Assessment of water content using remote sensing Normalized Difference Water Index: Preliminary study

2011 ◽  
Author(s):  
Sheriza Mohd Razali ◽  
Ahmad Ainuddin Nuruddin
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Preliminary Study

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.

Application of Normalized Difference Water Index for Vegetation Water Content Monitoring from MODIS Data

2011 ◽  
Vol 204-210 ◽  
pp. 2128-2132
Author(s):  
Yi Ding ◽  
Hui Li Gong
Keyword(s):  
Water Content ◽  
Forest Fire ◽  
Fire Risk ◽  
Ignition Point ◽  
Water Index ◽  
Vegetation Water Content ◽  
Normalized Difference Water Index ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Vegetation Water

The needs for vegetation water content monitoring originates from forest fire assessment: Firstly, the vegetation water content affects the forest ignition point; secondly, it affects the spread rate if the forest is on fire. Based on the above reasons, the inversion of vegetation water content in Da Hinggan Ling region of China was studied, using the Normalized Difference Water Index from MODIS (Moderate resolution Imaging Spectroradiometer) data, the relationship between the water content of vegetation and forest fire risk was preliminary analyzed.

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 Applicability of Remote Sensing-Based Vegetation Water Content in Modeling Lightning-Caused Forest Fire Occurrences

2019 ◽  
Vol 8 (3) ◽  
pp. 143 ◽  
Author(s):  
Masoud Abdollahi ◽  
Ashraf Dewan ◽  
Quazi Hassan
Keyword(s):  
Water Content ◽  
Forest Fire ◽  
Distinct Pattern ◽  
Water Index ◽  
Vegetation Water Content ◽  
Normalized Difference Water Index ◽  
Annual Dynamic ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Vegetation Water

In this study, our aim was to model forest fire occurrences caused by lightning using the variable of vegetation water content over six fire-dominant forested natural subregions in Northern Alberta, Canada. We used eight-day composites of surface reflectance data at 500-m spatial resolution, along with historical lightning-caused fire occurrences during the 2005–2016 period, derived from a Moderate Resolution Imaging Spectroradiometer. First, we calculated the normalized difference water index (NDWI) as an indicator of vegetation/fuel water content over the six natural subregions of interest. Then, we generated the subregion-specific annual dynamic median NDWI during the 2005–2012 period, which was assembled into a distinct pattern every year. We plotted the historical lightning-caused fires onto the generated patterns, and used the concept of cumulative frequency to model lightning-caused fire occurrences. Then, we applied this concept to model the cumulative frequencies of lightning-caused fires using the median NDWI values in each natural subregion. By finding the best subregion-specific function (i.e., R2 values over 0.98 for each subregion), we evaluated their performance using an independent subregion-specific lightning-caused fire dataset acquired during the 2013–2016 period. Our analyses revealed strong relationships (i.e., R2 values in the range of 0.92 to 0.98) between the observed and modeled cumulative frequencies of lightning-caused fires at the natural subregion level throughout the validation years. Finally, our results demonstrate the applicability of the proposed method in modeling lightning-caused fire occurrences over forested regions.

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