Spatial Distance ◽  
Remote Sensing Images ◽  
Distance Weighted

The spatial fragmentation of high-resolution remote sensing images makes the segmentation algorithm put forward a strong demand for noise immunity. However, the stronger the noise immunity, the more serious the loss of detailed information, which easily leads to the neglect of effective characteristics. In view of the difficulty of balancing the noise immunity and effective characteristic retention, an adaptive distance-weighted Voronoi tessellation technology is proposed for remote sensing image segmentation. The distance between pixels and seed points in Voronoi tessellation is established by the adaptive weighting of spatial distance and spectral distance. The weight coefficient used to control the influence intensity of spatial distance is defined by a monotone decreasing function. Following the fuzzy clustering framework, a fuzzy segmentation model with Kullback–Leibler (KL) entropy regularization is established by using multivariate Gaussian distribution to describe the spectral characteristics and Markov Random Field (MRF) to consider the neighborhood effect of sub-regions. Finally, a series of parameter optimization schemes are designed according to parameter characteristics to obtain the optimal segmentation results. The proposed algorithm is validated on many multispectral remote sensing images with five comparing algorithms by qualitative and quantitative analysis. A large number of experiments show that the proposed algorithm can overcome the complex noise as well as better ensure effective characteristics.

scholarly journals Remote Sensing Landslide Recognition Based on Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Wang ◽  
Xiaofei Wang ◽  
Junfan Jian
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Vegetation Index ◽  
Near Infrared ◽  
Spatial Information ◽  
Spectral Characteristics ◽  
Remote Sensing Images ◽  
Landslide Event

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.

scholarly journals Application of Remote Sensing on El Niño Extreme Effect in Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)

2021 ◽  
Vol 6 (1) ◽  
pp. 46-56
Author(s):  
Ricky Anak Kemarau ◽  
Oliver Valentine Eboy
Keyword(s):  
Remote Sensing ◽  
El Niño ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Information ◽  
El Nino ◽  
Extreme Temperature ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
The Impact

The years 1997/1998 and 2015/2016 saw the worst El Niño occurrence in human history. The occurrence of El Niño causes extreme temperature events which are higher than usual, drought and prolonged drought. The incident caused a decline in the ability of plants in carrying out the process of photosynthesis. This causes the carbon dioxide content to be higher than normal. Studies on the effects of El Niño and its degree of strength are still under-studied especially by researchers in the tropics. This study uses remote sensing technology that can provide spatial information. The first step of remote sensing data needs to go through the pre-process before building the NDVI (Normalized Difference Vegetation Index) and Normalized Difference Water Index (NDWI) maps. Next this study will identify the relationship between Oceanic Nino Index (ONI) with Application Remote Sensing in The Study Of El Niño Extreme Effect 1997/1998 and 2015/2016 On Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)NDWI and NDWI landscape indices. Next will make a comparison, statistical and spatial information space between NDWI and NDVI for each year 1997/1998 and 2015/2016. This study is very important in providing spatial information to those responsible in preparing measures in reducing the impact of El Niño.

scholarly journals Analysis of riverbank changes in Ho Chi Minh city in the period 1989 – 2015

2019 ◽  
Vol 2 (2) ◽  
pp. 80-88
Author(s):  
Thu Trang Hoang ◽  
Khoi Nguyen Dao ◽  
Loi Thi Pham ◽  
Hong Van Nguyen
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Ho Chi Minh City ◽  
Landsat 8 ◽  
River Bank ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Multi Temporal ◽  
Erosion And Accretion ◽  
Landsat Satellite

The objective of this study was to analyze the changes of riverbanks in Ho Chi Minh City for the period 1989-2015 using remote sensing and GIS. Combination of Modified Normalized Difference Water Index (MNDWI) and thresholding method was used to extract the river bank based on the multi-temporal Landsat satellite images, including 12 Landsat 4-5 (TM) images and 2 Landsat 8 images in the period 1989-2015. Then, DSAS tool was used to calculate the change rates of river bank. The results showed that, the processes of erosion and accretion intertwined but most of the main riverbanks had erosion trend in the period 1989-2015. Specifically, the Long Tau River, Sai Gon River, Soai Rap River had erosion trends with a rate of about 10.44 m/year. The accretion process mainly occurred in Can Gio area, such as Dong Tranh river and Soai Rap river with a rate of 8.34 m/year. Evaluating the riverbank changes using multi-temporal remote sensing data may contribute an important reference to managing and protecting the riverbanks.

scholarly journals Investigation on Perceptron Learning for Water Region Estimation Using Large-Scale Multispectral Images

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4333 ◽  
Author(s):  
Poliyapram Vinayaraj ◽  
Nevrez Imamoglu ◽  
Ryosuke Nakamura ◽  
Atsushi Oda
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Landsat 8 ◽  
Multispectral Images ◽  
Water Index ◽  
Tuning Parameters ◽  
Water Classification ◽  
Water Region ◽  
Normalized Difference Water Index ◽  
Perceptron Learning

Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth’s surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches are not sufficient to produce a globally adaptable water classification. The aim of this study is to develop a formula with automatically derived tuning parameters using perceptron neural networks for water/non-water region estimation, which we call the Perceptron-Derived Water Formula (PDWF), using Landsat-8 images. Water/non-water region estimates derived from PDWF were compared with three different approaches—Modified Normalized Difference Water Index (MNDWI), Automatic Water Extraction Index (AWEI), and Deep Convolutional Neural Network—using various case studies. Our proposed method outperforms all three approaches, showing a significant improvement in water/non-water region estimation. PDWF performance is consistently better even in cases of challenging conditions such as low reflectance due to hill shadows, building-shadows, and dark soils. Moreover, our study implemented a sunglint correction to adapt water/non-water region estimation over sunglint-affected pixels.

scholarly journals Comparison of methods of Humidity Index processing in the Irrigated Perimeter Nilo Coelho, Northeast of Brazil

2018 ◽  
Vol 7 (6) ◽  
pp. 315
Author(s):  
Antonio Celso de Sousa Leite ◽  
Leidjane Maria Maciel De Oliveira ◽  
Ulisses Alencar Bezerra ◽  
Débora Natália O. de Almeida Oliveira De Almeida ◽  
Ana Lucia B. Candeias ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Comparison Of Methods ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Humidity Index ◽  
Irrigated Perimeter ◽  
Remote Sensing Techniques ◽  
Object Of Study ◽  
Remote Sensing Image Processing

The use of remote sensing techniques in support of hydrological studies became common in recent years, through the application of orbital images that are used the reflectance values of the water, for mapping, delineation of water bodies and moisture monitoring in the internal structure of vegetable biomass. Among the methods and techniques of remote sensing image processing with the hydrological analysis, here the Normalized Difference Water Index (NDWI), object of study in the present work, in order to compare the application of the index through different methods. The present study was developed in the territorial part of the Nilo Coelho Irrigated Perimeter located in the Semiarid Northeastern region and covers the municipalities of Casa Nova-BA, Petrolina-PE and Juazeiro-BA, using TM-Landsat 5 sensor images from 30/07/2006. This comparison of the NDWI provided the best application for each index, as well as evaluate the potentiality of the index according to the applied method. 

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