scholarly journals Study on Monitoring Water Area in Irrigation Area by Local Space Self-correlation Index

2022 ◽  
Author(s):  
tao su ◽  
Jian Wang ◽  
Xingyuan Cui ◽  
Lei Wang
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Hot Spot ◽  
Water Extraction ◽  
Vegetation Coverage ◽  
Hot Spot Analysis ◽  
Water Index ◽  
Water Classification ◽  
Normalized Difference Water Index ◽  
Spot Analysis

Abstract Landsat remote sensing image is a widely used data source in water remote sensing. Normalized difference water index (NDWI), modified normalized difference water index (MNDWI) and automated water extraction index (AWEI) are commonly used water extraction classifiers. In the process of their application, because the threshold varies with the location and time of the research object, how to select the threshold with the highest classification accuracy is a time-consuming and challenging task. The purpose of this study was to explore a method that can not only improve the accuracy of water extraction, but also provide a fixed threshold, and can meet the requirements of automatic water extraction. We introduced the local spatial auto correlation statistics and calculate the Getis-Ord Gi* index to have hot spot analysis. Comparative analysis showed that the accuracy of water classification had been greatly improved through hot spot analysis. AWEIsh classifier had the best classification accuracy under the condition of INVERSE_DISTANCE neighborhood rule and Z>1.96, and the accuracy changes least in different time, different location and different vegetation coverage images. Therefore, in the process of regional water extraction, hot spot analysis method was effective, which was helpful to improve the accuracy of water extraction.

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 The Application of Modified Normalized Difference Water Index (MNDWI) by Leaf Area Index in the Retrieval of Regional Drought Monitoring

2015 ◽  
Vol XL-7/W3 ◽  
pp. 141-147
Author(s):  
H.-w. Zhang ◽  
H.-l. Chen
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Coverage ◽  
Area Index ◽  
Water Index ◽  
Retrieval Accuracy ◽  
Normalized Difference Water Index ◽  
Green Stage

The vegetation coverage is one of the important factors that restrict the accuracy of remote sensing retrieval of soil moisture. In order to effectively improve the accuracy of the remote sensing retrieval of soil moisture and to reduce the impact of vegetation coverage variation on the retrieval accuracy, the Leaf Area Index (LAI) is introduced to the Normalized Difference Water Index (NDWI) to greatly improve the accuracy of the soil moisture retrieval. In its application on the regional drought monitoring, the paper uses the relative LAI from two places which locate in the north and south of Henan Province respectively (Xin Xiang and Zhu Ma Dian) as indicators. It uses the days after turned-green stage to conduct difference value correction on the Relative Leaf Area Index (RLAL) of the entire province, so as to acquire the distribution of RLAI of the province’s wheat producing area. After this, the local remote sensing NDWI will be Modified （MNDWI = NDWI ×RLAI ） to acquire the soil moisture distribution status of the entire province’s wheat producing area. The result shows that, the Modified Normalized Difference Water Index of LAI which based on the days after turned-green stage can improve the real time retrieval accuracy of soil moisture under different vegetation coverage.

Fusion of Sentinel-1 radar and Sentinel-2 MSI imagery for water extraction in Tibetan plateau

2020 ◽  
Author(s):  
Dan Li ◽  
Baosheng Wu ◽  
Bowei Chen ◽  
Yanjun Wang ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radar Data ◽  
Google Earth ◽  
Water Extraction ◽  
Tibet Plateau ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Google Earth Engine ◽  
Optical Imagery ◽  
Sentinel 2

<p><strong>Abstract:</strong> Water plays a vital role in plants, animals and human survival, as well as water resources planning and protection. The spatial and temporal changes of rivers have a profound impact on climate change and the scientific protection of the regional ecological environment in Qingzang-Tibet plateau. Due to the influence of snow and cloud cover, optical remote sensing images in this region have less effective coverage. Many researches in the past mainly faced the challenge of misclassification caused by shadows from cloud and mountain. In this study, we proposed a method to improve the extraction of rivers by reducing the effect of shadows by fusing Sentinel-1 radar data and Sentinel-2 optical imagery. For the optical imagery, water indices including MNDWI (Modified Normalized Difference Water Index) and RNDWI (Revised Normalized Difference Water Index) and morphological operations were used to extract the river coverage. In addition, radar data is used to extract water in areas where there is no optical image coverage or where optical images are misclassified by using a combination of both the histogram and Otsu threshold methods. The GEE (Google Earth Engine) platform is used to implement the analysis using two classification datasets at a regional level. Relevant results from Sentinel-1 and Sentinel-2 data showed that the RNDWI has a more accurate water extraction results in this region. We further compared the final river width results with the manually measured samples from Google Earth and situ data of hydrological stations for accuracy assessment. The R<sup>2 </sup>value is 0.90, and the standard deviation is 18.663m. The river width can be estimated well by this method, which can provide basic data for the study of water in depopulated zone.</p><p><strong>Keywords: </strong>Remote sensing, shadow removal, water extraction, water index, Otsu threshold, Google Earth Engine</p>

Hot-spot analysis of motorcyclist crashes involving fixed objects using multinomial logit and data mining tools

2021 ◽  
pp. 1-19
Author(s):  
Bahar Dadashova ◽  
Chiara Silvestri-Dobrovolny ◽  
Jayveersinh Chauhan ◽  
Marcie Perez ◽  
Roger Bligh
Keyword(s):  
Data Mining ◽  
Hot Spot ◽  
Multinomial Logit ◽  
Hot Spot Analysis ◽  
Mining Tools ◽  
Spot Analysis

Early stage hot spot analysis through standard cell base random pattern generation

2017 ◽  
Author(s):  
Joong-Won Jeon ◽  
Jaewan Song ◽  
Jeong-Lim Kim ◽  
Seongyul Park ◽  
Seung-Hune Yang ◽  
...  
Keyword(s):  
Early Stage ◽  
Hot Spot ◽  
Pattern Generation ◽  
Random Pattern ◽  
Standard Cell ◽  
Hot Spot Analysis ◽  
Cell Base ◽  
Spot Analysis

scholarly journals Geographic variation in preventable hospitalisations across Canada: a cross-sectional study

BMJ Open ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. e037195
Author(s):  
Piotr Wilk ◽  
Shehzad Ali ◽  
Kelly K Anderson ◽  
Andrew F Clark ◽  
Martin Cooke ◽  
...  
Keyword(s):  
Geographic Variation ◽  
Small Area ◽  
Service Providers ◽  
Hot Spot ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Discharge Abstract Database ◽  
Cross Sectional ◽  
Hot Spot Analysis ◽  
Spot Analysis

ObjectiveThe objective of this study is to examine the magnitude and pattern of small-area geographic variation in rates of preventable hospitalisations for ambulatory care-sensitive conditions (ACSC) across Canada (excluding Québec).Design and settingA cross-sectional study conducted in Canada (excluding Québec) using data from the 2006 Canadian Census Health and Environment Cohort (CanCHEC) linked prospectively to hospitalisation records from the Discharge Abstract Database (DAD) for the three fiscal years: 2006–2007, 2007–2008 and 2008–2009.Primary outcome measurePreventable hospitalisations (ACSC).ParticipantsThe 2006 CanCHEC represents a population of 22 562 120 individuals in Canada (excluding Québec). Of this number, 2 940 150 (13.03%) individuals were estimated to be hospitalised at least once during the 2006–2009 fiscal years.MethodsAge-standardised annualised ACSC hospitalisation rates per 100 000 population were computed for each of the 190 Census Divisions. To assess the magnitude of Census Division-level geographic variation in rates of preventable hospitalisations, the global Moran’s I statistic was computed. ‘Hot spot’ analysis was used to identify the pattern of geographic variation.ResultsOf all the hospitalisation events reported in Canada during the 2006–2009 fiscal years, 337 995 (7.10%) events were ACSC-related hospitalisations. The Moran’s I statistic (Moran’s I=0.355) suggests non-randomness in the spatial distribution of preventable hospitalisations. The findings from the ‘hot spot’ analysis indicate a cluster of Census Divisions located in predominantly rural and remote parts of Ontario, Manitoba and Saskatchewan and in eastern and northern parts of Nunavut with significantly higher than average rates of preventable hospitalisation.ConclusionThe knowledge generated on the small-area geographic variation in preventable hospitalisations can inform regional, provincial and national decision makers on planning, allocation of resources and monitoring performance of health service providers.

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