Validation of satellite-derived soil-vegetation indices for prognosis of spring cereals yield reduction under drought conditions – Case study from central-western Poland

Wang, H., He, Y., Qian, B., McConkey, B., Cutforth, H., McCaig, T., McLeod, G., Zentner, R., DePauw, R., Lemke, R., Brandt, K., Liu, T., Qin, X., White, J., Hunt, T. and Hoogenboom, G. 2012. Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan. Can. J. Plant Sci. 92: 421–425. This study assessed potential impacts of climate change on wheat production as a biofuel crop in southern Saskatchewan, Canada. The Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) was used to simulate biomass and grain yield under three climate change scenarios (CGCM3 with the forcing scenarios of IPCC SRES A1B, A2 and B1) in the 2050s. Synthetic 300-yr weather data were generated by the AAFC stochastic weather generator for the baseline period (1961–1990) and each scenario. Compared with the baseline, precipitation is projected to increase in every month under all three scenarios except in July and August and in June for A2, when it is projected to decrease. Annual mean air temperature is projected to increase by 3.2, 3.6 and 2.7°C for A1B, A2 and B1, respectively. The model predicted increases in biomass by 28, 12 and 16% without the direct effect of CO2 and 74, 55 and 41% with combined effects (climate and CO2) for A1B, A2 and B1, respectively. Similar increases were found for grain yield. However, the occurrence of heat shock (>32°C) will increase during grain filling under the projected climate conditions and could cause severe yield reduction, which was not simulated by DSSAT-CSM. This implies that the future yield under climate scenarios might have been overestimated by DSSAT-CSM; therefore, model modification is required. Several measures, such as early seeding, must be taken to avoid heat damages and take the advantage of projected increases in temperature and precipitation in the early season.

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FEATURE FILTERING AND SELECTION FOR DRY MATTER ESTIMATION ON PERENNIAL RYEGRASS: A CASE STUDY OF VEGETATION INDICES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-1827-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 1827-1831 ◽

Cited By ~ 3

Author(s):

G. T. Alckmin ◽

L. Kooistra ◽

A. Lucieer ◽

R. Rawnsley

Keyword(s):

Feature Selection ◽

Perennial Ryegrass ◽

Dry Matter ◽

Selection Process ◽

Vegetation Indices ◽

Robust Model ◽

Trade Offs ◽

Initial Dataset ◽

High Degree

Abstract. Vegetation indices (VIs) have been extensively employed as a feature for dry matter (DM) estimation. During the past five decades more than a hundred vegetation indices have been proposed. Inevitably, the selection of the optimal index or subset of indices is not trivial nor obvious. This study, performed on a year-round observation of perennial ryegrass (n&thinsp;=&thinsp;900), indicates that for this response variable (i.e. kg.DM.ha&minus;1), more than 80% of indices present a high degree of collinearity (correlation&thinsp;&gt;&thinsp;|0.8|.) Additionally, the absence of an established workflow for feature selection and modelling is a handicap when trying to establish meaningful relations between spectral data and biophysical/biochemical features. Within this case study, an unsupervised and supervised filtering process is proposed to an initial dataset of 97 VIs. This research analyses the effects of the proposed filtering and feature selection process to the overall stability of final models. Consequently, this analysis provides a straightforward framework to filter and select VIs. This approach was able to provide a reduced feature set for a robust model and to quantify trade-offs between optimal models (i.e. lowest root mean square error &ndash; RMSE&thinsp;=&thinsp;412.27&thinsp;kg.DM.ha&minus;1) and tolerable models (with a smaller number of features &ndash; 4 VIs and within 10% of the lowest RMSE.)

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The effect of atmospheric correction methods on the relationship between vegetation indices and canopy cover (Case study: Marjan rangelands of Borujen)

Journal of Geospatial Information Technology ◽

10.29252/jgit.7.2.133 ◽

2019 ◽

Vol 7 (2) ◽

pp. 133-153

Author(s):

Fatemeh Pordel ◽

Ataollah Ebrahimi ◽

Zahra Azizi ◽

◽

...

Keyword(s):

Vegetation Indices ◽

Atmospheric Correction ◽

Canopy Cover ◽

The Relationship

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Estimation of Above-Ground Mangrove Biomass Using Landsat-8 Data- Derived Vegetation Indices: A Case Study in Quang Ninh Province, Vietnam

Forest and Society ◽

10.24259/fs.v5i2.13755 ◽

2021 ◽

pp. 506-525

Author(s):

Hai-Hoa Nguyen ◽

Huy Duc Vu ◽

Achim Röder

Keyword(s):

Vegetation Indices ◽

Landsat 8 ◽

Mangrove Forests ◽

Estimation Model ◽

The Status ◽

Good Potential ◽

The Mean ◽

Accuracy Assessments ◽

Quang Ninh

This study aimed to map the status of mangrove forests over the coasts of Hai Ha District and Mong Cai City in Quang Ninh Province by using 2019 Landsat-8 imagery. It then developed the AGB estimation model of mangrove forests based on the AGB estimation-derived plots inventory and vegetation indices-derived from Landsat-8 data. As results, there were five land covers identified, including mangrove forests, other vegetation, wetlands, built-up, and water, with the overall accuracy assessments of 80.0% and Kappa coefficient of 0.74. The total extent of mangrove forests was estimated at 4291.2 ha. The best AGB estimation model that was selected to estimate the AGB and AGC of mangrove forests for the whole coasts of Hai Ha District and Mong Cai City is AGB= 30.38 + 911.95*SAVI (R2=0.924, PValue <0.001). The model validation assessment has confirmed that the selected AGB model can be applied to Hai Ha and Mong Cai coasts with the mean difference between AGB observed and AGB predicted at 16.0 %. This satisfactory AGB model also suggests a good potential for AGB and AGC mapping, which offer the carbon trading market in the study site. As the AGB model selected, the total AGB and AGC of mangrove forests were estimated at about 14,600,000 tons and 6,868,076 tons with a range of from 94.0 - 432.0 tons ha-1, from 44.2 - 203.02 tons ha-1, respectively. It also suggests that the newly-developed AGB model of mangrove forests can be used to estimate AGC stocks and carbon sequestration of mangrove forests for C-PFES in over the coasts of Hai Ha District and Mong Cai City, which is a very importantly financial source for mangrove forest managers, in particular for local mangrove protectors.

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Water quality risk assessment for drought conditions: A case study of the Korean Nakdong River basin

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/351/1/012017 ◽

2019 ◽

Vol 351 ◽

pp. 012017

Author(s):

J -S Kim ◽

J -H Lee ◽

S -Y Park ◽

J Chen ◽

H Chen

Keyword(s):

Water Quality ◽

Risk Assessment ◽

River Basin ◽

Nakdong River ◽

Nakdong River Basin ◽

Drought Conditions ◽

Quality Risk

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Impacts of Drought on Vegetation Assessed by Vegetation Indices and Meteorological Factors in Afghanistan

Remote Sensing ◽

10.3390/rs12152433 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2433 ◽

Cited By ~ 3

Author(s):

Iman Rousta ◽

Haraldur Olafsson ◽

Md Moniruzzaman ◽

Hao Zhang ◽

Yuei-An Liou ◽

...

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Condition Index ◽

Vegetation Coverage ◽

P Value ◽

Human Society ◽

Time Series Regression ◽

Drought Conditions

Drought has severe impacts on human society and ecosystems. In this study, we used data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) sensors to examine the drought effects on vegetation in Afghanistan from 2001 to 2018. The MODIS data included the 16-day 250-m composites of the Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI) with Land Surface Temperature (LST) images with 1 km resolution. The TRMM data were monthly rainfalls with 0.1-degree resolution. The relationship between drought and index-defined vegetation variation was examined by using time series, regression analysis, and anomaly calculation. The results showed that the vegetation coverage for the whole country, reaching the lowest levels of 6.2% and 5.5% were observed in drought years 2001 and 2008, respectively. However, there is a huge inter-regional variation in vegetation coverage in the study period with a significant rising trend in Helmand Watershed with R = 0.66 (p value = 0.05). Based on VCI for the same two years (2001 and 2008), 84% and 72% of the country were subject to drought conditions, respectively. Coherently, TRMM data confirm that 2001 and 2008 were the least rainfall years of 108 and 251 mm, respectively. On the other hand, years 2009 and 2010 were registered with the largest vegetation coverage of 16.3% mainly due to lower annual LST than average LST of 14 degrees and partially due to their slightly higher annual rainfalls of 378 and 425 mm, respectively, than the historical average of 327 mm. Based on the derived VCI, 28% and 21% of the study area experienced drought conditions in 2009 and 2010, respectively. It is also found that correlations are relatively high between NDVI and VCI (r = 0.77, p = 0.0002), but slightly lower between NDVI and precipitation (r = 0.51, p = 0.03). In addition, LST played a key role in influencing the value of NDVI. However, both LST and precipitation must be considered together in order to properly capture the correlation between drought and NDVI.

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Assessing agricultural drought at a regional scale using LULC classification, SPI, and vegetation indices: case study in a rainfed agro-ecosystem in Central Mexico

Geomatics Natural Hazards and Risk ◽

10.1080/19475705.2015.1073799 ◽

2015 ◽

Vol 7 (4) ◽

pp. 1460-1488 ◽

Cited By ~ 5

Author(s):

Andres Sierra-Soler ◽

Jan Adamowski ◽

Julien Malard ◽

Zhiming Qi ◽

Hossein Saadat ◽

...

Keyword(s):

Regional Scale ◽

Vegetation Indices ◽

Agricultural Drought ◽

Central Mexico

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A multiple case study analysis of Six Sigma practices in Indian manufacturing companies

International Journal of Quality & Reliability Management ◽

10.1108/ijqrm-10-2014-0157 ◽

2016 ◽

Vol 33 (8) ◽

pp. 1138-1149 ◽

Cited By ~ 10

Author(s):

Jiju Antony ◽

E.V. Gijo ◽

Vikas Kumar ◽

Abhijeet Ghadge

Keyword(s):

Six Sigma ◽

Positive Impact ◽

Empirical Studies ◽

Yield Reduction ◽

Manufacturing Companies ◽

Study Analysis ◽

Organisational Performance ◽

Content Type ◽

Case Study Analysis

Purpose The purpose of this paper is to explore the fundamental barriers/challenges, benefits, commonly used tools and techniques, organisational infrastructure and impact on organisational performance in three Indian manufacturing companies. Design/methodology/approach A multi-case study analysis using the exploratory case study research was adopted by the authors to obtain a deeper insight into the Six Sigma implementation within three distinctive manufacturing organisations in India. Interviews were conducted with relevant staff (Six Sigma Deployment Champions, Six Sigma Master Black Belts and Six Sigma Black Belts) in all three companies. Findings Some of the barriers in implementing and sustaining Six Sigma identified from the case studies include: lack of accuracy of data generated from the processes, lack of understanding of the benefits of Six Sigma in the early stages of its adoption, high-attrition rate of Six Sigma Black Belts and so on. The benefits of Six Sigma included improvement of process yield, reduction of rework and rejection, reduction of raw material inventory, improved on-time delivery, on-time availability of material for production and so on. Supplier-input-process-output-customer, cause and effect diagram, process mapping, hypothesis tests (two sample test, F-test, etc.), control charts (X-bar-R chart, individual chart, etc.), simple graphical tools such as histograms, box plots and dot plots were the most commonly used tools of Six Sigma across the companies that participated for this research. All three companies have reported that Six Sigma had a positive impact on organisational performance and moreover the study also revealed that Six Sigma had positive impact on customer satisfaction, return-on-investment, productivity and product quality. Research limitations/implications The study was carried out in three Indian companies and therefore the findings cannot be generalised. The authors are extending the study to three more companies and the findings will be reported in the forthcoming months. Practical implications The findings of the study provide a good foundation to understand the fundamental barriers, benefits, commonly used tools and whether Six Sigma is having any impact on business performance in the Indian context. Very few empirical studies have been carried out on Six Sigma implementation in the Indian manufacturing companies and this research sets an agenda for a number of studies to follow on in the forthcoming years. Originality/value In authors’ opinion, this is possibly one of the first multi-case empirical studies on Six Sigma implementation in the Indian manufacturing companies. The results of the study can be used to benchmark with similar studies in other countries to understand the good and bad management practices of Six Sigma implementation.

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