Seasonal normalized difference vegetation index responses to air temperature and precipitation in Baghdad

Naif, Salwa S.; Mahmood, Dalia A.; Al-Jiboori, Monim H.

doi:10.1515/opag-2020-0065

Cited by 18 publications

(18 citation statements)

References 14 publications

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“…The NDVI mentioned values were calculated using Landsat remote sensing images with a resolution of 30 m by 30 m which is moderateresolution data that can represent the properties of vegetation coverage in a specific area. The choice of fine resolution will aid in not only increasing the accuracy of field features but also in improving the efficiency of crop and hydrological models [70], resulting in more conclusive outputs and planners in pertaining vegetation cover in this study.…”

Section: Ndvi Derivationmentioning

confidence: 99%

Utilizing TVDI and NDWI to Classify Severity of Agricultural Drought in Chuping, Malaysia

et al. 2021

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Agricultural drought is crucial in understanding the relationship to crop production functions which can be monitored using satellite remote sensors. The aim of this research is to combine temperature vegetation dryness index (TVDI) and normalized difference water index (NDWI) classifications for identifying drought areas in Chuping, Malaysia which has regularly recorded high temperatures. TVDI and NDWI are assessed using three images of the dry spell period in March for the years 2015, 2016 and 2017. NDWI value representing water content in vegetation decreases numerically to −0.39, −0.37 and −0.36 for the year 2015, 2016 and 2017. Normalized difference vegetation indices (NDVI) values representing vegetation health status in the given area for images of years 2015 to 2017 decreases significantly (p ≤ 0.05) from 0.50 to 0.35 respectively. Overall, TVDI in the Chuping area showed agricultural drought with an average value of 0.46. However, Kilang Gula Chuping area in Chuping showed a significant increase in dryness for all of the three years assessed with an average value of 0.70. When both TVDI and NDWI were assessed, significant clustering of spots in Chuping, Perlis for all the 3 years was identified where geographical local regressions of 0.84, 0.70 and 0.70 for the years 2015, 2016 and 2017 was determined. Furthermore, Moran’s I values revealed that the research area had a high I value of 0.63, 0.30 and 0.23 with respective Z scores of 17.80, 8.63 and 6.77 for the years 2015, 2016 and 2017, indicating that the cluster relationship is significant in the 95–99 percent confidence interval. Using both indices alone was sufficient to understand the drier spots of Chuping over 3 years. The findings of this research will be of interest to local agriculture authorities, like plantation and meteorology departments to understand drier areas in the state to evaluate water deficits severity and cloud seeding points during drought.

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Section: Ndvi Derivationmentioning

confidence: 99%

Utilizing TVDI and NDWI to Classify Severity of Agricultural Drought in Chuping, Malaysia

et al. 2021

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“…As the value increases towards +1 or increasing positive NDVI values indicate the dense vegetation (vegetated the plant canopy), and close to zero or decreasing negative values (-1) indicates the non-vegetation surface such as water and bare ground [92]. A high positive value of NDVI is also computed from vegetated agricultural cover crops [93]. NDVI is calculated based on the difference in the ratio of Red (R) and near-infrared (NIR) reflectance, equations ( 9) and ( 10 The study site is found in semi-arid region, where climate variables are limiting factors for vegetation cover determination.…”

Section: Normalized Difference Vegetation Index (Ndvi) Analysismentioning

confidence: 99%

Seasonal land use land cover change and the drivers in Kafta-Sheraro National Park, Tigray, Ethiopia

Temesgen

Warkineh

Hailemicael

2021

Preprint

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Kafta-sheraro national park (KSNP) is one of the homes of the African elephant has experienced extensive destruction of woodland following regular land use & land cover change in the past three decades, however, up to date, data and documentation detailing for these changes are not addressed. This study aims to evaluate the land use land cover change and drivers of change that occurred between 1988 and 2018. Landsat 5(TM), Landsat7 (ETM+), and Landsat 8 (OLI/TIRs) imagery sensors, field observation, and socio-economic survey data were used. The temporal and spatial Normalized difference vegetation index (NDVI) was calculated and tested the correlation between NDVI and precipitation/temperature. The study computed a kappa coefficient of the dry season (0.90) and wet season (0.845). Continuous decline of woodland (29.38%) and riparian vegetation (47.11%) whereas an increasing trend of shrub-bushland (35.28%), grassland (43.47%), bareland (27.52%), and cultivated land (118.36 km2) were showed over thirty years. More results showed bare land was expanded from wet to drier months, while, cultivated land and grazing land increased from dry to wet months. Based on the NDVI result high-moderate vegetation was decreased by 21.47% while sparse & non-vegetation was expanded by 19.8% & 1.7% (36.5 km2) respectively. Settlement & agricultural expansion, human-induced fire, firewood collection, gold mining, and charcoal production were the major proximate drivers that negatively affected the park resources. Around KSNP, the local community livelihood depends on farming, expansion of agricultural land is the main driver for woodland dynamics/depletion and this leads to increase resources competition and challenges for the survival of wildlife. Therefore, urgent sustainable conservation of park biodiversity via encouraging community participation in conservation practices and preparing awareness creation programs should be mandatory.

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“…(1) Difference Vegetation Index DVI DVI values range from 0 to 1, where 0 represents no vegetation coverage; 1 represents a high degree of vegetation coverage [8][9].…”

Section: Commonly Used Vegetation Indicesmentioning

confidence: 99%

“…(1) Univariate linear model Use statistical software to generate a scatter plot from the data, add a linear trend line, and fit these scatter points to a straight line to ensure that the sum of the vertical distances from the scatter points to the straight line is the smallest [16][17]. In the establishment of a univariate linear model of grassland aboveground biomass, the measured grassland aboveground biomass is selected as the dependent variable, and the NDVI and RVI of the coordinate points corresponding to the measured value in the remote sensing image are selected as independent variables, and the univariate linear regression equation is:…”

Section: Establishment Of Grassland Aboveground Biomass Model Based O...mentioning

confidence: 99%

Biomass Model of Typical Grassland Area Based on Vegetation Index

2020

AJEB

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Grassland ecosystem is one of the most important and widely distributed ecosystem types in terrestrial ecosystems. Grassland biomass is an important indicator to characterize and evaluate grassland ecosystems. The purpose of this paper is to study a typical grassland biomass model based on vegetation index. Grassland A is used as the research area, the research object is grassland aboveground biomass, and the aboveground biomass data of field measured sampling points are used as modeling data. Based on the NDVI vegetation index, the correlation analysis between RVI and the measured value of above-ground biomass in grasslands, respectively, established a linear model, an exponential model and a quadratic polynomial model. Through SPSS statistical analysis, the accuracy of the model was tested, and the aboveground biomass of remote sensing grassland was inverted using the optimal model. The research results show that the aboveground grassland biomass simulated by the power function model with RVI-Y=102.45x1.35 is basically close to the measured aboveground biomass. The average error of the estimated error/measured value is 6.79%, and the fitting accuracy is 94.6%. Therefore, it is feasible to estimate grassland biomass using an exponential function model based on RVI-above-ground biomass.

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Seasonal normalized difference vegetation index responses to air temperature and precipitation in Baghdad

Cited by 18 publications

References 14 publications

Utilizing TVDI and NDWI to Classify Severity of Agricultural Drought in Chuping, Malaysia

Utilizing TVDI and NDWI to Classify Severity of Agricultural Drought in Chuping, Malaysia

Seasonal land use land cover change and the drivers in Kafta-Sheraro National Park, Tigray, Ethiopia

Biomass Model of Typical Grassland Area Based on Vegetation Index

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