scholarly journals Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

2016 ◽  
Vol 11 (3) ◽  
Author(s):  
Thandi Kapwata ◽  
Michael T. Gebreslasie
Keyword(s):  
South Africa ◽  
Random Forest ◽  
Malaria Transmission ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mean Squared Error ◽  
Predictor Variable ◽  
Variable Importance ◽  
Mpumalanga Province

Malaria is an environmentally driven disease. In order to quantify the spatial variability of malaria transmission, it is imperative to understand the interactions between environmental variables and malaria epidemiology at a micro-geographic level using a novel statistical approach. The random forest (RF) statistical learning method, a relatively new variable-importance ranking method, measures the variable importance of potentially influential parameters through the percent increase of the mean squared error. As this value increases, so does the relative importance of the associated variable. The principal aim of this study was to create predictive malaria maps generated using the selected variables based on the RF algorithm in the Ehlanzeni District of Mpumalanga Province, South Africa. From the seven environmental variables used [temperature, lag temperature, rainfall, lag rainfall, humidity, altitude, and the normalized difference vegetation index (NDVI)], altitude was identified as the most influential predictor variable due its high selection frequency. It was selected as the top predictor for 4 out of 12 months of the year, followed by NDVI, temperature and lag rainfall, which were each selected twice. The combination of climatic variables that produced the highest prediction accuracy was altitude, NDVI, and temperature. This suggests that these three variables have high predictive capabilities in relation to malaria transmission. Furthermore, it is anticipated that the predictive maps generated from predictions made by the RF algorithm could be used to monitor the progression of malaria and assist in intervention and prevention efforts with respect to malaria.

scholarly journals Spatial Downscaling of TRMM Precipitation Using Geostatistics and Fine Scale Environmental Variables

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
No-Wook Park
Keyword(s):  
Environmental Variables ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Tropical Rainfall Measuring Mission ◽  
Predictive Performance ◽  
Environmental Modeling ◽  
Fine Scale ◽  
Coarse Scale ◽  
Uncertainty Measures ◽  
Trmm Precipitation

A geostatistical downscaling scheme is presented and can generate fine scale precipitation information from coarse scale Tropical Rainfall Measuring Mission (TRMM) data by incorporating auxiliary fine scale environmental variables. Within the geostatistical framework, the TRMM precipitation data are first decomposed into trend and residual components. Quantitative relationships between coarse scale TRMM data and environmental variables are then estimated via regression analysis and used to derive trend components at a fine scale. Next, the residual components, which are the differences between the trend components and the original TRMM data, are then downscaled at a target fine scale via area-to-point kriging. The trend and residual components are finally added to generate fine scale precipitation estimates. Stochastic simulation is also applied to the residual components in order to generate multiple alternative realizations and to compute uncertainty measures. From an experiment using a digital elevation model (DEM) and normalized difference vegetation index (NDVI), the geostatistical downscaling scheme generated the downscaling results that reflected detailed characteristics with better predictive performance, when compared with downscaling without the environmental variables. Multiple realizations and uncertainty measures from simulation also provided useful information for interpretations and further environmental modeling.

scholarly journals Rainfall Trend and Its Relationship with Normalized Difference Vegetation Index in a Restored Semi-Arid Wetland of South Africa

2020 ◽  
Vol 12 (21) ◽  
pp. 8919
Author(s):  
Florence M. Murungweni ◽  
Onisimo Mutanga ◽  
John O. Odiyo
Keyword(s):  
South Africa ◽  
Quantile Regression ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rainfall Data ◽  
Ecosystem Change ◽  
Annual Rainfall ◽  
Rainfall Trend ◽  
Vegetation Productivity ◽  
Rainfall Trends

Clearance of terrestrial wetland vegetation and rainfall variations affect biodiversity. The rainfall trend–NDVI (Normalized Difference Vegetation Index) relationship was examined to assess the extent to which rainfall affects vegetation productivity within Nylsvley, Ramsar site in Limpopo Province, South Africa. Daily rainfall data measured from eight rainfall stations between 1950 and 2016 were used to generate seasonal and annual rainfall data. Mann-Kendall and quantile regression were applied to assess trends in rainfall data. NDVI was derived from satellite images from between 1984 and 2003 using Zonal statistics and correlated with rainfall of the same period to assess vegetation dynamics. Mann-Kendall and Sen’s slope estimator showed only one station had a significant increasing rainfall trend annually and seasonally at p < 0.05, whereas all the other stations showed insignificant trends in both rainfall seasons. Quantile regression showed 50% and 62.5% of the stations had increasing annual and seasonal rainfall, respectively. Of the stations, 37.5% were statistically significant at p < 0.05, indicating increasing and decreasing rainfall trends. These rainfall trends show that the rainfall of Nylsvley decreased between 1995 and 2003. The R2 between rainfall and NDVI of Nylsvley is 55% indicating the influence of rainfall variability on vegetation productivity. The results underscore the impact of decadal rainfall patterns on wetland ecosystem change.

scholarly journals Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery

2020 ◽  
Vol 12 (19) ◽  
pp. 3153
Author(s):  
André Duarte ◽  
Luis Acevedo-Muñoz ◽  
Catarina I. Gonçalves ◽  
Luís Mota ◽  
Alexandre Sarmento ◽  
...  
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forest Density ◽  
Random Forest Classification ◽  
Otsu Thresholding ◽  
Forest Classification ◽  
Density Maps ◽  
Kappa Value

Eucalyptus Longhorned Borers (ELB) are some of the most destructive pests in regions with Mediterranean climate. Low rainfall and extended dry summers cause stress in eucalyptus trees and facilitate ELB infestation. Due to the difficulty of monitoring the stands by traditional methods, remote sensing arises as an invaluable tool. The main goal of this study was to demonstrate the accuracy of unmanned aerial vehicle (UAV) multispectral imagery for detection and quantification of ELB damages in eucalyptus stands. To detect spatial damage, Otsu thresholding analysis was conducted with five imagery-derived vegetation indices (VIs) and classification accuracy was assessed. Treetops were calculated using the local maxima filter of a sliding window algorithm. Subsequently, large-scale mean-shift segmentation was performed to extract the crowns, and these were classified with random forest (RF). Forest density maps were produced with data obtained from RF classification. The normalized difference vegetation index (NDVI) presented the highest overall accuracy at 98.2% and 0.96 Kappa value. Random forest classification resulted in 98.5% accuracy and 0.94 Kappa value. The Otsu thresholding and random forest classification can be used by forest managers to assess the infestation. The aggregation of data offered by forest density maps can be a simple tool for supporting pest management.

scholarly journals Land Use Classification of VHR Images for Mapping Small-Sized Abandoned Citrus Plots by Using Spectral and Textural Information

2021 ◽  
Vol 13 (4) ◽  
pp. 681 ◽  
Author(s):  
Sergio Morell-Monzó ◽  
María-Teresa Sebastiá-Frasquet ◽  
Javier Estornell
Keyword(s):  
Random Forest ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Majority Voting ◽  
Land Abandonment ◽  
Land Use Classification ◽  
Blue Band ◽  
Lower Accuracy

Agricultural land abandonment is an increasing problem in Europe. The Comunitat Valenciana Region (Spain) is one of the most important citrus producers in Europe suffering this problem. This region characterizes by small sized citrus plots and high spatial fragmentation which makes necessary to use Very High-Resolution images to detect abandoned plots. In this paper spectral and Gray Level Co-Occurrence Matrix (GLCM)-based textural information derived from the Normalized Difference Vegetation Index (NDVI) are used to map abandoned citrus plots in Oliva municipality (eastern Spain). The proposed methodology is based on three general steps: (a) extraction of spectral and textural features from the image, (b) pixel-based classification of the image using the Random Forest algorithm, and (c) assignment of a single value per plot by majority voting. The best results were obtained when extracting the texture features with a 9 × 9 window size and the Random Forest model showed convergence around 100 decision trees. Cross-validation of the model showed an overall accuracy of the pixel-based classification of 87% and an overall accuracy of the plot-based classification of 95%. All the variables used are statistically significant for the classification, however the most important were contrast, dissimilarity, NIR band (720 nm), and blue band (620 nm). According to our results, 31% of the plots classified as citrus in Oliva by current methodology are abandoned. This is very important to avoid overestimating crop yield calculations by public administrations. The model was applied successfully outside the main study area (Oliva municipality); with a slightly lower accuracy (92%). This research provides a new approach to map small agricultural plots, especially to detect land abandonment in woody evergreen crops that have been little studied until now.

scholarly journals Multi-Year Mapping of Disturbance and Reclamation Patterns over Tronox’s Hillendale Mine, South Africa with DBEST and Google Earth Engine

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 760
Author(s):  
Sifiso Xulu ◽  
Philani T. Phungula ◽  
Nkanyiso Mbatha ◽  
Inocent Moyo
Keyword(s):  
South Africa ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Google Earth ◽  
Abandoned Mines ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Petroleum Resources ◽  
Google Earth Engine ◽  
The Government

This study was devised to examine the pattern of disturbance and reclamation by Tronox, which instigated a closure process for its Hillendale mine site in South Africa, where they recovered zirconium- and titanium-bearing minerals from 2001 to 2013. Restoring mined-out areas is of great importance in South Africa, with its ominous record of almost 6000 abandoned mines since the 1860s. In 2002, the government enacted the Mineral and Petroleum Resources Development Act (No. 28 of 2002) to enforce extracting companies to restore mined-out areas before pursuing closure permits. Thus, the trajectory of the Hillendale mine remains unstudied despite advances in the satellite remote sensing technology that is widely used in this field. Here, we retrieved a collection of Landsat-derived normalized difference vegetation index (NDVI) within the Google Earth Engine and applied the Detecting Breakpoints and Estimating Segments in Trend (DBEST) algorithm to examine the progress of vegetation transformation over the Hillendale mine between 2001 and 2019. Our results showed key breakpoints in NDVI, a drop from 2001, reaching the lowest point in 2009–2011, with a marked recovery pattern after 2013 when the restoration program started. We also validated our results using a random forests strategy that separated vegetated and non-vegetated areas with an accuracy exceeding 78%. Overall, our findings are expected to encourage users to replicate this affordable application, particularly in emerging countries with similar cases.

scholarly journals COVERAGE CHANGES DETECTION AT CIÉNAGA GRANDE, SANTA MARTA – COLOMBIA USING AUTOMATIC CLASSIFICATION

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 195-200
Author(s):  
J. S. Vinasco ◽  
D. A. Rodríguez ◽  
S. Velásquez ◽  
D. F. Quintero ◽  
L. R. Livni ◽  
...  
Keyword(s):  
Random Forest ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Confusion Matrix ◽  
Automatic Classification ◽  
Detection Methods ◽  
Landsat 8 ◽  
High Resolution Data ◽  
Water Index ◽  
Normalized Difference Water Index

Abstract. The Ciénaga Grande, Santa Marta is the largest and most diverse ecosystem of its kind in Colombia. Its primary function is acting as a filter for the organic carbon cycle. Recently, this place has been suffering disruptions due to the anthropic activities taking place in its surroundings. The present study, the changes in the surface of Ciénaga Grande, Santa Marta, Magdalena, Colombia between 2013 and 2018 were determined using semiautomatic detection methods with high resolution data from remote sensors (Landsat 8). The zone of studies was classified in six kinds of surfaces: 1) artificial territories, 2) agricultural territories, 3) forests and semi-natural areas, 4) wet areas, 5) deep water surfaces &amp; 6) wich is related to clouds as a masking method. Random Forest classifiers were utilized and the Feed For Ward multilayer perceptron neuronal network (ANN) was simultaneously assessed. The training stage for both methods was performed with 300 samples, distributed in equal quantities, over each coverage class. The semi-automatic classification was carried out with an annual frequency, but the monitoring was carried out throughout the analysis period through the performance of three indicators Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI). It was found from the confusion matrix that the Random Forest method more accurately classified four classes while Neural Networks Analysis (NNA) just three. Finally, taking the Random Forest results into account, it was found that the agricultural expansion increased from 7% to 9% and the urban zone increased from 20% to 30% of the total area. As well as a decrease of damp areas from 27% to 12% and forests from 4% to 3% of the total area of study.

scholarly journals Discriminación de trigo y cebada empleando imágenes satelitales ópticas y radar

2021 ◽  
Author(s):  
Mario Fabián Marini
Keyword(s):  
Support Vector Machines ◽  
Maximum Likelihood ◽  
Random Forest ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Buenos Aires ◽  
Support Vector ◽  
Vector Machines ◽  
Sentinel 2

El partido de Coronel Rosales (Buenos Aires, Argentina) se halla localizado dentro de la región pampeana austral, una de las de mayor relevancia agro productiva del país. En este contexto, el conocimiento de la superficie cultivada adquiere significativa importancia para la posterior planificación agrícola y económica. En tal sentido, la discriminación de cultivos mediante teledetección se dificulta cuando se trata de los de ciclo fenológico muy similar, como el trigo y la cebada. En este estudio se realizó una discriminación de dichos cultivos empleando imágenes de Radar de Apertura Sintética (SAR) Sentinel-1A SLC, imágenes ópticas Sentinel-2 y una combinación de ambos tipos de datos. Se incorporaron medidas de coherencia, textura e intensidad de retrodispersión extraídas de los datos SAR durante el ciclo fenológico completo. Sobre cada escena Sentinel-2 se obtuvo el Índice de Diferencia Normalizada de Vegetación (Normalized Difference Vegetation Index - NDVI). Se emplearon tres algoritmos de clasificación: Máxima Verosimilitud (Maximum Likelihood - MLC), Máquinas de Soporte Vectorial (Support Vector Machines - SVM) y Random Forest (RF). Los mejores resultados se obtuvieron al combinar imágenes ópticas y SAR empleando el clasificador RF. La combinación de las retrodispersiones VV y VH junto a la coherencia y la textura de las imágenes SAR, sumada al apilado de NDVI de imágenes ópticas, arrojó los máximos valores de precisión de la clasificación. El valor de F1 fue de 87.27% para el trigo y de 89.20% para la cebada.

scholarly journals Geographical Distribution of Zebu Breeds in Brazil And Their Relationship With Environmental Variables and Human Development Indicator

2021 ◽  
Author(s):  
Paulo Ricardo Martins Lima ◽  
Vanessa Peripolli ◽  
Luiz Antônio Josahkian ◽  
Concepta McManus
Keyword(s):  
Human Development ◽  
Geographical Distribution ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Zebu Cattle ◽  
Forest Protection ◽  
Dual Purpose ◽  
Family Agriculture ◽  
Development Indicator

Abstract The aim of this study was to evaluate the geographical distribution of zebu breeds in Brazil and correlate their occurrence with environmental variables and human development indicator. The herds of purebred zebu cattle in Brazil were classified as beef breeds (Brahman, Polled Brahman, Nelore, Polled Nelore and Tabapuã), dairy breeds (Gir and Polled Gir), and dual-purpose breeds (Guzerá, Indubrasil, Polled Indubrasil, Sindhi and Polled Sindhi), all breeds being spatialized in ArcGIS program. Variables examined included environmental and human development indicator. The statistical analysis included analysis and logistic regression.The lower distribution of zebu cattle in the states of Northeast compared to other locations is probably due to its extreme climate, highly susceptible to long periods of high temperatures and lower precipitation, which directly affects local livestock. The beef breeds were evenly spread throughout the country. The location occupied for beef breeds was influenced by environmental variables, showing a higher incidence with increased precipitation, normalized difference vegetation index (NDVI), temperature, relative humidity and temperature humidity index (THI), as well as establishments without family agriculture and rivers and streams with forest protection. The location used for dual-purpose and dairy breeds was influenced by areas with cultivated cutting forages, areas with integrated crop-livestock forest systems and areas with rotational grazing system, indicating a higher occupation in fertile lands. The Gir breed, the only one with dairy exploration in this study, showed herds in establishments with family agriculture, characterized by small to medium farms, and in regions with higher altitude.

scholarly journals Fig. 3. Relation between per capita income and measures of private (A, B) and public (C) green infrastructure, including the normalized difference vegetation index (NDVI) and fractional tree cover. NDVI and tree cover values are expressed as percentage relative differences to respective district municipality means. Data points represent census tracts and are faded to highlight densities.

2021 ◽  
Author(s):  
Zander S Venter ◽  
Charlie M. Shackleton ◽  
Francini Van Staden ◽  
Odirilwe Selomane ◽  
Vanessa A Masterson
Keyword(s):  
South Africa ◽  
Low Income ◽  
Green Infrastructure ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Tree Cover ◽  
Racial Groups ◽  
Public And Private ◽  
Data Points ◽  
Relative Differences

<p>Urban green infrastructure provides ecosystem services that are essential to human wellbeing. A dearth of national-scale assessments in the Global South has precluded the ability to explore how political regimes, such as the forced racial segregation in South Africa during and after Apartheid, have influenced the extent of and access to green infrastructure over time. We investigate whether there are disparities in green infrastructure distributions across race and income geographies in urban South Africa. Using open-source satellite imagery and geographic information, along with national census statistics, we find that public and private green infrastructure is more abundant, accessible, greener and more treed in high-income relative to low-income areas, and in areas where previously advantaged racial groups (i.e. White citizens) reside.</p>

scholarly journals Identification of South African Bermudagrass Germplasm with Shade Tolerance

HortScience ◽  
2015 ◽  
Vol 50 (10) ◽  
pp. 1419-1425 ◽  
Author(s):  
Jeffrey C. Dunne ◽  
W. Casey Reynolds ◽  
Grady L. Miller ◽  
Consuelo Arellano ◽  
Rick L. Brandenburg ◽  
...  
Keyword(s):  
South Africa ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Economic Value ◽  
Fertility Treatments ◽  
Turf Quality ◽  
Poor Tolerance ◽  
Adequate Quality ◽  
Added Benefit ◽  
Susceptible Control

Bermudagrass, Cynodon spp. is one of the most commonly grown turfgrass genera in the southern United States having excellent drought tolerance, but poor tolerance to shade. Developing cultivars tolerant to shade would allow bermudagrass to become more prevalent in home lawns or other recreational areas in the southeast, where trees dominate the landscape. In this field study, nine accessions collected from Pretoria, South Africa were evaluated for their ability to grow under shade with varying fertility treatments. These accessions and cultivars ‘Celebration’, ‘TifGrand’, and ‘Tifway’ were evaluated under 0%, 63%, and 80% continuous shade during 2011–12. For both years, significant differences among shade levels, genotypes, and the interaction of the two were observed. As expected, the progression from 0% to 63% to 80% shade reduced normalized difference vegetation index (NDVI), percent turfgrass cover (TC), and turf quality (TQ) readings for all accessions. Some genotypes, however, were able to maintain adequate quality and aggressiveness under 63% shade. ‘Celebration’, WIN10F, and STIL03 performed better than ‘Tifway’ (P ≤ 0.05), the susceptible control. Overall, our results indicate that there are promising genotypes among the bermudagrass materials collected from South Africa. These accessions represent additional sources of shade hardiness to be used in bermudagrass breeding. Furthermore, higher nitrogen fertility provided increased NDVI and TQ in some instances suggesting an added benefit of fertility under low-light conditions. However, the increased economic value attributed to the added inputs associated with these increases is outweighed by the low impacts offered.

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