Regression model accuracy comparison on mangrove canopy density mapping

Digital Press Physical Sciences and Engineering ◽

10.29037/digitalpress.11249 ◽

2018 ◽

Vol 1 ◽

pp. 00001

Author(s):

Deha Agus Umarhadi ◽

Akbar Muammar

Keyword(s):

Regression Analysis ◽

Satellite Imagery ◽

Regression Models ◽

Vegetation Index ◽

Quadratic Regression ◽

Mapping Accuracy ◽

Canopy Density ◽

Exponential Regression ◽

Density Mapping ◽

Sentinel 2A

Mangrove canopy density condition is often considered in the coastal environment management, so that the accurate data of spatial distribution of mangrove canopy density is needed. This condition need to be studied further related to methods in the mangrove canopy density mapping. However, did not much research compare the mapping accuracy about mangrove canopy density using vegetation index and the combination of statistical regression models, especially using Sentinel-2A satellite imagery. The purpose of this study is to compare the accuracy of mangrove canopy density mapping using NDVI, MSAVI, and MSARVI with simple linear, quadratic, logarithmic, and exponential regression applied to Sentinel-2A satellite imagery. Mangrove canopy density data resulted from a field survey at Jor and Kecebing Bay, East Lombok. The result of accuracy analysis presented NDVI was the best vegetation index in mapping compared MSAVI and MSARVI with an accuracy above 80 % (linear regression analysis of NDVI: 81.66 %, quadratic regression analysis of NDVI: 80.84 %, exponential regression analysis of NDVI: 80.71 %, logarithmic regression analysis of NDVI: 80.68 %). Mapping the mangrove canopy density through the combination of another vegetation index (MSAVI and MSARVI) with four regression models had accuracy of between 70 % to 80 %, except a mangrove canopy density mapping accuracy using quadratic regression between MSARVI and field data, only reached 62.78 %. <o:p></o:p>

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The Effect of Topographic Correction on Canopy Density Mapping Using Satellite Imagery in Mountainous Area

International Journal on Advanced Science Engineering and Information Technology ◽

10.18517/ijaseit.10.3.7739 ◽

2020 ◽

Vol 10 (3) ◽

pp. 1317

Author(s):

Deha Agus Umarhadi ◽

Projo Danoedoro

Keyword(s):

Satellite Imagery ◽

Mountainous Area ◽

Topographic Correction ◽

Canopy Density ◽

Density Mapping

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Digital mapping of solonchak complexes using Sentinel-2A data

E3S Web of Conferences ◽

10.1051/e3sconf/202022201010 ◽

2020 ◽

Vol 222 ◽

pp. 01010

Author(s):

Azamat Suleymanov ◽

Ilyusya Gabbasova ◽

Mikhail Komissarov

Keyword(s):

Satellite Data ◽

Regression Models ◽

Vegetation Index ◽

Arable Land ◽

Quadratic Function ◽

Steppe Zone ◽

Linear Quadratic ◽

Republic Of Bashkortostan ◽

Soil Conductivity ◽

Sentinel 2A

Land salinization is an up-to-date issue being broadly studied all over the world. In Russia, salinization processes are predominantly observed in the southern regions, where the main areas of arable land are situated. This research is devoted to mapping of saline lands with the help of satellite data. The study was performed on a 100-hectare plot in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). A correlation was determined between the level of soil salinity and the main spectral indices associated with Sentinel-2A satellite data. Regression models used 5 salinity indices, vegetation index NDVI, and values of soil conductivity. Linear, quadratic, and logarithmic functions were used. By calculation, the salinity index 5 (G×R)/B demonstrated the best correlation values with the salinity level of (R=0.88, R2=0.77) while using the quadratic function. The vegetation index NDVI revealed no correlation, owing to the poor development or dried-up condition of vegetation. On the basis of the developed regression models, salinity maps are drawn, in which the areas of solonchak complexes are defined.

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Distribusi Spasial Kerapatan Mangrove Menggunakan Citra Sentinel-2A Di TAHURA Ngurah Rai Bali

Journal of Marine and Aquatic Sciences ◽

10.24843/jmas.2019.v05.i02.p05 ◽

2019 ◽

Vol 5 (2) ◽

pp. 192

Author(s):

I Gede Merta Yoga Pratama ◽

I Wayan Gede Astawa Karang ◽

Yulianto Suteja

Keyword(s):

Sampling Method ◽

Vegetation Index ◽

High Density ◽

Low Density ◽

Canopy Density ◽

Ndvi Index ◽

Sentinel 2A ◽

Fish Eye ◽

Very High ◽

Better Than

The mangrove forest of TAHURA Ngurah Rai is one of the mangrove ecosystems in Bali that suffered damages and density changes due to natural factors and human activities. Remote sensing is one of the technology that can be used to estimate the density of mangrove canopy in TAHURA Ngurah Rai. The purpose of this study was to find the best vegetation index for estimating mangrove canopy density out and map it spatially using Sentinel-2A image. The method of this research is using vegetation index NDVI, EVI and mRE-SR to estimate mangrove canopy density. Field data was collected using Stratified Random and Proportional Sampling method by taking photo of the density of canopy using camera with Fish Eye lens on 34 plot. The results of this study show the satistic test of the linear model of the vegetation index with the mangrove canopy density value on the NDVI index (r = 0.8165, R2 = 0.6667, RMSE = ± 8.1508), EVI (r = 0.8597, R2 = 0.7390, RMSE = ± 7.8117), and mRE-SR (r = 0.9277, R2 = 0.8607, RMSE = ± 4.9571). The conclusion of this research is mRE-SR vegetation index able to map mangrove canopy density better than NDVI and EVI vegetation index with 86.07% accuracy. The mangrove spatial distribution generated from the mRE-SR model is 1002.22 Ha with 3.24 Ha categorized as very high density, 94.82 Ha categorized as high density, 333 Ha categorized as medium density, 402.38 Ha categorized as low density, and categorized as very low density is up to 168.76 Ha.

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Relationships between test positivity rate, total laboratory confirmed cases of malaria, and malaria incidence in high burden settings of Uganda: an ecological analysis

Malaria Journal ◽

10.1186/s12936-021-03584-7 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Jaffer Okiring ◽

Adrienne Epstein ◽

Jane F. Namuganga ◽

Victor Kamya ◽

Asadu Sserwanga ◽

...

Keyword(s):

Regression Models ◽

Malaria Incidence ◽

Temporal Changes ◽

Linear Regression Models ◽

High Burden ◽

Exponential Regression ◽

Malaria Morbidity ◽

Laboratory Test Results ◽

Combining Data ◽

Catchment Areas

Abstract Background Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings. Methods This study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models. Results A total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R2 value reduced to 0.38. Conclusions In high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.

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Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables

Mathematics ◽

10.3390/math9060609 ◽

2021 ◽

Vol 9 (6) ◽

pp. 609

Author(s):

María del Mar Rueda ◽

Beatriz Cobo ◽

Antonio Arcos

Keyword(s):

Regression Analysis ◽

Regression Models ◽

Regression Coefficients ◽

Randomized Response ◽

Statistical Tool ◽

Complex Survey ◽

Complex Sample ◽

Qualitative Variables ◽

World Survey ◽

Sensitive Variable

Randomized response (RR) techniques are widely used in research involving sensitive variables, such as drugs, violence or crime, especially when a population mean or prevalence must be estimated. However, they are not generally applied to examine relationships between a sensitive variable and other characteristics. This type of technique was initially applied to qualitative variables, and studies later showed that a logistic regression may be performed with RR data. Since many of the variables considered in this context are quantitative, RR techniques were extended to these cases to estimate the values required. Regression analysis is a valuable statistical tool for exploring relationships among variables and for establishing associations between responses and covariates. In this article, we propose a design-based regression analysis for complex sample designs based on the unified RR approach. We present estimators of the regression coefficients, study their theoretical properties and consider different ways to estimate their variance. The properties of these estimation techniques were simulated using various quantitative randomized models. The method proposed was also used to analyse the findings from a real-world survey.

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Estimation of Outdoor PM2.5 Infiltration into Multifamily Homes Depending on Building Characteristics Using Regression Models

Sustainability ◽

10.3390/su13105708 ◽

2021 ◽

Vol 13 (10) ◽

pp. 5708

Author(s):

Bo-Ram Park ◽

Ye-Seul Eom ◽

Dong-Hee Choi ◽

Dong-Hwa Kang

Keyword(s):

Regression Analysis ◽

Correlation Analysis ◽

Field Test ◽

Outer Surface ◽

Regression Models ◽

Field Tests ◽

Test Results ◽

Correlation And Regression Analysis ◽

Building Characteristics ◽

Pm2.5 Concentration

The purpose of this study was to evaluate outdoor PM2.5 infiltration into multifamily homes according to the building characteristics using regression models. Field test results from 23 multifamily homes were analyzed to investigate the infiltration factor and building characteristics including floor area, volume, outer surface area, building age, and airtightness. Correlation and regression analysis were then conducted to identify the building factor that is most strongly associated with the infiltration of outdoor PM2.5. The field tests revealed that the average PM2.5 infiltration factor was 0.71 (±0.19). The correlation analysis of the building characteristics and PM2.5 infiltration factor revealed that building airtightness metrics (ACH50, ELA/FA, and NL) had a statistically significant (p < 0.05) positive correlation (r = 0.70, 0.69, and 0.68, respectively) with the infiltration factor. Following the correlation analysis, a regression model for predicting PM2.5 infiltration based on the ACH50 airtightness index was proposed. The study confirmed that the outdoor-origin PM2.5 concentration in highly leaky units could be up to 1.59 times higher than that in airtight units.

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Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

10.5194/egusphere-egu21-15589 ◽

2021 ◽

Author(s):

Brianna Pagán ◽

Adekunle Ajayi ◽

Mamadou Krouma ◽

Jyotsna Budideti ◽

Omar Tafsi

Keyword(s):

Machine Learning ◽

Satellite Imagery ◽

Cloud Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Learning Approaches ◽

Area Index ◽

Spatial And Temporal Scales ◽

Field Level ◽

Crop Health

The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level.&#160;Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.&#160;

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Drought risk in the Bolivian Altiplano associated with El Niño Southern Oscillation using satellite imagery data

10.5194/nhess-2018-403 ◽

2019 ◽

Cited By ~ 1

Author(s):

Claudia Canedo-Rosso ◽

Stefan Hochrainer-Stigler ◽

Georg Pflug ◽

Bruno Condori ◽

Ronny Berndtsson

Keyword(s):

Satellite Imagery ◽

El Niño ◽

Crop Production ◽

Vegetation Index ◽

Crop Yields ◽

El Nino ◽

Southern Oscillation ◽

Drought Risk ◽

Bolivian Altiplano ◽

Imagery Data

Abstract. Drought is a major natural hazard in the Bolivian Altiplano that causes large losses to farmers, especially during positive ENSO phases. However, empirical data for drought risk estimation purposes are scarce and spatially uneven distributed. Due to these limitations, similar to many other regions in the world, we tested the performance of satellite imagery data for providing precipitation and temperature data. The results show that droughts can be better predicted using a combination of satellite imagery and ground-based available data. Consequently, the satellite climate data were associated with the Normalized Difference Vegetation Index (NDVI) in order to evaluate the crop production variability. Moreover, NDVI was used to target specific drought hotspot regions. Furthermore, during positive ENSO phase (El Niño years), a significant decrease in crop yields can be expected and we indicate areas where losses will be most pronounced. The results can be used for emergency response operations and enable a pro-active approach to disaster risk management against droughts. This includes economic-related and risk reduction strategies such as insurance and irrigation.

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