Identification of South African Bermudagrass Germplasm with Shade Tolerance

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

Sustainability ◽

10.3390/su12218919 ◽

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.

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

Land ◽

10.3390/land10070760 ◽

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.

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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.

10.21504/rur.14413580 ◽

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>

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IDENTIFIKASI PERUBAHAN TUTUPAN DAN PENGGUNAAN LAHAN SEBAGAI DASAR PENENTUAN STRATEGI PENGELOLAAN KPHP WAY TERUSAN

EnviroScienteae ◽

10.20527/es.v13i3.4307 ◽

2017 ◽

Vol 13 (3) ◽

pp. 208

Author(s):

Tri Santoso ◽

Melya Riniarti ◽

Indra Gumay Febryano

Keyword(s):

Land Cover ◽

Supervised Classification ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Economic Value ◽

Market Demand ◽

Landsat Images ◽

Economic Interests ◽

Literature Searches ◽

Landsat Satellite

Encroachment on forest areas in Indonesia occurs due to various factors mainly related to tenure issues and economic interests. That encroachment occurred in all regions of Indonesia with vary in intensity and amount. Register 47 Way Terusan which has been designated as a KPHP model Way Terusan also being occupied by squatters since the 1990s. The communities within and around the KPHP Way Terusan area has highly dependency on forest resources. The data collection is done in several ways, namely: interviews, literature searches, downloads Landsat satellite imagery and field verification activities. Landsat images Scene: Path 123 and Row 063 for the year 1994, 1999, 2004, 2009 and 2014. Data analysis was conducted using NDVI (Normalized Difference Vegetation Index) and supervised classification. The results of the analysis of land cover in 1994 until 2014 shows the intensity of dynamics of land cover change in the region KPHP Way Terusan. Land cover changes caused as a result of choice of the type of vegetation that has higher economic value. In 2014, the use of cassava cultivation was the highest (55.24%) because of its high economic value, convenient cultivation and market demand. Partnership with agroforestry pattern most likely applied as management strategy policies to accommodate the interests of various stakeholders in KPHP Way Terusan.

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Salinity Tolerance and Recovery Attributes of Warm-season Turfgrass Cultivars

HortScience ◽

10.21273/hortsci13963-19 ◽

2019 ◽

Vol 54 (9) ◽

pp. 1625-1631 ◽

Cited By ~ 1

Author(s):

Manuel Chavarria ◽

Benjamin Wherley ◽

James Thomas ◽

Ambika Chandra ◽

Paul Raymer

Keyword(s):

Salinity Stress ◽

Salinity Tolerance ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Recovery Period ◽

Warm Season ◽

Shoot Biomass ◽

Seashore Paspalum ◽

Turf Quality ◽

Elevated Salinity

As population growth places greater pressures on potable water supplies, nonpotable recycled irrigation water is becoming widely used on turfgrass areas including golf courses, sports fields, parks, and lawns. Nonpotable recycled waters often have elevated salinity levels, and therefore turfgrasses must, increasingly, have good salinity tolerance to persist in these environments. This greenhouse study evaluated 10 commonly used cultivars representing warm-season turfgrass species of bermudagrass (Cynodon spp.), zoysiagrass (Zoysia spp.), st. augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze], and seashore paspalum (Paspalum vaginatum Swartz) for their comparative salinity tolerance at electrical conductivity (EC) levels of 2.5 (control), 15, 30, and 45 dS·m–1. Salinity treatments were imposed on the grasses for 10 weeks via subirrigation, followed by a 4-week freshwater recovery period. Attributes, including turf quality, the normalized difference vegetation index (NDVI), canopy firing, and shoot biomass reductions were evaluated before and after salinity stress, as well as after the 4-week freshwater recovery period. Results showed considerable differences in salinity tolerance among the cultivars and species used, with the greatest tolerance to elevated salinity noted within seashore paspalum cultivars and Celebration® bermudagrass. In comparison with growth in 2.5-dS·m–1 control conditions, increased shoot growth and turf quality were noted for many bermudagrass and seashore paspalum cultivars at 15 dS·m–1. However, st. augustinegrass and some zoysiagrass cultivars responded to elevated salinity with decreased growth and turf quality. No cultivars that had been exposed to 30- or 45-dS·m–1 salinity recovered to acceptable levels, although bermudagrass and seashore paspalum recovered to acceptable levels after exposure to 15-dS·m–1 salinity. More severe salinity stress was noted during year 2, which coincided with greater greenhouse temperatures relative to year 1.

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Crop coefficient of pomegranate in the Comarca Lagunera

Journal-Agrarian and Natural Resource Economics ◽

10.35429/janre.2020.6.4.1.6 ◽

2021 ◽

pp. 1-6

Author(s):

Norma Guadalupe Sifuentes-Morín ◽

José Alfredo Montemayor-Trejo ◽

Alan Joel Servín-Prieto ◽

Jorge Arnaldo Orozco-Vidal

Keyword(s):

Agricultural Development ◽

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Regional Scale ◽

Economic Value ◽

Crop Coefficient ◽

Production Costs ◽

Promising Tool ◽

Landsat Satellite

For agricultural development, water is the most important thing, so today farmers are looking for crops that have some degree of resistance to drought and high economic value such as pomegranate, however, there is poor literature on its production. The Crop Coefficient (Kc) helps us determine the water requirement during plant development, which is critical for reducing production costs and saving water. The objective of this study was to know the Kc during the phenological development of the pomegranate, in an orchard located in the municipality of Gómez Palacio, Durango, Mexico, using 8 Landsat satellite images and geographic information systems. The estimation of Kc based on the Normalized Difference Vegetation Index (NDVI), was performed as proposed by Calera (2016). The KC values obtained range from 0.33 to 0.65. Its evolution with satellite images is consistent according to the development stages of the crop. The relationship between the NDVI and KC may be a promising tool for farmers to estimate water use of pomegranate trees on a regional scale based on satellite imagery.

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Response of 110 Kentucky Bluegrass Varieties and Winter Annual Weeds to Methiozolin

Weed Technology ◽

10.1614/wt-d-16-00089.1 ◽

2016 ◽

Vol 30 (4) ◽

pp. 965-978 ◽

Cited By ~ 4

Author(s):

Sandeep S. Rana ◽

Shawn D. Askew

Keyword(s):

Weed Control ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Kentucky Bluegrass ◽

Annual Bluegrass ◽

Turf Quality ◽

Relative Cover ◽

Winter Annual ◽

Winter Annual Weeds ◽

Annual Weeds

Methiozolin is a new isoxazoline herbicide that has scarcely been tested in Kentucky bluegrass turf. A field trial was conducted in Blacksburg, VA, to determine response of 110 Kentucky bluegrass varieties and winter annual weeds to sequential fall applications of methiozolin. At 1.5 and 6 mo after initial treatment (MAIT), Kentucky bluegrass injury I30values (predicted methiozolin rate that causes 30% Kentucky bluegrass injury) ranged between 3.4 to more than 10 times the recommended methiozolin rate for annual bluegrass control. Methiozolin at all rates reduced cover of annual bluegrass, common chickweed, corn speedwell, hairy bittercress, mouseear chickweed, and Persian speedwell but increased cover of parsley-piert. For all varieties, methiozolin at 2 kg ai ha−1increased Kentucky bluegrass cover, turf quality, and turf normalized difference vegetation index (NDVI) relative to the nontreated check at 6 MAIT. Kentucky bluegrass relative cover change (RCC) was attributed primarily to weed control but was inversely correlated with methiozolin rates because of increased weed control and reduced Kentucky bluegrass growth. Despite the decline in RCC with increasing methiozolin rates, most Kentucky bluegrass varieties treated with the highest methiozolin rate (6 kg ha−1) still had greater Kentucky bluegrass cover than the nontreated check at 6 MAIT. Results from this study indicate that two fall applications of methiozolin at rates beyond that previously reported for annual bluegrass control can safely be applied to a broad range of Kentucky bluegrass varieties spanning most of the known genetic classifications.

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Timing and Patterns of the ENSO Signal in Africa over the Last 30 Years: Insights from Normalized Difference Vegetation Index Data

Journal of Climate ◽

10.1175/jcli-d-13-00365.1 ◽

2014 ◽

Vol 27 (7) ◽

pp. 2509-2532 ◽

Cited By ~ 19

Author(s):

N. Philippon ◽

N. Martiny ◽

P. Camberlin ◽

M. T. Hoffman ◽

V. Gond

Keyword(s):

South Africa ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Democratic Republic Of Congo ◽

Horn Of Africa ◽

The North ◽

Positive Correlations ◽

The Impact ◽

Rainfall Region

Abstract A more complete picture of the timing and patterns of the ENSO signal during the seasonal cycle for the whole of Africa over the three last decades is provided using the normalized difference vegetation index (NDVI). Indeed, NDVI has a higher spatial resolution and is more frequently updated than in situ climate databases, and highlights the impact of ENSO on vegetation dynamics as a combined result of ENSO on rainfall, solar radiation, and temperature. The month-by-month NDVI–Niño-3.4 correlation patterns evolve as follows. From July to September, negative correlations are observed over the Sahel, the Gulf of Guinea coast, and regions from the northern Democratic Republic of Congo to Ethiopia. However, they are not uniform in space and are moderate (~0.3). Conversely, positive correlations are recorded over the winter rainfall region of South Africa. In October–November, negative correlations over Ethiopia, Sudan, and Uganda strengthen while positive correlations emerge in the Horn of Africa and in the southeast coast of South Africa. By December with the settlement of the ITCZ south of the equator, positive correlations over the Horn of Africa spread southward and westward while negative correlations appear over Mozambique, Zimbabwe, and South Africa. This pattern strengthens and a dipole at 18°S is well established in February–March with reduced (enhanced) greenness during ENSO years south (north) of 18°S. At the same time, at ~2°N negative correlations spread northward. Last, from April to June negative correlations south of 18°S spread to the north (to 10°S) and to the east (to the south of Tanzania).

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

Geospatial health ◽

10.4081/gh.2016.434 ◽

2016 ◽

Vol 11 (3) ◽

Cited By ~ 2

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.

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Use of remotely sensed derived metrics to assess wetland vegetation responses to climate variability induced drought at the Soetendalsvlei wetland system in the Western Cape province of South Africa

10.21203/rs.3.rs-247465/v1 ◽

2021 ◽

Author(s):

Noluthando Conelia Ndlala ◽

Timothy Dube

Keyword(s):

South Africa ◽

Climate Variability ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Wetland Vegetation ◽

Western Cape ◽

Wetland Ecosystems ◽

Dry Seasons ◽

Vegetation Health ◽

Wet And Dry Seasons

Abstract Wetland areas are the most vital ecosystems and they provide important functions towards stabilizing the environment. Hydrological processes in these wetland systems directly affects the productivity of plants. Therefore, assessing vegetation response to climate variability induced drought is vital in wetlands. In this paper, the subtle changes in vegetation distribution were used as a proxy to examine and quantify the extent of drought impacts on wetland ecosystems within the Heuningnes catchment, South Africa. First, vegetation health information was extracted by calculating the normalized difference vegetation index (NDVI) during the wet and dry seasons for the period between 2014 and 2018. The derived NDVI results were further statistical linked to the corresponding rainfall and evapotranspiration (ET) observed during the study period. An analysis of NDVI results revealed that gradual vegetation health change occurred across the study area. The highest derived NDVI (0.5) for wetland vegetation was observed during the year 2014 but progressively declined over the years. Change in vegetation health indicated a significant (α = 0.05) and positive correlation to the amount of rainfall received over the same period. The results of this study showed that healthy vegetation deteriorated between the study periods due to the 2015–2017 Western Cape drought.

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